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English Pages 5257 [5329] Year 2021
Fred R. Volkmar Editor
Encyclopedia of Autism Spectrum Disorders Second Edition
Encyclopedia of Autism Spectrum Disorders
Fred R. Volkmar Editor
Encyclopedia of Autism Spectrum Disorders With 59 Figures and 144 Tables
Editor Fred R. Volkmar Yale University New Haven, CT, USA
ISBN 978-3-319-91279-0 ISBN 978-3-319-91280-6 (eBook) ISBN 978-3-319-91284-4 (print and electronic bundle) https://doi.org/10.1007/978-3-319-91280-6 1st edition: © Springer Science+Business Media New York 2013 2nd edition: © Springer Nature Switzerland AG 2021 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG. The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Preface to Second Edition
Eight years have now passed since the first edition of this Encyclopedia. During that time the field has continued to grow – almost exponentially in some areas! In doing this second edition of the Encyclopedia, we are mindful of the growth as well as some of the advantages for updating entries and including new ones in this much used reference work. It has been gratifying to see this resource being heavily used with around 400,000 downloads since it first appeared! In this second edition we have added nearly 400 new entries and updates on over 450 previous entries reflecting activity in the field since the first edition. As with the first edition we have attempted to be comprehensive in scope with entries on a range of topics including not only research issues but biographies of important contributors to the field, legal and social policy issues, educational, behavioral, and medical interventions, treatments, and advances in basic sciences of behavior, communication, neurobiology, genetics, epidemiology, and so forth. For this edition, we have also included a new set of entries on countries giving brief overviews of the history of autism work and the current state of the field in both developed and developing countries. This latter group of entries also reflects the growing interest in autism around the world specifically in developing countries where infrastructure for both service, teaching, and research has become increasingly important. With the addition of our new entries, we have reached nearly 1800 entries in total. As with the first edition we hope that this work provides an invaluable resource for parents, students, educators, researchers, and professionals alike. Even though these volumes appear in hard copy, in this new second edition we continually update entries and add new ones as these are needed. For this addition, I particularly thank our supporters at Springer – Judy Jones, Tina Shelton, and Sindhu Ramachandran, at Yale my helpful assistants Lori Klein and Monica Mleczek, and at the Autism Center at Southern Connecticut State University my assistant Eileen Farmer. I also particularly thank my Associate Editor Dr. Michael Powers who has assumed an important leadership role in the production of this edition. All of us hope you find this unique resource a valuable and helpful one. We are delighted to welcome you to this second edition. New Haven, CT, USA September 1, 2020
Fred R. Volkmar
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Preface to First Edition
Why an encyclopedia of autism? There are several answers to this question. They include the need to provide a comprehensive and current guide to the diverse knowledge now available. There has been a significant upsurge in research in autism during the past two decades. Several hundred papers were published in 1991 compared to more than 2,000 articles during 2011. The quantity of research (not even counting non-peer-reviewed publications) has increased so dramatically that it is difficult, if not impossible, for researchers and clinicians to keep up. Access to a reference work that provides an introduction to relevant information is clearly needed. Although several excellent handbooks and textbooks have been published in recent years, these are, almost intrinsically, fated to become increasingly out of date more and more quickly. Fortunately, many of the same technological advances that have been adapted for use with individuals with autism have uses for those of us who support them. The ability to produce both a print reference work as well as an online version with additional content was a major attraction for us in undertaking this project. It also can be updated easily and will have additional content. The electronic format also provides for an extensive cross-referencing system, which is designed to facilitate rapid searching and information retrieval. With contributions on a range of topics from leaders in the field, this reference work breaks new ground as a resource. The Encyclopedia contains several thousand entries relevant to autism and related conditions, including new research findings; entries on development and behavior; assessment methods and instruments; treatments and educational interventions; biographies of leaders in the field; and information relevant to epidemiology, social policy, and treatment planning. Both I and the associate editors of this work hope that you will benefit from using the encyclopedia and welcome your feedback. By the time the print publication of this work appears, the online edition will already have had entries added reflecting new knowledge in various areas. We hope that this resource enhances the work of clinicians and researchers alike. New Haven, CT, USA September 2012
Fred R. Volkmar M.D. Editor
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About the Editor
Fred R. Volkmar is the Irving B. Harris Professor of Child Psychiatry, Pediatrics, and Psychology at the Yale Child Study Center, Yale University School of Medicine, and the Dorothy Goodwin Family Chair of Special Education at Southern Connecticut State University. An international authority on Asperger’s disorder and autism, Dr. Volkmar was the primary author of the DSM-IV autism and pervasive developmental disorders section. He has authored several hundred scientific papers and has coedited numerous books, including Asperger Syndrome, Healthcare for Children on the Autism Spectrum: A Guide to Medical, Nutritional, and Behavioral Issues, and the recently released third edition of the Handbook of Autism and Pervasive Developmental Disorders. He serves as associate editor of the Journal of Autism, the Journal of Child Psychology and Psychiatry, and the American Journal of Psychiatry. He also serves as co-chairperson of the autism/MR committee of the American Academy of Child and Adolescent Psychiatry. Since 2007 he has served editor of the Journal of Autism and more recently of the Encyclopedia of Autism.
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List of Field Editors
George M. Anderson Laboratory of Developmental Neurochemistry, Yale Child Study Center, New Haven, CT, USA Nirit Bauminger-Zviely School of Education, Bar-Illan University, RamatGan, Israel Susan Y. Bookheimer Cognitive Neuroscience, UCLA School of Medicine, Los Angeles, CA, USA Alice S. Carter Department of Psychology, University of Massachusetts Boston, Boston, MA, USA Tony Charman Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, UK Katarzyna Chawarska Yale Child Study Center, New Haven, CT, USA Joshua J. Diehl Child and Adolescent Services, LOGAN Community Resources, Inc., South Bend, IN, USA Peter Doehring ASD Roadmap, Chadds Ford, PA, USA Andrew L. Egel Dept. of Counseling, Higher Education and Special Education, University of Maryland, College Park, MD, USA Inge-Marie Eigsti Department of Psychological Sciences, University of Connecticut, Storrs, CT, USA Ruth Eren Center of Excellence on Autism Spectrum Disorders, Southern Connecticut State University, New Haven, CT, USA Adam Feinstein Autism Cymru and Looking Up, London, UK Eric Fombonne Department of Psychiatry, Oregon Health and Science University, Portland, OR, USA Michael Fitzgerald Department of Psychiatry, Trinity College, Dublin, Ireland Grace Gengoux Child and Adolescent Psychiatry, Stanford University, Stanford, CA, USA Howard Goldstein College of Behavioral and Community Sciences, University of South Florida, Tampa, FL, USA xi
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Francesca Happé SGDP Centre, Institute of Psychiatry, Psychology and Neuroscience, London, UK Pamela Heaton Department of Psychology, University of London, London, UK Patricia Howlin Institute of Psychiatry, Psychology and Neuroscience King’s College, London, UK Susan Hyman Developmental and Behavioral Pediatrics, University of Rochester Golisano Children’s Hospital, Rochester, NY, USA Gagan Joshi Psychiatry, Massachusetts General Hospital, Boston, MA, USA Connie Kasari Human Development and Psychology GSE&IS, Center for Autism Research and Treatment Semel Institute, UCLA, Los Angeles, CA, USA Lauren Kenworthy Department of Pediatrics, Neurology, Psychiatry, George Washington University Medical School, Center for Autism Spectrum Disorders, Division of Pediatric Neuropsychology, Children’s National Health System, Rockville, MD, USA Robert L. Koegel Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA Ronald Leaf Autism Partnership Foundation, Seal Beach, CA, USA Ann S. Le-Couteur Population Health Sciences Institute, Newcastle University, Royal Victoria Infirmary, Newcastle upon Tyne, UK Luc Lecavalier Nisonger Center, Ohio State University, Columbus, OH, USA Rachel Loftin AARTS Center, Rush University Medical Center, Chicago, IL, USA James W. Loomis Center for Children with Special Needs, Glastonbury, CT, USA Catherine Lord UCLA, Los Angeles, CA, USA Christopher J. McDougle Lurie Center for Autism, Massachusetts General Hospital, Lexington, MA, USA James C. McPartland Yale Child Study Center, New Haven, CT, USA Nancy J. Minshew Departments of Psychiatry and Neurology, University of Pittsburgh, Pittsburgh, PA, USA Thomas Morgan Vanderbilt Department of Pediatrics, Division of Medical Genetics and Genomic Medicine, Nashville, TN, USA Hope Morris Communication Sciences and Disorders, University of Vermont, Burlington, VT, USA Paul A. Offit Division of Infectious Diseases, Department of Pediatrics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA Kristen M. Powers Cognitive Behavioral and Occupational Therapy Services, CCSN, Glastonbury, CT, USA
List of Field Editors
List of Field Editors
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Kevin A. Pelphrey Yale Child Study Center, New Haven, CT, USA Patricia Prelock University of Vermont, Burlington, VT, USA Brian Reichow University of Florida, Gainesville, FL, USA Lawrence Scahill Children’s Healthcare of Atlanta, Marcus Autism Center, Atlanta, GA, USA Tristram Smith Department of Pediatics, University of Rochester Medical Center, Rochester, NY, USA Wendy L. Stone Department of Psychology, UW READi Lab, University of Washington, Seattle, WA, USA John W. Thomas Quinnipiac University School of Law, Hamden, CT, USA Geralyn Timler Speech Pathology and Audiology, Miami University, Oxford, OH, USA Rutger Jan van der Gaag Department of Psychiatry and Karakter University Center for Child and Adolescent Psychiatry, Radboud University Medical Centre, Utrecht, The Netherlands Ernst O. VanBergeijk Threshold Program, Lesley University, Cambridge, MA, USA Gerrit van Schalkwyk Butler Hospital, Brown University, Providence, RI, USA Ty W. Vernon Koegel Autism Center/Department of Counseling, Clinical, and School Psychology, University of California Santa Barbara, Santa Barbara, CA, USA Giacomo Vivanti Early Detection and Intervention Program, AJ Drexel Autism Institute, Drexel University, Philadelphia, PA, USA Deborah Weiss Department of Communication Disorders, SCSU Faculty Senate, Judaic Studies, Southern Connecticut State University, New Haven, CT, USA Jeffrey J. Wood Department of Psychiatry, UCLA/Geffen School of Medicine, Los Angeles, CA, USA Marc Woodbury-Smith Translational and Clinical Sciences Institute, Newcastle University, Newcastle upon Tyne, UK Sara J. Webb Seattle Children’s Research Institute, University of Washington, Seattle, WA, USA Mary Jane Weiss Institute for Applied Behavioral Sciences, Endicott College, Beverly, MA, USA Virginia C.N. Wong Division of Paediatric Neurology, Developmental Behavioural Paediatrics and Paediatric Neurohabilitation, The University of Hong Kong, Queen Mary Hospital, Hong Kong, China Tristram Smith: deceased.
Associate Editor
Michael Powers The Center for Children with Special Needs (CCSN), Glastonbury, CA, USA Yale Child Study Center, Yale University School of Medicine, New Haven, CA, USA
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Contributors
Benjamin Aaronson Psychiatry and Behavioral Sciences, UW Autism Center, University of Washington, Seattle, WA, USA Ahmed A. Abdel-Rahman Department of Neuropsychiatry, Faculty of Medicine, Assiut University, Assiut, Egypt Sebiha M. Abdullahi Child Study Center, Yale University, New Haven, CT, USA Amy Accardo Department of Interdisciplinary and Inclusive Education, College of Education, Rowan University, Glassboro, NJ, USA Pasquale Accardo Virginia Commonwealth University, Richmond, VA, USA Silvia Adaes Quinnipiac University School of Law, Hamden, CT, USA Catherine Adams Human Communication Development and Hearing/ School of Health Sciences, University of Manchester, Manchester, UK Gail Fox Adams Department of Applied Linguistics, University of California, Los Angeles, CA, USA Lynn Adams New Orleans, LA, USA Ryan Adams Division of Developmental and Behavioral Pediatrics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA Ayodola A. Adigun Yale Child Study Center, New Haven, CT, USA Albert J. Solnit Children’s Center, Middletown, CT, USA Ralph Adolphs Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, USA Bill Ahearn The New England Center for Children, Southborough, MA, USA Rashid Akbari Child Study Center, Yale University School of Medicine, New Haven, CT, USA Abdulrahman A. Al-Atram Department of Psychiatry, College of Medicine, Majmaah University, Majmaah, Kingdom of Saudi Arabia xvii
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Mohamd A. Alblihed Department of Medical Biochemistry, School of Medicine, Taif University, Taif, Kingdom of Saudi Arabia Lilia Albores Gallo Research in Genetic, Clinical and Community Epidemiology, Hospital Psiquiátrico Infantil Dr. Juan N. Navarro, México City, Mexico Kimberly Aldinger Department of Cell and Neurobiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA Center for Integrative Brain Research, Seattle Children’s Research Institute, Seattle, WA, USA Mashal Salman Aljehany University of Jeddah, Jeddah, Makkah, Kingdom of Saudi Arabia Mariam Aljunied Special Educational Needs Division, Ministry of Education, Singapore, Singapore Melissa L. Allen Department of Psychology, Lancaster University Fylde College, Lancaster, UK Shirley Alleyne School of Clinical Medicine and Research, The University of the West Indies, Cave Hill, St. Michael, Barbados Samira Al-Saad Kuwait Center for Autism, Kuwait, Kuwait Fouad A. W. Alshaban Neurological Disorders Research Center, Qatar Biomedical Research Institute, Hamad Bin Khalifa University, Doha, Qatar Christine Alter Vocational Independence Program, New York Institute of Technology, Old Westbury, NY, USA D. O. Alvi Azad Yale Child Study Center, The Edward Zigler Center in Child Development and Social Policy, Yale University, New Haven, CT, USA Michael G. Aman Nisonger Center, UCEDD, The Ohio State University, Columbus, OH, USA Evdokia Anagnostou Department of Peadiatrics, University of Toronto, Clinician Scientist, Bloorview Research Institute, Toronto, ON, Canada Allan M. Andersen Department of Psychiatry, University of Iowa, Iowa City, IA, USA Connie Anderson Post-Baccalaureate Certificate Program in Autism Studies, College of Health Professions, Towson University, Towson, MD, USA Cynthia M. Anderson May Institute, Randolph, MA, USA George M. Anderson Laboratory of Developmental Neurochemistry, Yale Child Study Center, Yale University, New Haven, CT, USA Ligia Antezana Department of Psychology, Virginia Tech, Blacksburg, VA, USA
Contributors
Contributors
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Karthikeyan Ardhanareeswaran Autism Program, Child Study Center, Yale School of Medicine, New Haven, CT, USA Program in Neurodevelopment and Regeneration, Yale School of Medicine, New Haven, CT, USA Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT, USA Jennifer Arnold Department of Psychology, University of North Carolina, Chapel Hill, NC, USA Larry Arnold Autism Centre for Education and Research, University of Birmingham, Edgebaston, Birmingham, UK Sudha Arunachalam New York University, New York, NY, USA Miya Asato Pediatrics and Psychiatry, Division of Child Neurology, School of Medicine, Children’s Hospital of Pittsburgh, University of Pittsburgh, Pittsburgh, PA, USA Kristen Ashbaugh Koegel Autism Center, University of California, Santa Barbara, CA, USA Chris Ashwin Centre for Applied Autism Research, Department of Psychology, University of Bath, Bath, UK Danielle Asklar Southern Connecticut State University, New Haven, CT, USA Takeshi Atsumi Department of Medical Physiology, Faculty of Medicine, Kyorin University, Mitaka/Tokyo, Japan Karla K. Ausderau Department of Kinesiology, Occupational Therapy Program, Waisman Center, University of Wisconsin-Madison, Madison, WI, USA Sarita Austin Unlocking Language, London, UK Bonnie Auyeung Autism Research Centre, University of Cambridge, Cambridge, UK Mitrah E. Avini Yale Child Study Center, New Haven, CT, USA Alvi Azad Yale Child Study Center, The Edward Zigler Center in Child Development and Social Policy, Yale University, New Haven, CT, USA Gazi F. Azad Center for Autism and Related Disorders, Kennedy Krieger Institute’s, Baltimore, MD, USA Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
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Muhammad Waqar Azeem Sidra Medical and Research Center, Cornell Weill Medical College, Doha, Qatar Department of Psychiatry, Sidra Medicine, Doha, Qatar Weill Cornell Medicine, Doha, Qatar Marina Azimova ABA Services of CT, West Hartford, CT, USA Nur‘aini Azizah Faculty of Psychology, Universitas Islam Negeri Sunan Gunung Djati, Bandung, Indonesia Inmaculada Baixauli Catholic University of Valencia, Valencia, Spain Savana M. Y. Bak University of Minnesota, Twin Cities, Educational Psychology, Minneapolis, MN, USA Muideen O. Bakare Child and Adolescent Unit, Federal Neuropsychiatric Hospital, Enugu, Enugu, Nigeria Childhood Neuropsychiatric Disorders Initiatives (CNDI), Enugu, Nigeria Bruce L. Baker Department of Psychology, University of California Los Angeles, Los Angeles, CA, USA Jason K. Baker Department of Child and Adolescent Studies, California State University, Fullerton, Fullerton, CA, USA Vanessa Hus Bal University of Michigan, Ann Arbor, MI, USA Michelle Sondra Ballan Columbia University School of Social Work, New York, NY, USA Abigail Bangerter Department of Neuroscience, Janssen Research and Development, LLC, Titusville, NJ, USA Claudio Banzato Psychiatry, University of Campinas – Unicamp, Campinas, São Paulo, Brazil Grace T. Baranek Mrs. T.H. Chan Division of Occupational Science and Occupational Therapy, University of Southern California (USC), Los Angeles, CA, USA Aurélie Baranger Autism-Europe, Bruxelles, Belgium Gregory Barnes Department of Neurology, School of Medicine, Vanderbilt University, Nashville, TN, USA Mihaela Barokova Center for Autism Research Excellence, Boston University, Boston, MA, USA Simon Baron-Cohen Autism Research Centre, University of Cambridge, Cambridge, UK Monica Barreto Yale Child Study Center, New Haven, CT, USA Amy C. Barrett Koegel Autism Center/Department of Counseling, Clinical, and School Psychology, University of California Santa Barbara, Santa Barbara, CA, USA
Contributors
Contributors
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Anjali Barretto Department of Special Education, Gonzaga University, Spokane, WA, USA Kevin Barry Quinnipiac University School of Law, Hamden, CT, USA Lawrence Bartak Faculty of Education, Monash University, Clayton, VIC, Australia Christine Barthold Center for Disabilities Studies, University of Delaware, Newark, DE, USA Erin E. Barton University of Colorado Denver, Denver, CO, USA Marianne Barton Department of Psychological Sciences, University of Connecticut, Storrs, CT, USA Ran Barzilay Department of Psychiatry, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel Association for Children at Risk (R.A.), Tel Aviv, Israel Magali Batty Université de Toulouse, CERPPS, Toulouse, France Nirit Bauminger-Zviely School of Education, Bar-Illan University, RamatGan, Israel Kimberly M. Bean Department of Special Education, Center of Excellence on Autism Spectrum Disorders, Southern Connecticut State University, New Haven, CT, USA Yvette F. Bean Department of Educational Psychology, University of Georgia, Athens, GA, USA Allison Bean Ellawadi Speech and Hearing Science, The Ohio State University, Columbus, OH, USA Luke Beardon The Autism Centre, Institute of Education, Sheffield Hallam University, Sheffield, South Yorkshire, UK Emily Beaudoin McGill University, Montreal, QC, Canada Kelly B. Beck Department of Rehabilitation Science and Technology, University of Pittsburgh, Pittsburgh, PA, USA Daniel F. Becker Department of Psychiatry, University of California, San Francisco, USA Cynthia Beesley Benhaven, Inc., North Haven, CT, USA Marlene Behrman Department of Psychology, Carnegie Mellon University Center for the Neual Basis of Cognition, Pittsburgh, PA, USA Jennifer S. Beighley Department of Psychology, Louisiana State University, Baton Rouge, LA, USA Sara Beltran Southern Connecticut State University, New Haven, CT, USA Julie Bender Department of Communication Disorders, Southern Connecticut State University, New Haven, CT, USA
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Contributors
Stephanie Bendiske The Center For Children With Special Needs, Glastonbury, CT, USA Esther Ben-Itzchak Bruckner Center for Research in Autism, Department of Communication Disorders, Ariel University, Ariel, Israel Kyle D. Bennett Department of Teaching and Learning, Florida International University, Miami, FL, USA Matthew Bennett The University of Wollongong, Australia
Wollongong, NSW,
Randi Bennett Child Neuroscience Laboratory, Yale Child Study Center, New Haven, CT, USA Terry Bennett Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada Loisa Bennetto Department of Clinical and Social Sciences in Psychology, University of Rochester, Rochester, NY, USA Eric Benninghoff Yale University, New Haven, CT, USA Betsey A. Benson Nisonger Center, UCEDD, The Ohio State University, Columbus, OH, USA Carmen Berenguer University of Valencia, Valencia, Spain Michael Berger Department of Psychology, Royal Holloway University of London, Egham, Surrey, UK Ella Maja Viktoria Bergman Department of Education, UiT – The Arctic University of Norway, Tromsø, Norway Thomas Bergmann Berlin Treatment Center for Mental Health in Developmental Disabilities, Ev. Krankenhaus Königin Elisabeth Herzberge, Berlin, Germany Thomas P. Berney Institute of Health and Society, Sir James Spence Institute, Newcastle University, Royal Victoria Infirmary, Newcastle upon Tyne, UK Raphael Bernier Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA Armando Bertone McGill University, Montreal, QC, Canada Frank Besag Child and Adolescent Mental Health Services, SEPT. (South Essex Partnership University NHS Foundation Trust), Bedford, UK Chad Beyer Faculty of Medicine and Health Sciences, Stellenbosch University, Parow, South Africa Linas A. Bieliauskas Department of Psychiatry (F6248, MCHC-6), University of Michigan Health System, Ann Arbor, MI, USA Elizabeth E. Biggs Department of Special Education, University of Illinois, Urbana-Champaign, Champaign, IL, USA
Contributors
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Dorothy Bishop Department of Experimental Psychology, University of Oxford, Oxford, UK Somer Bishop Department of Psychiatry, University of California, San Francisco, CA, USA Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA Vicki Bitsika Faculty of Humanities and Social Sciences, Bond University, Robina, QLD, Australia Jan Blacher Graduate School of Education, University of California, Riverside, Riverside, CA, USA Caitlyn Black Southern Connecticut State University, New Haven, CT, USA Melissa H. Black School of Occupational Therapy, Social Work and Speech Pathology, Faculty of Health Sciences, Curtin Autism Research Group, Curtin University, Perth, WA, Australia Amanda Blackwell School of Behavioral and Brain Sciences, Callier Center for Communication Disorders, University of Texas-Dallas, Dallas, TX, USA Bryan J. Blair Institute for Behavioral Studies, The Van Loan School, Endicott College, Beverly, MA, USA Long Island University, Brooklyn, NY, USA Michael Bloch Yale OCD Research Clinic, New Haven, CT, USA Sarah Boland Yale Child Study Center, New Haven, CT, USA Danielle Bolling Yale Child Study Center, New Haven, CT, USA Sven Bölte Center of Neurodevelopmental Disorders (KIND), Department of Women’s and Children’s Health and Child and Adolescent Psychiatry, Center for Psychiatry Research, Karolinska Institutet and Stockholm County Council, Stockholm, Sweden Laura Bonazinga Bouyea Vermont Speech Language Pathology, University of Vermont, South Burlington, VT, USA Alex Bonnin Keck School of Medicine, University of Southern California, Los Angeles, CA, USA Susan Y. Bookheimer Department of Psychiatry and Biobehavioral Sciences, UCLA School of Medicine, Los Angeles, CA, USA Susan Boorin School of Nursing, Yale University, West Haven, CT, USA Hilary Boorstein Children’s Mercy Hospital, Kansas, MO, USA Kerri Booth Center for Children with Special Needs, Glastonbury, CT, USA Tereza-Maria Booules-Katri Department of Clinical and Health Psychology, Psychopathology and Neuropsychology Research Unit, Universitat Autonoma de Barcelona, Barcelona, Spain Jill Boucher Developmental Psychology, Autism Research Group, City University, London, UK
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Gordon Bourland Trinity Behavioral Associates, Arlington, TX, USA Linda Bowers LinguiSystems, Inc, East Moline, IL, USA Dermot Bowler Autism Research Group, City University London, London, UK Lisa Bowman-Perrott Texas A&M University, College Station, TX, USA Jessica Bradshaw Clinical Psychology, UCSB Koegel Autism Center, University of California, Santa Barbara, Santa Barbara, CA, USA John Bradshaw Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia Meghan Brahm Department of Special Education, Southern Connecticut State University, New Haven, CT, USA Marcel Brass Ghent University, Ghent, Belgium Helena Brentani Department of Psychiatry, Faculty of Medicine, University of Sao Paulo, Sao Paulo, Brazil Neil Brewer Flinders University, Adelaide, SA, Australia Jennifer Brielmaier Laboratory of Behavioral Neuroscience, National Institute of Mental Health, NIH, Porter Neuroscience Research Center, Bethesda, MD, USA Kate O’. Brien Mary Immaculate College, Limerick, Ireland Darlene Brodeur Department of Psychology, Acadia University, Wolfville, NS, Canada Erik Bromberg University of California, Santa Barbara, Santa Barbara, CA, USA Kabie Brook Autism Rights Group Highland (ARGH), Inverness, Scotland, UK Rechele Brooks Institute for Learning and Brain Sciences, University of Washington, Seattle, WA, USA Whitney T. Brooks Nisonger Center, UCEDD, The Ohio State University, Columbus, OH, USA Jeffrey P. Brosco Department of Pediatrics, Miller School of Medicine, University of Miami, Mailman Center for Child Development, Miami, FL, USA Mark Brosnan Centre for Applied Autism Research, Department of Psychology, University of Bath, Bath, UK Gregory Brower School of Medicine, Texas Tech University Health Sciences Center, Lubbock, TX, USA Ted Brown Department of Occupational Therapy, School of Primary and Allied Health Care, Faculty of Medicine, Nursing and Health Sciences, Monash University – Peninsula Campus, Frankston, VIC, Australia
Contributors
Contributors
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Lauren Turner Brown Department of Psychiatry, Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Ted Brown School of Primary and Allied Health Care, Faculty of Medicine, Nursing and Health Sciences, Monash University – Peninsula Campus, Frankston, VIC, Australia Pamela Brucker Special Education and Reading, Southern Connecticut State University, New Haven, CT, USA Crystal I. Bryce School of Social and Family Dynamics, Arizona State University, Tempe, AZ, USA Paulina L. Buffle Faculty de Psychology and Educational Sciences, University of Geneva, Geneva, Switzerland Jacob A. Burack Department of Educational and Counselling Psychology, McGill University, Montreal, QC, Canada Shakeia Burgin Division of Speech and Hearing Sciences, Department of Allied Health Sciences, University of North Carolina-Chapel Hill, School of Medicine, Chapel Hill, NC, USA Mack D. Burke Texas A&M University, College Station, TX, USA Meghan M. Burke Department of Special Education, University of Illinois at Urbana-Champaign, Champaign, IL, USA Karen Burner Department of Psychology, University of Washington, Seattle, WA, USA Courtney Burnette University of Nebraska, Medical Center Munroe-Meyer Institute, Omaha, NE, USA Anthony Burns Department of Psychiatry, AARTS Center, Rush University Medical Center, Chicago, IL, USA Casey Burrows Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA Sarah Butler Center for Autism and the Developing Brain, New YorkPresbyterian Hospital/Westchester Division, White Plains, NY, USA Eilidh Cage Department of Psychology, University of Stirling, Stirling, Scotland, UK Ru Ying Cai Autism Spectrum Australia (Aspect), Aspect Research Centre for Autism Practice, Flemington, VIC, Australia Department of Educational Studies, Macquarie University, Sydney, NSW, Australia Marina Calac Center for Early Intervention Volnickel, Chisinau, Republic of Moldova Susan Calhoun Psychiatry, Penn State Health and College of Medicine, Hershey, PA, USA
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Claudia Califano Yale-New Haven Hospital, New Haven, CT, USA Kevin Callahan University of North Texas, Kristin Farmer Autism Center, Denton, TX, USA Daniel Campbell Yale Child Study Center, Yale University, New Haven, CT, USA Ricardo Canal-Bedia Clinical Psychology Department, Department of Personality, Assessment, and Psychological Treatment, Centro de Atención Integral al Autismo (INFOAUTISMO), University Institute of Community Integration (INICO), University of Salamanca, Salamanca, Spain Allison R. Canfield Department of Pediatrics, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA Maria Canon Yale Child Study Center, New Haven, CT, USA Lindsey Capece Quinnipiac University, Hamden, CT, USA Matthew R. Capriotti Department of Psychology, University of WisconsinMilwaukee, Milwaukee, WI, USA Laurie Cardona Yale Child Study Center, Yale University, New Haven, CT, USA Michael Carley Green Bay, WI, USA L. Lee Carlisle Division of Child and Adolescent Psychiatry, Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA Joana C. Carmo Faculdade de Psicologia, Universidade de Lisboa, Lisbon, Portugal Departamento de Psicologia e Ciências da Educação, Faculdade de Ciências Humanas e Sociais, Universidade do Algarve, Faro, Portugal Christi Carnahan University of Cincinnati, Cincinnati, OH, USA Staci Carr UniqueKids Inc, Moseley, VA, USA Themba Carr University of Michigan Center for Human Growth and Development, Ann Arbor, MI, USA Alice S. Carter Department of Psychology, University of Massachusetts Boston, Boston, MA, USA Mark Carter School of Education, Macquarie University, Sydney, NSW, Australia Manuel Casanova Department of Psychiatry, University of Louisville, Louisville, KY, USA Carissa J. Cascio Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
Contributors
Contributors
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Jane Case-Smith Division of Occupational Therapy, School of Health and Rehabilitation Sciences, Columbus, OH, USA Arlette Cassidy The Gengras Center, University of Saint Joseph, West Hartford, CT, USA Lisa Castagnola Child Study Center, The Edward Zigler Center in Child Development and Social Policy, School of Medicine, Yale University, New Haven, CT, USA A. Charles Catania Department of Psychology, UMBC (University of Maryland, Baltimore County), Baltimore, MD, USA Paul K. Cavanagh Vocational Independence Program, New York Institute of Technology, Central Islip, NY, USA Antonio Cerasa Institute for Biomedical Research and Innovation (IRIB), National Research Council of Italy (CNR), Mangone, Italy S. Anna Institute and Research in Advanced Neurorehabilitation (RAN), Crotone, Italy Paige Cervantes Department of Child and Adolescent Psychiatry, Child Study Center, NYU Langone Health, New York, NY, USA Raymond Won Shing Chan ASD Services, New Life Psychiatric Rehabilitation Association, Kowloon, Hong Kong Marie Moore Channell Department of Speech and Hearing Science, University of Illinois at Urbana-Champaign, Champaign, IL, USA S. Michael Chapman TEACCH Autism Program, University of North Carolina Chapel Hill, Chapel Hill, NC, USA Marjorie H. Charlop Claremont McKenna College, Claremont, CA, USA Tony Charman Centre for Research in Autism and Education, Department of Psychology and Human Development, Institute of Education, University of London, London, UK Marek Chawarski Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA Liam R. Chawner University of Leeds, Leeds, UK Kuan-Lin Chen Department of Occupational Therapy, College of Medicine, National Cheng Kung University, Tainan City, Taiwan Institute of Allied Health Sciences, College of Medicine, National Cheng Kung University, Tainan City, Taiwan Department of Physical Medicine and Rehabilitation, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan City, Taiwan Karen Chenausky Boston University, Boston, MA, USA Tessa Chesher Tulane University, New Orleans, LA, USA
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Coralie Chevallier SGDP Centre, Institute of Psychiatry, King’s College, London, UK Center for Autism Research, Children’s Hospital of Philadelphia, Philadelphia, PA, USA Stephanie N Child May Institute, Randolph, MA, USA Youngsun T. Cho Yale Child Study Center, New Haven, CT, USA Sylvia Henn Tean Choo Department of Child Development, KK Women’s and Children’s Hospital, Singapore, Singapore Nick Chown Palau-solità i Plegamans, Lliçà de Vall, Barcelona, Spain Rob Christian Department of Psychiatry, The Carolina Institute for Developmental Disabilities, University of North Carolina School of Medicine, Chapel Hill, NC, USA Domenic V. Cicchetti Departments of Psychiatry and Biometry, Yale Child Study Center, Yale University, New Haven, CT, USA Marina Ciccarelli School of Occupational Therapy, Social Work and Speech Pathology, Curtin University, Perth, WA, Australia Joseph H. Cihon Autism Partnership Foundation, Seal Beach, CA, USA Emily Coderre Department of Communication Sciences and Disorders, University of Vermont, Burlington, VT, USA Keith A. Coffman Department of Pediatrics, School of Medicine, Pittsburgh, PA, USA Jared Cohen Yale Child Study Center, Yale University, New Haven, CT, USA Carla Colomer Universitat Jaume I, Castellon, Spain Emma Condy Neurodevelopmental and Behavioral Phenotyping Service, Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA Caitlin M. Conner Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA John N. Constantino Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA Barbara A. Cook Department of Communication Disorders, Center of Excellence on Autism Spectrum Disorders, Southern Connecticut State University, New Haven, CT, USA Elaine Coonrod Department of Psychiatry, School of Medicine, TEACCH, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Kelly D. Coons-Harding Department of Psychology, Laurentian University, Sudbury, ON, Canada
Contributors
Contributors
xxix
Judith Cooper NIDCD (National Institute on Deafness and Other Communication Disorders), National Institute of Health EPS – Executive Plaza South, Rockville, MD, USA Eugenia Corbett Franklin County Home Health, St. Albans, VT, USA Cara Cordeaux Child Neuroscience Lab, Yale Child Study Center, New Haven, CT, USA Joseph A. Cornett Psychology and Global Health, Yale College, Yale University, New Haven, CT, USA Lauren Cornew Radiology Department, Children’s Hospital of Philadelphia, Philadelphia, PA, USA Christoph U. Correll Psychiatry Research, The Zucker Hillside Hospital, Glen Oaks, NY, USA Christina Corsello Department of Psychiatry, Child and Adolescent Services Research Center, University of San Diego, San Diego, CA, USA Elin Cortijo-Doval Bioethics Center, Yale University, New Haven, CT, USA Kleio Cossburn Keele University, Keele, Newcastle-under-Lyme, UK Andreia P. Costa Institute for Health and Behavior, University of Luxembourg, Esch-sur-Alzette, Luxembourg Kirsty Coulter Department of Psychological Sciences, University of Connecticut, Storrs, CT, USA Emma Craig Queen’s University Belfast, Belfast, UK Kym Craig Heriot-Watt University, Edinburgh, Scotland, UK Madison Crandall Vanderbilt University, Nashville, TN, USA Laura Crane Centre for Research in Autism and Education (CRAE), UCL Institute of Education, University College London, London, UK Department of Psychology, Goldsmiths, University of London, New Cross, London, UK Hayley Crawford Coventry University, Coventry, UK Jacqueline N. Crawley Laboratory of Behavioral Neuroscience, National Institute of Mental Health, NIH, Porter Neuroscience Research Center, Bethesda, MD, USA Lisa Croen Autism Research Program, Kaiser Permanente Division of Research, Oakland, CA, USA Michael J. Crowley Developmental Electrophysiology Laboratory, Yale Child Study Center, New Haven, CT, USA Alyson Crozier School of Health Sciences, University of South Australia, Adelaide, SA, Australia
xxx
Kristen D’Eramo The Center for Children with Special Needs, Glastonbury, CT, USA Sarah Dababnah University of Maryland School of Social Work, Baltimore, MD, USA Yael Dai Department of Psychological Sciences, University of Connecticut, Storrs, CT, USA Tamara C. Daley Westat, Durham, NC, USA Paulo Dalgalarrondo University of Campinas Cidade Universitária “Zeferino Vaz”, Campinas, São Paulo, Brazil Jeffrey Danforth Department of Psychology, Eastern Connecticut State University, Willimantic, CT, USA John T. Danial Psychological Studies in Education, University of California, Los Angeles, Los Angeles, CA, USA Clarissa Dantas Department of Psychiatry, Faculty of Medical Sciences, University of Campinas (Unicamp), Campinas, São Paulo, Brazil Catherine Davies Indiana Resource Center for Autism Indiana University, Bloomington, IN, USA Cheryl Davis 7 Dimensions Consulting, Worcester, MA, USA Luann Ley Davis University of Memphis, Memphis, TN, USA Naomi Davis Institute for Social Development, Cary, NC, USA Leann Smith DaWalt Waisman Center, University of Wisconsin-Madison, Madison, WI, USA Geraldine Dawson Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA Michelle Dawson Hôpital Rivière des Prairies, Centre de recherche du CIUSS du Nord de l’île de Montréal et département de psychiatrie de l’Université de Montréal, Montréal, QC, Canada Talena C. Day School of Medicine, Child Study Center, Yale University, New Haven, CT, USA Annelies de Bildt Child and Adolescent Psychiatry, University Medical Center Groningen, Groningen, The Netherlands Concetta de Giambattista Child Neuropsychiatry Unit, University of Bari “Aldo Moro”, Bari, Italy Maretha de Jonge Department of Psychiatry, University Medical Center, Utrecht, Netherlands
Contributors
Contributors
xxxi
Ad De Jongh Department of Social Dentistry and Behavioral Sciences, Academic Centre for Dentistry Amsterdam (ACTA), University of Amsterdam and VU University Amsterdam, Amsterdam, The Netherlands School of Health Sciences, Salford University, Manchester, UK Institute of Health and Society, University of Worcester, Worcester, UK School of Psychology, Queen’s University, Belfast, Ireland Naama de la Fontaine Yale Child Study Center, New Haven, CT, USA Ashley B. de Marchena Department of Behavioral and Social Sciences, University of the Sciences, Philadelphia, PA, USA Oana De Vinck Department of Pediatrics, Yale University School of Medicine, New Haven, CT, USA Petrus J. de Vries Division of Child & Adolescent Psychiatry, University of Cape Town, Rondebosch, South Africa Rebecca DeAquair The Center for Children with Special Needs, Glastonbury, CT, USA W. Thornton N. Deegan Yale Child Study Center, New Haven, CT, USA Michelle DeFelice Southern Connecticut State University, New Haven, CT, USA Emma Delemere School of Social Sciences, Education and Social Work, Queen’s University Belfast, Belfast, UK Kristin Dell’Armo The Ohio State University Nisonger Center – UCEDD, Columbus, OH, USA Lara Delmolino Douglass Developmental Disabilities Center, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA Elizabeth A. DeLucia Yale Child Study Center, New Haven, CT, USA Virginia Polytechnic Institute and State University, Blacksburg, VA, USA K. Mark Derby Department of Special Education, Gonzaga University, Spokane, WA, USA Mieke Dereu Experimental Clinical and Health Psychology, Ghent University, Ghent, Belgium Whitney J. Detar Gevirtz Graduate School of Education, The University of California Center for Special Education, Disabilities, and Development, Santa Barbara, CA, USA Gabriel S. Dichter UNC Departments of Psychiatry, Psychology and Neuroscience, UNC-Chapel Hill, Carolina Institute for Developmental Disabilities, Chapel Hill, NC, USA
xxxii
Contributors
Joshua J. Diehl Autism Services, LOGAN Community Resources, Inc., South Bend, IN, USA Department of Psychology, University of Notre Dame, Notre Dame, IN, USA Carolyn DiGuiseppi Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA Anthony DiLollo Wichita State University, Department of Communication Sciences and Disorders, Wichita, Kansas, USA Nicholas M. DiLullo Child Study Center, Yale University School of Medicine, New Haven, CT, USA Ilan Dinstein Psychology Department, Carnegie Mellon University, Pittsburgh, PA, USA Amiris Dipuglia Pennsylvania Training and Technical Assistance Network, Harrisburg, PA, USA Leyla Akoury Dirani Division of Child and Adolescent Psychiatry, Department of Psychiatry, American University of Beirut Medical Center, Beirut, Lebanon Cheryl Dissanayake Olga Tennison Autism Research Centre, La Trobe University, Melbourne, VIC, Australia Mark R. Dixon Behavior Analysis and Therapy Program, Southern Illinois University, Carbondale, IL, USA Peter Doehring Foundations Behavioral Health, Doylestown, PA, USA ASD Roadmap, Chadds Ford, PA, USA Sam Doernberg Cornell University, Ithaca, NY, USA Rebecca Doggett Koegel Autism Center, Gevirtz Graduate School of Education University of California, Santa Barbara, Santa Barbara, CA, USA Elizabeth Howell Dohrmann Treatment and Research Institute for Autism Spectrum Disorders (TRIAD), Nashville, TN, USA Department of Psychiatry and Biobehavioral Sciences, Child and Adolescent Psychiatry Fellowship Program, Semel Institute for Neuroscience and Human Behavior, Resnick Neuropsychiatric Hospital, UCLA David Geffen School of Medicine, Los Angeles, CA, USA J. Don Richardson Department of Psychiatry, Ontario, London, ON, Canada
University of Western
John Donvan Washington, DC, USA Michael F. Dorsey Institute for Behavioral Studies, The Van Loan School, Endicott College, Beverly, MA, USA Amego Inc., The Best Clinical Network, Attleboro, MA, USA Constance Doss Department of Psychology, University of Alabama-Birmingham, Birmingham, AL, USA
Contributors
xxxiii
Katerina Dounavi School of Social Sciences, Education and Social Work, Queen’s University Belfast, Belfast, UK Peter W. Dowrick University of Auckland, Auckland, New Zealand Carolyn A. Doyle Indiana University School of Medicine, Indianapolis, IN, USA Jessica Dreaver School of Occupational Therapy, Social Work and Speech Pathology, Curtin University, Perth, WA, Australia Curtin Autism Research Group, Curtin University, Perth, WA, Australia Katerina M. Dudley Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Ana D. Dueñas Education and Human Services, Lehigh University, Bethlehem, PA, USA Jodi M. Duke Division of Special Education and disability Research, Fairfax, VA, USA Eric Duku Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada Amie Duncan Cincinnati Children’s Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, OH, USA Ed Duncan Children’s Centre, La Trobe University, Melbourne, VIC, Australia Debra Dunn The Center for Autism Research, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA Patrick Dwyer Centre for Autism Research, Technology and Education, Department of Psychology, Victoria, Canada Department of Psychology, University of California, Davis, Davis, CA, USA Kathleen Dyer River Street Autism Program at Coltsville, Capitol Region Education Council/Elms College, Hartford, CT, USA Endicott College, Bloomfield, CT, USA Jaclyn M. Dynia Crane Center for Early Childhood Research and Policy, The Ohio State University, Columbus, OH, USA Shaun M. Eack School of Social Work and Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA Maureen Early Christian Sarkine Autism Treatment Center, Indianapolis, IN, USA Lisa Edelson-Fries Department of Psychology, Boston University, Boston, MA, USA Neurocognition, Department of Brain Health, Nestlé Institute for Health Sciences, Lausanne, Switzerland
xxxiv
Elizabeth R. Eernisse Department of Language and Literacy, Cardinal Stritch University, Milwaukee, WI, USA Shaunessy Egan Center for Children with Special Needs, Glastonbury, CT, USA Inge-Marie Eigsti Department of Psychological Sciences, University of Connecticut, Storrs, CT, USA Svein Eikeseth Department of Behavioral Science, Oslo and Akershus University College, Lillestrøm, Norway Ingólfur Einarsson The State Diagnostic and Counseling Center, Kópavogur, Iceland Martin Eisemann Department of Psychology, UiT – The Arctic University of Norway, Tromso, Norway Naomi V. Ekas Department of Psychology, Texas Christian University, Fort Worth, TX, USA Rob El Fattal Cultivate Behavioral Health and Education, Bee Cave, TX, USA Ismail El Hailouch School of Public Health, Child Study Center, Yale University School of Medicine, New Haven, CT, USA Paul El-Fishawy State Laboratory, Child Study Center, Yale University, New Haven, CT, USA Amira Elhoufey Department of Community Health Nursing, Faculty of Nursing, Assiut University, Assiut, Egypt Department of Community Health Nursing, Sabia University College, Jazan University, Jazan, Kingdom of Saudi Arabia Stephen N. Elliott Sanford School of Social and Family Dynamics, Learning Sciences Institute, Arizona State University, Tempe, AZ, USA Kimberly Ellison Yale Child Study Center, New Haven, CT, USA Eric Emerson Centre for Disability Research, Lancaster University, Lancaster, LA, UK Centre for Disability Research and Policy, University of Sydney, Lidcombe, NSW, Australia Paul Edward Engelhardt School of Psychology, University of East Anglia, Norwich, Norfolk, UK Peter Enticott Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia Ruth Eren Center of Excellence on Autism Spectrum Disorders, Southern Connecticut State University, New Haven, CT, USA Patricio Erhard University of Texas at Austin, Austin, TX, USA
Contributors
Contributors
xxxv
Craig A. Erickson Christian Sarkine Autism Treatment Center, Indianapolis, IN, USA Department of Psychiatry, University of Cincinnati School of Medicine, Cincinnati, OH, USA Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA Gianluca Esposito Nanyang Technological University, Wako, Saitama, Singapore University of Trento, Rovereto, TN, Italy Kuroda Research Unit, RIKEN Brain Science Institute, Wako-shi Saitama, Japan Joshua Ewen Kennedy Krieger Institute, Baltimore, MD, USA Mariah Eykelhoff Southern Connecticut State University, New Haven, CT, USA Reina S. Factor Virginia Polytechnic Institute and State University, Blacksburg, VA, USA Virginia Tech Autism Clinic and Center for Autism Research, Blacksburg, VA, USA Michelle D. Failla Department of Psychiatry and Behavioral Science, Vanderbilt University Medical School, Nashville, TN, USA Terry S. Falcomata University of Texas at Austin, Austin, TX, USA Marita Falkmer School of Occupational Therapy, Social Work and Speech Pathology, Curtin University, Perth, WA, Australia Torbjörn Falkmer School of Occupational Therapy, Social Work and Speech Pathology, Curtin University, Perth, WA, Australia Pain and Rehabilitation Centre, and Department of Medical and Health Sciences, Linköping University, Linköping, Sweden Megan Farley Psychiatry, University of Utah School of Medicine, University Neuropsychiatric Institute, Salt Lake City, UT, USA Cristan Farmer The National Institute of Mental Health (NIMH), National Institutes of Health (NIH), Bethesda, MD, USA Nisonger Center Psychology, Ohio State University, Columbus, OH, USA Janet Farmer Thompson Center for Autism and Neurodevelopmental Disorders, University of Missouri, Columbia, MO, USA Miranda Farmer Yale Child Study Center, New Haven, CT, USA Jesslyn N. Farros Endicott College, Beverly, MA, USA Deborah Fein Department of Psychological Sciences, University of Connecticut, Storrs, CT, USA Adam Feinstein Autism Cymru and Looking Up, London, UK
xxxvi
Maurice Feldman Department of Child and Youth Studies and Department of Applied Disability Studies, Brock University, St. Catharines, ON, Canada Eunice Feng Koegel Autism Center, Eli and Edythe L. Broad Center for Asperger Research, University of California, Santa Barbara, CA, USA Rachel M. Fenning Department of Child and Adolescent Studies, California State University, Fullerton, Fullerton, CA, USA Jenny Ferguson School of Social Sciences, Education and Social Work, Queen’s University Belfast, Belfast, UK Julia L. Ferguson Autism Partnership Foundation, Seal Beach, CA, USA Thomas Fernandez Yale Child Study Center, Yale University School of Medicine, New Haven, CT, USA Summer Ferreri Department of Counseling, Educational Psychology and Special Education, College of Education Michigan State University, East Lansing, MI, USA Sean Field The School at Springbrook, Oneonta, NY, USA Carlos N. Filipe Faculdade de Ciências Médicas, NOVA Medical School, Universidade Nova de Lisboa, Lisbon, Portugal Joseph J. Fins Division of Medical Ethics, Weill Cornell Medical College, New York, NY, USA Michael B. First Department of Psychiatry, Columbia University, New York State Psychiatric Institute, New York, NY, USA Nicole Fischer Department of Communication Disorders, Southern Connecticut State University, New Haven, CT, USA Patricio Fischman Yale University Child Study Center, New Haven, CT, USA Private Practice, Santiago, Chile Ronit Fischman Child and Adolescent Psychologist, Private Practice, Santiago, Chile Veronica P. Fleury Florida State University, Tallahassee, FL, USA Renee Folsom Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles (UCLA) The Help Group/UCLA Neuropsychology Program, Los Angeles, CA, USA Laura Fontil Department of Educational and Counselling Psychology, School/Applied Child Psychology, McGill University, Montreal, QC, Canada Joy Fopiano Department of Elementary Education, Southern Connecticut State University, New Haven, CT, USA Danielle Forbes Psychology, University of Massachusetts Boston, Boston, MA, USA
Contributors
Contributors
xxxvii
Solandy Forte The Center for Children with Special Needs, Glastonbury, CT, USA Milestones Behavioral Services, Inc., Milford, CT, USA Jennifer H. Foss-Feig Department of Psychiatry, Icahn School of Medicine at Mount Sinai Hospital, New York, NY, USA Richard M. Foxx University of Pennsylvania, Harrisburg, PA, USA Christina Fragale University of Texas at Austin, Austin, TX, USA Kathleen B. Franke The Unumb Center for Neurodevelopment, Columbia, SC, USA Thomas Frazier Autism Speaks, New York, NY, USA Cleveland Clinic Children’s, Cleveland, OH, USA Stephanny Freeman Center for Autism Research and Treatment (CART), University of California, Los Angeles, Los Angeles, CA, USA Megan Freeth Department of Psychology, University of Sheffield, Sheffield, UK Hannah Friedman Yale Child Study Center, New Haven, CT, USA Uta Frith Division of Biosciences, Institute of Cognitive Neuroscience UCL, London, UK Cori Fujii Division of Psychological Studies in Education, University of California, Los Angeles, Los Angeles, CA, USA Daniel Shuen Sheng Fung Department of Developmental Psychiatry, Institute of Mental Health, Singapore, Singapore Rosaria Furlano Department of Psychology, Queen’s University, Kingston, ON, Canada Maria Fusaro Department of Psychiatry and Behavioral Sciences, UC Davis M.I.N.D. Institute, Sacramento, CA, USA Cheryl Smith Gabig Department of Speech, Language, and Hearing Sciences, Lehman College/The City University of New York, Bronx, NY, USA Sebastian Gaigg Autism Research Group, City University London, London, UK Eynat Gal Department of Occupational Therapy, University of Haifa, Haifa, Israel Cédric Galera Department of Child and Adolescent Psychiatry, Université de Bordeaux, Bordeaux, France Jennifer Gallup Idaho State University, Pocatello, ID, USA Tanuja Gandhi Child Study Centre, Yale School of Medicine, New Haven, CT, USA
xxxviii
Cristina García-López Joint Research Institute National University for Distance Education and Health Institute Carlos III (IMIENS), Madrid, Spain Hospital Sant Joan de Déu, UTAE, Barcelona, Spain Lauren Gardner Child Development and Rehabilitation Center, Johns Hopkins All Children’s Hospital, Saint Petersburg, FL, USA Dana Rose Garfin Sue & Bill Gross School of Nursing, University of California, Irvine, Irvine, CA, USA Gabriela Garrido Department of Child and Adolescent Psychiatry, ASD Department, Pereira Rossell Hospital – ASSE, Universidad de la República, School of Medicine, Montevideo, Uruguay Beth Garrison Hartford Hospital Pain Treatment Center, Bristol, CT, USA Grant Gautreaux Nicholls State University, Thibodaux, LA, USA David C. Gavisk College of Contemporary Liberal Studies, Department of Education, Regis University, Denver, CO, USA Erin Gelinas Department of Communication Disorders, Southern Connecticut State University, New Haven, CT, USA Grace W. Gengoux Child and Adolescent Psychiatry, Stanford University School of Medicine, Lucile Packard Children’s Hospital, Stanford, CA, USA Danielle Geno The College of Arts and Sciences, The University of Vermont, Burlington, VT, USA Stelios Georgiades Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada Sima Gerber Department of Linguistics and Communication Disorders, Queens College, Flushing, NY, USA Jennifer Varley Gerdts Department of Psychology, University of Washington, CHDD, Seattle, WA, USA Meital Gewirtz Yale University, New Haven, CT, USA Golnaz Ghaderi Department of Social Sciences, University of Ottawa, Ottawa, ON, Canada Ahmad Ghanizadeh School of Medicine, Research Center for Psychiatry and Behavioral Sciences, Shiraz University of Medical Sciences, Shiraz, Iran Parisa Ghanouni Occupational Science and Occupational Therapy Department, University of British Columbia, Vancouver, Canada Occupational Science and Occupational Therapy Department, Dalhousie University, Halifax, NS, Canada Mohammad Ghaziuddin University of Michigan, Ann Arbor, MI, USA Jenna Gilder Claremont Graduate University, Claremont, CA, USA
Contributors
Contributors
xxxix
Christopher Gillberg Department of Child and Adolescent Psychiatry, Gillberg Neuropsychiatry Centre, University of Gothenburg, Gothenburg, Sweden Madelyn A Gillentine Department of Genome Sciences, University of Washington, Seattle, WA, USA Walter Gilliam Child Study Center, Yale University School of Medicine, New Haven, CT, USA Regina Gilroy Quinnipiac University School of Law, Hamden, CT, USA Sonya Girdler School of Occupational Therapy, Social Work and Speech Pathology, Faculty of Health Sciences, Curtin University, Perth, WA, Australia Curtin Autism Research Group, Curtin University, Perth, WA, Australia Ivy Giserman Kiss Department of Psychology, University of Massachusetts Boston, Boston, MA, USA Jalisa Gittens McGill University, Montreal, QC, Canada Theresa R. Gladstone Yale Child Study Center, New Haven, CT, USA Jeffrey Glennon Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands Tara J. Glennon Occupational Therapy, Quinnipiac University, Hamden, CT, USA Centre of Pediatric Therapy, Fairfield and Wallingford, Wallingford, CT, USA Dorie Glover Psychiatry and Biobehavioral Sciences, University of California at Los Angeles, Los Angeles, CA, USA Lindsay B. Glugatch Department of Special Education and Clinical Sciences, University of Oregon, Eugene, OR, USA Nitin Gogtay Division of Child and Adolescent Psychiatry, National Institutes of Mental Health, Bethesda, MD, USA Tze Jui Goh Department of Developmental Psychiatry, Institute of Mental Health, Singapore, Singapore Ofer Golan Department of Psychology, Bar-Ilan University, Ramat Gan, Israel Association for Children at Risk (R.A.), Tel Aviv, Israel Melissa C. Goldberg Kennedy Krieger Institute, Baltimore, MD, USA Wendy A. Goldberg Department of Psychological Science, University of California, Irvine, Irvine, CA, USA Yael Goldfarb Department of Occupational Therapy, University of Haifa, Haifa, Israel Rachel L. Goldin Department of Psychology, Louisiana State University, Baton Rouge, LA, USA
xl
Tina R. Goldsmith Center for Development and Disability, University of New Mexico, Albuquerque, NM, USA Howard Goldstein Human Development and Family Science, The Ohio State University, Columbus, OH, USA Sam Goldstein Neurology Learning and Behavior Center, University of Utah, Salt Lake City, UT, USA Peyman Golshani David Geffen School of Medicine at UCLA, Los Angeles, CA, USA José Luis Cuesta Gómez Faculty of Education, Universidad de Burgos, Burgos, Spain Ana Maria Gonzalez-Barrero Department of Psychology, Concordia University, Montreal, QC, Canada Emma Goodall The University of Wollongong, Wollongong, NSW, Australia Cara Damiano Goodwin Virginia Institute of Autism, Charlottesville, VA, USA Amanda E. Gordon Quinnipiac University School of Law, Hamden, CT, USA Ilanit Gordon Child Study Center, Yale University, New Haven, CT, USA Judith Gould NAS Lorna Wing Centre for Autism, Bromley, UK Michele Goyette-Ewing Yale Child Study Center, New Haven, CT, USA Richard B. Graff The New England Center for Children, Southborough, MA, USA Catherine Grainger Psychology, University of Stirling, Stirling, Scotland, UK Temple Grandin Department of Animal Sciences, Colorado State University, Fort Collins, CO, USA Kylie M. Gray Centre for Developmental Psychiatry and Psychology, Department of Psychiatry, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia Sarah A. O. Gray Department of Psychology, Tulane University, New Orleans, LA, USA Department of Psychology, University of Massachusetts Boston, Boston, MA, USA Ashley Dawn Greathouse Combined-Integrated Clinical and Counseling Psychology Doctoral Program, University of South Alabama, Mobile, AL, USA
Contributors
Contributors
xli
Kirstin Greaves-Lord Jonx Department of (Youth) Mental Health and Autism, Lentis Psychiatric Institute, Groningen, The Netherlands Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC, Rotterdam, The Netherlands Yulius Autisme, Dordrecht, The Netherlands Emma Green Department of Psychology, University of Waterloo, Waterloo, ON, Canada Evelynne Green The University of Vermont, Burlington, VT, USA Shulamite A. Green Department of Psychology, University of California, Los Angeles, CA, USA Alissa Greenberg Juvo, Sacramento, CA, USA Jan S. Greenberg Waisman Center, University of Wisconsin-Madison, Madison, WI, USA Alyse Greer Quinnipiac University School of Law, Hamden, CT, USA Frank M. Gresham Department of Psychology, Louisiana State University, Baton Rouge, LA, USA Elena L. Grigorenko University of Houston, Houston, TX, USA Yale Child Study Center, Psychology, and Epidemiology and Public Health, Yale University, New Haven, CT, USA Jemma Grindstaff Chapel Hill TEACCH Center, Carrboro, NC, USA Roy Grinker Anthropology, The George Washington University, Washington, DC, USA Roseann R. Groh Center for Children with Special Needs, Glastonbury, CT, USA Mark Groskreutz Special Education and Reading Department, The Center of Excellence on Autism Spectrum Disorders, Southern Connecticut State University, New Haven, CT, USA Matthew Grover Otterbein University, Westerville, OH, USA Manon Grube Center for Music in the Brain, Faculty of Health, Aarhus University, Aarhus, Denmark Rinatte Gruen Yale Child Study Center, New Haven, CT, USA Ouriel Grynszpan Laboratoire d’Informatique pour la Mécanique et les Sciences de l’Ingénieur, LIMSI, CNRS, Université Paris-Sud, Orsay, France Rebecca Grzadzinski Carolina Institute for Developmental Disabilities, University of North Carolina, Chapel Hill, NC, USA
xlii
Amanda C. Gulsrud UCLA Semel Institute for Neuroscience and Human Behavior, Los Angeles, CA, USA Yuqing Guo Sue & Bill Gross School of Nursing, University of California, Irvine, Irvine, CA, USA Abha R. Gupta Developmental-Behavioral Pediatrics, Child Study Center, Yale University, New Haven, CT, USA Nouchine Hadjikhani Martinos Center for Biomedical Imaging, Harvard Medical School, Boston, MA, USA Gillberg Neuropsychiatry Center, Sahlgrenska Academy, University of Gothenburg, Göteborg, Sweden Eileen Haebig Department of Speech, Language, and Hearing Sciences, Purdue University, West Lafayette, IN, USA Deborah Hales Division of Education, American Psychiatric Association, Arlington, VA, USA Jane Hamilton Quinnipiac University School of Law, Hamden, CT, USA Daniela Han Private Practice, Santiago, Chile Yu Han Neuroscience, University of Vermont, Burlington, VT, USA Gregory P. Hanley Western New England University, Springfield, MA, USA Robin Hansen Pediatrics, Center for Excellence in Developmental Disabilities, UC Davis M.I.N.D. Institute, Sacramento, CA, USA Francesca Happé MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK Antonio Y. Hardan Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA Sarah Hardy Department of Psychological Sciences, University of Connecticut, Storrs, CT, USA Annville Psychological Services, Annville, PA, USA Toya Harmon Texas State University, San Marcos, TX, USA Sandra Harris Douglass Developmental Disabilities Center, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA Ashley J. Harrison Department of Educational Psychology, University of Georgia, Athens, GA, USA Catharina Hartman Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands Ahmad Hassan Department of Neuroscience, Yale University School of Medicine, New Haven, CT, USA
Contributors
Contributors
xliii
Wassim Hassan Department of Neuroscience, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA Tyler A. Hassenfeldt Virginia Polytechnic Institute and State University, Blacksburg, VA, USA Kathleen Hastings Southern Connecticut State University, New Haven, CT, USA Megan Hatfield School of Occupational Therapy, Social Work and Speech Pathology, Curtin University, Perth, WA, Australia Susan M. Havercamp Nisonger Center, UCEDD, The Ohio State University, Columbus, OH, USA Brett Heasman Centre for Research in Autism and Education (CRAE), UCL, London, UK Pamela Heaton Department of Psychology, University of London, London, UK Amy Heberle Clinical Psychology, University of Massachusetts, Boston, MA, USA Darren Hedley School of Psychological Science, Olga Tennison Autism Research Centre, La Trobe University, Melbourne, VIC, Australia John P. Hegarty Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA Stephen Hegedus School of Education, Southern Connecticut State University, New Haven, CT, USA S. M. J. Heijnen-Kohl Mondriaan Geriatric Mental Health Care, HeerlenMaastricht, The Netherlands Sascha Hein Freie Universität Berlin, Berlin, Germany David T. Helm Division of Developmental Medicine, Boston Children’s Hospital, Boston, MA, USA Heather A. Henderson Department of Psychology, University of Waterloo, Waterloo, ON, Canada Department of Psychology, University of Miami, Coral Gables, FL, USA Dawn Hendricks Department of Special Education and Disability Policy, VCU Autism Center for Excellence, Virginia Commonwealth University, Richmond, VA, USA Susan Hepburn Department of Psychiatry and Pediatrics, JFK Partners, University of Colorado at Denver, Aurora, CO, USA Colorado State University, Department of Human Development and Family Services, Fort Collins, CO, USA Katelyn Herchel Center for Children with Special Needs, Glastonbury, CT, USA
xliv
Irva Hertz-Picciotto Department of Public Health Sciences and the MIND Institute, University of California, Davis, Davis, CA, USA Amaia Hervas Child and Adolescent Mental Health Unit, University Hospital Mutua of Terrassa, Barcelona, Spain Sean Hess Rehabilitation Services, Wesley Woodlawn Hospital & ER, Wichita, KS, USA Ashley Durkee Hester Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Steven D. Hicks Penn State College of Medicine, Hershey, PA, USA Trenesha L. Hill Department of Psychology, Tulane University, New Orleans, LA, USA Manon H. J. Hillegers Department of Child and Adolescent Psychiatry/ Psychology, Erasmus Medical Center-Sophia Children’s Hospital, Rotterdam, The Netherlands Ashleigh Hillier Department of Psychology, University of Massachusetts Lowell, Lowell, MA, USA Jennifer Hillman Applied Psychology Program, The Pennsylvania State University, Berks College, Reading, PA, USA Claudia Hilton Occupational Therapy Department, University of Texas Medical Branch, Galveston, TX, USA Kimberly Ho Misiaszek Yale Child Study Center, New Haven, CT, USA Michal Hochhauser Department of Occupational Therapy, Ariel University, Ariel, Israel Ginny Hodge Chapel Haven, Inc, New Haven, CT, USA Abby Hodges University of Denver, Denver, CO, USA Sandra Hodgetts Pediatrics, University of Alberta, Edmonton, AB, Canada Kristin Hodgson UNC TEACCH Autism Program-Charlotte, Charlotte, NC, USA Ellen J. Hoffman Albert J. Solnit Integrated Training Program, Yale Child Study Center, Program on Neurogenetics, Yale School of Medicine, New Haven, CT, USA Abigail L. Hogan Department of Psychology, University of South Carolina, Columbia, SC, USA Kerry Hogan Wilmington Psych, Wilmington, NC, USA Thomas P. Hogan Department of Psychology, University of Scranton, Scranton, PA, USA Katherine C. Holman Department of Special Education, Towson University, Towson, MD, USA
Contributors
Contributors
xlv
Anne Holmes Eden Autism Services, Princeton, NJ, USA David L. Holmes Lifespan Services, Princeton, NJ, USA Jinkuk Hong Waisman Center, University of Wisconsin-Madison, Madison, WI, USA Lisa Honigfeld Child Health and Development Institute of Connecticut, Farmington, CT, USA Stephen R. Hooper Department of Allied Health Sciences, School of Medicine, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA Daniel W. Hoover Center for Child and Family Traumatic Stress, Kennedy Krieger Institute, Baltimore, MD, USA M. S. Hope Morris Communication Sciences and Disorders, The University of Vermont, Burlington, VT, USA Andrea Horvath Department of Paediatrics, The Medical University of Warsaw, Warsaw, Poland Ernst Horwitz Department of Psychiatry, Groningen University Medical Center, Groningen, The Netherlands Katherine Howells Deakin Child Study Centre, School of Psychology, Faculty of Health, Deakin University, Geelong, VIC, Australia Patricia Howlin Institute of Psychiatry, Psychology and Neuroscience, King’s College, London, UK Youjia Hua Department of Curriculum, Instruction and Special Education, Curry School of Education and Human Development, University of Virginia, Charlottesville, VA, USA Kristelle Hudry Olga Tennison Autism Research Centre, School of Psychological Science, La Trobe University, Bundoora, VIC, Australia Marisela Huerta Center for Autism and the Developing Brain, Weill Cornell Medicine, New York, NY, USA Samantha Huestis Yale Child Study Center, New Haven, CT, USA Rosemary Huisingh LinguiSystems, Inc, East Moline, IL, USA Laura Hull Research Department of Clinical, Educational and Health Psychology, University College London, London, UK Kara Hume University of North Carolina, Chapel Hill, NC, USA Rachel Hundley Division of Developmental Medicine, Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, USA Hillary Hurst Department of Psychology, University of Massachusetts Boston, Boston, MA, USA Vanessa Hus Department of Psychology, University of Michigan, Ann Arbor, MI, USA
xlvi
Contributors
Tiffany Hutchins Department of Communication Sciences and Disorders, The University of Vermont, Burlington, VT, USA Ted Hutman Department of Psychiatry and Biobehavioral Science, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA Semel Institute of Neuroscience, Los Angeles, CA, USA Soonjo Hwang Massachusetts General Hospital, Boston, MA, USA Wei-Chin Hwang Department of Psychology, Claremont McKenna College, Claremont, CA, USA Susan Hyman Developmental and Behavioral Pediatrics, Division Chief Neurodevelopmental and Behavioral Pediatrics, University of Rochester Golisano Children’s Hospital, Rochester, NY, USA Suzannah Iadarola Department of Pediatrics, Medical Center, Rochester, NY, USA
University of Rochester
Dorothea A. Iannuzzi Division of Academic Pediatrics, Autism Intervention Research Network on Physical Health (AIR-P), Autism Treatment Network (ATN), Mass General Hospital for Children, Boston, MA, USA Karim Ibrahim Child Study Center, Yale School of Medicine, Yale University, New Haven, CT, USA Masakazu Ide Department of Disabilities of Brain Functions, Research Institute of National Rehabilitation Center for Persons with Disabilities, Tokorozawa/Saitama, Japan Nazish Imran Child and Family Psychiatry Department, King Edward Medical University/Mayo Hospital, Lahore, Pakistan Sheree Incorvaia Vocational Independence Program, New York Institute of Technology, Central Islip, NY, USA Brooke Ingersoll Department of Psychology, Michigan State University, East Lansing, MI, USA Barry Ingham Northumberland, Tyne and Wear NHS Foundation Trust, Newcastle, UK Irma Isasa Child and Adolescent Service, Polyclinic Gipuzkoa, San Sebastián, Spain Andrew Iskandar Division TEACCH, CB 7180, UNC-CH, TEACCH Early Intervention Program, Chapel Hill, NC, USA Scott Luther James Jackson Child Study Center, Yale University School of Medicine, New Haven, CT, USA Laudan B. Jahromi School of Social and Family Dynamics, Arizona State University, Tempe, AZ, USA Mark Jaime Division of Science, Indiana University-Purdue University, Columbus, Columbus, IN, USA
Contributors
xlvii
T. Rene Jamison Center for Child Health and Development, University of Kansas Medical Center, Kansas City, KS, USA Sara Jelinek Department of Psychology, Michigan State University, East Lansing, MI, USA Heather H. Jia Illinois State University, Normal, IL, USA Ronnie Jia Illinois State University, Normal, IL, USA Cynthia R. Johnson Pediatrics, Psychiatry, and Education, University of Pittsburgh, Pittsburgh, PA, USA Ellen Johnson Section of Social Work, Mayo Clinic, Rochester, MN, USA Kimberly Johnson Neurodevelopmental and Behavioral Pediatrics, Children’s Hospital Colorado, Aurora, CO, USA Kristin Johnson Yale University, New Haven, CT, USA Catherine R. G. Jones Department of Psychology, University of Essex, Colchester, UK Emily Jones Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA Rebecca Jordan Child Study Center, Yale School of Medicine, Yale University, New Haven, CT, USA Rita Jordan School of Education, University of Birmingham, Edgbaston, Birmingham, UK Roger J. Jou Child Study Center, Yale University School of Medicine, New Haven, CT, USA Martha Bates Jura Department of Psychiatry, UCLA/Geffen School of Medicine, Los Angeles, CA, USA Tobi Gilbert Juris Quinnipiac University School of Law, Hamden, CT, USA Aaron Kaat Nisonger Center, Ohio State University, Columbus, OH, USA Allison Kahl New York University School of Law, New York, NY, USA Martha D. Kaiser Child Neuroscience Laboratory, Yale Child Study Center, New Haven, CT, USA Luke Kalb Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Kennedy Krieger Institute’s Center for Autism and Related Disorders, Baltimore, MD, USA Rajesh Kana Department of Psychology, University of Alabama-Birmingham, Birmingham, AL, USA Xin Kang Department of Applied Social Sciences, The Hong Kong Polytechnic University, Hung Hom, Hong Kong
xlviii
Steve Kanne Department of Health Psychology, School of Health Professions Thompson Center for Autism and Neurodevelopmental Disorders, University of Missouri, Columbia, MO, USA Sara Kaplan-Levy Clinical Psychology, University of Massachusetts Boston, Boston, MA, USA Annette Karmiloff-Smith Birkbeck College, London, UK Christie P. Karpiak Department of Psychology, University of Scranton, Scranton, PA, USA Connie Kasari Graduate School of Education and Information Studies and the Semel Institute, University of California, Los Angeles, Los Angeles, CA, USA Juli Katon Department of Special Education, University of Maryland, College Park, MD, USA Alice Kau Intellectual and Developmental Disabilities (IDD) Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD, USA Elizabeth Kauffman AJ Drexel Autism Institute, Drexel University, Philadelphia, PA, USA Alan S. Kaufman Yale University School of Medicine, New Haven, CT, USA Carson Kautz Yale Child Study Center, Yale University, New Haven, CT, USA Brandon Keehn Department of Speech, Language, and Hearing Sciences, Department of Psychological Sciences, Purdue University, West Lafayette, IN, USA Jacqueline Kelleher Education, Sacred Heart University Isabelle Farrington School of Education, Southern Connecticut State University, Fairfield, CT, USA Annemarie M. Kelly College of Health and Human Services, Eastern Michigan University, Ypsilanti, MI, USA Daniel P. Kennedy Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, USA Maureen C. Kenny Department of Counseling, Recreation and School Psychology, Florida International University, Miami, FL, USA Danielle Geno Kent The College of Arts and Sciences, The University of Vermont, Burlington, VT, USA Connor M. Kerns Department of Psychology, University of British Columbia, Vancouver, BC, Canada
Contributors
Contributors
xlix
Stephenie Koon Miang Khoo Autism Resource Centre, Singapore, Singapore Meena Khowaja Nemours/A.I. duPont Hospital for Children, Wilmington, DE, USA Emily Kilroy Mayes Lab, Yale Child Study Center, New Haven, CT, USA Jinah Kim Department of Creative Arts Therapy, College of Cultural Convergence, Jeonju University, Jeonju, Jeollabukdo, Republic of Korea Mina Kim College of Education Temple University, Philadelphia, PA, USA So Hyun Sophy Kim Department of Psychiatry, Weill Cornell Medicine, White Plains, NY, USA Department of Psychology, University of Michigan, Ann Arbor, MI, USA Sunny Kim Koegel Autism Center, University of California, Santa Barbara, Santa Barbara, CA, USA Young-Shin Kim Yale Child Study Center, New Haven, CT, USA Yael Kimhi Education, Levinsky College of Education, Tel-Aviv, Israel Jessica Lynn Kinard The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Bryan King Department of Psychiatry and Behavioral Sciences and Seattle Children’s Hospital, University of Washington, Seattle, WA, USA Robert King School of Applied Psychology, University College Cork, Cork, Ireland Usha Kini Consultant Clinical Geneticist, Oxford Radcliffe Hospitals NHS Trust University of Oxford, Oxford, UK Anne V. Kirby Department of Occupational and Recreational Therapies, University of Utah, Salt Lake City, UT, USA Raymond M. Klein Department of Psychology and Neuroscience, Dalhousie University, Halifax, NS, Canada Harvey J. Kliman Reproductive and Placental Research Unit, Department of Obstetrics, Gynecology and Reproductive Sciences, Yale University School of Medicine, New Haven, CT, USA Laura G. Klinger TEACCH Autism Program, Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Vicki Madaus Knapp Applied Behavior Analysis (ABA) Program, Daemen College, Amherst, NY, USA Rebecca Knickmeyer Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA Victoria Knight Faculty of Education, University of British Columbia, Vancouver, BC, Canada
l
Ryan Knighton The Center for Children with Special Needs, Glastonbury, CT, USA Newton Public Schools, Newton, MA, USA Jordan A. Ko Koegel Autism Center/Department of Counseling, Clinical, and School Psychology, University of California Santa Barbara, Santa Barbara, CA, USA Brittany L. Koegel University of California, Santa Barbara, Santa Barbara, CA, USA Lynn Kern Koegel Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA Koegel Autism Center, Eli and Edythe L. Broad Center for Asperger Research, University of California, Santa Barbara, CA, USA Robert L. Koegel Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA Koegel Autism Center/Clinical Psychology, Gevirtz Graduate School of Education, University of California, Santa Barbara, CA, USA Frances L. Kohl Department of Special Education, University of Maryland, College Park, MD, USA Natasha Kolivas Olga Tennison Autism Research Centre, La Trobe University, Melbourne, VIC, Australia Judah Koller Seymour Fox School of Education, Clinical Child Psychology, The Hebrew University of Jerusalem, Jerusalem, Israel Koorosh Kooros Pediatric Gastroenterology and Nutrition, Rady Children’s Hospital, San Diego, University of California San Diego, San Diego, CA, USA Jonathan Kopel Texas Tech University Health Sciences Center (TTUHSC), Lubbock, TX, USA Kellie Kotwicki Applied Behavior Analysis, Daemen College, Amherst, NY, USA Positive ABA, LLC, Queen Creek, AZ, USA Klara Kovarski Fondation Ophtalmologique A. de Rothschild, Institut de Neuropsychologie, Neurovision et NeuroCognition, Paris, France CNRS (Integrative Neuroscience and Cognition Center, UMR 8002), Paris, France Université Paris Descartes, Sorbonne Paris Cité, Paris, France David J. Krainski Vocational Independence Program, New York Institute of Technology, Central Islip, NY, USA Cate Kraper Clinical Psychology, University of Massachusetts Boston, Boston, MA, USA
Contributors
Contributors
li
Anna M. Krasno The Gevirtz School, UC Santa Barbara Koegel Autism Center, Santa Barbara, CA, USA Jennifer M. D. Kremkow Department of Communication Sciences and Disorders, Elmhurst College, Elmhurst, IL, USA M. Kristen Center for Children with Special Needs, Glastonbury, CT, USA Kimberly Kroeger-Geoppinger Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA Steve Kroupa School of Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Graduate School of Human-Environment Studies, Kyushu University, Fukuoka, Japan Lydia Kruse Human Development and Family Science, Schoenbaum Family Center, The Ohio State University, Columbus, OH, USA S. Jay Kuder Department of Interdisciplinary and Inclusive Education, College of Education, Rowan University, Glassboro, NJ, USA Grace Kuravackel Pediatrics, University of Louisville, Louisville, KY, USA Sarah Kuriakose Department of Counseling, Clinical, and School Psychology (CCSP), University of California, Santa Barbara, CA, USA Hiroshi Kurita Graduate School of Medicine, The University of Tokyo, Tokyo, Japan Onur Kurt Research Institute for Individuals with Disabilities, Anadolu University, Eskisehir, Turkey Emily S. Kuschner Center for Autism Spectrum Disorders, Division of Neuropsychology, Children’s National Medical Center, Washington, DC, USA Metehan Kutlu Department of Special Education, Hakkari University, Hakkari, Turkey Jennifer M. Kwon Department of Neurology and Pediatrics (SMD), University of Rochester, School of Medicine and Dentistry, Rochester, NY, USA Hidemi Kyotani Centre for Autism Research, Technology and Education, Department of Psychology, Victoria, Canada Szu-Shen Lai Department of Physical Medicine and Rehabilitation, Taoyuan Chang Gung Memorial Hospital, Taoyuan City, Taiwan Chee Meng Lam Autism Resource Centre, Singapore, Singapore Kristen Lam UNC Neurodevelopmental Disorders Research Center, UNCChapel Hill, Chapel Hill, NC, USA Rebecca Landa Center for Autism and Related Disorders, Kennedy Krieger Institute’s, Baltimore, MD, USA
lii
Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA Chloe Lane Department of Psychology, University of Sheffield, Sheffield, UK Russell Lang Clinic for Autism Research Evaluation and Support, Texas State University, San Marcos, TX, USA Traci Lanner The School at Springbrook, Oneonta, NY, USA Kyle Lanning Quinnipiac University School of Law, Hamden, CT, USA Nathaniel Laor Department of Psychiatry, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel Department of Medical Education, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel Yale Child Study Center, New Haven, CT, USA Association for Children at Risk (R.A.), Tel Aviv, Israel Amanda P. Laprime The Center for Children with Special Needs, Glastonbury, CT, USA University of Rochester Medical Center, Rochester, NY, USA Kenneth Larsen Oslo University Hospital, Oslo, Norway Robert H. LaRue Douglass Developmental Disabilities Center, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA Susan Latham Department of Communication Disorders, St. Mary’s College (IN), Notre Dame, IN, USA Elizabeth Laugeson UCLA Semel Institute for Neuroscience and Human Behavior, Los Angeles, CA, USA Margaret Holmes Laurie Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK Tara A. Lavelle Center for Value and Risk in Health (CEVR), Tufts Medical Center, Boston, MA, USA J. Kiely Law Department of Medical Informatics, Kennedy Krieger Institute, Baltimore, MD, USA Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD, USA Kathy Lawton Special Education and Nisonger Center, The Ohio State University, Columbus, OH, USA Kathy Leadbitter Social Development Research Group, University of Manchester, Manchester, UK Geraldine Leader Irish Centre for Autism and Neurodevelopmental Research (ICAN), National University of Ireland, Galway (NUI Galway), Galway, Ireland
Contributors
Contributors
liii
Justin B. Leaf Autism Partnership Foundation, Seal Beach, CA, USA Ronald Leaf Autism Partnership Foundation, Seal Beach, CA, USA Eli R. Lebowitz Yale School of Medicine, Child Study Center, Yale University, New Haven, CT, USA Emma Lecarie Yale Child Study Center, New Haven, CT, USA Luc Lecavalier Nisonger Center, Ohio State University, Columbus, OH, USA Ann S. Le-Couteur Institute of Health and Society, Sir James Spence Institute, Newcastle University, Royal Victoria Infirmary, Newcastle upon Tyne, UK Katherine Ledbetter-Cho Clinic for Autism Research Evaluation and Support, Texas State University, San Marcos, TX, USA Elinda Ai Lim Lee School of Occupational Therapy, Social Work and Speech Pathology, Faculty of Health Sciences, Curtin University, Perth, WA, Australia Curtin Autism Research Group, Curtin University, Perth, WA, Australia Evon Batey Lee Pediatrics, Kennedy Center/Vanderbilt University, Nashville, TN, USA Hoe Lee School of Occupational Therapy, Social Work and Speech Pathology, Curtin University, Perth, WA, Australia James Hyun Lee Mayo Clinic School of Medicine, Rochester, MN, USA Jordan Lee Southern Connecticut State University, New Haven, CT, USA Michelle Lee NYU School of Medicine, New York, NY, USA Department of Communication Sciences and Disorders, Northwestern University, Evanston, IL, USA Su Mei Lee Child Neuroscience Lab, Yale Child Study Center, New Haven, CT, USA Susan Leekam School of Psychology, Cardiff University, Cardiff, UK Jiedi Lei Centre for Applied Autism Research, Department of Psychology, University of Bath, Bath, UK Yale Child Study Center, School of Medicine, Yale University, New Haven, CT, USA Michelle Lestrud The Gengras Center, University of Saint Joseph, West Hartford, CT, USA Cecilia Nga Wing Leung The Jockey Club iREACH Social Competence Development and Employment Support Center, New Life Psychiatric Rehabilitation Association, Kowloon, Hong Kong
liv
Bennett Leventhal Nathan Kline Institute for Psychiatric Research (NKI), Orangeburg, NY, USA Harriet Levin University of Connecticut, Storrs, CT, USA Philip Levin The Help Group – UCLA Neuropsychology Program, Los Angeles, CA, USA Michael Levine Quinnipiac University School of Law, Hamden, CT, USA Brianna Lewis Yale Child Study Center, Yale School of Medicine, New Haven, CT, USA Laura Foran Lewis College of Nursing and Health Sciences, University of Vermont, Burlington, VT, USA Mark Lewis College of Medicine, University of Florida, Gainesville, FL, USA Michael Lewis Department of Pediatrics, Institute for the Study of Child Development, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA Moira Lewis Speech-Language Pathologist, Marcus Autism Center Children’s Healthcare of Atlanta, Atlanta, GA, USA Boxing Li Neuroscience Program, Guangdong Provincial Key Laboratory of Brain Function and Disease, Zhongshan School of Medicine and The Fifth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China Ya-Min Li Clinical Nursing Teaching and Research Section, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China Yong-Jiang Li Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China Diane M. Lickenbrock Human Development and Family Studies, The Pennsylvania State University, University Park, PA, USA Rebecca Lieb NeuroDevelopmental Science Center, Akron Children’s Hospital, Akron, OH, USA Joan Lieber Counseling, Higher Education and Special Education, University of Maryland, College Park, MD, USA Nataly Lim University of Texas at Austin, Austin, TX, USA Sok Bee Lim Department of Child Development, KK Women’s and Children’s Hospital, Singapore, Singapore Yi Huey Lim School of Occupational Therapy, Social Work and Speech Pathology, Curtin University, Perth, WA, Australia Charlotte Limosani Department of Communication Disorders, Southern Connecticut State University, New Haven, CT, USA Christie Enjey Lin Departments of Education and Psychiatry, Child and Adolescent Psychiatry, University of California, Los Angeles, CA, USA
Contributors
Contributors
lv
Sigvard Lingh Uppsala, Sweden Karen M. Lionello-DeNolf Psychology Department, Assumption College, Worcester, MA, USA Paul H. Lipkin Department of Medical Informatics, Kennedy Krieger Institute, Baltimore, MD, USA Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD, USA Guodong Liu Division of Health Services and Behavioral Research, Department of Public Health Sciences, The Pennsylvania State University, College of Medicine, Hershey, PA, USA Ting Liu Department of Health and Human Performance, Texas State University, San Marcos, TX, USA Patricia Sánchez Lizardi School Panamericana, Mexico City, Mexico
of
Psychology,
Universidad
Ella Lobregt-van Buuren Dimence Institute of Mental Health, Deventer, The Netherlands Rachel Loftin AARTS Center, Rush University Medical Center, Chicago, IL, USA Andrew Lolli Quinnipiac University School of Law, Hamden, CT, USA Michael Lombardo Autism Research Centre, University of Cambridge, Cambridge, UK Steven Long Speech Pathology and Audiology, Marquette University, Milwaukee, WI, USA James W. Loomis Center for Children with Special Needs, Glastonbury, CT, USA Amaia Lopetegui GAUTENA, Donostia, Gipuzkoa, Spain Catherine Lord Center for Autism and the Developing Brain, New YorkPresbyterian Hospital/Westchester Division, White Plains, NY, USA UCLA, Los Angeles, CA, USA Erin Loring Yale Department of Genetics, New Haven, CT, USA Molly Losh The Roxelyn and Richard Pepper Department of Communication Sciences and Disorders, Northwestern University, Evanston, IL, USA Susan Luger Susan Luger Associates, New York, NY, USA James Luiselli May Institute, Randolph, MA, USA Jan Łukasik Department of Paediatrics, The Medical University of Warsaw, Warsaw, Poland Joyce Lum UNC TEACCH Autism Program-Charlotte, Charlotte, NC, USA
lvi
Stanley E. Lunde Psychology, UCLA-MRRC Laboratories, Lanterman Developmental Center (Ret.), Pomona, CA, USA Yona Lunsky Centre for Addiction and Mental Health, Toronto, ON, Canada Viktor Lushin Long Island University, New York, NY, USA Rhiannon J. Luyster Department of Communication Sciences and Disorders, Emerson College, Boston, MA, USA Kristen Lyall AJ Drexel Autism Institute, Drexel University, Philadelphia, PA, USA Megan Lyons Laboratory of Developmental Communication Disorders, Yale Social and Affective Neurodevelopment of Autism Program, Yale Child Study Center, New Haven, CT, USA Suzanne Macari Yale Child Study Center, New Haven, CT, USA Tim MacLaughlin Department of Special Education, Gonzaga University, Spokane, WA, USA Kailey MacNeill Department of Communication Sciences and Disorders, The University of Vermont, Burlington, VT, USA Kelly Macy Department of Communication Sciences, The University of Vermont, Burlington, VT, USA Brenna B. Maddox Penn Center for Mental Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA Psychology Department, Virginia Tech, Blacksburg, VA, USA Leen Maes Department of Rehabilitation Sciences, Ghent University, Ghent, Belgium Department of Otorhinolaryngology, Ghent University Hospital, Ghent, Belgium María Magán-Maganto Department of Personality, Assessment, and Psychological Treatment, Centro de Atención Integral al Autismo (INFOAUTISMO), University Institute of Community Integration (INICO), University of Salamanca, Salamanca, Spain Iliana Magiati Department of Psychology, National University of Singapore, Singapore, Singapore School of Psychological Science, University of Western Australia, Crawley, WA, Australia Caroline I. Magyar Magyar Psychological Services, LLC, Rochester, NY, USA Kelly Mahler Mahler Occupational Therapy, Hershey, USA Marsha R. Mailick Waisman Center, University of Wisconsin-Madison, Madison, WI, USA Zoe Mailloux Private Practice, Redondo Beach, CA, USA
Contributors
Contributors
lvii
Mark Malady Florida Institute of Technology, Melbourne, FL, USA Savita Malhotra Fortis Hospital, Mohali, Punjab, India Beth Malow Sleep Disorders Division, Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA David S. Mandell Center for Autism Research, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA University of Pennsylvania, Philadelphia, PA, USA William Mandy Research Department of Clinical, Educational and Health Psychology, University College London, London, UK Melissa Manjarrés Speech Pathology and Audiology, Marquette University, Milwaukee, WI, USA Deepali Mankad Holland Bloorview Kids Rehabilitation Hospital, Bloorview Research Institute, Toronto, ON, Canada Arlene Mannion Irish Centre for Autism and Neurodevelopmental Research (ICAN), National University of Ireland, Galway (NUI Galway), Galway, Ireland Katie L. Maras Centre for Applied Autism Research (CAAR), Department of Psychology, University of Bath, Bath, UK Mariana Marchitto Department of Communication Disorders, Southern Connecticut State University, New Haven, CT, USA Lee Marcus TEACCH Autism Program, University of North Carolina, Chapel Hill, NC, USA Lucia Margari Child Neuropsychiatry Unit, University of Bari “Aldo Moro”, Bari, Italy C Amigo María Child and Adolescents Psychiatrist, ASD Department, Pereira Rossell Hospital – ASSE, Montevideo, Uruguay Flavia Marino Institute for Biomedical Research and Innovation (IRIB), National Research Council of Italy (CNR), Messina, Italy Richard Marks Florida State University, Tallahassee, FL, USA Michelle Marlborough Operational Stress Injury Clinic, Parkwood Institute, St. Joseph’s Health Care London, London, ON, Canada Christina N. Marsack-Topolewski College of Health and Human Services, Eastern Michigan University, Ypsilanti, MI, USA Carolyn L. Marsh Yale Child Study Center, New Haven, CT, USA Kimberly Marshall Center for Children with Special Needs, Glastonbury, CT, USA Itxaso Marti Neuropediatrics, Hospital Universitario Donostia, San Sebastian, Spain
lviii
Andres Martin Yale Child Study Center, New Haven, CT, USA Marta Martinez Southern Connecticut State University, New Haven, CT, USA Nicole Martins Indiana University, Bloomington, IN, USA Lisa E. Mash San Diego State University/University of California, San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, USA David Mason Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK Susan A. Mason Services for Students with Autism Spectrum Disorders, Montgomery County Public Schools, Silver Spring, MD, USA Natasa Mateljevic Yale University, New Haven, CT, USA Leny Mathew AJ Drexel Autism Institute, Drexel University, Philadelphia, PA, USA Johnny L. Matson Department of Psychology, Louisiana State University, Baton Rouge, LA, USA Tara Matthews Children’s Specialized Hospital, Mountainside, NJ, USA Jennifer Gillis Mattson Institute for Child Development, Department of Psychology, Binghamton University, Binghamton, NY, USA Melissa Maye Clinical Psychology, University of Massachusetts Boston, Boston, MA, USA Carla A. Mazefsky Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA Micah O. Mazurek Curry School of Education and Human Development, University of Virginia, Charlottesville, VA, USA David McAdam Department of Pediatrics, University of Rochester Medical Center, Rochester, NY, USA Bonnie McBride Intervention Services for Autism, University of Oklahoma College of Medicine, Oklahoma City, OK, USA Gregory McCarthy Department of Psychology, Yale University, New Haven, CT, USA Maryellen Brunson McClain Department of Psychology, Utah State University, Logan, UT, USA Iain McClure The Royal Hospital for Sick Children, Edinburgh, UK University of Edinburgh, Edinburgh, Scotland, UK Jennifer McCullagh Department of Communication Disorders, Southern Connecticut State University, New Haven, CT, USA Christin A. McDonald Nationwide Children’s Hospital’s Center for Autism Spectrum Disorders, Westerville, OH, USA
Contributors
Contributors
lix
Christina G. McDonnell Virginia Polytechnic Institute and State University, Blacksburg, VA, USA Christopher J. McDougle Lurie Center for Autism, Massachusetts General Hospital, Lexington, MA, USA Nancy Lurie Marks Professorship in the Field of Autism, Harvard Medical School, Boston, MA, USA Andrea McDuffie MIND Institute University of California-Davis, Sacramento, CA, USA John McEachin Autism Partnership Foundation, Seal Beach, CA, USA Kate McFadden Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA Tyler McFayden Department of Psychology, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA Elizabeth McGarry Koegel Autism Center/Department of Counseling, Clinical, and School Psychology, University of California Santa Barbara, Santa Barbara, CA, USA Jenny McGinley Physiotherapy, Centre for Movement Disorders and Gait Research, Southern Health, The University of Melbourne, Parkville, VIC, Australia Cali McGinn Center for Children with Special Needs, Glastonbury, CT, USA Richard McGrath School of Health Sciences, University of South Australia, Adelaide, SA, Australia John McGrew Indiana University – Purdue University at Indianapolis, Indianapolis, IN, USA Nancy S. McIntyre Frank Porter Graham Child Development Institute, University of North Carolina, Chapel Hill, NC, USA Heather McKay Quinnipiac University School of Law, Hamden, CT, USA Desmond McKernan Asperger Syndrome Association of Ireland (Aspire), Dublin, Ireland Elizabeth P. McKernan Department of Psychology, Syracuse University, Syracuse, NY, USA William McMahon Department of Psychiatry, University of Utah, Salt Lake City, UT, USA Edward McNulty Quinnipiac University School of Law, Hamden, CT, USA James C. McPartland School of Medicine, Child Study Center, Yale University, New Haven, CT, USA Shantel E. Meek School of Social and Family Dynamics, Arizona State University, Tempe, AZ, USA
lx
Karen Meers Center of Excellence on Autism Spectrum Disorders, Southern Connecticut State University, New Haven, CT, USA Nagwa Abdel Meguid Research on Children with Special Needs, National Research Centre, Cairo, Egypt Smita Shukla Mehta Department of Educational Psychology, University of North Texas, Denton, TX, USA Lisa J. Meier Department of Psychology, George Mason University, Falls Church, VA, USA Sarah Melchior School Psychologist, Services for Students with Autism Spectrum Disorders Montgomery County Public Schools, Silver Spring, MD, USA Alicia Melis Department of Developmental and Comparative Psychology, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany Kellen Mermin-Bunnell Yale Child Study Center, New Haven, CT, USA Liesbeth Mevissen Trajectum, (Forensic) Treatment Facility for Adults with Intellectual Disabilities, Zwolle, The Netherlands Judith Meyers Child Health and Development Institute of Connecticut, Farmington, CT, USA Euripedes Constantino Miguel Department of Psychiatry, Faculty of Medicine, University of Sao Paulo, Sao Paulo, Brazil Helga O. Miguel Section on Analytical and Functional Biophotonics, National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA Michael Miklos Pennsylvania Training and Technical Assistance Network, Harrisburg, PA, USA Judith H. Miles Pediatrics, Medical Genetics and Pathology, The Thompson Center for Autism and Neurodevelopmental Disorders, Columbia, MO, USA Margaret Millea Department of Psychology, University of Notre Dame, Notre Dame, IN, USA Amber R. Miller Koegel Autism Center/Department of Counseling, Clinical, and School Psychology, University of California Santa Barbara, Santa Barbara, CA, USA Kaitlin Koffer Miller Policy and Analytics Center, A.J. Drexel Autism Institute, Drexel University, Philadelphia, PA, USA Lauren E. Miller Department of Psychological Sciences, University of Connecticut, Storrs, CT, USA Lucy Jane Miller STAR Institute for Sensory Processing Disorder, Greenwood Village, CO, USA Department of Pediatrics, University of Colorado Denver, Denver, CO, USA
Contributors
Contributors
lxi
Trube C. Miller Department of Educational Studies, Hardin-Simmons University, Abilene, TX, USA Catherine Miltenberger Trumpet Behavioral Health, Lakewood, CO, USA Damian Milton Tizard Centre, University of Kent, Canterbury, Kent, UK Damian Elgin Maclean Milton Tizard Centre, University of Kent, Canterbury, Kent, UK Ruud Minderaa Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands Helen Minnis Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK Nancy J. Minshew Departments of Psychiatry and Neurology, University of Pittsburgh, Pittsburgh, PA, USA Ana Miranda University of Valencia, Valencia, Spain Sarah S. Mire University of Houston, Houston, TX, USA Jacquelyn Moffitt Department of Child and Adolescent Studies, California State University, Fullerton, Fullerton, CA, USA John Molteni Institute for Autism and Behavioral Studies, University of Saint Joseph, West Hartford, CT, USA Guillermo Montes St. John Fisher College, Rochester, NY, USA David Moore School of Psychology, Liverpool John Moores University, Liverpool, UK Marcel Moran Indiana University School of Medicine, Indianapolis, IN, USA Montana T. Morris UCSF Weill Institute for Neurosciences, San Francisco, CA, USA Susan Morris School of Physiotherapy and Exercise Science, Curtin University, Perth, WA, Australia Maya G. Mosner UNC-Chapel Hill, Carolina Institute for Developmental Disabilities, Chapel Hill, NC, USA Philippa Moss Psychology, Great Ormond Street Hospital, London, UK Stewart Mostofsky Kennedy Krieger Institute, Baltimore, MD, USA Laurent Mottron Center of Excellence in Pervasive Developmental Disorders of University of Montreal, Montreal, QC, Canada Department of Psychiatry, Riviere-des-Prairies Hospital, University of Montreal, Montreal, QC, Canada Svend Erik Mouridsen Child and Adolescent Psychiatry Centre, Bispebjerg University Hospital, Copenhagen, Denmark
lxii
Maura Moyle Speech Pathology and Audiology, Marquette University, Milwaukee, WI, USA Dennis Mozingo Integrated Behavioral Solutions, Atlanta, GA, USA Daniel W. Mruzek Department of Pediatrics (SMD), University of Rochester, School of Medicine and Dentistry, Rochester, NY, USA Vannesa T. Mueller Speech-Language Pathology Program, University of Texas at El Paso College of Health Science, El Paso, TX, USA Janyl Mukashova Autism Program, Bulat Utemuratov Foundation, Almaty, Kazakhstan Cora Mukerji Yale Child Study Center, New Haven, CT, USA James Anton Mulick Child Development Center Columbus Children’s Hospital, Columbus, OH, USA Ralph-Axel Müller Department of Psychology, San Diego State University, San Diego, CA, USA Rebecca Munday The Center for Children with Special Needs, Glastonbury, CT, USA Peter Mundy Psychiatry and School of Education, UC Davis, Davis, CA, USA Kerim M. Munir Division of Developmental Medicine, Boston Children’s Hospital, Boston, MA, USA Harvard Medical School, Boston, MA, USA John D. Murdoch Child Study Center, Yale University School of Medicine, New Haven, CT, USA Dinah Murray National Autistic Taskforce, London, UK Donna S. Murray Division of Developmental and Behavioral Pediatrics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA Kim Musheno Legislative Affairs, Association of University Centers on Disabilities, Silver Spring, MD, USA Michelle Myers The School at Springbrook, Oneonta, NY, USA Marie Nabbout-Cheiban School of Education, Southern Connecticut State University, New Haven, CT, USA Josh Nadeau Rogers Behavioral Health, Tampa, FL, USA Psychology, University of South Florida, St. Petersburg, FL, USA Aparna Nadig School of Communication Sciences and Disorders, McGill University, Montreal, QC, Canada Tzipi Nagel-Edelstein Association for Children at Risk (R.A.), Tel Aviv, Israel
Contributors
Contributors
lxiii
Jo Anne Nakagawa Tuberous Sclerosis Alliance, Silver Spring, MD, USA Adam Naples Yale Child Study Center, Yale University, New Haven, CT, USA Deborah Napolitano Applied Behavior Analysis, Daemen College, Amherst, NY, USA Golisano Institute for Developmental Disability Nursing, St. John Fisher College, Rochester, NY, USA Anahita Navab Department of Psychology, University of California, Los Angeles, CA, USA Ahsan Nazeer Department of Psychiatry, Sidra Medicine, Doha, Qatar Weill Cornell Medicine, Doha, Qatar Nicole Neil Faculty of Education, Western University, London, ON, Canada Seth Ness Department of Neuroscience, Janssen Research and Development, LLC, Titusville, NJ, USA Maureen Nevers Center on Disability and Community Inclusion, University of Vermont, Burlington, VT, USA Augmentative Communication Consultant, Center on Disability and Community Inclusion, Burlington, VT, USA Rose E. A. Nevill Curry School of Education and Human Development University of Virginia, Charlottesville, VA, USA Diana B. Newman Communication Disorders Department, Southern Connecticut State University, New Haven, CT, USA Tina Newman The Center for Children with Special Needs, Glastonbury, CT, USA Brandon Nichols The School at Springbrook, Oneonta, NY, USA Antonio Gennaro Nicotera Oasi Research Institute – IRCCS, Troina, Italy Child Neuropsychiatry Unit, Department of Human Pathology of the Adult and Developmental Age, University Hospital “G. Martino”, Messina, Italy Jacqueline A. Noonan Department of Pediatrics, University of Kentucky, College of Medicine, Lexington, KY, USA Courtenay Norbury Psychology Department, Royal Holloway, University of London, Egham, Surrey, UK Anders Nordahl-Hansen Faculty of Education, Østfold University College, Halden, Norway Department of Special Needs Education, UiO University of Oslo, Oslo, Norway Eva Nordin-Olsson Department of Child and Adolescent Psychiatry, Gillberg Neuropsychiatry Centre, University of Gothenburg, Gothenburg, Sweden
lxiv
Elizabeth C. Nulty Center for Children with Special Needs, Glastonbury, CT, USA Heather J. Nuske Penn Center for Mental Health, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA Lena Nylander Department of Psychiatry, Clinical Sciences, Lund University, Lund, Sweden Marisa O’Boyle Clinical Psychology, University of Massachusetts Boston, Boston, MA, USA Kirsten O’Hearn Laboratory of Neurocognitive Development, Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA Carmel O’Sullivan Trinity College, University of Dublin, Dublin, Ireland Leona Oakes Department of Clinical and Social Sciences in Psychology, University of Rochester, Rochester, NY, USA Jordi E. Obiols Department of Clinical and Health Psychology, Psychopathology and Neuropsychology Research Unit, Universitat Autonoma de Barcelona, Barcelona, Spain Emily Ochi Mrs. T.H. Chan Division of Occupational Science and Occupational Therapy, University of Southern California (USC), Los Angeles, CA, USA Samuel L. Odom Child Development, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Paul A. Offit Division of Infectious Diseases, Department of Pediatrics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA Roald A. Øien Department of Psychology/Department of Education, UIT – The Arctic University of Norway, University of Tromsø, Tromsø, Norway Yale Child Study Center, Yale Autism Program, Yale University School of Medicine, New Haven, CT, USA Melody Oliphant Yale Child Study Center, Yale University School of Medicine, New Haven, CT, USA Kim E. Ono Department of Psychology, University of Miami, Coral Gables, FL, USA Devon Oosting Yale Child Study Center, Center for Translational Developmental Neuroscience, New Haven, CT, USA Alyssa Orinstein Department of Psychological Sciences, University of Connecticut, Storrs, CT, USA Boston University School of Medicine, Boston, MA, USA Felice Orlich Autism Psychology Services, Seattle Children’s Hospital CAC – Autism Center, Seattle, WA, USA
Contributors
Contributors
lxv
Mitsuhiko Ota School of Philosophy, Psychology and Language Sciences, University of Edinburgh, Edinburgh, UK Ria Pal University of Rochester School of Medicine and Dentistry, Rochester, NY, USA Jessica Palilla Departments of Psychology and Neuroscience, Brigham Young University, Provo, UT, USA Shannon Palmer Central Michigan University, Mount Pleasant, MI, USA Kate Palmer GRASP, New York, NY, USA Mark Palmieri Feeding Clinic, Center for Children with Special Needs, Glastonbury, CT, USA Gahan Pandina Department of Neuroscience, Janssen Research and Development, LLC, Titusville, NJ, USA Vincent Pandolfi Department of Psychology, Rochester Institute of Technology, Rochester, NY, USA Brittany Panuzio Department of Communication Disorders, Southern Connecticut State University, New Haven, CT, USA Chris Papadopoulos University of Bedfordshire | Luton Campus, Luton, UK Mi Na Park Department of Counseling, Clinical, and School Psychology, University of California The Gevirtz School, Santa Barbara, CA, USA Jeremy Parr Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK Sir James Spence Institute, Institute of Health and Society, Newcastle University, Royal Victoria Infirmary, Newcastle Upon Tyne, UK Rizwan Parvez Yale Child Study Center, New Haven, CT, USA Bernadeta Patro-Gołąb Department of Paediatrics, The Medical University of Warsaw, Warsaw, Poland Vanessa Patrone Applied Behavior Analysis, Daemen College, Amherst, NY, USA Kartik Pattabiraman Child Study Center, Yale School of Medicine, New Haven, CT, USA Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA Diane R. Paul Clinical Issues in Speech-Language Pathology, American Speech-Language-Hearing Association, Rockville, MD, USA Rhea Paul Department of Speech and Language Pathology, College of Health Professions, Sacred Heart University, Fairfield, CT, USA Markus Paulus Department Psychology, Ludwig-Maximilians-Universität München, Munich, Germany
lxvi
Deborah A. Pearson Department of Psychiatry and Behavioral Sciences, University of Texas Medical School at Houston, Houston, TX, USA Melanie Pellecchia University of Pennsylvania, Philadelphia, PA, USA Elizabeth Pellicano Macquarie School of Education, Macquarie University, Sydney, NSW, Australia Liz Pellicano Centre for Research in Autism and Education (CRAE), Department of Psychology and Human Development, Institute of Education, University of London, London, UK Kevin A. Pelphrey Harris Child Study Center, Yale University School of Medicine, New Haven, CT, USA Sue Peppé High Appin, Tynron, Thornhill, UK Celal Perihan Idaho State University, Pocatello, ID, USA Kate S. Perri Christian Sarkine Autism Treatment Center, Riley Hospital for Children, Indianapolis, IN, USA Danielle Perszyk Yale Child Study Center, New Haven, CT, USA Mario C. Petersen Child Development and Rehabilitation Center, Oregon Health Science University, Eugene, OR, USA Neysa Petrina University of Sydney, Sydney, NSW, Australia Laura Phipps Munroe Meyer Institute, University of Nebraska Medical Center, Omaha, NE, USA Denise Phua Autism Association Singapore, Pathlight School and Eden School, Autism Resource Centre, Singapore, Singapore Janice N. Phung Department of Psychology, California State University San Marcos, San Marcos, CA, USA Andrew Pickles School of Epidemiology and Health Science, University of Manchester, Manchester, UK Madison Pilato Neurodevelopmental and Behavioral Pediatrics, University of Rochester Medical Center, Rochester, NY, USA Melanie Pinkett-Davis Center for Autism and Related Disorders, Kennedy Krieger Institute’s, Baltimore, MD, USA Giovanni Pioggia Institute for Biomedical Research and Innovation (IRIB), National Research Council of Italy (CNR), Messina, Italy Ozgur Pirgon Department of Pediatrics, Division of Pediatric Endocrinology, S. Demirel University, Isparta, Turkey Rachel Plant Southern Connecticut State University, New Haven, CT, USA Joshua B. Plavnick Michigan State University, East Lansing, MI, USA
Contributors
Contributors
lxvii
Bertram O. Ploog Department of Psychology, Center for Developmental Neuroscience, College of Staten Island and Graduate Center, City University of New York, Staten Island, NY, USA Claire Plowgian Speech Pathology and Audiology, Marquette University, Milwaukee, WI, USA Guilherme Vanoni Polanczyk Department of Psychiatry, Faculty of Medicine, University of Sao Paulo, Sao Paulo, Brazil Anamiguel Pomales-Ramos Yale Child Study Center, New Haven, CT, USA Kenneth K. Poon Early Childhood and Special Needs Education, National Institute of Education (NIE), Nanyang Technological University, Singapore, Singapore Ben Popple White Oak Pediatric Dentistry, Newnan, GA, USA Sue Porr Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Kristen M. Powers Coordinator of Rehabilitative Services, Center for Children with Special Needs, Glastonbury, CT, USA Michael D. Powers The Center for Children with Special Needs, Glastonbury, CT, USA Shirley Poyau Clinical Psychology, University of Massachusetts Boston, Boston, MA, USA Pilar Pozo Joint Research Institute National University for Distance Education and Health Institute Carlos III (IMIENS), Madrid, Spain Faculty of Psychology, National University for Distance Education (UNED), Madrid, Spain Cathy Pratt Indiana Resource Center for Autism, Indiana University, Bloomington, IN, USA Patricia Prelock Communication Sciences and Disorders, Dean’s Office, College of Nursing and Health Sciences, Burlington, VT, USA Rebecca Edmondson Pretzel Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Josh Pritchard Applied Behavior Analysis, Florida Institute of Technology, Orlando, FL, USA Zheala Qayyum Department of Psychiatry, Yale University, New Haven, CT, USA Rachael Quicquaro Southern Connecticut State University, New Haven, CT, USA Colleen Quinn Rivendale, Arc of the Ozarks, Springfield, MO, USA
lxviii
Ana Figueroa Quintana Child and Adolescent Psychiatry Unit, Hospital Perpetuo Socorro, Las Palmas, Spain E. M. Quintin Department of Educational and Counselling Psychology, McGill University, Montreal, QC, Canada Hala Raad Division of Child and Adolescent Psychiatry, Department of Psychiatry, American University of Beirut Medical Center, Beirut, Lebanon Carol Rabideau Vanderbilt Kennedy Center, Nashville, TN, USA Diana Rafaela Centro Hospitalar do Baixo Vouga, Aveiro, Portugal Deborah Rafferty Department of Psychology, Texas Christian University, Fort Worth, TX, USA Ramkripa Raghavan Center on Early Life Origins of Disease, Department of Population, Family and Reproductive Health, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA Adithyan Rajaraman UMBC, Baltimore, MD, USA Isabelle Rapin Neurology and Pediatrics (Neurology), Albert Einstein College of Medicine, Bronx, NY, USA Karen Ratcliff Occupational Therapy Department, University of Texas Medical Branch, Galveston, TX, USA Kristin Ratliff Research and Development, Western Psychological Services, Torrance, CA, USA Reinhold Rauh Department of Child and Adolescent Psychiatry, Psychotherapy, and Psychosomatics; Medical Center, University of Freiburg, Faculty of Medicine University of Freiburg, Germany, Freiburg, Germany Corey Ray-Subramanian Waisman Center, University of Wisconsin-Madison, Madison, WI, USA Sarah Raza Department of Pediatrics, University of Alberta, Edmonton, AB, Canada Autism Research Centre, Glenrose Rehabilitation Hospital, Edmonton, AB, Canada Devon Hartford Redmond Child and Family Development, Charlotte, NC, USA Brian Reichow Child Study Center, Yale University School of Medicine, New Haven, CT, USA Anita Zucker Center for Excellence in Early Childhood Studies, University of Florida, Gainesville, FL, USA Beau Reilly Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA Noah Remnick Ezra Stiles College, Yale University, New Haven, CT, USA
Contributors
Contributors
lxix
Patricia Renno Department of Education, University of California, Los Angeles, Los Angeles, CA, USA Ann Reynolds Pediatrics, Child Development Unit, Aurora, CO, USA Catherine E. Rice National Center on Birth Defects and Developmental Disabilities, Centers for Disease Control and Prevention, Atlanta, GA, USA Amanda Richdale Olga Tennison Autism Research Centre, La Trobe University, Melbourne, VIC, Australia Raili Riikonen Department of Child Neurology, University of Kuopio, Kuopio, Finland Nicole Rinehart Deakin Child Study Centre, School of Psychology, Faculty of Health, Deakin University, Geelong, VIC, Australia Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia Mandy Rispoli Purdue University, West Lafayette, IN, USA Ariella Riva Ritvo Child Study Center, Yale School of Medicine, Yale University, Los Angeles, CA, USA Edward R. Ritvo UCLA School of Medicine, Los Angeles, CA, USA Jane Roberts Department of Psychology, University of South Carolina, Columbia, SC, USA Timothy P. L. Roberts Radiology Department, Children’s Hospital of Philadelphia, Philadelphia, PA, USA Ashley E. Robertson School of Psychological, Social and Behavioural Sciences, Faculty of Health and Life Sciences, Coventry University, Coventry, UK Diana L. Robins AJ Autism Drexel Institute, Drexel University, Philadelphia, PA, USA Anna Robinson Centre for Autism Studies, Scottish Centre for Applied Autism Research, University of Strathclyde, Glasgow, UK Janine Robinson CLASS, Cambridgeshire and Peterborough NHS Foundation Trust, Fulbourn, Cambridgeshire, UK NHS England, London, UK Adriano Rodrigues Health Sciences Center, Federal University of Piaui – UFPI, Teresina, Brazil Jessica L. Roesser Department of Pediatrics (SMD), University of Rochester, School of Medicine and Dentistry, Rochester, NY, USA Edward R. Ritvo: deceased.
lxx
Amanda Roestorf Department of Psychology, Faculty of Natural Sciences, University of Stirling, Stirling, Scotland Bernadette Rogé CERPPS, Université Toulouse Jean Jaurès, Toulouse, France CeRESA (Centre Régional d’Education et de Services pour l’Autisme), Institut Universitaire de France (IUF), Toulouse, France Sally J. Rogers Department of Psychiatry and Behavioral Sciences, UC Davis M.I.N.D. Institute, Sacramento, CA, USA Anna Rogulina Southern Connecticut State University, New Haven, CT, USA Jessica Rohrer The Center for Children with Special Needs, Glastonbury, CT, USA Johannes Rojahn Department of Psychology, George Mason University, Fairfax, VA, USA Raymond G. Romanczyk Institute for Child Development, Department of Psychology, Binghamton University, Binghamton, NY, USA Elizabeth M. G. Romero Attention, Behavior and Cognition, LLC, Worcester, MA, USA Jenny R. Root School of Teacher Education, College of Education, Florida State University, Tallahasee, FL, USA Danielle Ropar School of Psychology, University of Nottingham, Nottingham, UK Michael Rosanoff Autism Speaks, New York, NY, USA Belen Rosello University of Valencia, Valencia, Spain Sara D. Rosenblum-Fishman Psychology, University of Massachusetts Boston, Boston, MA, USA April Rosenkrantz Quinnipiac University School of Law, Hamden, CT, USA Allyson Ross Florida Institute of Technology, Melbourne, FL, USA Edoardo Rosso ECH Inc., Adelaide, South Australia, Australia Erin Rotheram-Fuller School Psychology, Department of Psychological Studies in Education, College of Education Temple University, Philadelphia, PA, USA Justin Rowberry Developmental and Behavioral Pediatrics, New Haven, CT, USA Sonia Rowley Child Study Center, Yale School of Medicine, Yale University, New Haven, CT, USA Eric Rubenstein Waisman Center, University of Wisconsin-Madison, Madison, WI, USA
Contributors
Contributors
lxxi
Lisa Ruble Educational School and Counseling Psychology, University of Kentucky, Lexington, KY, USA Kristin Ruedel Department of Special Education, University of Maryland Washington State University, Richland, WA, USA Ailsa Russell Centre for Applied Autism Research, Department of Psychology, University of Bath, Bath, UK Natalie Russo Department of Psychology, Syracuse University, Syracuse, NY, USA Samantha Russo Institute for Behavioral Studies, Endicott College, Beverly, MA, USA Ikram Rustamov Child Mental Health and Development Center, Azerbaijan Medical University, Baku, Azerbaijan Liliana Ruta Institute for Biomedical Research and Innovation (IRIB), National Research Council of Italy (CNR), Messina, Italy Marion Rutherford Queen Margaret University, Edinburgh, Scotland, UK Khaled Saad Faculty of Medicine, Assiut University, Assiut, Egypt Mohammad Nasser Saadatzi Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA Lori-Ann Sacrey Department of Pediatrics, University of Alberta, Edmonton, AB, Canada Autism Research Centre, Glenrose Rehabilitation Hospital, Edmonton, AB, Canada Mustafa Sahin Department of Neurology, Children’s Hospital Boston, Harvard Medical School, Boston, MA, USA Catalina Sakai Child Study Center, Program on Neurogenetics, Yale School of Medicine, New Haven, CT, USA Carlos Marcín Salazar Clínica Mexicana de Autismo y Alteraciones del Desarrollo, A. C., Mexico City, Mexico Stephan Sanders Child Study Center, Yale University, New Haven, CT, USA Tanja Sappok Berlin Center for Mental Health in Intellectual Developmental Disabilities, Ev. Krankenhaus Königin Elisabeth Herzberge (KEH), Berlin, Germany Geeta Sarphare Department of Child and Adolescent Psychiatry, Kennedy Krieger Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA Encarnación Sarriá Joint Research Institute National University for Distance Education and Health Institute Carlos III (IMIENS), Madrid, Spain Faculty of Psychology, National University for Distance Education (UNED), Madrid, Spain
lxxii
Noah J. Sasson The University of Texas at Dallas, Richardson, TX, USA Celine A. Saulnier Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA David Saunders Yale Child Study Center, New Haven, CT, USA Sarah Savage Institute of Psychiatry, Psychology and Neuroscience King’s College, London, UK Lawrence David Scahill Nursing and Child Psychiatry, Yale Child Study Center, Yale University School of Nursing, New Haven, CT, USA Marcus Autism Center, Children’s Healthcare of Atlanta, Atlanta, GA, USA Department of Pediatrics, Emory University, Atlanta, GA, USA Angela Scarpa Virginia Polytechnic Institute and State University, Blacksburg, VA, USA Virginia Tech Autism Clinic and Center for Autism Research, Blacksburg, VA, USA Christian Patrick Schaaf Institute of Human Genetics, Heidelberg University, Heidelberg, Germany Roseann Schaaf Department of Occupational Therapy, Faculty, Farber Institute for Neurosciences, Jefferson College of Rehabilitation Sciences, Thomas Jefferson University, Philadelphia, PA, USA Ulrich Max Schaller Department of Psychiatry and Psychotherapy Medical Center, University of Freiburg, Faculty of Medicine University of Freiburg, Germany, Freiburg, Germany David Schelly Department of Occupational Therapy, Clarkson University, Potsdam, NY, USA David Schena Department of Psychology, University of Alabama, Tuscaloosa, AL, USA Synnve Schjølberg Child Health and Development, Mental and Physical Health, Norwegian Institute of Public Health, Oslo, Norway Rebecca Schmidt Department of Public Health Sciences and the MIND Institute, University of California, Davis, Davis, CA, USA Lauren Schmitt Psychiatry, UT Southwestern Medical Center, Dallas, TX, USA Naomi Schneider College of Education and Human Ecology, The Ohio State University, Columbus, OH, USA Sarah A. Schoen Sensory Processing Disorder Foundation, Rocky Mountain University of Health Professions, Denver, CO, USA Elizabeth Schoen Simmons Department of Psychological Sciences, University of Connecticut, Storrs, CT, USA
Contributors
Contributors
lxxiii
Winifred Schultz-Krohn Department of Occupational Therapy, San José State University, San José, CA, USA Cyndi Schumann UC Davis M.I.N.D. Institute, Sacramento, CA, USA Jessica Oeth Schuttler Center for Child Health and Development, University of Kansas Medical Center, Kansas City, KS, USA Tobias Schuwerk Department Psychology, Universität München, Munich, Germany
Ludwig-Maximilians-
Caley B. Schwartz Department of Psychology, University of Miami, Coral Gables, FL, USA Ilene Sharon Schwartz Haring Center for Applied Research and Training in Education, University of Washington, Seattle, WA, USA John W. Scibak State Representative, Commonwealth of Massachusetts, South Hadley, MA, USA Haleigh M. Scott Department of Disability and Human Development, University of Illinois at Chicago, Chicago, IL, USA Melissa Scott School of Occupational Therapy, Social Work and Speech Pathology, Curtin University, Perth, WA, Australia Curtin Autism Research Group, Curtin University, Perth, WA, Australia Felicity Sedgewick School of Education, University of Bristol, Bristol, UK Ifat Seidman Department of Psychology, The Hebrew University of Jerusalem, Jerusalem, Israel Trisha Self Wichita State University, Department of Communication Sciences and Disorders, Wichita, Kansas, USA Marsha Mailick Seltzer Waisman Center, University of Wisconsin-Madison, Madison, WI, USA Atsushi Senju Centre for Brain and Cognitive Development, Birkbeck, University of London, London, UK Kapila Seshadri Department of Pediatrics, Division of Developmental Behavioral Pediatrics, TJH Medical Services, Jamaica, NY, USA Amitta Shah The NAS Lorna Wing Centre for Autism, Bromley, Kent, UK Ruchita Shah Department of Psychiatry, Postgraduate Institute of Medical Education and Research, Chandigarh, India Jeffrey D. Shahidullah Department of Psychiatry, Dell Medical School, The University of Texas at Austin, Austin, TX, USA Wendy E. Shaia Social Work Community Outreach Service, University of Maryland School of Social Work, Baltimore, MD, USA Aditya Sharma Academic Child and Adolescent Mental Health, Sir James Spence Institute Newcastle University, Newcastle upon Tyne, UK
lxxiv
Katie Shattuck School of Nursing, University of North Carolina-Chapel Hill, University of North Carolina School of Medicine, Chapel Hill, NC, USA Paul Shattuck George Warren Brown School of Social Work, Washington University, St. Louis, MO, USA Ramzi Shawahna Department of Physiology, Pharmacology and Toxicology, Faculty of Medicine and Health Sciences, An-Najah National University, Nablus, Palestine Lindsay Shea Policy and Analytics Center, A.J. Drexel Autism Institute, Drexel University, Philadelphia, PA, USA Victoria Shea Department of Psychiatry, TEACCH Autism Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Daniel Tan Lei Shek Department of Applied Social Sciences, The Hong Kong Polytechnic University, Hung Hom, Hong Kong Daniel Shepherd Auckland University of Technology, Auckland, New Zealand Elizabeth Sheppard School of Psychology, University of Nottingham, Nottingham, UK Mark Sherry Department of Sociology and Anthropology, University of Toledo, Toledo, OH, USA Lori S. Shery ASPEN (asperger/Autism SPectrum Education Network), Edison, NJ, USA Frederick Shic School of Medicine, Yale Child Study Center, Yale University, School of Medicine, New Haven, CT, USA Stephanie Y. Shire Special Education and Clinical Sciences, College of Education, University of Oregon, Eugene, OR, USA Carolyn M. Shivers Virginia Tech Center for Autism Research, Virginia Tech, Blacksburg, VA, USA Department of Psychology, Vanderbilt University, Nashville, TN, USA Timothy Shriver Special Olympics, Inc, Washington, DC, USA Oren Shtayermman New York Institute of Technology Mental Health Counseling, Old Westbury, NY, USA Lisa Shull Division of Neurodevelopmental and Behavioral Pediatrics, Golisano Children’s Hospital, University of Rochester School of Medicine, Jamaica, NY, USA Clinical Psychology, Long Island University, Brooklyn, NY, USA Cory Shulman The Paul Baerwald School of Social Work, The Hebrew University of Jerusalem, Jerusalem, Israel Sarah Shultz Department of Psychology, Yale University, New Haven, CT, USA
Contributors
Contributors
lxxv
Reet Sidhu Department of Pediatric Neurology, Columbia University, New York, NY, USA Bryna Siegel Autism Clinic, Department of Child and Adolescent Psychiatry, University of California, San Francisco, San Francisco, CA, USA Laura B. Silverman Department of Pediatrics, University of Rochester, School of Medicine and Dentistry, Rochester, NY, USA Zi Lin Sim Autism Resource Centre, Singapore, Singapore David R. Simmons School of Psychology, University of Glasgow, Glasgow, UK Kathryn A. Simon Yale Child Study Center, New Haven, CT, USA Linda Rumanoff Simonson Benhaven, Inc., North Haven, CT, USA Alison Singer Autism Science Foundation, New York, NY, USA Anjileen Singh Counseling, Clinical, and School Psychology, UC Santa Barbara, Santa Barbara, CA, USA Vini Singh Center for Autism and Related Disorders, Kennedy Krieger Institute’s, Baltimore, MD, USA Danielle Sipsock Child and Adolescent Psychiatry, Warren Alpert Medical School of Brown University, Riverside, RI, USA Carmel Sivaratnam Deakin Child Study Centre, School of Psychology, Faculty of Health, Deakin University, Geelong, VIC, Australia Angelo T. R. Sivathasan Department of Child and Adolescent Psychiatry/ Psychology, Erasmus Medical Center-Sophia Children’s Hospital, Rotterdam, The Netherlands Bram Sizoo Psychiatry, Center for Developmental Disorders, Deventer, The Netherlands Ingjerd Skafle Faculty of Education, Østfold University College, Halden, Norway David Skuse UCL Great Ormond Street Institute of Child Health, London, UK Nicole Slade Department of Psychology, University of Massachusetts Boston, Boston, MA, USA Ingrid E. Sladeczek McGill University, Montreal, QC, Canada Alexandra M. Slaughter University of Houston, Houston, TX, USA Virginia Slaughter University of Queensland, Brisbane, QLD, Australia Jonathan Sliva Quinnipiac University School of Law, Hamden, CT, USA Martyna Smielewska Quinnipiac University School of Law, Hamden, CT, USA
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Elizabeth G. Smith Department of Psychology, University of Rochester (NY), Rochester, NY, USA Holly Smith Psychology Department, University of Canterbury, Christchurch, New Zealand Isaac C. Smith Department of Psychology, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA Jonathan Smith University of Rochester Medical Center, Rochester, NY, USA Tristram Smith Department of Pediatics, University of Rochester Medical Center, Rochester, NY, USA Wanda L. Smith Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada Anne Snow Child Study Center, Autism Program, Yale University, New Haven, CT, USA Kate Snyder University of Cincinnati, Cincinnati, OH, USA Martine Solages Child Study Center, Yale University, New Haven, CT, USA Marjorie Solomon Department of Psychiatry and Behavioral Sciences, UC Davis M.I.N.D. Institute, Sacramento, CA, USA Richard Solomon Ann Arbor Center for Developmental and Behavioral Pediatrics, Ann Arbor, MI, USA Youeun Song Child Study Center, Yale University School of Medicine, New Haven, CT, USA Latha Soorya Department of Psychiatry, Rush University Medical Center, Chicago, IL, USA Alexander Sorokin Federal Resource Center for Autism, Moscow State University of Psychology and Education, Moscow, Russia Timothy Soto Clinical Psychology, University of Massachusetts Boston, Boston, MA, USA Isabelle Soulières Centre d’excellence en troubles envahissants du développement de l’université de Montréal, Hôpital Riviére-des-Prairies, Montréal, QC, Canada Department of Psychology, Université du Québec à Montréal, Montréal, QC, Canada Mikle South Departments of Psychology and Neuroscience, Brigham Young University, Provo, UT, USA César Soutullo Child and Adolescent Psuychiatry Unit, Department of Psychiatry and Medical Psychology, University of Navarra Clinic, Pamplona, Spain Tristram Smith: deceased.
Contributors
Contributors
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Louise Spear-Swerling Southern Connecticut State University, New Haven, CT, USA Sarah Spence Department of Neurology, Children’s Hospital Boston Harvard Medical School, Boston, MA, USA Elizabeth Spencer College of Education and Human Ecology, The Ohio State University, Columbus, OH, USA Trina D. Spencer Rightpath Research and Innovation Center, University of South Florida, Tampa, FL, USA Institute for Human Development, Northern Arizona University, Flagstaff, AZ, USA Laurie A. Sperry Department of Psychiatry, School of Medicine, Yale University, New Haven, CT, USA Ania Spina Southern Connecticut State University, New Haven, CT, USA Beth Springate Department of Psychology, University of Connecticut, Storrs, CT, USA Dorrey Sproatt Psychological Studies in Education, University of California, Los Angeles, CA, USA Melissa A. Sreckovic Education Department, University of Michigan – Flint, Flint, MI, USA Helaine St. Amant UC Davis Department of Pediatrics, Sacramento, CA, USA Kate St. Cyr Parkwood Institute Operational Stress Injury Clinic – GTA Services, Toronto, ON, Canada Margaret St. John Quinnipiac University School of Law, Hamden, CT, USA Wouter Staal Neuroscience, Radboud University Nijmegen Medical Centre Karakter, Nijmegen, The Netherlands Aaron Stabel The M.I.N.D. Institute, University of California Davis Medical Center, Sacramento, CA, USA Lawrence H. Staib Department of Diagnostic Radiology, Yale University School of Medicine, New Haven, CT, USA Caleb R. Stanley Applied Behavior Analysis Program, Utah Valley University, Orem, UT, USA Zachary R. Steelman University of Arkansas, Fayetteville, AR, USA Georges Steffgen Institute for Health and Behavior, University of Luxembourg, Esch-sur-Alzette, Luxembourg Amanda Steiner Yale Child Study Center, New Haven, CT, USA Jennifer Stephenson School of Education, Macquarie University, Sydney, NSW, Australia
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Kevin G. Stephenson Department of Psychology, Nationwide Children’s Hospital and The Ohio State University, Columbus, OH, USA Lindsey Sterling Department of Psychiatry, Jane and Terry Semel Institute for Neuroscience and Human Behavior UCLA, Los Angeles, CA, USA Kyle Sterrett University of California, Los Angeles, Los Angeles, CA, USA Arianne Stevens Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA Bradley Stevenson University of North Carolina Charlotte, Charlotte, NC, USA Lisa Steward Indiana Behavior Analysis Academy, Kokomo, IN, USA Mary Elizabeth Stewart Psychology, School of Social Sciences, HeriotWatt University, Edinburgh, UK Kimberly Stigler Christian Sarkine Autism Treatment Center, Riley Hospital for Children, Indianapolis, IN, USA Lavinia Stoicescu Children’s Specialized Hospital, Warren, NJ, USA Mark A. Stokes School of Psychology, Deakin University, Burwood, VIC, Australia Wendy L. Stone Department of Psychology, UW READi Lab, University of Washington, Seattle, WA, USA Eric A. Storch Department of Pediatrics and Psychiatry, University of South Florida, St. Petersburg, FL, USA Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA Michael Storz Chapel Haven, Inc, New Haven, CT, USA Susan M. Strahosky School of Medicine and Dentistry, University of Rochester, Rochester, NY, USA Cara G. Streit Threshold Program, Lesley University, Cambridge, MA, USA Dorothy Stubbe Yale University School of Medicine Child Study Center, New Haven, CT, USA Denis G. Sukhodolsky Child Study Center, Yale School of Medicine, Yale University, New Haven, CT, USA Stephen Sulkes Division of Developmental and Behavioral Pediatrics, Golisano Children’s Hospital, University of Rochester, Rochester, NY, USA Simone D. Sun NYU Langone Health, Neuroscience Institute, New York, NY, USA Connie Sung Department of Counseling, Educational Psychology and Special Education, Michigan State University, East Lansing, MI, USA
Contributors
Contributors
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Min Sung Department of Developmental Psychiatry, Institute of Mental Health, Singapore, Singapore Faja Susan Developmental Medicine, Boston Children’s Hospital/Harvard Medical School, Boston, MA, USA Hanna Swaab Social and Behavioural Sciences, Leiden University, Leiden, The Netherlands Deanna M. Swain Virginia Polytechnic Institute and State University, Blacksburg, VA, USA Virginia Tech Autism Clinic and Center for Autism Research, Blacksburg, VA, USA Hania Szajewska Department of Paediatrics, The Medical University of Warsaw, Warsaw, Poland Peter Szatmari Department of Psychiatry and Behavioural Neurosciences, McMaster University Hamilton Health Sciences Corporation, Hamilton, ON, Canada Nathalie Szilagyi Yale Child Study Center, New Haven, CT, USA Christen Szymanski Department of Pediatrics (SMD), University of Rochester, School of Medicine and Dentistry, Rochester, NY, USA Nicole Takahashi Thompson Center for Autism and Neurodevelopmental Disorders, Columbia, MO, USA Yoshihiro Takeuchi Division of Developmental and Behavioral Pediatrics, Department of Pediatrics, Shiga University of Medical Science, Otsu, Shiga, Japan Celia Tam Yale Child Study Center, New Haven, CT, USA Leanne Tamm Cincinnati Children’s Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, OH, USA James W. Tanaka Centre for Autism Research, Technology and Education, Department of Psychology, Victoria, Canada Karen Tang Department of Psychology, University of Notre Dame, Notre Dame, IN, USA Center for Autism and the Developing Brain, Weill Cornell Medicine, White Plains, NY, USA Digby Tantam School of Health and Related Research, University of Sheffield, Sheffield, UK Pamela Targett Virginia Commonwealth University, Richmond, VA, USA Gennaro Tartarisco Institute for Biomedical Research and Innovation (IRIB), National Research Council of Italy (CNR), Messina, Italy Marc J. Tassé Nisonger Center – UCEDD, Departments of Psychology and Psychiatry, The Ohio State University, Columbus, OH, USA
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Marc B. Taub Southern College of Optometry, Memphis, TN, USA Mitchell Taubman Actum Clinical and Behavioral Services, Calabasas, CA, USA Johanna Patricia Taylor Pediatrics and Education, University of Pittsburgh, Pittsburgh, PA, USA Julie Lounds Taylor Department of Pediatrics, Vanderbilt Kennedy Center, Vanderbilt University, Nashville, TN, USA Margot J. Taylor Department of Diagnostic Imaging, Neuroscience and Mental Health Programme, The Hospital for the Sick Children Research Institute, Toronto, ON, Canada Department of Medical Imaging, Department of Psychology, University of Toronto, Toronto, ON, Canada Elizabeth Allen Technical Test Development, PRO-ED, Inc, Austin, TX, USA Elizabeth J. Teh Department of Otolaryngology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore Ito Tetsuya Department of Neonatology and Pediatrics, Graduate School of Medical Sciences, Nagoya City University, Aichi, Japan Linda Thibodeau Callier Advanced Hearing Research Center, Dallas, TX, USA Kathy Thiemann-Bourque Schiefelbusch Institute for Life Span Studies Juniper Gardens Children’s Project, University of Kansas, Lawrence, KS, USA Benjamin R. Thomas Claremont Graduate University, Claremont, CA, USA Brynn Thomas The Neurodevelopmental Disabilities Laboratory, Laboratory for Understanding Neurodevelopment (FUN Lab), Northwestern, and the University of Notre Dame, Chicago, IL, USA John W. Thomas Independent Educational Consultant, Durham, NC, USA Quinnipiac University School of Law, Hamden, CT, USA Kenneth Thomas Quinnipiac University School of Medicine, Hamden, CT, USA Yale School of Medicine, New Haven, CT, USA John Thorne Department of Speech and Hearing Sciences, University of Washington Fetal Alcohol Syndrome Diagnostic and Prevention Network, Seattle, WA, USA Audrey Thurm Neurodevelopmental and Behavioral Phenotyping Service, Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
Contributors
Contributors
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Elaine Tierney Department of Psychiatry, Kennedy Krieger Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA Geralyn Timler Speech Pathology and Audiology, Miami University, Oxford, OH, USA Iris Charlotte Tjaarda University of Applied Sciences Leiden, Leiden, Zuid-Holland, The Netherlands James T. Todd Psychology Department, College of Arts and Sciences, Eastern Michigan University, Ypsilanti, MI, USA Mariana Torres-Viso The Center for Children with Special Needs, Glastonbury, CT, USA Karen Toth Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA Jeanne Townsend Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA Joshua Trachtenberg David Geffen School of Medicine at UCLA, Los Angeles, CA, USA Vladimir Trajkovski Macedonian Scientific Society for Autism, Institute of Special Education and Rehabilitation, Faculty of Philosophy, Ss. Cyril and Methodius University, Skopje, Republic of Macedonia Frank Tran St. Joseph’s Healthcare, Hamilton, ON, Canada Darold A. Treffert St. Agnes Hospital, Fond du Lac, WI, USA Eva Troyb Neuropsychology, EASTCONN Regional Education Service Center, Columbia, CT, USA Katherine Tsatsanis Yale Child Study Center, New Haven, CT, USA Richard W. Tsien NYU Langone Health, Neuroscience Institute, New York, NY, USA Roberto Tuchman Department of Neurology, Miami Children’s Hospital, Weston, FL, USA Tychele N. Turner University of Washington, Seattle, WA, USA Katherine Tyson Chapel Hill Pediatric Psychology, P.A., Chapel Hill, NC, USA Mirko Uljarević Melbourne School of Psychological Sciences, Faculty of Medicine, Dentistry, and Health Sciences, The University of Melbourne, Melbourne, VIC, Australia Pamela Ullmann Colors of Play, Oakland, NJ, USA David Vagni Institute for Biomedical Research and Innovation (IRIB), National Research Council of Italy (CNR), Messina, Italy Joanne Valdespino Test Development, PRO-ED, Inc., Austin, TX, USA
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Valentina Valentovich Department of Psychological Science, University of California, Irvine, Irvine, CA, USA Karen S. van der Aalst Department of Child and Adolescent Psychiatry/ Psychology, Erasmus Medical Center-Sophia Children’s Hospital, Rotterdam, The Netherlands Jan Rutger Van der Gaag Department of Psychiatry and Karakter University Center for Child and Adolescent Psychiatry, Radboud University Medical Centre, Utrecht, Netherlands Stradina University of Riga, Riga, Latvia Marilyn Van Dyke Psychological Studies, UCLA’s Graduate School of Education and Information Systems, University of California, Los Angeles, Los Angeles, CA, USA Annemarie van Elburg Child and Adolescent Psychiatry, Rintveld Center for Eating Disorders, Altrecht Mental Health Institute, University Medical Center Utrecht, Utrecht, The Netherlands Nissa Van Etten Cultivate Behavioral Health and Education, Bee Cave, TX, USA Ruth Van Hecke Department of Rehabilitation Sciences, Ghent University, Ghent, Belgium Megan Van Ness Department of Psychology, University of Notre Dame, Notre Dame, IN, USA Gerrit Ian van Schalkwyk Department of Psychiatry, Yale School of Medicine, Yale Child Study Center, Yale University, New Haven, CT, USA Butler Hospital, Brown University, Providence, RI, USA Guus van Voorst Clinical Psychology, Center for Autistic Disorders, GGZ Centraal, Amersfoort, Netherlands Ernst O. VanBergeijk Vocational Independence Program, New York Institute of Technology, Central Islip, NY, USA Threshold Program, Lesley University, Cambridge, MA, USA Brent Vander Wyk Yale Child Study Center, Center for Translational Developmental Neuroscience, New Haven, CT, USA Douglas Vanderbilt Developmental-Behavioral Pediatrics, Children’s Hospital Los Angeles/USC, Los Angeles, CA, USA Tricia Vause Department of Child and Youth Studies and Department of Applied Disability Studies, Brock University, St. Catharines, ON, Canada Pamela E. Ventola Yale Child Study Center, School of Medicine, Yale University, New Haven, CT, USA Patrizia Ventura Child Neuropsychiatry Unit, University of Bari “Aldo Moro”, Bari, Italy Ty W. Vernon Yale Child Study Center, New Haven, CT, USA
Contributors
Contributors
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Koegel Autism Center/Department of Counseling, Clinical, and School Psychology, University of California Santa Barbara, Santa Barbara, CA, USA Michaela Viktorinova Yale Child Study Center Temple Medical Center, New Haven, CT, USA Michele Villalobos The Edward Zigler Center in Child Development and Social Policy, Yale Child Study Center, New Haven, CT, USA Micaela Violette Yale Child Study Center, New Haven, CT, USA Benedetto Vitiello Child and Adolescent Treatment and Preventive Intervention Research Branch, NIMH, NIH, Bethesda, MD, USA Donata Pagetti Vivanti European Disability Forum, Brussels, Belgium Giacomo Vivanti A.J. Drexel Autism Institute, Drexel University, Philadelphia, PA, USA David H. V. Vogel Institute of Neuroscience and Medicine (INM3), Research Center Jülich, Jülich, Germany Faculty of Medicine and University Hospital Cologne, Department of Psychiatry, University of Cologne, Cologne, Germany Kai Vogeley Institute of Neuroscience and Medicine (INM3), Research Center Jülich, Jülich, Germany Faculty of Medicine and University Hospital Cologne, Department of Psychiatry, University of Cologne, Cologne, Germany Dawn Vogler-Elias Communication Sciences and Disorders, Nazareth College, Rochester, NY, USA Fred R. Volkmar Child Study Center, Irving B. Harris Professor of Child Psychiatry, Pediatrics and Psychology, Yale Child Study Center, School of Medicine, Yale University, New Haven, CT, USA Lucy Volkmar Achievement First East New York Elementary School, Brooklyn, NY, USA Avery Voos Yale Child Study Center, New Haven, CT, USA Kayla Wagner SUNY Upstate Medical University, Syracuse, NY, USA Allison Wainer Department of Psychiatry, Rush Medical College, Chicago, IL, USA Krysia Emily Waldock Tizard Centre, University of Kent, Canterbury, UK Michael F. Walker Child Study Center, Yale University School of Medicine, New Haven, CT, USA Gregory L. Wallace Psychiatry and Behavioral Sciences and Pediatrics, School of Medicine and Health Sciences, The George Washington University, Washington, DC, USA Katherine S. Wallace Department of Psychiatry and Behavioral Sciences, UC Davis M.I.N.D. Institute, Sacramento, CA, USA
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Margaret Walsh May Institute, Randolph, MA, USA Pat Walsh Centre of Medical Law and Ethics, Dickson Poon School of Law, Somerset House East Wing, Kings College London, London, UK Katherine Walton Department of Psychology, Michigan State University, East Lansing, MI, USA Kai Wang Department of Psychiatry and Department of Preventive Medicine, The Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA Xiaobin Wang Center on Early Life Origins of Disease, Department of Population, Family and Reproductive Health, Division of General Pediatrics & Adolescent Medicine, Department of Pediatrics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA Tracey Ward Simons Autism Family Collaboration, University of Washington Autism Research Center, Seattle, WA, USA Felix Warneken Department of Psychology, Harvard University, Cambridge, MA, USA Zachary Warren Vanderbilt Kennedy Center, Treatment and Research Institute for Autism Spectrum Disorders (TRIAD), Nashville, TN, USA Renee Watling Division of Occupational Therapy, Department of Rehabilitation Medicine, University of Washington, Seattle, WA, USA Linda R. Watson The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Sara Jane Webb Psychiatry and Behavioral Sciences and UW Autism, Seattle Children’s Research Institute, University of Washington, Seattle, WA, USA Paul Wehman Department of Physical Medicine and Rehabilitation, Virginia Commonwealth University, Richmond, VA, USA Deborah Weiss Department of Communication Disorders, Southern Connecticut State University, New Haven, CT, USA Jonathan A. Weiss Department of Psychology, York University, Toronto, ON, Canada Mary Jane Weiss Institute for Behavioral Studies, Endicott College, Beverly, MA, USA Therese R. Welch School of Medicine and Dentistry, University of Rochester Medical Center, Rochester, NY, USA Aurelie Welterlin Chapel Hill TEACCH Center, Carrboro, NC, USA Julia Wenegrat Psychiatry, University of Washington, CHDD, Seattle, WA, USA
Contributors
Contributors
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Alexander Westphal Division of Law and Psychiatry, Yale Child Study Center, Yale School of Medicine, New Haven, CT, USA Susan W. White Department of Psychology, University of Alabama, Tuscaloosa, AL, USA Psychology Department, Virginia Tech, Blacksburg, VA, USA Andrew Whitehouse Telethon Kids Institute, University of Western Australia, Nedlands, WA, Australia Research Section (Psychology), University of Western Australia, Crawley, WA, Australia Siena Whitham Psychological Studies in Education, University of California, Los Angeles, Los Angeles, CA, USA Jennifer Wick Community Consultation Program, Division of Neurodevelopmental and Behavioral Pediatrics, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA Andrea Trubanova Wieckowski Psychology Department, Virginia Tech, Blacksburg, VA, USA Serena Wieder Profectum Foundation, Mendham, NJ, USA Lisa Wiesner Pediatric and Adolescent Medicine, Orange, CT, USA Kristin Wier Department of Psychology, University of Notre Dame, Notre Dame, IN, USA LOGAN Autism Learning Center - Southwest Michigan, Benton Harbor, MI, USA Jan R. Wiersema Ghent University, Ghent, Belgium Katrina Williams Developmental Medicine, University of Melbourne, The Royal Children’s Hospital and Murdoch Childrens Research Institute, Parkville, VIC, Australia Zachary J. Williams Medical Scientist Training Program, Vanderbilt University School of Medicine, Nashville, TN, USA Yale Child Study Center, New Haven, CT, USA Meagan C. Wills Yale Child Study Center, New Haven, CT, USA A. Jeremy Willsey Department of Psychiatry, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA Dawn Wimpory School of Psychology, University of Wales Bangor, Gwynedd, UK Gayle C. Windham Division of Environmental and Occupational Disease Control, CA Department of Public Health, Richmond, CA, USA Lorna Wing Centre for Social and Communication Disorders, Bromley, Kent, UK
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Logan Wink Department of Psychiatry, University of Cincinnati School of Medicine, Cincinnati, OH, USA Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA Vincent Winterling Delaware Autism Program, Newark, DE, USA Julie M. Wolf Yale Child Study Center, New Haven, CT, USA Connie Wong FPG Child Development Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Jeffrey J. Wood Departments of Psychiatry and Education, UCLA/Geffen School of Medicine, UCLA Center for Autism Research and Treatment, University of California, Los Angeles, CA, USA Marc Woodbury-Smith Department of Psychiatry and Behavioural Neuroscience, McMaster University, Hamilton, ON, Canada Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK Douglas W. Woods Department of Psychology, University of WisconsinMilwaukee, Milwaukee, WI, USA Richard Woods Independent Scholar, Nottingham, UK Julie Worley Department of Psychology, Louisiana State University, Baton Rouge, LA, USA John Wright School of Social and Behavioral Sciences, Marist College, Poughkeepsie, NY, USA Brent Vander Wyk Yale Child study Center, New Haven, CT, USA Maya Yaari Department of Psychology, The Hebrew University of Jerusalem, Jerusalem, Israel Ayako Yaguchi Department of Disabilities of Brain Functions, Research Institute of National Rehabilitation Center for Persons with Disabilities, Tokorozawa/Saitama, Japan Department of Contemporary Psychology, Rikkyo University, Niiza/Saitama, Japan Japan Society for the Promotion of Science, Chiyoda/Tokyo, Japan Brett Yamane Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA Yanki Yazgan Faculty of Medicine (ret), Marmara University, Istanbul, Turkey Yale Child Study Center (adjunct), New Haven, CT, USA Benjamin E. Yerys Center for Autism Research, Children’s Hospital of Philadelphia, Philadelphia, PA, USA Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
Contributors
Contributors
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Nurit Yirmiya Department of Psychology, The Hebrew University of Jerusalem, Jerusalem, Israel Robyn L. Young Flinders University, Adelaide, SA, Australia Lu Yu Department of Applied Social Sciences, The Hong Kong Polytechnic University, Hung Hom, Hong Kong Chengan Yuan Division of Educational Leadership and Innovation, Mary Lou Fulton Teachers College, Arizona State University, Tempe, AZ, USA Eunice Yuen Yale Child Study Center, New Haven, CT, USA Nicola Yuill Children and Technology Lab, School of Psychology, University of Sussex, Brighton, UK Brian A. Zaboski School of Special Education, School Psychology, and Early Childhood Studies, University of Florida, Gainesville, FL, USA Ditza A. Zachor The Autism Center/Alut, Department of Pediatrics, Shamir (Assaf Harofeh) Medical Center, Sackler Faculty of Medicine, Tel Aviv University, Zerifin, Israel Asmaa M. Zahran Clinical Pathology Department, South Egypt Cancer Institute, Assiut University, Assiut, Egypt Casey Zampella Department of Clinical and Social Sciences in Psychology, University of Rochester, Rochester, NY, USA Thomas Zane Van Loan School of Graduate and Professional Studies, Endicott College, The Institute for Behavioral Studies, Beverly, MA, USA Department of Applied Behavior Science, University of Kansas, Lawrence, KS, USA Charles H. Zeanah Department of Neurology and the Department of Psychiatry and Behavioral Sciences, School of Medicine, Tulane University, New Orleans, LA, USA Shoshana Zhang Yale University, New Haven, CT, USA Sonja Ziegler Marcus Autism Center, Emory University, Atlanta, GA, USA Cynthia Zierhut Department of Psychiatry and Behavioral Sciences, UC Davis M.I.N.D. Institute, Sacramento, CA, USA Cristofer Zillo Yale Child Study Center, New Haven, CT, USA Kimberly Zlomke Combined-Integrated Clinical and Counseling Psychology Doctoral Program, University of South Alabama, Mobile, AL, USA Caren Zucker New Jersey, USA Lonnie Zwaigenbaum Department of Pediatrics, University of Alberta, Autism Research Centre, Glenrose Rehabilitation Hospital, Edmonton, AB, Canada
A
m
Definition
▶ Mu Rhythm
15q13.3 microdeletion syndrome (OMIM 612001, DECIPHER coordinates: chr15: 30,901,306-32,445,407, hg19) is the result of heterozygous deletions at chromosome 15q13.3, ranging in size from 350 kb to 3.9 Mb. These deletions are mediated by nonallelic homologous recombination (NAHR) between four low copy repeat (LCR) elements: breakpoints (BPs) 3, 4, and 5, as well as the D-CHRNA7-LCR. The most common of these deletions, spanning 1.5 Mb to 2 Mb are mediated by BPs 4 and 5 and encompass six genes: FAN1, MTMR10, TRPM1, KLF13, OTUD7A, and CHRNA7, as well as one microRNA: hsa-miR-211. Of these genes, CHRNA7 and OTUD7A are the top candidate genes (Yin et al. 2018; Gillentine and Schaaf 2015). The estimated frequency of the most common 15q13.3 microdeletions is 1 in 5525 live births (0.19%) and is estimated to be higher (0.29%) among individuals with intellectual disability and idiopathic generalized epilepsy (1%) (Gillentine et al. 2018). However, these deletions also exhibit incomplete penetrance, with about 20% of individuals not having any diagnosed phenotypes. This contributes to 15q13.3 microdeletions being de novo (15%) and inherited (85%). Of note, studies have found that a large proportion of 15q13.3 microdeletion probands are adopted; so, while having unknown inheritance, it is likely that these are inherited from affected parents (Ziats et al. 2016).
m Rhythm ▶ Mu Rhythm
1-[1-[4,4-Bis(p-fluorophenyl) butyl]-4-piperidyl]-2benzimidazolinone ▶ Pimozide
15q13.3 Microdeletion Syndrome Christian Patrick Schaaf1 and Madelyn A Gillentine2 1 Institute of Human Genetics, Heidelberg University, Heidelberg, Germany 2 Department of Genome Sciences, University of Washington, Seattle, WA, USA
Synonyms CHRNA7 deletions
© Springer Nature Switzerland AG 2021 F. R. Volkmar (ed.), Encyclopedia of Autism Spectrum Disorders, https://doi.org/10.1007/978-3-319-91280-6
2
Individuals carrying 15q13.3 microdeletions have a wide range of phenotypes, including intellectual disability/developmental delay, seizures/ epilepsy, autism spectrum disorder (ASD), and schizophrenia (Ziats et al. 2016). In general, probands with 15q13.3 microdeletion syndrome have height, weight, and fronto-occipital circumference within the normal range. Over half of 15q13.3 microdeletion probands exhibit cognitive deficits, with a study of 18 probands finding the average full-scale IQ to be 60 (Ziats et al. 2016; Gillentine and Schaaf 2015). The next most prevalent phenotype is seizures/epilepsy, affecting about one third of probands. Language or speech impairments are also common, affecting just under one third of probands. Other neuropsychiatric phenotypes include schizophrenia, ASD or autistic features, ADHD or attention difficulties, and mood disorders in less than 20% of probands each. Abnormal behaviors, including aggression, and impulsiveness have been observed in about a quarter of the cases. Dysmorphic features are present in about one third of probands, although there is not a consistent pattern of dysmorphia. While deletions between 1.5 Mb and 2 Mb are the most common, but both larger and smaller deletions are reported with similar clinical phenotypes. Notably, homozygous deletions at 15q13.3 have been reported and are phenotypically more severe, with probands exhibiting neonatal encephalopathy. Additionally, the reciprocal microduplication is also pathogenic, with incomplete penetrance as well and a similar range of phenotypes, although typically less severe (Gillentine and Schaaf 2015). Several hypotheses have been proposed to explain the variable expressivity observed among 15q13.3 microdeletion syndrome probands. These include additional copy number changes and/or single nucleotide variants contributing to phenotypes and epigenetic changes. However, the most prominent hypothesis is the effect of modifier genes, in particular the humanspecific fusion gene CHRFAM7A, consisting exons 5 through 10 of CHRNA7 and a sequence of unknown function, FAM7A. The fusion gene is copy variable and polymorphic among the
15q13.3 Microdeletion Syndrome
population. Functional studies have shown that increasing amounts of CHRFAM7A can contribute to CHRNA7 dysfunction (Ihnatovych et al. 2019). Currently, there is no consistent treatment for 15q13.3 microdeletion syndrome. CHRNA7, encoding for the α7 nicotinic acetylcholine receptor (nAChR), has been suggested as a candidate gene. Dysfunction of the α7 nAChR is supported molecularly, with a decrease of the receptor resulting in decreased calcium flux through the channel (Gillentine et al. 2017). Due to this, α7 agonists and positive allosteric modulators (PAMs) have been suggested as a possible treatment and assessed among a few individuals with mixed results. One individual carrying a 15q13.3 microdeletion who exhibited recurrent rage outbursts was treated with galantamine, a nAChR allosteric modulator and acetylcholinesterase inhibitor, with positive results, although such drugs are known to have severe side effects (Cubells et al. 2011). Individuals with schizophrenia or autism spectrum disorder have also been treated with nAChR agonists in small studies with positive, but limited results (Olincy et al. 2016). To date, no large clinical trials have been performed using such compounds.
See Also ▶ Angelman/Prader-Willi Locus ▶ Angelman/Prader-Willi Syndromes ▶ Cholinergic System ▶ Chromosomal Abnormalities ▶ Chromosome 15q11–q13
References and Reading Cubells, J. F., DeOreo, E. H., Harvey, P. D., et al. (2011). Pharmaco-genetically guided treatment of recurrent rage outbursts in an adult male with 15q13.3 deletion syndrome. American Journal of Medical Genetics. Part A, 155, 805–810. https://doi.org/10.1002/ajmg.a. 33917. Gillentine, M. A., Schaaf, C. P. (2015). The human clinical phenotypes of altered CHRNA7 copy number. Biochem. Pharmaacol, 97(4), 352–362. https://doi. org/10.1016/j.bcp.2015.06.012
16p11.2 Gillentine, M. A., Schaaf, C. P., & Patel, A. (2017). The importance of phase analysis in multiexon copy number variation detected by aCGH in autosomal recessive disorder loci. American Journal of Medical Genetics. Part A, 173, 2485–2488. https://doi.org/10.1002/ajmg.a.38328. Gillentine, M. A., Lupo, P. J., Stankiewicz, P., & Schaaf, C. P. (2018). An estimation of the prevalence of genomic disorders using chromosomal microarray data. Journal of Human Genetics, 63, 795–801. https://doi.org/10.1038/s10038-018-0451-x. Ihnatovych, I., Nayak, T. K., Ouf, A., et al. (2019). iPSC model of CHRFAM7A effect on α7 nicotinic acetylcholine receptor function in the human context. Translational Psychiatry, 9, 59. https://doi.org/10.103 8/s41398-019-0375-z. Olincy, A., Blakeley-Smith, A., Johnson, L., et al. (2016). Brief report: Initial trial of Alpha7-nicotinic receptor stimulation in two adult patients with autism spectrum disorder. Journal of Autism and Developmental Disorders, 46, 3812–3817. https://doi.org/10.1007/s10803016-2890-6. Yin, J., Chen, W., Chao, E. S., et al. (2018). Otud7a knockout mice recapitulate many neurological features of 15q13.3 microdeletion syndrome. American Journal of Human Genetics, 102, 296–308. https://doi.org/10.1 016/j.ajhg.2018.01.005. Ziats, M. N., Goin-Kochel, R. P., Berry, L. N., et al. (2016). The complex behavioral phenotype of 15q13.3 microdeletion syndrome. Genetics in Medicine, 18, 1111–1118. https://doi.org/10.1038/gim.2016.9.
16p11.2 Stephan Sanders Child Study Center, Yale University, New Haven, CT, USA
Definition 16p11.2 refers to a particular region on the short (p) arm of chromosome 16 that corresponds to an approximately 500 kilobase copy number variation (CNV) that is strongly associated with the risk for ASD. The region contains 28 genes and is flanked by segmental duplications (stretches of near-identical DNA). These are known to increase the likelihood of a process known as nonhomologous allelic recombination, which can lead to gains or losses of the chromosomal segment flanked by these repeats.
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The importance of deletions and duplications at 16p11.2 in ASD was recognized simultaneously by three research groups (Kumar et al. 2008; Marshall et al. 2008; Weiss et al. 2008). These findings have since been replicated multiple times. 16p11.2 CNVs are found in about 1% of individuals with autism, compared with less than 0.1% of the population. CNVs in this region have also been associated with intellectual disability, developmental delay, schizophrenia (duplications only), and obesity (deletions only) (http://www. ncbi.nlm.nih.gov/books/NBK11167/). CNVs involving this interval are among the most well-established risk factors for ASD. They also highlight the complexity of the genetic contribution to these syndromes: the CNVs are neither necessary (ASD can occur without 16p11.2 CNVs) nor sufficient (ASD is not always present with the CNV) to cause ASD. Both deletions and duplications can contribute to risk, and these variations may either be de novo or transmitted within families. Moreover, in some families in which one affected child carries a 16p11.2, there may be other affected family members who do not. The region contains multiple biologically plausible gene candidates for ASD (see list below). At this time, it is not known whether a single gene is responsible for the ASD phenotype or if a combination of genes within the region accounts for the risk. The genes in the 16p11.2 region are ALDOA, ASPHD1, C16orf53, C16orf54, CDIPT, CORO1A, DOC2A, FAM57B, FLJ25404, GDPD3, HIRIP3, INO80E, KCTD13, LOC100271831, LOC440356, MAPK3, MAZ, MVP, PPP4C, PRRT2, QPRT, SEZ6L2, SLC7A5P1, SPN, TAOK2, TBX6, TMEM219, and YPEL3.
See Also ▶ Candidate Genes in Autism ▶ Chromosomal Abnormalities ▶ Common Disease-Rare Variant Hypothesis ▶ Copy Number Variation ▶ DNA ▶ Genetics
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3-(2-Chloro-10 H-phenothiazin-10-yl)-N,N-dimethylpropan-1-amine Hydrochloride
References and Reading Kumar, R. A., KaraMohamed, S., Sudi, J., Conrad, D. F., Brune, C., Badner, J. A., et al. (2008). Recurrent 16p.112 microdeletions in autism. Human Molecular Genetics, 17(4), 628–638. Marshall, C. R., Noor, A., Vincent, J. B., Lionel, A. C., Feuk, L., Skaug, J., et al. (2008). Structural variation of chromosomes in autism spectrum disorder. American Journal of Human Genetics, 82(2), 477–488. Weiss, L. A., Shen, Y., Korn, J. M., Arking, D. E., Miller, D. T., Fossdal, R., et al. (2008). Association between microdeletion and microduplication at 16p.112 and autism. The New England Journal of Medicine, 358(7), 667–675.
3-(2-Chloro-10 Hphenothiazin-10-yl)-N,Ndimethylpropan-1-amine Hydrochloride
with disabilities. Enforced by the Office of Civil Rights (OCR) within the US Department of Health and Human Services, Section 504 states that “No otherwise qualified individual with a disability in the United States . . . shall, solely by reason of her or his disability, be excluded from the participation in, be denied the benefits of, or be subjected to discrimination under any program or activity receiving Federal financial assistance. . .” (29 U.S.C. § 794(a)). Section 504 applies to any organization receiving federal funding; thus, it has important implications for individuals with autism spectrum disorders (ASD) and their participation in various educational, recreational, community, and employment settings.
Historical Background ▶ Chlorpromazine
3-Chloro-5-[3(dimethylamino)propyl]10,11-dihydro-5H-dibenz[b,f] azepine Monohydrochloride ▶ Clomipramine
3-Day Measles ▶ Rubella
504 Plan Kate Snyder1, Kara Hume2 and Christi Carnahan1 1 University of Cincinnati, Cincinnati, OH, USA 2 University of North Carolina, Chapel Hill, NC, USA
Definition Section 504 is a regulation of the Rehabilitation Act of 1973 that extends civil rights to individuals
The Civil Rights Act of 1964 and its prohibition of discrimination based on race, color, or national origin was a catalyst for the development of Section 504 of the 1973 Rehabilitation Act. Senator Hubert Humphrey (D., Minnesota) led the work to add an amendment to the Rehabilitation Act of 1973 that would address the discrimination of individuals with disabilities who had not been included under the Civil Rights Act. Section 504 was the first piece of legislation that specifically addressed the civil rights of individuals with disabilities. Implementation of Section 504 was wrought with challenges. Initial responsibility for writing implementation regulations was left to the US Department of Health, Education, and Welfare (HEW). Though drafts of the regulations were written as early as 1975 (Pfeiffer 2002), by 1977, the regulations had yet to be signed and implementation of Section 504 had stalled. In response, on April 5, 1977, the American Coalition of Citizens with Disabilities (ACCD) led demonstrations in HEW regional offices across the country. These demonstrations and other lobbying efforts led to the signing of the regulations on April 28, 1977. Delays in the creation of government-wide implementation slowed the process of issuing regulations within individual federal agencies (National Council on Disability
504 Plan
2003). Each department within the executive branch of the federal government now has its own regulations for implementing the provisions of Section 504 (Yell 2006). As the first civil rights legislation for individuals with disabilities, Section 504 of the 1973 Rehabilitation Act paved the way for future legislation for individuals with disabilities, including the 1990 adoption of the Americans with Disabilities Act (ADA) and the Individuals with Disabilities Education Act (IDEA). Together, Section 504, ADA, and IDEA protect the rights and equal participation of individuals with disabilities in employment, in education, and in the community.
Current Knowledge Qualification Under Section 504 Section 504 specifically states that to be protected under the law, an individual must be determined to (1) have a physical or mental impairment that substantially limits one or more major life activities, (2) have a record of such an impairment, or (3) be regarded as having such an impairment. Though no exhaustive list of specific “mental or physical impairments” covered by Section 504 exists, regulatory provision 34 C.F.R. 104.3(j)(2) (i) defines a physical or mental impairment as “any physiological disorder or condition, cosmetic disfigurement, or anatomical loss . . .or any mental or psychological disorder.” Major life activities, as defined by the Section 504 regulations at 34 C.F.R. 104.3(j)(2)(ii), include functions such as caring for one’s self, performing manual tasks, walking, seeing, hearing, speaking, breathing, learning, and working. It is important to note that this list is also not considered exhaustive, and thus other activities or functions not explicitly stated may be considered “major life activities” under Section 504. Since autism is a brain-based disorder (Wass 2011), individuals with a diagnosis of ASD would “have record” of a “mental impairment” that could potentially qualify them for protection under Section 504. Qualification is determined based upon the influence of an individual’s autism on his or her ability to perform a “major life activity.”
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The characteristics of autism manifest in social interactions, communicative exchanges, and through restricted or stereotyped patterns of behavior, interests, or activities (American Psychiatric Association 2000). Though to qualify for Section 504 each person on the autism spectrum must be evaluated on an individual basis, the disorder could potentially influence many “major life activities.” Application of Section 504 in Education (From Preschool Through Postsecondary) The provisions of Section 504 extend civil rights to individuals with disabilities to ensure access to activities and programs for which they “otherwise qualify” (29 U.S.C. § 794(a)). In other words, an individual meets program or employment criteria despite his or her disability. Applied to public education, this means that the individual with a disability is of public school age. Schools providing a public education must ensure that students with disabilities have equal opportunity to benefit from educational programs and facilities under Section 504 (Yell 2006). A central component of Section 504 as it applies to public schools is the provision of a free appropriate public education (FAPE). FAPE, as defined by Section 504, requires that a student with a disability be provided with regular or special education and related aids and services that are designed to meet his or her individual educational needs. These provisions must meet the individual’s needs as adequately as the needs of students without disabilities are met. Examples relevant to learners with ASD include using visuals to supplement verbal instruction, providing tape recorders, modifying textbooks, using behavior support techniques such as reinforcement, adjusting class schedules, and increasing classroom organization/structure. Section 504 also requires that all educational programs be accessible to all learners. This does not mean that schools are required to make every room or program accessible to all students but that all learners have equal access to programming. For example, a school may offer multiple sections of a biology lab in three different classrooms. If one of the lab classrooms is accessible and two are not, the school still meets the expectation
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of Section 504 because the educational program is accessible to all students. It is not permissible, however, to create a scenario where a disproportionate number of students with disabilities are assigned to the same program or activity because of accessibility issues. Returning to the example of the biology lab, it would not be acceptable for the school to create one section of the lab in which students with disabilities were overrepresented. This issue of disproportionality, or overrepresentation, is related to the FAPE provision within Section 504 that students with disabilities and students without disabilities should be placed in the same setting, to the maximum extent appropriate to meet the needs of the students with disabilities. In addition, students with disabilities may not be excluded from participating in any school activities, including extracurricular programs such as recreational sports or special interest clubs, in which students without disabilities would participate (US Department of Education, Office of Civil Rights 2010). Section 504 also requires that students with disabilities access programs and services in “comparable facilities.” In the event that a student with a disability is educated in a separate facility from their peers, a district must ensure that the facility is comparable (i.e., in terms of space, location, size) to the district’s other facilities. Thus, Section 504 protects students with disabilities from the historical practice of establishing special education classrooms in areas not conducive to learning, such as storage rooms or partitioned areas (Yell 2006). Eligibility Determination Since Section 504 and IDEA both protect the rights of individuals with disabilities in public education settings (through age 21), there is often confusion about eligibility requirements. It is important to note that not all students with disabilities who qualify for an individualized plan under Section 504 will meet the requirements for special education under IDEA. However, all students protected by IDEA also qualify for protections under Section 504. One reason for this distinction is that under IDEA, a disability must have an adverse impact on a student’s learning
504 Plan
that requires special education and related services. If a student does not require specialized instruction as a result of their disability, then he or she would not meet the requirements of IDEA. While IDEA explicitly requires the involvement of special education programming, implementation of Section 504 is general education responsibility (Yell 2006). Essentially, Section 504 provides access to an education (“to and through the schoolhouse door,” Wright and Wright 2008); however, Section 504 includes no guarantee that the individual will receive educational benefit, as specified in IDEA. In order to determine a student’s eligibility under Section 504, schools are required to follow certain procedural safeguards related to the identification, evaluation, or educational placement of students with a disability (U.S. Department of Education, Office for Civil Rights 2010). An evaluation must occur if a parent or teacher has referred a student, if a student has a medical diagnosis, or if a student has missed an excessive number of school days due to illness. Schools must use an evaluation procedure to determine whether a student’s disability (or perceived disability) limits his or her ability to perform a major life activity, but there is no standardized protocol for how this evaluation should take place. The FAPE provision requires that once students have been evaluated and determined to meet the criteria for Section 504, school teams must develop an individualized plan that outlines how services and accommodations will be provided. Many students who meet the criteria of Section 504 are also protected under IDEA. These students will therefore have an individualized education program (IEP) that will also constitute their written plan. If a student’s educational needs can be met with accommodations and related services that do not include specialized instruction, they do not typically qualify for special education. These students have only a Section 504 plan that reflects their needs. Finally, a number of rights and safeguards provided by IDEA are not similarly provided to individuals under Section 504, including prior written notice, rights to independent educational evaluations, and protections from permanent expulsion. Table 1
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504 Plan, Table 1 Overview of major differences between Section 504 and IDEA Eligibility
Major provisions
Funding Overall responsibility
Section 504 Individuals must qualify under the broad definition: (1) have a physical or mental impairment that substantially limits one or more major life activities, (2) have a record of such an impairment, or (3) be regarded as having such an impairment. Need for special education is not a requirement No otherwise qualified individual with disability shall solely by reason of his or her disability be: • Excluded from participation in • Denied the benefits of • • Be subjected to discrimination under any program or activity receiving federal financial assistance No funding provided for Section 504 Local education agency (LEA); general education
provides an overview of the supports and services provided under Section 504 and under IDEA. Application in Postsecondary Education Any postsecondary institution that receives federal funding is required to apply the regulations of Section 504 for qualifying individuals. Qualifying individuals at the postsecondary education level are those individuals with a disability who also meet the academic or technical standards that are required for admission by the institution. Individuals must also meet the participation requirements for the institution’s activity or program. FAPE does not apply to postsecondary educational settings; instead, institutions are required to provide “appropriate academic adjustments and auxiliary aids and services that are necessary to afford an individual with a disability an equal opportunity to participate in a school’s program” (US Department of Education, Office of Civil Rights 2011). The accommodations and services provided by a postsecondary institution should not alter the individual’s program in a fundamental way nor should they create an “undue burden” on the institution. Individuals with autism who meet the requirements for Section 504 while in elementary or secondary education should recognize that they might not receive the same services or
IDEA Students (aged 3–21) must qualify under one of the fourteen disability categories; students must demonstrate need for special education services
Procedural safeguards and the right to free appropriate public education in the least restrictive environment as defined by IDEA
Both state and federal funding State education agency (SEA); special education
accommodations at the postsecondary level. For example, some individuals with autism may be provided support from an educational assistant while in high school. Postsecondary institutions are not required to provide the same service because it may result in an undue financial burden to the institution (US Department of Education, Office of Civil Rights 2007). Another difference in provisions at the postsecondary level is the shift in responsibility. At the elementary and secondary school level, school districts are required to identify, evaluate, and ensure services for an individual with a disability under Section 504. At the postsecondary level, individuals must disclose their disability to the university and follow the institution’s procedures for requesting academic adjustments. Individuals with ASD must be prepared to discuss their individual needs when transitioning to the postsecondary education setting (Adreon and Durocher 2007). Application in Employment Settings Any employer who receives federal funding must also fulfill the mandates of Section 504 that protect qualified individuals with a disability. The disability criterion for protection under Section 504 in an employment setting is the same as in educational settings; however, the definition of “qualified” is changed. For the purposes
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of employment, in order to be “qualified” an individual with a disability must be able to perform the essential function of the job with reasonable accommodation (US Department of Health and Human Services, Office of Civil Rights 2006). An employer is required to take steps to accommodate an employee’s disability unless doing so would cause an undue burden to the employer. Workplace accommodations for individuals with disabilities are somewhat intuitive in certain situations (i.e., providing a sign language interpreter for an individual who is deaf or an access ramp for an individual with a physical disability). Workplace accommodations can sometimes be less obvious in the case of an individual with ASD but are no less important in ensuring the individual’s success in the workplace. Accommodations for individuals with autism in the workplace could include minor modifications to work materials or physical changes in the workplace that make the position more accessible. For example, an employer could make the reasonable accommodation of providing a quieter workspace that reduces distractions if such a change would be an appropriate accommodation for the individual with autism.
Future Directions Increased Prevalence A recent prevalence study estimated that 2–3% (1:38) of the total school-age population have an autism spectrum disorder (Kim et al. 2011). Many of these students are served in the general education setting (i.e., two-thirds of the sample in the Kim et al. study, 2011) and may not qualify for services under IDEA. This increases the likelihood that individuals with ASD will receive protections under Section 504, which has vast implications for school staff. This resurgence in 504 cases will require that school staff is adept in identifying and implementing appropriate accommodations and modifications for students with ASD – likely requiring additional staff training and expertise. In addition, an increase in litigation around 504 protections is expected as families and schools struggle to identify what accommodations
504 Plan
and auxiliary aids are required. For example, Section 504 does not mandate specific education programs or models nor does it require that students with ASD receive individualized instruction in specialized settings (Katsiyannis and Reid 1999). As this population ages, the demand for Section 504 protections at postsecondary settings, including universities, community colleges, and trade schools, will likely also increase. The resources required to implement these plans, both human resources and financial resources, may create new challenges for these institutions. Finally, employers will likely face similar challenges in supporting employees on the autism spectrum protected by Section 504. Technology The use of personal and portable technology with individuals with ASD is on the rise (e.g., iPad, iPod, personal digital assistants, communication devices) (Mechling et al. 2009). These tools are often used to support processing, communication, self-management, self-care, independent functioning, and other “major life activities” (e.g., learning and working, per Section 504). It is not clear, however, whether provisions in Section 504 provide for the procurement/use of these devices, and this ambiguity is likely to be discussed and debated in upcoming years. Though Section 504 requires that auxiliary aids such as technology are provided to individuals with specific disabilities (i.e., hearing or vision impairments) at no additional cost, there is no mention of such supports for individuals with broader developmental delays such as ASD or communication impairments as result of such delays. The fact that no funding is allocated to school districts, postsecondary institutions, or workplaces in association with Section 504 may further complicate the issue of providing technological supports for individuals with ASD. Social Skills Instruction Similar questions are likely to arise around the issue of social skills instruction. Because socializing and/or social functioning is described as one of the major life activities under Section 504, accommodations and modifications in this area are
7-Dehydrocholesterol Reductase Deficiency
recommended for individuals with ASD (Bellini et al. 2007). These may include peer-mediated strategies, direct social skills instruction, behavioral modification, self-management, and/or other evidence-based social skill strategies. Currently, however, the state of social skills instruction for individuals with ASD who do qualify under IDEA is bleak (Bellini et al.), and little is known about the status of this type of instruction for those who are protected under Section 504. It is safe to assume that services for this population would not exceed that of those who qualify under IDEA and likely also safe to assume that social skills services for the 504 protected group are close to nonexistent. As discussed above, as this population continues to increase, particularly a higher functioning group of students who may not receive services under IDEA, an increased focus on this type of instruction will fall to those implementing Section 504 plans.
See Also ▶ Academic Supports ▶ Americans with Disabilities Act ▶ Employment ▶ Individual Education Plan ▶ Individuals with Disabilities Education Act (IDEA) ▶ Toilet Training
9 Journal of Autism & Developmental Disorders, 39, 1420–1434. Rehabilitation Act of 1973. Section 504 (34 C.F.R. Part 104), 93rd Congress, H. R. 8070. Turnbull, R., Wilcox, B., & Stowe, M. (2002). A brief overview of special education law with focus on autism. Journal of Autism & Developmental Disorders, 32, 479–494. U.S. Department of Education, Office for Civil Rights. (2010). Free appropriate public education for students with disabilities: Requirements under section 504 of the rehabilitation act of 1973. Washington, D.C: U.S. Department of Education, Office for Civil Rights. U.S. Department of Health & Human Services. (June 2006). Your rights under the Section 504 and the Americans with Disabilities Act. In Office for Civil Rights Fact Sheet. Retrieved May 3, 2011, from http://www. hhs.gov/ocr/civilrights/resources/factsheets/504.pdf. Wass, S. (2011). Distortions and disconnections: Disrupted brain connectivity in autism. Brain & Cognition, 75, 18–28. Wright, P. & Wright, P. (March 2, 2008). Key differences between Section 504, the ADA, and the IDEA. In Wrightslaw.com. Retrieved May 3, 2011, from http:// wrightslaw.com/. Yell, M. L. (2006). The law and special education (2nd ed.). Upper Saddle River: Pearson.
5-HT ▶ Serotonin
5-Hydroxytryptamine References and Reading Adreon, D., & Durocher, J. S. (2007). Evaluating the college transition needs of individuals with high-functioning autism spectrum disorders. Intervention in School and Clinic, 42, 271–279. Bellini, S., Peters, J., Benner, L., & Hopf, A. (2007). A meta-analysis of school-based social skills interventions for children with autism spectrum disorders. Remedial & Special Education, 28, 153–162. Katsiyannis, A., & Reid, R. (1999). Autism and section 504: Rights and responsibilities. Focus on Autism and Other Developmental Disabilities, 14, 66–72. Kim, Y. S., Leventhal, B., Koh, Y. J., Fombonne, E., Laska, E., et al. (2011). Prevalence of autism spectrum disorders in a total population sample. AJP in Advance. https://doi.org/10.1176/appi.ajp.2011.10101532. Mechling, L., Gast, D., & Seid, N. (2009). Using a Personal Digital Assistant to increase independent task completion by students with autism spectrum disorder.
▶ Serotonin
7-[4-[4-(2,3-Dichlorophenyl)1-piperazinyl]butoxy]-3,4dihydro-2(1H)-quinolinone ▶ Aripiprazole
7-Dehydrocholesterol Reductase Deficiency ▶ Smith-Lemli-Opitz Syndrome
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7q11.23 Duplications Stephan Sanders Child Study Center, Yale University, New Haven, CT, USA
Synonyms Williams-Beuren region duplication
7q11.23 Duplications
NSUN5, RFC2, STX1A, TBL2, TRIM50, VPS37D, WBSCR22, WBSCR26, WBSCR27, and WBSCR28.
See Also • • • • • •
▶ Candidate Genes in Autism ▶ Chromosomal Abnormalities ▶ Common Disease-Rare Variant Hypothesis ▶ Copy Number Variation ▶ DNA ▶ Genetics
Definition 7q11.23 duplications are copy number variations (CNVs) in which an extra copy of 1,400 kb of DNA from the long arm of chromosome 7 is present. Duplications in this region are associated with “non-syndromic” ASD (Sanders et al. 2011). The region contains 26 genes, listed below, and is flanked by two segmental duplications (stretches of near-identical DNA). These are known to increase the likelihood of a process known as nonhomologous allelic recombination, which can lead to gains or losses of the chromosomal segment flanked by these repeats and account for the common breakpoints seen in the vast majority of individuals carrying duplications in this region. 7q11.23 duplications have also been seen in combination with intellectual disability, speech delay, and cardiac malformations (http://www.omim.org/entry/ 609757?search¼7q11.23&highlight¼7q1123). Reciprocal deletions at 7q11.23 cause Williams-Beuren syndrome characterized by distinctive facial features, supravalvular aortic stenosis, and intellectual disability (http://www.omim.org/ entry/194050?search¼7q11.23&highlight¼7q1123). Of note, these individuals also are known for highly sociable personalities. The distinctive phenotypes resulting from opposite changes in the number of copies of this region raise the intriguing possibility that the level of expression of a gene, or genes, within the 7q11.23 region plays a key role in the development and/or functioning of the social brain. The genes in the 7q11.23 region are ABHD11, BAZ1B, BCL7B, CLDN3, CLDN4, CLIP2, DNAJC30, EIF4H, ELN, FKBP6, FZD9, GTF2I, GTF2IRD1, LAT2, LIMK1, MLXIPL,
References and Reading Sanders, S. J., Ercan-Sencicek, A. G., Hus, V., Luo, R., Murtha, M. T., Moreno-De-Luca, D., et al. (2011). Multiple recurrent de novo CNVs, including duplications of the 7q11.23 Williams syndrome region, are strongly associated with autism. Neuron, 70(5), 863–885.
8-Chloro-1-methyl-6-phenyl4H-s-triazolo [4,3-α] [1,4] Benzodiazepine ▶ Alprazolam
AACAP Fred R. Volkmar Child Study Center, Irving B. Harris Professor of Child Psychiatry, Pediatrics and Psychology, Yale Child Study Center, School of Medicine, Yale University, New Haven, CT, USA
Synonyms AACAP practice parameters
Definition One of the first comprehensive guidelines to care of individuals with autism and related disorder, the Practice Parameters of the American Academy
AACAP
of Child and Adolescent Psychiatry first appears in 1999 (Volkmar et al. 1999) with recommendations for ascertainment and screening, diagnosis, and clinical care. The second version (Volkmar et al. 2014) appeared 15 years later and provided updated guidance for practioners. The original version synthesized available evidence in making recommendations for care anticipating some of the findings and recommendations made by the National Research Council 2 years later (National Research Council 2001). The initial version was intended to aide in the diagnosis and care and treatment of individuals with autism and related disorder. It provided an overview of the assessment and treatment recommendations with an emphasis on evidence-based treatment practices based on available scientific research. It also noted the need for involvement of multiple care providers with attendant issues of care coordination and so forth. The second version was updated to reflect the considerable advances in research – particularly treatment research and practice. It focused more specifically on the strength of evidence available in support to the various recommendations in the decade and a half since the first version appeared. The second version explicitly differed in that it explicitly noted the strength of the recommendation – ranging from clinical standard (rigorous evidence), clinical guideline (strong evidence), and clinical option (some but weak or emerging evidence) and not endorsed for treatments that appeared to have no little efficacy based on available research. Explicit distinctions were made based on the strength of the evidence ranging from randomized clinical controlled trials, controlled trials with nonrandomized assignment, uncontrolled trials, and case reports. Issues like care coordination and co-morbidity were also explicitly discussed. Many of the recommendations made were also consistent with the use of the medical home model of care (Hyman and Johnson 2012). These practice guidelines have many similarities and a few differences from other official guidelines, e.g., relative to issues of screening and early diagnosis; this guideline recommends early screen and encourages early diagnosis while others do not (see, Wilson et al. 2014; McClure 2014, for a discussion). Differences often largely
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come from the standards for levels of scientific evidence explicitly adopted by the formulators. As with all such official guides to care, recommendations should be evaluated in light of current research and practice and the circumstances of the individual case. With that, caveat attempts of this kind are most welcome as they provide clinical guidance for a range of care providers and provide basic recommendations for care.
See Also ▶ Medical Home and ASD ▶ National Guideline for the Assessment and Diagnosis of Autism Spectrum Disorders in Australia ▶ Screening Measures ▶ Sign Language
References and Reading Hyman, S. L., & Johnson, J. K. (2012). Autism and pediatric practice: Toward a medical home. Journal of Autism and Developmental Disorders, 42(6), 1156–1164. McClure, I. (2014). Developing and implementing practice guidelines. In Handbook of autism and pervasive developmental disorders, volume 2: Assessment, interventions, and policy (4th ed., pp. 1014–1035). Hoboken: Wiley. National Research Council. (2001). Educating young children with autism. Washington, DC: National Academy Press. Volkmar, F., Cook, E., Jr., Pomeroy, J., Realmuto, G., & Tanguay, P. (1999). Summary of the practice parameters for the assessment and treatment of children, adolescents, and adults with autism and other pervasive developmental disorders. Journal of the American Academy of Child & Adolescent Psychiatry, 38(12), 1611–1616. Volkmar, F., Siegel, M., Woodbury-Smith, M., King, B., McCracken, J., & State, M. (2014). Practice parameter for the assessment and treatment of children and adolescents with autism spectrum disorder. Journal of the American Academy of Child & Adolescent Psychiatry, 53(2), 237–257. https://doi.org/10.1016/j.jaac.2013. 10.013. Wilson, C., Roberts, G., Gillan, N., Ohlsen, C., Robertson, D., & Zinkstok, J. (2014). The NICE guideline on recognition, referral, diagnosis and management of adults on the autism spectrum. Advances in Mental Health and Intellectual Disabilities, 8(1), 3–14. https://doi.org/10.1108/AMHID-05-2013-0035.
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AACAP Practice Parameters ▶ AACAP
Aarskog Syndrome Marc B. Taub Southern College of Optometry, Memphis, TN, USA
Synonyms Aarskog-Scott syndrome; Faciogenital dysplasia
Definition Aarskog syndrome was first reported in 1970 by Aarskog in a seven-patient case series. The syndrome is characterized by short stature with peculiar facies, “shawl” scrotum (the scrotal folds encircle the penis ventrally), cryptorchidism (the testis fails to descend into its normal position in the scrotum), and abnormalities of the hands and feet (Aarskog 1970). Aarskog syndrome can be inherited as an X-linked disorder caused by FGD1 mutations (Xu et al. 2010; Volter et al. 2014) or possibly in an autosomal dominant or recessive pattern (Xu et al. 2010). Population surveys estimate that Aarskog occurs in approximately 1 per million in the general population (Gorski et al. 2000). Intelligence ranges from normal to mild mental retardation. A normal IQ distribution has been found (Pilozzi-Edwards et al. 2011). Mild learning difficulties and attention deficit hyperactivity disorder have been reported (Pilozzi-Edmonds et al.). Comorbidity has been documented with autism (Schwartz et al. 2000). Birth size is often normal. Alterations occur when individuals are 2–4 years old (Shalev et al. 2006). Until puberty, most patients are short with height at or below the third “centile.” Puberty is often delayed, but these patients do display a growth spurt in the late teens resulting in
AACAP Practice Parameters
adult height in the low-to-normal range. Final height is around the 10th “centile.” Serum growth hormone levels are reported as normal and treatment with growth hormone is ineffective. Spina bifida occulta, cervical spine abnormalities, and scoliosis have been documented (Taub and Stanton 2008). The nose is often described as short and stubby, with a broad nasal bridge and anteversion of the nostrils. The ears are low set and protuberant. They are fleshy superiorly and referred to as “jug-handle ears.” Maxillary hypoplasia and dental malocclusion has been reported as well as a transverse crease below the lower lip (Taub and Stanton 2008). Associated ophthalmic conditions include hypertelorism, telecanthus, blepharoptosis, and antimongoloid (downward) obliquity of the palpebral fissures. Ophthalmoplegia, strabismus, hyperopic astigmatism, retinal vessel tortuosity, nystagmus, and Brown’s syndrome have also been reported. The hands and feet are affected by this condition in several ways. The hands are often short and broad with mild syndactyly (interdigital webbing) and/or brachydactyly (shortness in comparison to the other bones and body parts). Hyperextensible joints with concomitant flexion of the distal joints (a hallmark sign), single palmer creases, and short medially incurved fifth fingers are also found. The feet are broad and flat with metatarsus versus short, splayed bulbous toes (Taub and Stanton 2008). Genital anomalies include a “shawl” scrotum, bilateral or unilateral cryptorchidism, and macroorchidism (abnormally large testes). Inguinal hernia (a condition in which part of the intestine bulges through a weak area in muscles in the abdomen, specifically the groin) has been found in association with the syndrome. No characteristic anomaly has been documented in females (Taub and Stanton 2008). There are no specific therapies for Aarskog syndrome. Some features may require surgical intervention (Orrico et al. 2007.
See Also ▶ Genetics ▶ Strabismus
Aberrant Behavior Checklist
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References and Reading
ABAS, Second Edition Aarskog, D. (1970). A familial syndrome of short stature associated with facial dysplasia and genital anomalies. The Journal of Pediatrics, 77, 856–861. Gorski, J. L., Estrada, L., Changhzi, H., & Zhou, L. (2000). Skeletal-specific expression of FDG1 during bone formation and skeletal defects in faciogential dysplasia (FDGY; Aarskog syndrome). Developmental Dynamics, 218, 573–586. Orrico, A., Galli, L., Obregon, M. G., de Castro Perez, M. F., Falciani, M., & Sorrentino, V. (2007). Unusually severe expression of craniofacial features in Aarskog-Scott syndrome due to a novel truncating variant of the FDG1 gene. American Journal of Medical Genetics, 143, 58–63. Pilozzi-Edwards, L., Maher, T. A., Basran, R. K., Milunsky, A., Al-Thihli, K., Braverman, N. E., et al. (2011). Fraternal twins with Aarskog-Scott syndrome due to maternal germline mosaicism. American Journal of Medical Genetics Part A, 155, 1987–1990. Schwartz, C. E., Gillessen-Kaesbach, G., May, M., Cappa, M., Gorski, J., Steindl, K., et al. (2000). Two novel mutations confirm FGD1 is responsible for the Aarskog syndrome. European Journal of Human Genetics, 8, 869–874. Shalev, S. A., Chevinski, E., Weiner, E., Mazor, G., Friez, M. J., & Schwartz, C. E. (2006). Clinical variation of Aarskog syndrome in a large family with 2189delA in the FGD1 gene. American Journal of Medical Genetics Part A, 140(2), 162–165. Taub, M. B., & Stanton, A. (2008). Aarskog syndrome: A case report and literature review. Optometry, 79, 371–377. Volter, C., Martinez, R., Hagen, R., & Kress, W. (2014). Aarskog-Scott syndrome: A novel mutation in the FGD1 gene associated with severe craniofacioal dysplasia. European Journal of Pediatrics, 173, 1373–1376. Xu, M., Qi, M., Zhou, H., Qui, H., et al. (2010). Familial syndrome resembling Aarskog syndrome. American Journal of Medical Genetics Part A, 152A, 2017–2022.
Aarskog-Scott Syndrome ▶ Aarskog Syndrome
▶ Adaptive Behavior Assessment System, Second Edition
ABAS-II ▶ Adaptive Behavior Assessment System, Second Edition
ABC ▶ Aberrant Behavior Checklist ▶ Autism Behavior Checklist
ABC-C ▶ Aberrant Behavior Checklist
ABC-R ▶ Aberrant Behavior Checklist
Aberrant Behavior Checklist Cristan Farmer1 and Michael G. Aman2 1 The National Institute of Mental Health (NIMH), National Institutes of Health (NIH), Bethesda, MD, USA 2 Nisonger Center, UCEDD, The Ohio State University, Columbus, OH, USA
Abbreviations
ABA ▶ Didactic Approaches
ASD DD
Autism spectrum disorder Developmental disability
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Synonyms ABC; ABC-C; ABC-R; Aberrant behavior checklist – community; Aberrant behavior checklist – residential
Description The Aberrant Behavior Checklist (ABC) is an informant rating instrument that was empirically derived by principal component analysis (Aman et al. 1985a). It contains 58 items that resolve onto five subscales. The subscales and the respective number of items are as follows: (a) Irritability (15 items), (b) Social Withdrawal (16 items), (c) Stereotypic Behavior (7 items), (d) Hyperactivity/Noncompliance (16 items), and (e) Inappropriate Speech (4 items). A total score for this instrument was not psychometrically derived and is not valid. The ABC was designed to be completed by any adult who knows the client well. This could be a parent, teacher, workshop supervisor, case worker, or informants in other roles. Depending upon reading ability, completion time varies, but most raters complete the ABC in 10–15 min the first time. Thereafter, rating times usually decline. A revised version of the ABC was published in 2017, along with a detailed manual (Aman and Singh 2017) and freely available annotated bibliography (https://psychmed.osu.edu/index.php/ instrument-resources). With respect to the actual content of the scale, although the wording of a handful of items was generalized (e.g., references to “the ward” and “patients” have been altered), the meaning of all items remains the same as in the original version. Subscale titles similarly underwent slight changes; “Irritability, Agitation, Crying” is now entitled Irritability and “Lethargy, Social Withdrawal” is now Social Withdrawal. Finally, substantive changes to the face sheet were undertaken in an effort to create more usable data. More general terms for school and other settings were used to facilitate comparison, as such terms are variable over time and across geographic location. Rather than querying individual diagnoses, the face sheet now requests explanations and,
Aberrant Behavior Checklist
where relevant, severity about various conditions that might impact behavior (e.g., sensory or physical impairments, developmental disabilities, medical diagnoses). The remainder of the face page is unchanged from the previous version; the rater is asked to provide the client’s sex, date of birth, and the rater’s relationship to the client, and a listing of any medicines being used by the client. In the context of treatment studies, this information (other than the subject’s name and date) is often not collected. Instructions for completing the ABC and its 58 items are found on the second page of the instrument. The period over which informants rate the client defaults to 4 weeks. However, depending on the clinical or research needs, this period can be increased or decreased. The instructions ask the informant to rate the client on a scale ranging from 0 (not at all a problem) to 3 (the problem is severe in degree). Further, the instructions ask raters to take relative frequency into account, such that if a given behavior occurs more than the client’s reference group (e.g., other children of the same age and sex), scores greater than or equal to 1 are warranted. The instructions also encourage informants to consider observations and reports of other responsible adults who know the client well when making their ratings. Finally, the instructions indicate that behaviors which interfere with the client’s development, functioning, and/or social relationships should be rated as a problem, even if these behaviors do not interfere with other people around the client. The 58 behavior items consume about 1½ pages of the form. Initially, the ABC was developed primarily as a measure of treatment effects, especially as an outcome measure for pharmacological intervention. With time, the use of the ABC has expanded, and it has been employed, fairly frequently, for the following applications: (a) to examine psychometric characteristics of other instruments and/or the ABC itself, (b) to study the behavioral phenotypes of individuals with genetic and metabolic conditions, (c) to examine the effects of different environmental variables (e.g., size of housing arrangements) on behavior, (d) to characterize
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the composition of subjects within studies and/or programs, (e) to assess the effects of sleep disruption on client behavior, (f) to characterize individuals with different types of psychiatric disorders, and (g) to evaluate quality of life. There are at least 35 languages into which it has been translated, including the following: Afrikaans, Arabic, Chinese, Czech, Danish, Dutch, Filipino, Finnish, French (Belgian, Canadian, and European), German, Greek, Hebrew, Hungarian, Indonesian, Italian, Japanese, Korean, Lithuanian, Norwegian, Persian (Farsi), Polish, Portuguese, Romanian, Russian, Serbian, Slovak, Slovenian, Spanish (Colombian, Mexican, Spanish, and USA), Swedish, Thai, Turkish, Telugu (regional language of Andhra Pradesh, India), Ukrainian, Urdu, Vietnamese, and Zulu. At the time of this writing, the following language translations were revised for compatibility with the 2017 ABC revision: Afrikaans, Arabic, Canadian French, European French, Chinese (Traditional), English (USA), Filipino, Hebrew, Kannada, Korean, Norwegian, Polish, Portuguese, Russian, Spanish (Spain and USA), and Urdu. In 2017, a single manual for the community and residential versions of the ABC replaced previous separate versions (Aman and Singh 1986, 1994). This new manual addresses an array of subjects not covered in the original manuals, including sections on giving instructions to raters, practices to avoid, and using the ABC for characterizing change at the individual and group levels. The ABC-Second Edition Community/Residential Manual (Aman and Singh 2017) gives the history of the ABC’s development and elaborates upon the meanings of all 58 items. Average subscale scores and standard deviations (normative data) are provided for adults, sourced from developmental centers in the United States and New Zealand. Normative data for teacher ratings of children and adolescents in special educational classes are provided in the following formats: (a) T-scores and percentiles by sex and age, (b) T-scores with all ages and sexes combined, and (c) means and standard deviations broken out by age and sex, as well as collapsed across all ages. The group home norms are presented in
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the following ways: (a) T-scores and percentiles by age (10-year groupings) and functional levels (mild, moderate, severe, and profound intellectual disability); (b) T-scores and percentiles collapsed across functional level, summarized for age alone and for sex alone; and (c) means and standard deviations broken out by combinations of functional level and age and summarized by sex alone. Normative data for parent ratings of children and adolescents with intellectual disability are provided as means and standard deviations broken out by age and sex. The manual is also a comprehensive source for information on studies of the psychometric properties of the ABC, including internal consistency, interrater reliability, testretest reliability, criterion group validity, concurrent and discriminant validity, and correspondence of ratings with direct observation scores. We summarize some of the information contained in the manual herein. There have been about 450 scientific studies conducted with the ABC, providing a rich literature against which new work can be compared.
Historical Background The development of the ABC grew out of a practical need for an instrument to assess treatment effects in people with DD (e.g., Singh and Aman 1981). Development of the ABC was closely modeled on the Behavior Problem Checklist of Quay and Peterson (Quay 1977) and the enormously popular Conners’ Parent and Teacher Rating Scales (Conners 1969, 1970). The initial form of the ABC contained 125 items, developed after a review of residential center case records, a survey of existing instruments, and consultation with direct care staff regarding content and wording. A pilot study obtained ratings from caregivers of 418 adolescents and adults with DDs. Items endorsed for fewer than 10% of subjects were dropped, and a principal factoring method was conducted with oblique rotation, leaving 76 items. The intermediate 76-item scale was then used to rate a new group of 509 adolescents and adults. The data from both samples were analyzed independently by a principal factoring method
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followed by oblique rotation. A five-factor solution seemed most interpretable in both analyses. Items that failed to load on the same respective factors across analyses were deleted, leaving 58 items in the ultimate ABC. Two important subsequent changes took place more or less simultaneously. First, the original ABC contained some language that was distinctly institutional in flavor (e.g., “excessively active on the ward”). This language was modified in the early 1990s (e.g., “excessively active at home, school, work, or elsewhere”) to form what was then called the ABC-community. At about the same time, investigators assessed the ABC in child samples and found that the original factor structure was maintained for children and adolescents (e.g., Marshburn and Aman 1992; Brown et al. 2002). The earlier version of the ABC was dubbed the ABC-Residential to distinguish it from the newer ABC-Community. Thus, at this stage, there were residential and community versions available, and the Community version’s structure was validated for children, adolescents, and adults. With time, the ABC came to be used more and more in pharmacological research involving people with intellectual disability and/or autism spectrum disorders (ASDs). Other uses are described under Clinical Uses, below. Much of the published research with the ABC can be accessed through the Annotated Bibliography on the ABC (Aman 2015; available at http://psychmed.osu. edu/resources.htm). One important development was the adoption of the ABC’s Irritability subscale as the primary outcome measure by the Research Units on Pediatric Psychopharmacology (RUPP et al. 2002, 2005), a network of experienced psychopharmacology laboratories funded by the US National Institute of Mental Health. In two studies, the RUPP network showed that risperidone was highly effective in reducing agitated and irritable behavior for children and adolescents with autistic disorder chosen for high initial scores on the Irritability subscale. Using data from these pivotal investigations and from another clinical trial, Johnson & Johnson Pharmaceuticals obtained a clinical indication from the United
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States Food and Drug Administration for the use of risperidone in children and adolescents with autism and significant agitation and irritability. At that point, it was the only medication approved by the FDA for treating patients with autism. Subsequently, Bristol-Myers Squibb Company launched two pivotal clinical trials of aripiprazole in children and adolescents with autism and agitated/irritable behavior, again with the ABC Irritability subscale as the primary outcome measure. Bristol-Myers Squibb was also able to obtain a clinical indication for its product. These developments have made the ABC a popular choice as an outcome measure for the pharmaceutical industry when targeting behavior problems in patients with DD. However, it is important to realize that individual academic investigators were using the ABC long before it was adopted as an outcome by industry. In 2015, Bearss et al. published an experiment showing that psychosocial training, administered by parents of children with autism spectrum disorder, was highly effective in reducing disruptive behavior in the children as assessed by parent ratings on the Irritability subscale of the ABC. It seems probable that the ABC will be used widely in future to assess the impact of psychosocial treatments in children with DDs. As noted under Clinical Uses, below, the ABC has been used for approximately 450 pharmacological and nonpharmacological purposes over the last 30+ years.
Psychometric Data There is a wealth of psychometric data on the ABC. Construct Validity. There have been several independent factor analyses with the ABC which have supported its construct validity (a) across versions of the ABC, (b) across settings (large residential vs. small, within the community), and (c) across age groups. Most of these studies have been referenced and summarized in the Annotated Bibliography on the ABC (Aman 2015; freely available at http://psychmed.osu.edu/resources. htm), and they are summarized in Table 1.
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Aberrant Behavior Checklist, Table 1 Studies of the construct validity of the ABC
Authors Aman et al. (1987a) Newton and Sturmey (1988) Bihm and Poindexter (1991) Freund and Reiss (1991) Rojahn and Helsel (1991) Marshburn and Aman (1992) Aman et al. (1995) Ono (1996) Siegfrid (2000) Brown et al. (2002) Brinkley et al. (2007)
Sansone et al. (2012) Kaat et al. (2014) Wheeler et al. (2014)
Residential/ community children/adults Res, Adults
Number of factors 5 (Same)
% of items on same factor (mean factor loading) 86% (0.58)
Coefficient of congruence (mdn) 0.88–0.96 (0.94)
Res, Adults
5 (Same)
78% and 81%a
NR
Res, Adults
5 (Same)
NR
NR
Comm, Childr Comm, Childr Res, Childr
5 (Same) (parent) 5 (Same) (teacher) 5 (Same)
91% 80% NR
0.88–0.82 (0.86) 0.65–0.91 (0.81) 0.80–0.89 (0.82)
Comm, Childr
4 (1–4 Same)
84% (0.65)
0.87–0.96 (0.90)
Comm, Adults
5 (Same)
95% (0.59)
0.84–0.97 (0.90)
Res, Childr/Adults Comm, Adults Comm, Childr
5 (Same) 5 (Same) 4 (1–4 Same)
83% 84% (0.69) 71% (0.51)
NR NR 0.62–0.91 (0.85)
Comm, Childr
5 (Same for low SIB subjects) 4 (Subscales 2–5 same for high SIB subjects) 6 (1–5 same)
78%
NR
60%
NR
76%
NR
5 (Same)
90%
NR
5 (Same)
97%
NR
Comm, Childr/ Adults Comm, Childr Comm, Childr/ Adults
Same, same factor composition; NR, not reported; mdn, median value Using ordinal and dichotomous coding (absent/present), respectively
a
As shown in Table 1, all studies of construct validity essentially verify the ABC factor structure as described in the original report (Aman et al. 1985a). Two studies failed to find the Inappropriate Speech factor in children, possibly because of a lack of participants with ASDs; it is worth noting that a very large study (n ¼ 1,893) of children with ASD demonstrated excellent support for the original factor structure (Kaat et al. 2014). One study (Brinkley et al. 2007) found significant changes to the Irritability factor when subjects with high rates of selfinjury (SIB) were included, but the factor structure was confirmed when these subjects were excluded.
Other Forms of Validity. The original ABC development study included several validity comparisons (Aman et al. 1985b). Concurrent validity was established through moderate correlations with existing standardized scales (e.g., the AAMD Adaptive Behavior Scale), and comparisons of criterion groups yielded predictable patterns of difference (e.g., individuals who attended formal training activities received lower subscale scores than those who did not). Further, direct observations of the individuals in their residences were well-correlated with ABC scores. Finally, compared to unmedicated individuals, those
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prescribed psychotropic medications had significantly higher ABC scores on all domains except Repetitive Speech. Subsequently, numerous studies have demonstrated the validity of the ABC, and the manual cites about 35 studies addressing validity. Examples of this include concurrent validity between the ABC and other formal instruments, including (a) the Psychopathology Instrument for Mentally Retarded Adults, (b) the Nisonger Child Behavior Rating Form, (c) Conners’ Teacher Rating Scale, (d) Diagnostic Assessment for the Severely Handicapped-II, (e) Reiss Screen for Maladaptive Behavior, (f) Stereotyped Behavior Scale, (g) Teacher Report Form, and (h) The ADD-H Comprehensive Teacher’s Rating Scale. Reliability Assessments. Many researchers, especially those who conducted factor analysis with the ABC, reported alpha coefficients – a measure of internal consistency. Generally, coefficient alpha ranged from the low 0.80s to the middle 0.90s, indicating a high level of consistency. Interrater Reliability. Many of the studies that examined cross-informant reliability are summarized in Table 2. These generally fell into the low 0.50s to high 0.60s range, which is quite adequate for both research and clinical practice. Using criteria established by Cicchetti and Sparrow (1981), these reliabilities fall into the fair to good ranges.
Test-Retest Reliability. Several studies that examined test-retest reliability are summarized in Table 3. Median reliability ranged from the mid-0.60s to highs in the 0.90s. In general, testretest reliably was quite high, falling within ranges characterized by Cicchetti and Sparrow (1981) as good to excellent.
Clinical Uses As noted, the ABC was developed as an outcome measure for pharmacological trials in people with developmental disabilities, and it has been used heavily for this purpose (see Annotated Bibliography, Aman 2015). However, use of the scale is not confined to research. The ABC can be used, in combination with other data-based approaches, to monitor the effects of routine clinical care in people with intellectual disabilities and/or ASD. Its early use was primarily among individuals with intellectual disabilities alone, but in recent years it has been used a great deal to assess treatment outcomes in individuals with ASD. This is supported by the available data; one large study (n ¼ 1,893) produced very strong evidence for the factor validity of the ABC when used in children and adolescents with ASD. However, it is worth
Aberrant Behavior Checklist, Table 2 Summary of interrater reliability studies with the ABC Authors Aman et al. (1985b) Aman et al. (1987b) Freund and Reiss (1991)a Rojahn and Helsel (1991) Ono (1996) Schroeder et al. (1997) Siegfrid (2000)c Miller et al. (2004)
Sample size (a) 35 (b) 40 (a) 28 (b) 28 94? 130 33 30 90 22
Ages of subjects Adults Adults Adults Adults Children Children/Adolescents Children/Adults Adults Adults Children
Correlation range 0.54–0.67 0.51–0.88 0.52–0.74 0.40–0.66 0.39–0.49b 0.39–0.61 0.58–0.78b 0.12–0.53 0.67–0.90 0.72–0.80
Median correlation 0.59 0.71 0.60 0.59 0.45b 0.49 0.68 0.45 0.73 NR
All references can be found in Annotated Bibliography on the ABC (Aman 2015). Unless indicated otherwise, all correlations were Pearson correlation coefficients. Unless coded otherwise, raters had the same roles a Parent-teacher agreement b Spearman correlation coefficients c Intraclass correlation coefficients
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Aberrant Behavior Checklist, Table 3 Summary of test-retest reliability studies with the ABC
Ono (1996)
Lag 4 week 1 month 1 month 4 weeks
Sample size 28 30a 25b 43
Schroeder et al. (1997) Siegfrid (2000)c Miller et al. (2004)
30 days 4 week 2 weeks
30 20 48
Age group Adults Children Children Children, Adults Adults Adults Children
Berry-Kravis et al. (2006)
5 week; 2 week
49
Adults
Authors Aman et al. (1987b) Freund and Reiss (1991)
Correlation range 0.55–0.83 0.80–0.95 0.50–0.67 0.84–0.90
Median correlation 0.72 (mean) 0.88 0.61 0.85
0.52–0.76 0.84–0.98 0.68–0.85b 0.74–1.00d 0.60–0.90e
0.59 0.94 NR NR 0.90
All references can be found in the Annotated Bibliography on the ABC (Aman 2015). Unless indicated otherwise, all were Pearson correlation coefficients a Parent ratings b Teacher ratings c Intraclass correlation coefficients d Teaching assistants e Intraclass correlation coefficient
noting that although several subscales assess features of ASDs (e.g., Social Withdrawal, Stereotypic Behavior, Inappropriate Speech), the ABC was not intended to be a measure of overall autism severity. As research on specific genetic conditions becomes more common, investigators have attempted to identify syndrome-specific factor structures rather than employing the validated existing structure. This practice is likely to yield unstable results, and researchers are cautioned against this practice (Aman and Singh 2017). Recently, Aman et al. (2020) analyzed extensive data from participants with fragile X syndrome and concluded that the classical scoring algorithm, as presented in the ABC Manual, is the optimal way of presenting ABC results. Periodically, the ABC had been used to assess the effects of behavior intervention, both in formal research (Aman et al. 2009; Bearss et al. 2015) and in everyday care. Obviously, it is important to document the efficacy of such treatment. The ABC has been used to select participants for various forms of research intervention, especially pharmacological investigations. It may serve a similar role in routine clinical care to identify individuals who warrant preventive care and/or
active intervention. As noted earlier, the ABC has been used to monitor behavior in those experiencing transition, such as moving from one living environment to another. It has also been used to assess co-occurring behavioral issues in people with genetic or metabolic syndromes, and this is another likely area of clinical application. The ABC has primarily been used to assess school-aged children, adolescents, and adults through late middle age. The largest psychometric study of the ABC in preschoolers (n ¼ 556, Kaat et al. 2014) produced convincing evidence that it is valid for use in this age group, at least for those with ASD. Although there have been a few studies among elderly people, its utility here has yet to be properly and thoroughly established. To conclude, the ABC is used to measure and document changes in behavior. These can be changes associated with pharmacological or behavioral intervention or those instigated by environmental alterations. The ABC appears well-suited to assessing a range of ages extending from school-age through late middle age. It has been useful for characterizing the behavior of people with ASD, ID, and a multitude of developmental disability-specific syndromes.
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References and Reading Aman, M. G. (2015, April). Annotated bibliography on the Aberrant Behavior Checklist (ABC) (Unpublished manuscript). The Nisonger Center UAP, Ohio State University, Colombus. Aman, M. G., & Singh, N. N. (1986). Aberrant Behavior Checklist manual. East Aurora: Slosson Educational Publications. Aman, M. G., & Singh, N. N. (1994). Aberrant Behavior Checklist – Community. Supplementary manual. East Aurora: Slosson Educational Publications. Aman, M. G., & Singh, N. N. (2017). Aberrant Behavior Checklist manual (2nd ed.). East Aurora: Slosson Educational Publications. Aman, M. G., Singh, N. N., Stewart, A. W., & Field, C. J. (1985a). The Aberrant Behavior Checklist: A behavior rating scale for the assessment of treatment effects. American Journal of Mental Deficiency, 89, 485–491. Aman, M. G., Singh, N. N., Stewart, A. W., & Field, C. J. (1985b). Psychometric characteristics of the Aberrant Behavior Checklist. American Journal of Mental Deficiency, 89, 492–502. Aman, M. G., Richmond, G., Stewart, A. W., Bell, J. C., & Kissel, R. C. (1987a). The Aberrant Behavior Checklist: Factor structure and the effect of subject variables in American and New Zealand facilities. American Journal of Mental Deficiency, 91, 570–578. Aman, M. G., Singh, N. N., & Turbott, S. H. (1987b). Reliability of the Aberrant Behavior Checklist and the effect of variations in instructions. American Journal of Mental Deficiency, 92, 237–240. Aman, M. G., Burrow, W., & Wolford, P. L. (1995). The Aberrant Behavior Checklist community: Factor validity and effect of subject variables for adults in group homes. American Journal on Mental Retardation, 100, 283–292. Aman, M. G., Novotny, S., Samango-Sprouse, C., Lecavalier, L., Leonard, E., Gadow, K., et al. (2004). Outcome measures for clinical drug trials in autism. CNS Spectrums, 9, 36–47. Aman, M. G., McDougle, C. J., Scahill, L., Handen, B., Arnold, L. E., Johnson, C., Stigler, K. A., Bearss, K., Butter, E., Swiezy, N. B., Sukhodolsky, D. D., Ramadan, Y., Pozdol, S. L., Nikolov, R., Lecavalier, L., Kohn, A. E., Koenig, K., Hollway, J. A., Korzekwa, P., Gavaletz, A., Mulick, J. A., Hall, K. L., Dziura, J., Ritz, L., Trollinger, S., Yu, S., Vitiello, B., Wagner, A., & Research Units on Pediatric Psychopharmacology. (2009). Medication and parent training in children with pervasive developmental disorders and serious behavior problems: Results from a randomized clinical trial. Journal of the American Academy of Child and Adolescent Psychiatry, 48, 1143–1154. Aman, M. G., Norris, M., Kaat, A., Andrews, H., Choo, T.-H., Chen, C., Wheeler, A., Bann, C. & Erickson, C. (2020). Factor structure of the Aberrant
Aberrant Behavior Checklist Behavior Checklist in individuals with fragile X syndrome: Clarifications and future guidance (Unpublished paper). Ohio State University. Bearss, K., Johnson, C., Smith, T., Lecavalier, L., Swiezy, N., Aman, M., McAdam, D. B., Butter, E., Stillitano, C., Minshawi, N., Sukhodolsky, D., Mruzek, D. W., Turner, K., Neal, T., Hallett, V., Mulick, J. A., Green, B., Handen, B., Deng, Y., Dziura, J., & Scahill, L. (2015). Parent training for young children with autism spectrum disorder and disruptive behavior: A randomized clinical trial. Journal of the American Medical Association, 313, 1524–1533. https://doi.org/10.1001/jama.2015.3150. Berry-Kravis, E., Krause, S., Block, S., Guter, S., Wuu, J., Leurgans, S., et al. (2006). Effect of CX516, an AMPA-modulating compound, on cognition and behavior in fragile X syndrome: A controlled trial. Journal of Child and Adolescent Psychopharmacology, 16, 525–540. Bihm, E. M., & Poindexter, A. R. (1991). Cross-validation of the factor structure of the Aberrant Behavior Checklist for persons with mental retardation. American Journal on Mental Retardation, 96, 209–211. Brinkley, J., Nations, L., Abramson, R. K., Hall, A., Wright, H. H., Gabriels, R., et al. (2007). Factor analysis of the Aberrant Behavior Checklist in individuals with autism spectrum disorders. Journal of Autism and Developmental Disorders, 37(10), 1949–1959. Brown, E. C., Aman, M. G., & Havercamp, S. M. (2002). Factor analysis and norms on parent ratings with the Aberrant Behavior Checklist community for young people in special education. Research in Developmental Disabilities, 23, 45–60. Cicchetti, D. V., & Sparrow, S. A. (1981). Developing criteria for establishing interrater reliability of specific items: Applications to assessment of adaptive behavior. American Journal of Mental Deficiency, 86, 127–137. Conners, C. K. (1969). A teacher rating scale for use in drug studies with children. American Journal of Psychiatry, 126, 884–888. Conners, C. K. (1970). Symptom patterns in hyperkinetic, neurotic, and normal children. Child Development, 141, 667–682. Freund, L. S., & Reiss, A. L. (1991). Rating problem behaviors in outpatients with mental retardation: Use of the Aberrant Behavior Checklist. Research in Developmental Disabilities, 12, 435–451. Kaat, A., Lecavalier, L., & Aman, M. (2014). Validity of the Aberrant Behavior Checklist in children with autism spectrum disorder. Journal of Autism and Developmental Disorders, 44, 1103–1116. Lecavalier, L., & Aman, M. G. (2005). Rating instruments. In J. L. Matson, R. B. Laud, & M. L. Matson (Eds.), Behavior modification for persons with developmental disabilities: Treatments and supports (Vol. I, pp. 160–189). Kingston: NADD.
ABLLS-R Marshburn, E. C., & Aman, M. G. (1992). Factor validity and norms for the Aberrant Behavior Checklist in a community sample of children with mental retardation. Journal of Autism and Developmental Disorders, 22, 357–373. Miller, M. L., Fee, V. E., & Netterville, A. K. (2004). Psychometric properties of ADHD rating scales among children with mental retardation I: Reliability. Research in Developmental Disabilities, 25, 459–476. Newton, J. T., & Sturmey, P. (1988). The Aberrant Behaviour (sic) Checklist: A British replication and extension of its psychometric properties. Journal of Mental Deficiency Research, 32, 87–92. Ono, Y. (1996). Factor validity and reliability for the Aberrant Behavior Checklist-community in a Japanese population with mental retardation. Research in Developmental Disabilities, 17, 303–309. Quay, H. C. (1977). Measuring dimensions of deviant behavior: The behavior problem checklist. Journal of Abnormal Child Psychology, 5, 277–287. Rojahn, J., & Helsel, W. J. (1991). The Aberrant Behavior Checklist with children and adolescents with dual diagnosis. Journal of Autism and Developmental Disorders, 21, 17–28. RUPP, McCracken, J. T., McGough, J., Shah, B., Cronin, P., Hong, D., et al. (2002). A double-blind, placebo-controlled trial of risperidone in children with autistic disorder. The New England Journal of Medicine, 347, 314–321. RUPP, Aman, M. G., Arnold, L. E., Lindsay, R., Nash, P., Hollway, J., et al. (2005). Risperidone treatment of autistic disorder: Longer term benefits and blinded discontinuation after six months. American Journal of Psychiatry, 162, 1361–1369. Sansone, S., Widaman, K., Hall, S., Reiss, A., Lightbody, A., Kaufmann, W., & Hessl, D. (2012). Psychometric study of the Aberrant Behavior Checklist in fragile x syndrome and implications for targeted treatment. Journal of Autism and Developmental Disorders, 42, 1377–1392. Schroeder, S. R., Rojahn, J., & Reese, R. M. (1997). Reliability and validity of instruments for assessing psychotropic medication effects on self injurious behavior in mental retardation. Journal of Autism and Developmental Disorders, 27, 89–102. Siegfrid, L. W. K. (2000). A study of the reliability and validity of the Chinese version Aberrant Behavior Checklist (M.Sc. Thesis, Hong Kong Polytechnic University, Hong Kong). Singh, N. N., & Aman, M. G. (1981). Effects of thioridazine dosage on the behavior of severely mentally retarded persons. American Journal of Mental Deficiency, 85, 580–587. Wheeler, A., Raspa, M., Bann, C., Bishop, E., Hessl, D., Sacco, P., & Bailey, D. (2014). Anxiety, attention problems, hyperactivity, and the Aberrant Behavior Checklist in fragile X syndrome. American Journal of Medical Genetics Part A, 164, 141–155.
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Aberrant Behavior Checklist – Community ▶ Aberrant Behavior Checklist
Aberrant Behavior Checklist – Residential ▶ Aberrant Behavior Checklist
ABI, Autism Behavior Inventory ▶ Autism Behavior Inventory (ABI)
Abilify ▶ Aripiprazole
ABI-S, Autism Behavior Inventory-Short Form ▶ Autism Behavior Inventory (ABI)
ABLLS: Assessment of Basic Language and Learning Skills ▶ PEAK Relational Training System
ABLLS-R ▶ Assessment of Basic Language and Learning Skills (ABLLS)
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Abnormal Involuntary Movement Scale Maureen Early1, Logan Wink2,3, Craig A. Erickson1,2,3 and Christopher J. McDougle4,5 1 Christian Sarkine Autism Treatment Center, Indianapolis, IN, USA 2 Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA 3 Department of Psychiatry, University of Cincinnati School of Medicine, Cincinnati, OH, USA 4 Lurie Center for Autism, Massachusetts General Hospital, Lexington, MA, USA 5 Nancy Lurie Marks Professorship in the Field of Autism, Harvard Medical School, Boston, MA, USA
Abnormal Involuntary Movement Scale Branch, P. R. (1975). Abnormal involuntary movement scale (AIMS). Early Clinical Drug Evaluation Unit Intercom, 4, 3–6. Martinez, M., Marangell, L. B., & Martinez, J. M. (2011). Psychopharmacology. In R. E. Hales, S. C. Yudofsky, & G. O. Gabbard (Eds.), Essentials of psychiatry (3rd ed., pp. 455–524). Washington, DC: American Psychiatric Publishing. Sadock, B. J., & Sadock, V. A. (2003). Biological therapies. In Synopsis of psychiatry: Behavioral sciences/ clinical psychiatry (pp. 974–1150). Philadelphia: Lippincott Williams & Wilkins. Wyatt, R. J. (1998). Instructions for using the abnormal involuntary movement scale (AIMS) and AIMSmodified (AIMS-M3D). In Practical psychiatric practice: Forms and protocols for clinical use (2nd ed., pp. 77–82). Washington, DC: American Psychiatric Press.
Abnormality ▶ Exceptionality
Synonyms AIMS
Definition A scale used by physicians for evaluating and monitoring abnormal movements such as those associated with tardive dyskinesia which rates the severity of abnormal movements from 0 to 4. The scale is used every 3–6 months to monitor patients taking antipsychotic medications for the development of movement-related side effects. The scale was developed by the Psychopharmacology Research Branch in 1975 and is currently in the public domain.
See Also ▶ Atypical Antipsychotics ▶ Tardive Dyskinesia
References and Reading Boyd, M. A. (2008). Appendix D. In Psychiatric nursing: Contemporary practice (pp. 891–892). Philadelphia: Wolters Kluwer Health/Lippincott Williams & Wilkins.
Abolishing Operations Amanda P. Laprime The Center for Children with Special Needs, Glastonbury, CT, USA University of Rochester Medical Center, Rochester, NY, USA
Definition Abolishing operations (AO): a general term to describe antecedent events which momentarily decrease the reinforcing or punishing effectiveness of a consequence and therefore alter the future frequency of behavior related to that consequence. AOs, in conjunction with establishing operations (EO; see establishing operation), fall under the greater omnibus term, motivating operation (MO; see motivating operations). AOs involve events which result in a decrease in the effectiveness of a reinforcer or punisher when delivered contingent on a behavior. There are many unconditioned abolishing operations identified in humans. Satiation of food, water, sleep, activity, oxygen, and warmth or cold all function
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as abolishing operations for related behavior and reinforcement (Cooper et al. 2007). For example, having just eaten lunch functions as an AO for food as a reinforcer which momentarily decreases any behavior reinforced by food.
Historical Background Skinner (1938) discussed abolishing operations under the framework of “Drive” and “Drive Conditions,” noting that satiation has an abative effect on behavior. Original work around motivation combined EOs and AOs into one general category. More recently, the AO has been defined and studied as a motivative variable in its own right (Laraway et al. 2003; see motivating operations for a further discussion of the evolution of motivating operations).
Current Knowledge The concept of the AO has been influential to behavior interventions for individuals with autism spectrum disorders (ASD). The functional assessment of behavior has clearly demonstrated the significance of reinforcement for challenging behavior. Many research studies have focused on reducing challenging behavior by teaching new behaviors which can result in the same reinforcer. Interventions that rely on an AO analysis differ from consequence-based interventions in that they involve the antecedent. Specifically, interventions involving the AO are referred to as antecedent interventions. Antecedent interventions modify or remove the environmental events which precede behavior, to decrease or remove the likelihood of the behavior occurring in the future (Kern and Clemens 2007). An intervention which relies on an AO would seek to abolish or reduce the value of a reinforcer. This reinforcer value-altering effect (Laraway et al. 2003) would subsequently result in a decrease of the target behavior. For the remainder of this entry, interventions relying on the AO for their effect will be referred to as AO-based interventions. For example, an individual with a history of overeating at parties
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may instead eat prior to attending a party. This intervention (i.e., satiation of food) would create an AO for food as a reinforcer, thereby decreasing the probability of overeating at the party as previously compared to when an EO for food was in place. The analysis of AOs has successfully contributed to the area of behavior assessment, interventions to reduce behaviors maintained by automatic reinforcement, and interventions to reduce behaviors maintained by social (i.e., positive or negative) reinforcement. These are discussed in detail below.
The Role of AOs in Behavior Assessment Functional behavior assessment (FBA) and functional analyses (FA) help clinicians to understand how a behavior looks and functions in the environment. These assessments set the foundation for individualized interventions in clinical settings. Furthermore, they have become a “best-practice” component of any program which involves behavior intervention for individuals with ASD. Iwata et al. (1994) established functional analysis (FA) technology to better understand the unique environmental variables that evoke and maintain behaviors of interest. An FA rotates across a variety of conditions which involve an EO for challenging behavior (i.e., demand, alone, denied access, attention). One important component of all functional analyses is a “control” condition (i.e., play condition). In a control condition, there are preferred items (i.e., toys) available, no demands, and social interaction delivered on a time-based schedule. The control condition is specifically set up to function as an AO for challenging behavior. Behavior should not be evoked in this condition unless other reinforcers or processes maintain behavior. Recent research has conceptualized that turning behavior “off” in the control condition (by identifying the relevant AO variables) is just as important as turning it on across other conditions (by identifying relevant EO variables, Hanley et al. 2014). Information about the environmental events acting as an AO for challenging behavior has guided the development of interventions to reduce challenging behavior in individuals with ASD.
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Reducing Challenging Behavior with AO-Based Interventions In addition to contributing to behavior assessment, AOs have been instrumental in developing effective treatment plans. AO-based interventions have demonstrated efficacy with challenging behaviors maintained by automatic reinforcement, as well as those maintained by social reinforcement. Automatic reinforcement is defined as that which is not mediated by another person. For example, when you feel an itch, scratch it, and it goes away, the reinforcer (i.e., removal of the itch) does not rely on another person. Therefore, the behavior is automatically reinforced. Meaning, in the future, when you feel an itch, you will continue to scratch, because discomfort is removed. This differs from a situation in which you feel an itch, ask someone to scratch the spot, and they scratch it. In the latter scenario, reinforcement is delivered via an individual and is therefore social mediated. Stereotypic behavior, which involves repeated, restricted, and repetitive responses, is frequently maintained by automatic reinforcement. In this case, the behavior may be reinforced by the feeling, sound, or some other quality it itself produces. Automatically maintained challenging behavior can interfere with prosocial repertoires for individuals with ASD and is often difficult to decrease. Historically, interventions targeting behaviors maintained by automatic reinforcement have had limited impact or have necessitated aversive consequences for their effect. When effective, interventions for these types of behaviors often require intensive adult support, which does not lead to maintenance and generalization of suppressed responses (Laprime and Dittrich 2014). AO-based interventions have provided an alternative way to reduce automatically maintained challenging behavior. Examples of these interventions include noncontingent reinforcement (NCR) and presession access to reinforcers. The commonality across these interventions is that a known reinforcer is delivered proactively. This differs from procedures which contingently omit reinforcement (i.e., extinction) or deliver an alternate reinforcer (i.e., differential
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reinforcement). By satiating an individual on a reinforcer prior to it occurring, an AO may be established which decreases the frequency of the behavior. For example, if a child has previously engaged in repeated scripting of their favorite television show (challenging behavior) because of the preferred sound it creates (reinforcer), delivering access to those sounds proactively may reduce echolalia for a duration of time following the reinforcer delivery. This would be an example of presession access to a reinforcer. NCR procedures involve delivering a known reinforcer on a time-based schedule, unrelated to the occurrence or nonoccurrence of a behavior (Cooper et al. 2007). For instance, if a child throws an object (challenging behavior) because of the sound it creates (reinforcer), an AO may be established by proactively providing set times in which designated objects can be thrown to create the same sound. This intervention would create an AO for the sounds as a reinforcer and subsequently reduce throwing behavior for a period of time following intervention. NCR has successfully reduced a variety of topographies of behavior maintained by automatic reinforcement. These include but are not limited to rumination (Carroll et al. 2011), vocal stereotypy (Lang et al. 2009), self-injury (Horner et al. 1991), mouthing (Simmons et al. 2003), as well as behaviors maintained by social reinforcers (discussed in more detail below). Presession access to reinforcers has been demonstrated to reduce echolalia (Laprime and Dittrich 2014), vocal stereotypy (Berg et al. 2000; Rispoli et al. 2013), property destruction (O’Reilly et al. 2009), and more general challenging behavior such as aggression or tantrums (Chung and Cannella-Malone 2010; Lang et al. 2010). AO-based interventions have also been prominently featured in the literature on challenging behaviors maintained by social reinforcement. Social positive reinforcement involves the addition of a preferred stimulus following a behavior (such as attention or a tangible item), while social negative reinforcement involves the removal of something aversive following a behavior (such as a difficult academic assignment). Both positive and negative reinforcement involve reinforcement
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that is mediated by someone else. For instance, if a child is presented with academic work, rips it up, and someone takes away the academic demand, the escape (negative reinforcement) is mediated by another person. Several of the same AO-based interventions previously discussed (i.e., NCR and presession access to reinforcers) have been used with behaviors maintained by social reinforcement. Vollmer et al. (1995) demonstrated that noncontingent access to escape reduced selfinjurious behavior maintained by escape from demands for two young men with developmental disabilities. The researchers provided escape from instruction on a time-based schedule (i.e., every 2 min). The intervention reliably reduced selfinjurious behavior across both participating, and over time, the researchers increased the schedule of escape to one that was more naturalistic (i.e., every 10 min), without the reoccurrence of selfinjury. Many studies have demonstrated the efficacy of NCR to create AOs for challenging behavior maintained by social negative (Butler and Luiselli 2007) and social positive (Derby et al. 1996; McComas et al. 2003) reinforcement. Along this line of research, several studies have focused on creating an AO for escape-maintained behavior during academic instruction with children with ASD. All interventions involve pairing an environment or environmental situation with high levels of reinforcement. These AO-based interventions have included errorless instruction (Ebanks and Fisher 2003), rate of instruction (Roxburgh and Carbone 2012), stimulus demand fading (Pace et al. 1993), and the high-p request sequence (Mace et al. 1988). One example of an AO-based intervention to decrease escapemaintained problem behavior during academic work involved the technology of presession pairing (Kelly et al. 2015). While presession pairing is often described a best-practice approach for working with children with ASD, Kelly and colleagues were the first to systematically evaluate the degree to which the intervention created an AO for escape from demands as a reinforcer. In this study the authors provided access to highly preferred reinforcers, in the presence of an instructor, for a set amount of time prior to academic instruction. They found that the intervention
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created an AO for escape and attention maintained challenging behavior during academic instruction for three children with ASD who engaged in a variety of challenging behavior (i.e., self-injury, refusal, aggression, and elopement). All participants had decreased levels of challenging behavior and increased responding to instruction follow precession pairing sessions. Across all studies referenced here, AO-based interventions not only reduced target behaviors but resulted in increased participation with instruction for participants. A series of studies have also employed the AO to reduce challenging behaviors maintained by social positive reinforcement (Edrisinha et al. 2011; Derby et al. 1996; McComas et al. 2003; McGinnis et al. 2010). McGinnis and colleagues (2010) found that providing presession attention to three children with ASD and other developmental disabilities reduced their challenging behavior (self-injury, aggression, property destruction, and tantrums), for up to 15 min in subsequent test sessions. The authors compared a low-AO presession condition (with less presession exposure to attention) to a high-AO presession condition (with more presession exposure to attention). The results of the study demonstrated that while both AO conditions resulted in lower levels of problem behavior following exposure, the high-AO sessions resulted in the lowest levels. Marcus and Vollmer (1996) found that noncontingent access to tangible items effectively reduced aggressive and self-injurious behavior in three young children with ASD (two of the participants) and down syndrome (one participant). These studies are example of those which have contributed to the research on AO-based interventions to decrease potentially dangerous and intense challenging behaviors. Importantly, behavior reductions were achieved in many of these studies without the need for punishment or extinction-based interventions. AO-based interventions have become a popular choice for reducing challenging behaviors for children with ASD. As previously stated, AO-based interventions fall under the umbrella of antecedent interventions. AO-based interventions provide an alternative to interventions that
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require intensive schedules of reinforcement, punishment, or extinction (Smith and Iwata 1997). Importantly, these interventions do not rely on another person to respond to each instance of behavior. For this reason, AO-based interventions may require less resources than other interventions such as differential reinforcement. Furthermore, they reduce the probability of a behavior occurring at all, which is important for potentially dangerous behavior (such as self-injury or rumination). Lastly, AO-based interventions may be easier for individuals to implement and reduce the need for hands-on, intensive intervention (Vollmer et al. 1995). One limitation to AO-based interventions is that their impact is relatively short-lived. The reason for this is that MOs function along a continuum. Immediately following the delivery of a reinforcer, an individual is satiated (AO) on that reinforcer. The AO (i.e., satiation) reduces the probability of the behavior related to the reinforcer occurring. As time increases from the delivery of the reinforcer, the satiation will wane and the individual will enter a state of deprivation. This change in states of motivation will create an establishing operation (EO). The EO, by nature of its effect, will increase the probability of the behavior reoccurring. This analysis is important in understanding the longterm effects of AO-based interventions (Michael 2000). Another limitation to these interventions is that they do not change the contingencies which maintain challenging behavior. Therefore, the effect of an AO-based intervention is limited unless the AO remains in place (Iwata et al. 1993).
Future Directions An understanding of AOs has been demonstrated to be an effective component of interventions to reduce challenging behavior for individuals with ASD. Continued research and application of interventions based in the conceptualization of AOs are necessary to expand the analysis of behavior as it relates to these areas. It is important that the longevity and generality of these interventions continue to be assessed in the literature and in practice with individuals with ASD.
Abolishing Operations
See Also ▶ Establishing Operations ▶ Functional Analysis ▶ Functional Behavior Assessment ▶ Motivating Operation ▶ Reinforcement
References and Reading Berg, W. K., Peck, S., Wacker, D. P., Harding, J., McComas, J., Richman, D., et al. (2000). The effects of presession exposure to attention on the results of assessments of attention as a reinforcer. Journal of Applied Behavior Analysis, 33, 463–477. Butler, L. R., & Luiselli, J. K. (2007). Escape-maintained problem behavior in a child with autism: Antecedent functional analysis and intervention evaluation of noncontingent escape and instructional fading. Journal of Positive Behavior Interventions, 9, 195–202. Carroll, R. A., Rapp, J. T., Rieck, T. M., & Siewer, B. N. (2011). The effects of noncontingent reinforcement with alternative oral stimulation in the treatment of rumination. Journal on Developmental Disabilities, 17, 72–76. Chung, Y. C., & Cannella-Malone, H. I. (2010). The effects of presession manipulations on automatically maintained challenging behavior and task responding. Behavior Modification, 34, 479–502. Cooper, J. O., Heron, T. E., & Heward, W. L. (2007). Applied behavior analysis (2nd ed.). Upper Saddle River: Pearson. Derby, K. M., Fisher, W. W., & Piazza, C. C. (1996). The effects of contingent and noncontingent attention on self-injury and self-restraint. Journal of Applied Behavior Analysis, 29, 107–110. Ebanks, M. E., & Fisher, W. W. (2003). Altering the timing of academic prompts to treat destructive behavior maintained by escape. Journal of Applied Behavior Analysis, 36, 355–359. Edrisinha, C., O’Reilly, M., Sigafoos, J., Lancioni, G., & Choi, H. Y. (2011). Influence of motivating operations and discriminative stimuli on challenging behavior maintained by positive reinforcement. Research in Developmental Disabilities, 32, 836–845. Hanley, G. P., Jin, C. S., Vanselow, N. R., & Hanratty, L. A. (2014). Producing meaningful improvements in problem behavior of children with autism via synthesized analyses and treatments. Journal of Applied Behavior Analysis, 47, 16–36. Horner, R. H., Day, H. M., Sprague, J. R., O’Brien, M., & Heathfield, L. T. (1991). Interspersed requests: A nonaversive procedure for reducing aggression and self-injury during instruction. Journal of Applied Behavior Analysis, 24, 265–278. Iwata, B. A., Dorsey, M. F., Slifer, K. J., Bauman, K. E., & Richman, G. S. (1994). Toward a functional analysis of
Absence Seizures, Second Edition self-injury. Journal of Applied Behavior Analysis, 27, 197–209. Iwata, B. A., Vollmer, T. R., Zarcone, J. R., & Rodgers, T. A. (1993). Treatment classification and selection based on behavioral function. In R. Van Houten & S. Axelrod (Eds.), Behavior analysis and treatment (pp. 101–125). New York: Plenum. Kelly, A. N., Axe, J. B., Allen, R. F., & Maguire, R. W. (2015). Effects of presession pairing on the challenging behavior and academic responding of children with autism. Behavioral Interventions, 30(2), 135–156. Kern, L., & Clemens, N. H. (2007). Antecedent strategies to promote appropriate classroom behavior. Psychology in the Schools, 44(1), 65–75. Lang, R., O’Reilly, M., Sigafoos, J., Lancioni, G. E., Machalicek, W., Rispoli, M., & White, P. (2009). Enhancing the effectiveness of a play intervention by abolishing the reinforcing value of stereotypy: A pilot study. Journal of Applied Behavior Analysis, 42, 889–894. Lang, R., O’Reilly, M., Sigafoos, J., Machalicek, W., Rispoli, M., Giulio, E. L., et al. (2010). The effects of an abolishing operation intervention component on play skills, challenging behavior, and stereotypy. Behavior Modification, 34, 267–289. Laprime, A. P., & Dittrich, G. A. (2014). An evaluation of a treatment package consisting of discrimination training and differential reinforcement with response cost and a social story on vocal stereotypy for a preschooler with autism in a preschool classroom. Education and Treatment of Children, 37(3), 407–430. Laraway, S., Snycerski, S., Michael, J., & Poling, A. (2003). Motivating operations and some terms to describe them: Some further refinements. Journal of Applied Behavior Analysis, 36, 407–414. Mace, F. C., Hock, M. L., Lalli, J. S., West, B. J., Belfiore, P., Pinter, E., et al. (1988). Behavioral momentum in the treatment of noncompliance. Journal of Applied Behavior Analysis, 21, 123–141. Marcus, B. A., & Vollmer, T. R. (1996). Combining noncontingent reinforcement and differential reinforcement schedules as treatment for aberrant behavior. Journal of Applied Behavior Analysis, 29(1), 43–51. McComas, J. J., Thompson, A., & Johnson, L. (2003). The effects of presession attention on problem behavior maintained by different reinforcer. Journal of Applied Behavior Analysis, 36, 297–307. McGinnis, M. A., Houchins-Juárez, N., McDaniel, J. L., & Kennedy, C. H. (2010). Abolishing and establishing operation analyses of social attention as positive reinforcement for problem behavior. Journal of Applied Behavior Analysis, 43(1), 119–123. Michael, J. (2000). Implications and refinements of the establishing operation concept. Journal of Applied Behavior Analysis, 33, 401–410. O’Reilly, M. F., Lang, R., Davis, T., Rispoli, M., Machalicek, W., Sigafoos, J., et al. (2009). A systematic examination of different parameters of presession exposure to tangible stimuli that maintain
27 problem behavior. Journal of Applied Behavior Analysis, 42, 773–783. Pace, G. M., Iwata, B. A., Cowdery, G. E., Andree, P. J., & McIntyre, T. (1993). Stimulus (instructional) fading during extinction of self-injurious escape behavior. Journal of Applied Behavior Analysis, 26, 205–212. Rispoli, M., Camargo, S. H., Neely, L., Gerow, S., Lang, R., Goodwyn, F., et al. (2013). Pre-session satiation as a treatment for stereotypy during group activities. Behavior Modification, 38(3), 392–411. Roxburgh, C., & Carbone, V. J. (2012). The effects of varying teacher presentation rates on responding during discrete trial training for two children with autism. Behavior Modification, 37, 1–26. Simmons, J. N., Smith, R. G., & Kliethermes, L. (2003). A multiple-schedule evaluation of immediate and subsequent effects of fixed-time food presentation on automatically maintained mouthing. Journal of Applied Behavior Analysis, 36, 541–544. Skinner, B. F. (1938). The behavior of organisms. Acton: Copley. Smith, R. G., & Iwata, B. A. (1997). Antecedent influences on behavior disorders. Journal of Applied Behavior Analysis, 30, 343–375. Vollmer, T. R., Marcus, B. A., & Ringdahl, J. E. (1995). Noncontingent escape as a treatment for self-injurious behavior maintained by negative reinforcement. Journal of Applied Behavior Analysis, 28, 15–26.
Absence Seizures, Second Edition Jennifer M. Kwon1 and Ria Pal2 1 Department of Neurology and Pediatrics (SMD), University of Rochester, School of Medicine and Dentistry, Rochester, NY, USA 2 University of Rochester School of Medicine and Dentistry, Rochester, NY, USA
Note: In 2017, the International League Against Epilepsy (ILAE) revised the naming of seizures to make them more understandable. Terms like “petit mal” and “pyknolepsy” be avoided
Short Description or Definition An absence seizure consists of staring as the behavioral change which accompanies abnormal generalized electrical activity in the brain. The
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Absence Seizures, Second Edition
electrical brain activity seen in “typical” absence seizures is generalized 3-Hz spike and wave discharges. Absence seizures are brief (usually less than 15 s) and do not usually result in falling, loss of muscle tone, or jerking of the arms and legs.
When absence seizures have atypical features, such as EEG findings that are not simply 3-Hz spike and wave, or when the seizures can also be associated with convulsions or myoclonic jerks, it may be harder to become truly seizure-free.
Categorization
Clinical Expression and Pathophysiology
Absence seizures are categorized as primarily generalized seizures. Childhood absence epilepsy has an onset between 3 and 8 years of age, and juvenile absence epilepsy has onset after 10 years.
Absence seizures are related to GABA and voltage-dependent calcium channel functions. Thalamocortical tracts are implicated. Most absence seizures are considered idiopathic or of unknown etiology. While a genetic association has been identified for a small number of patients, absence epilepsy is idiopathic at this time. It has been reported in children with Angelman syndrome but has not been specifically associated with autism. It may be difficult to clinically differentiate staring episodes from behaviors that occur for other reasons in individuals who are inattentive, who stare, and who might have motor mannerisms on the basis of autism. Absence seizures may be accompanied by a glassy expression (look absent), and affected children may drop things. They may have brief eyelid fluttering or other automatic, subtle movements.
Epidemiology Childhood absence epilepsy (CAE) has an incidence of 6.38/100,000 in children less than 15 years of age, and the majority are girls. CAE represents about 10% of all epilepsies and as such is among the most frequent types of epilepsy. Other seizure types may also occur in children with CAE. It is more common to have other seizure types with juvenile absence epilepsy (JAE). Absence epilepsy is not reported to occur with greater frequency among children and youth with autism spectrum disorders. It has been associated with specific genes related to GABA function and calcium channel function.
Natural History, Prognostic Factors, and Outcomes Absence seizures occur most commonly in people under age 20, usually in children ages 6–12. Children who develop typical childhood absence epilepsy (CAE) are usually normal in their development. The seizures are brief, lasting just seconds, but can occur many times a day. They can typically be triggered by hyperventilation. Many children can become seizure-free but the percentage varies. It has been reported that up to 90% of affected children will be seizure-free by adolescence. There may be school and learning difficulties, particularly with verbal memory, seen in patients with CAE. Inattention is reported.
Evaluation and Differential Diagnosis Evaluation is indicated if staring episodes lasting 5–30 s are observed in children or youth. They may or may not have repetitive motor movements. They will not turn to their name or alert when touched. People do not recall absence seizures. EEG is the diagnostic study of choice. Characteristic general synchronous, bilateral 2.5–4-Hz spike and slow-wave discharges are seen. Generalized activity on an EEG means that abnormal and synchronized epileptic activity is detected by all EEG electrodes (or most of the cortical surface). If they are frequent enough, a conventional EEG will capture an episode. If less frequent, prolonged monitoring with video may be necessary to identify if a staring episode is a seizure. As noted, it may be difficult to clinically distinguish absence seizures from inattention,
Academic Skills
overfocus, and staring in patients with ASD who might also have stereotyped movements. They are sometimes difficult to distinguish from atypical absence epilepsy and may occur with other seizure types as well. Absence seizures very rarely explain inattention in patients with ADHD. Since absence epilepsy may result in inattention, there may be a negative effect on school work and social interaction. This may also result in psychosocial stress. The medications used to treat the seizures may further impact attention and learning. Decision on what medication to use must balance all of these factors.
29 Matricardi, S., Verrotti, A., Chiarelli, F., Cerminara, C., & Curatolo, P. (2014). Current advances in childhood absence epilepsy. Pediatric Neurology, 50(3), 205–212. Spence, S. J., & Schneider, M. T. (2009). The role of epilepsy and epileptiform EEGs in autism spectrum disorders. Pediatric Research, 65, 599–606. Weiergraber, H., Stephani, U., & Kohling, R. (2010). Voltage gated calcium channels in etiopathogenesis and treatment of absence epilepsy. Brain Research Reviews, 62(2), 245–271. Yalcin, O. (2012). Genes and molecular mechanisms involved in the epileptogenesis of idiopathic absence epilepsy. Seizure, 21(2), 79–86.
Academic Disability Treatment ▶ Developmental Disabilities The medications used to treat petit mal or absence seizures include ethosuximide, valproic acid, and lamotrigine. The first two have equivalent efficacy but ethosuximide is the initial monotherapy of choice due to fewer cognitive side effects. Forty to seventy percent of children are seizure-free within 4–5 months of therapy. In small studies, topiramate monotherapy has been ineffective for absence seizures. The utility of other anticonvulsants, such as levetiracetam and zonisamide, are under study. Some children with absence seizures that cannot be controlled by any combination of medicines may benefit from a ketogenic diet.
See Also ▶ Electroencephalogram (EEG) ▶ Seizures
References and Reading Fisher, R. S., Cross, J. H., D’Souza, C., French, J. A., Haut, S. R., Higurashi, N., et al. (2017). Instruction manual for the ILAE 2017 operational classification of seizure types. Epilepsia, 58(4), 531–542. https:// doi.org/10.1111/epi.13671. Glauser, T. A., Cnaan, A., Shinar, S., Hiaz, D. G., Dlugos, P., Masur, D., et al. (2010). Ethosuccimide, valproic acid, and lamictal in children with absence epilepsy. The New England Journal of Medicine, 362(9), 790–799.
Academic Skills Rita Jordan School of Education, University of Birmingham, Edgbaston, Birmingham, UK
Definition Academic skills have the same meaning within the field of autism as without; they refer to skills in subject areas that form the academic curriculum, available to all children in that country. Increasingly, children and young people within the autism spectrum are entitled to the skills, knowledge, and understanding available to others as a matter of human rights, although there may be problems in exercising these rights where there are additional inherent problems (such as language or intellectual difficulties) or behavioral difficulties. There are also common comorbid conditions that may occur with autism (such as specific learning difficulties: dyslexia, dyspraxia) that may cause particular academic difficulties. However, there are no reasons why individuals with autism should be excluded from any academic area as a result of their autism alone. There may be difficulties in accessing certain
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subjects because of the way they are taught or the physical or social context in which they are taught. As with others, success in acquiring academic skills in autism depends on intellectual level, particular talents, and interests, as well as an autism-friendly teaching approach.
Historical Background Although Kanner (1943) had recognized the biological base of autism, he was later influenced by current psychological theories, which saw autism as a form of childhood schizophrenia with treatment confined to therapy for the child, or the family, depending on the theory of causation adopted. Thus, for two decades following the identification of autism, most children with autism were excluded from academic education of any kind. If there was treatment, it was of a clinical and/or therapeutic kind. It was left to a few pioneering schools (in the UK and Denmark) to demonstrate that these children were able to learn and benefit from education, although even then, the specialist curricula of such schools were largely concerned with teaching adaptive behaviors and practical occupation skills; academic skills were still regarded as largely inappropriate. Two things changed this picture. Wing (1988) introduced the notion of an autism spectrum that included children and young people with average or above average intellectual ability and good structural language skills (introducing the term “Asperger’s syndrome” to describe such children). It became clear that many of these children (albeit often undiagnosed or misdiagnosed) were already in mainstream schools. Secondly, there grew a worldwide movement for the social inclusion of all children in education with the same entitlement to the culturally valued skills, knowledge, and understanding available to other children and young people in that culture. Inclusion is not about integration alone, where a child may be “allowed” access, but about the designing of curricula and educational systems that take account of all children, in all their diversity and needs, from design to implementation. This is an ideal that is still a “work in progress” in most countries,
Academic Skills
but it did open the door to the realization that many children on the autism spectrum could and should benefit from access to the full academic curriculum. The goal was to identify barriers to this process and to seek ways of overcoming them. The effects of these developments were that children with autism in many countries began to be included in special needs legislation that recognized their entitlement to a broad and relevant curriculum, including academic skills. This did not always mean mainstream education since many children had learning and behavioral difficulties that made full integration problematic, and staffs in mainstream schools were then largely unaware of the special needs of those with autism, and lacked strategies to meet those needs or help the pupils overcome their many barriers to learning. However, special schools often (although not universally) adapted their curricula to include access to academic skills that enabled all their pupils to participate in the national curriculum of their country, albeit often adapted to individual needs. At the same time, as more children with autism were learning to be included in the general educational system available to others, a contrary movement developed from a clinical perspective, which claimed that education for those with autism should first focus on the remedial aspects, training the child in basic adaptive functioning as a precursor to any other form of learning. This was introduced with preschool children and made the claim that such programs would be so successful in remediating core difficulties that no special measures to access the academic curriculum would be needed. Some children appear to have benefited significantly from such intensive behavioral intervention at an early age, although there is no follow-up showing the later effects on learning academic skills (except of the most basic skills of reading and writing). However, research shows that not all children benefit equally (Parsons et al. 2011) and that for some children (especially those of higher ability) it is not relevant to their academic learning. The emphasis on developmental, as opposed to academic, skills, however, has influenced some educational practice, especially in special schools.
Academic Skills
The growth of autobiographies of those with autism has also had a profound effect on the understanding of what might be appropriate curricular content for those with autism. Many “successful” individuals with autism demonstrate that success (especially in terms of vocational success and being able to achieve financial independence) depends on building skills and expertise in particular academic subjects at least as much as overcoming supposed “deficits” in functioning. The influence of special interests in guiding and developing academic skills has been shown to be even more important in autism than with other learners and “interest-led” curricula are being developed. Information technology (IT) is not a universal interest of those with autism, but it has been shown to be a valuable medium for learning for many (Murray and Aspinall 2006) and its increasing role in the academic curriculum of many schools has aided participation by those with autism. The end result has been that many more students with autism are succeeding academically, gaining qualifications at school, and entering further and higher education. Although most people with autism will continue to need understanding and some support even in universities, greater numbers are able to qualify. Sadly, social difficulties and levels of anxiety remain high and may interfere with future job prospects and quality of life (Gelbar et al. 2014). Yet the chance to pursue areas of interest through academic study does of itself improve life for those with autism. Some do attempt to stay within academia, gaining more and more qualifications. Sometimes this is a positive outcome, but for some, it reflects fear and anxiety about moving on from university to the wider world. Those who do succeed provide role models for younger students but also help reinforce the value of academic skills to people with autism. For those with additional learning difficulties, high academic achievement may be out of reach, but academic skills still have relevance in their education. Daily living skills may have a higher priority, but interest and development are fostered by participation in academic tasks and basic academic skills are needed to live a life of dignity and some independence.
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Current Knowledge A study by the Council of Europe a few years ago (Jordan 2009) showed that almost all countries across Europe “included” children and young people with autism within their education systems, although the definition of “education” was varied. For some countries, especially where children with autism had additional learning difficulties, “education” was very like what other countries might describe as “clinical” practice. This was true of some countries that had developed treatment services for people with autism for many decades and where standards of individualized treatment were very high. It is almost as if successful “treatment” is seen as an alternative to inclusive education for some of these children. Even in countries where official policy is for full inclusion for all children with autism (including those with additional difficulties), there remain considerable barriers to its full and successful implementation (Jordan 2008), mostly related to insufficient understanding of autism in mainstream schools. Many developed countries have made significant efforts to increase understanding of autism across the education service with online in-service training of staff and the growth of accredited programs in autism studies. Even where inclusive practice is well developed, there are usually ways of excluding some children from some aspects of the academic curriculum, where these are not considered relevant to the individual pupil. There will always be some children and young people for whom it is more advantageous to concentrate on a narrower band of academic subjects than is generally taught as part of the national curriculum. This might be because of specific difficulties with subjects that are not considered vital for that individual’s development and future quality of life or it may be because dropping some subjects will allow concentration on other subjects that are more interesting and/or relevant to the individual. The problem is that not all such decisions are evidence based. Ultimately, each decision should be an individual one and there is no good scientific research that can decide which academic subjects will be of benefit to those with autism and which will not.
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In fact, it is unlikely that such generalized statements will apply across such a heterogeneous population. Too often, such decisions are made based on assumptions that have not been tested. Academic Subjects in Relation to Autism Mathematics: It is often assumed that mathematics will be a strength in autism but this is too broad an assumption. The early stages of mathematics (computation and rule-governed stages) are often areas of strength in autism. However, later stages may produce problems and the aspects that cause problems will vary according to learning style. For visual learners, geometry and graphical work may be strengths but for those who are not visual thinkers (and visual thinking is not universal across the spectrum) this may be a particular difficulty rather than a strength. For the larger group of visual thinkers, algebra rather than geometry may be a problem. Algebra represents a problem because to understand algebra, one has to understand reversibility of operations, which, in turn, requires explicit working memory ability – often a problem in autism. A recent development is software (GRID algebra: Hewett 2016) that makes these internal operations visible (the child can see what operation has been performed and so needs to be reversed), but this awaits evaluation with children with autism. Even computation skills may be compromised by context and time constraints. When a numeracy program was introduced as a core part of the National Curriculum in the UK, it was expected that this would pose no particular problems for those with autism. But this program emphasized mental arithmetic, conducted at speed in a class context. This proved disastrous for many with autism who could neither concentrate fully in such a group context nor access their answers at speed. It became clear that implicit knowledge of the answer might be there (and could be accessed given time) but there were problems in making the answer explicit and only responding when directed to (inhibiting responses if the teacher did not direct the question specifically at them). As a result many children with autism began to fail at a subject they had previously felt confident in, with disastrous effects on their morale and general learning ability.
Academic Skills
One aspect of mathematics, however, has largely unrealized potential in autism: statistics. It is well established that people with autism struggle with uncertainty and that many behavioral issues arise when expected circumstances change or when people find it hard to give definite answers and keep to them. Being told that something “may” happen or that we “will see” if an event unfolds will generally result in much distress in individuals with autism and even challenging behavior. Yet clearly not all of life’s events can be predicted with certainty and people with autism need to be prepared for situations that change. As long as the individual is intellectually able enough to understand, this can be solved by introducing the notion of probability and statistics. In reality, saying that an event has a 90% chance of occurring tomorrow and a 10% chance of not occurring may have little basis in fact, but the numbers seem to make it more acceptable to the person with autism than if one just said it might or might not happen. Degrees of certainty can be refined as the child is taught the variables on which the occurrence depends and the degrees of confidence in that statement. Using such numbers to replace indecisive language not only helps reduce distress and consequent challenging behavior but also gives an acceptable language of numbers for describing and predicting the world. In some cases it can lead to a lifelong interest in statistics and even an occupation using statistics. On a less positive note, a special ability to calculate at speed may seem like an expression of a high level of mathematical ability that could be utilized in a work situation or be useful for increasing academic ability. But high-speed calculators may have no insight into how answers are reached, that is, no ability to reflect or monitor their own learning. This can be a great drawback when it comes to examinations, where it is important to show working to demonstrate understanding: the actual correct answer carrying less weight than this working out. It can also prevent effective vocational uses of this computational ability. People with autism can sometimes have the capacity to add up a shopping list mentally, for example, but cannot follow the sequential process of recording each item on a cash register. The sad
Academic Skills
fact is that no shopper will trust the mental calculations of someone who does not record them on a cash register so an apparent strength ends up having little value. Literature: Just as mathematics may be assumed to be universally strong in autism, so literature may be seen as a universal problem, but that is equally untrue. Written language is often easier than speech for people with autism, because it does not vary so much between people and situations. Some children with autism come to develop speech through written language for this reason, reversing the typical progression of being able to tell a story by arranging pictures in sequence before learning to read. It is not the sequencing that is a difficulty but the “making sense” of the underlying narrative. It has been suggested, with some research support (Bruner and Feldman 1993; Losh and Capps 2003), that people with autism struggle with many aspects of narrative: understanding the basic narrative structure of events (steady state, event, restoration of the state marked by a coda); telling the gist of an event rather than verbatim details; understanding different roles within an event; keeping track of protagonists within a story by appropriate pronoun use; understanding emotional responses of protagonists; understanding agents and intentional acts. Reading in autism often emerges through reading instructions in computer games or on videos. However, this ability to read short phrases or to memorize large chunks of text is very different from the ability to make sense of longer connected texts such as fictional stories or novels. This is especially true if, as is often the case, there is associated dyslexia in autism. It is paradoxical that individuals with autism may also be hyperlexic, in that they can “read” large chunks of text but in a rote manner, without being able to perceive meaning in the text. Less commonly, some people with autism are verbal thinkers and have good verbal ability. For these individuals their verbal ability may help with their understanding of the world. For example, linguistic structure can help distinguish actual from reported, or imagined, events and this has been shown to be a factor in some able people with autism learning to develop an understanding of mental states (Theory of Mind). Inasmuch as
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literature does involve some of the key difficulties in autism, teaching literature can also be seen as an opportunity to address some of these difficulties: understanding motivations, intentional actions, and their consequences. In written form, these ideas can be addressed at the child’s own pace, rather than trying to be grasped in real-life situations which may pass too quickly and which may be harder to interpret in terms of key events and characters. Literature can provide a structure with which to interpret events and some approaches use written scripts to help the person with autism understand, prepare for, and carry out social actions. When it comes to writing, there may be dyspraxic or other motor or sensory problems that hinder the development of handwriting skills. It is useful to learn some basic handwriting skills, where possible, and teachers need to take advice from occupational therapists to look at supports (e.g., in posture, in pencil grips) to make this happen. Since typing or touch screen technology means that “writing” (or at least communicating in a visual form) is more accessible to children even with the most severe motor problems, difficulties in handwriting should not be allowed to hinder the expression of ideas. Such technological solutions have enabled some people with autism to demonstrate their ability to think and to express themselves, when it would otherwise have been assumed they were incapable of doing so. Using writing (or an equivalent form), children can also be taught skills such as making a précis of a text, which helps them understand how to extract meaning from a text in a very tangible way. History: Whether or not history presents a problem for people with autism depends on the nature of the curriculum and how it is taught. If it is presented as a list of facts that can be memorized, then most people with autism (unless they have severe learning difficulties) will manage this without difficulty. However, unless there are clear rules, it can be more difficult to try to assess possible causes for certain events or, even more problematically, try to imagine alternative outcomes. The most difficulties for those with autism, however, are caused by history teaching that requires the pupils to imagine, for example, what it might feel like to have been a Roman
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soldier on Hadrian’s Wall, or a pilgrim arriving in North America. As with literature, the very fact that history may present some difficulties for pupils with autism can also be seen as an opportunity for teaching. It can be a chance to make explicit some of the things that might affect how someone might feel. This allows pupils with autism to learn more about emotions and to develop a cognitive frame for developing empathy (or at least, sympathy). This does not lead to typical intuitive empathetic understanding, but research shows that a cognitive approach supported with many examples in practice can provide the best approach for people with autism to develop some understanding of others (Mesibov 1986; Ozonoff and Miller 1995); the explicit discussion of motivation and the effects of actions in history may provide this. Geography: Many individuals with autism prefer to be outdoors rather than confined in buildings (Evans 2015), so they appreciate opportunities to explore their natural environment. For some, this will extend to interest in the geographical features of the outdoors environment and particularly aspects of physical geography. Geographical features of the environment can be explored and explained through laws governing forces of climate, water, volcanoes, particular rock structures, and so on. All of this can provide a logical way of understanding the physical aspects of the environment, without the need for social understanding. On the other hand, the study of populations in social geography can enable some understanding of groups of people, if not of individuals. Science: Science (and engineering) is usually considered to be one of the most accessible academic subjects for individuals with autism. People with autism are often, mistakenly, thought to have problems with abstract concepts, which would make the abstract concepts of science difficult to master. However, it is not “abstraction” as an explicit description of a concept that causes problems in autism; rather it is the implicit process of abstraction through which everyday “fuzzy” concepts are normally acquired from experience that causes the problems. People with autism therefore have problems with everyday concepts, but
Academic Skills
scientific concepts do not rely on this process of abstraction; they are defined explicitly by criterial features and so fit the learning style of those with autism. It is the specificity and explicitness of science that makes it an attractive choice for those with autism. However, there can be some difficulties with the scientific process. People with autism find it difficult to tolerate uncertainty so the scientific method of hypothesis testing can be a problem for them. Once again, however, the process of scientific enquiry can help by specifying the conditions under which facts are established and by being rule governed. Statistics can also help with this understanding and the acceptance of uncertainty. Foreign Languages: There is a common view in education that, if there is pressure on the curriculum for those with autism because of the need to provide education in social and life skills, then learning a foreign language can be dropped to provide that curricular space. The argument is often made that the person with autism has struggled to master his/her first language so it would be a waste of time to attempt to teach them a second language. There may well be individual cases where this is the correct decision, and certainly curriculum subjects need to be prioritized. But such decisions should always be on an individual basis – not on an assumption that all pupils with autism will struggle with a foreign language. Some may indeed have struggled to acquire their first language and may still have problems with receptive language and with the pragmatic uses of language. A foreign language, however, is not generally taught in the way that a first language is acquired. Everything is made more explicit, so that the processes and structures of the language are much more apparent to the pupils with autism than the implicit understandings that characterize first language acquisition. It may be the first time that students with autism have understood these aspects of language and not only will this make the foreign language easier to acquire but may also help with the understanding of their first language. In addition, learning a foreign language in a mainstream school is often the only opportunity given to the pupil to be taught everyday social
Academic Skills
skills such as greetings, social rules and different language styles, adjusting language to context and useful skills like waiting in restaurants, gaining attention, expressing regret, asking directions, and so on. The fact that these vital social skills are being taught in a foreign language is a minor problem compared with the general failure in mainstream schools to address these important areas of learning at all. Once again, many individuals with autism become very interested in, and skilled at, foreign languages and some are able to obtain employment through acquiring this academic skill. Few schools remain that teach classical subjects such as Ancient Latin and Greek, but such “dead” languages are also often highly appealing to people with autism. These dead languages do not have the pragmatic learning opportunities of modern foreign languages, but they do offer “pure” academic skills. Because these languages are no longer live, they do not vary according to deictic factors like time, place, and person. Thus, they can be learnt as a system, almost divorced from social meaning, and one that remains unaltered over time. Information Technology: This relatively new academic subject is not universally attractive or accessible to all individuals with autism, but it has made academic study accessible to many people with autism as well as being a useful tool for accessing other parts of the academic curriculum. Computers can provide a patient, controllable, self-paced, and, above all, nonsocial environment for learning and thus provide access to a large part of the academic curriculum. Information technology can be a rigorous academic subject in its own right also and offer a potential vocational opportunity for many individuals with autism. Psychology: A minority of schools offer psychology as an academic subject. Although few people with autism will be suited to a career in psychology (in spite of the fact that some have done so), it can be a valuable subject to study as an academic subject. Knowledge of self and others is typically acquired through natural intuitive routes but difficulties in such routes of acquisition are at the heart of autism. People with autism, therefore, have to learn about themselves and others in an
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academic way, so the opportunity to engage in this systematically through psychology can be very beneficial. Natural understanding will always be superior (faster and able to happen without effort and alongside other cognitive tasks), but academic psychological skills may be the best route to increased understanding in people with autism. There may still need to be support in applying these academic skills to real-life understanding of self and others, but it is better than having no way to understand.
Future Directions Technological aids have enabled more individuals with autism gain and demonstrate their potential. This is likely to continue. Technology itself is likely to grow as an academic subject, and there will be more vocational opportunities to develop and apply such technological academic skills. The fact that typical children now also use more technologically driven and explicit ways of learning means that learning styles of students with autism will begin to merge with those of the typical majority of learners. This should aid the development of inclusive practices in education. People with autism may always remain at a disadvantage when it comes to understanding and operating in the social world, but they may be at an advantage when it comes to understanding and operating in the technological world. As technology takes over many low-level cognitive skills (storing and manipulating data, for example), there will be increased need for the exercise of higher-level academic skills – making sense of the data, problem-solving, and interrogating data in meaningful ways. These are high-level skills but they are teachable, and experience shows that what is clearly (and explicitly) taught can be learnt by people with autism, as long as there are no significant learning or other difficulties. Already it is seen that some academic skills (such as handwriting) have lost some value as other ways of expressing oneself have developed. There may be other academic skills that become redundant, but it is doubtful if humans can flourish and grow without the exercise of some academic skills. It may be that everyone does not need to
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learn how to be a historian, say, but everyone needs to understand about how to find sources, how to make sense of them, and to understand notions of trust and reliability in interpreting data. There will be different ways of teaching such skills, but they will be at least as valuable to children with autism as they will be to all.
See Also ▶ Academic Supports ▶ Computer-Based Intervention Assistive Technology ▶ Education ▶ Homework/Assignments, Modifying ▶ Inclusion ▶ Narrative Assessment ▶ Reading ▶ School-Aged Children
Academic Supports adults. In E. Schopler & G. B. Mesibov (Eds.), Social behaviour in autism. New York: Plenum Press. Murray, D., & Aspinall, A. (2006). Getting IT: Using information technology to empower people with communication difficulties. London: Jessica Kingsley. Ozonoff, S., & Miller, J. N. (1995). Teaching theory of mind: A new approach to social skills training for individuals with autism. Journal of Autism and Developmental Disorders, 25(4), 415–433. Parsons, S., Guldberg, K., MacLeod, A., Jones, G., Prunty, A., & Balfe, T. (2011). International review of the evidence on best practice provision for children on the autism spectrum. European Journal of Special Needs, 26(1), 47–63. Wing, L. (1988). The continuum of autistic characteristics. In E. Schopler & G. B. Mesibov (Eds.), Diagnosis & assessment in autism. New York: Plenum Press.
Academic Supports Kara Hume University of North Carolina, Chapel Hill, NC, USA
References and Reading Definition Bruner, J. S., & Feldman, C. (1993). Theories of mind and the problem of autism. In S. Baron-Cohen, H. TagerFlusberg, & D. J. Cohen (Eds.), Understanding other minds: Perspectives from autism. Oxford: Oxford University Press. Evans, G. (2015). Outdoor adventure programmes: One young man’s experiences. Good Autism Practice, 16(1), 6–11. Gelbar, N., Smith, I., & Reichow, B. (2014). Systematic review of articles describing experience and supports of individuals with autism enrolled in college and university programs. Journal of Autism and Developmental Disorders, 44(10), 2593–2601. Hewett, D. (2016). GRID algebra Association of Teachers of Mathematics. Jordan, R. (2008). Autism spectrum disorders: A challenge and a model for inclusion in education. British Journal of Special Education, 35(1), 11–15. Jordan, R. (2009). Education and social integration of children and youth with autism spectrum disorders: Definition, prevalence, rights, needs, provision and examples of good practice. Strasbourg: Council of Europe. Kanner, L. (1943). Autistic disturbance of affective contact. Nervous Child, 2, 217–250. Losh, M., & Capps, L. (2003). Narrative ability in highfunctioning children with autism or Asperger’s syndrome. Journal of Autism and Developmental Disorders, 33(3), 239–251. Mesibov, G. B. (1986). A cognitive program for teaching social behaviors to verbal autistic adolescents and
Academic supports provide students with additional help in specific skill areas or subject areas, such as reading, math, or writing. These may include a small group tutoring session, a test-taking skill program, or other adjustments to the length and difficulty of an assignment, all intended to assist students to reach proficiency in an academic area. Though the term academic supports is not used specifically in special education law, it is similar to the term “specially designed instruction,” which is defined in IDEA (Individuals with Disabilities Education Act of 2004) as: Adapting, as appropriate to the needs of an eligible child. . .the content, methodology, or delivery of instructioni. To address the unique needs of the child that results from the child’s disability; and ii. To ensure access of the child the general curriculum, so that the child can meet the educational standards within the jurisdiction of the public agency that applies to all children. [300.39 (b)(3)]
Academic Supports
Academic supports can also include accommodations and modifications to a student’s scheduling, setting, materials, instruction, and/or student response. Modifications change the content that is being taught and/or what is expected of the student, such as providing a text at a different reading level or offering shorter assignments. Accommodations change only how the information is received or how the student responds, without altering the content difficulty or student expectations. Accommodations may include providing audiotaped books, allowing answers to be given orally, and using a computer to complete written work. Finally, supplementary aids are an additional source of academic support available for students with disabilities, as described in IDEA. These include assistive technology, such as word processors or communication systems; adapted materials, including audio books or highlighted notes; and peer tutors.
Historical Background Prior to 1975, most individuals with autism spectrum disorders (ASD) in the United States were denied academic instruction in the public schools. These individuals were either not educated or were served in private institutions that focused less on academics and more on the reduction of challenging behavior and/or on the development of life skills (e.g., cooking, cleaning). The passage of the Education for All Handicapped Children Act in 1975 (reauthorized as IDEA in 1990 and including students with autism specifically for the first time) guaranteed for the first time that individuals with ASD and other disabilities could access a free and public education (FAPE). This law also requires that schools and families develop an Individualized Education Program (IEP) which clearly outlines the academic supports (e.g., accommodations, modifications, and supplementary aids) to be provided to the student with ASD. Finally, the law mandates that students with ASD have access to the least restrictive environment (LRE), essentially ensuring that to the maximum extent possible, students with ASD are educated in the general education setting with their nondisabled peers.
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Though the law has now been in place for over 30 years, progress in the education of individuals with ASD in the academic domain has been slow. The academic profile of individuals with ASD is complex, and academic skills are often difficult for individuals with ASD to fully demonstrate during assessments and classroom instruction. Historically, most individuals with ASD, as many as 75%, were thought to also have a diagnosis of mental retardation (Ghaziuddin 2000). Due to better instrumentation and understanding of the learning profiles of individuals with ASD, more recent research indicates that approximately 16–30% of the population with ASD has a comorbid condition of mental retardation (now termed “intellectual disability” in the United States) (de Bildt et al. 2004). Accurately identifying intellectual disabilities in individuals with ASD has been challenging, as has accurately indentifying their academic strengths and needs. Individuals with ASD often present an uneven profile of skills, as they may be reading at a very young age (i.e., hyperlexia) but may not be able to describe what they have read or respond verbally to comprehension questions. Similarly, individuals with ASD may have other splinter skills (i.e., a talent or ability in a specific area such as music or calendar knowledge) that may not translate to other areas such as math or reading. Without an accurate understanding of an individual’s present level of performance in academic domains, practitioners have had difficulty in developing and implementing appropriate academic supports for students on the autism spectrum.
Current Knowledge Research in the last decade focused on the cognitive profile of individuals with ASD has informed the field around important and often essential academic supports designed to benefit students with ASD. Following is a brief summary of the processing style of many on the spectrum as well as the state of academic supports currently in use by individuals with ASD. Lastly, a brief description of a number of currently used academic supports will be described.
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Academic Supports
The Cognitive Profile of Many Individuals with ASD Auditory and Visual Processing: Research indicates that individuals with ASD may process auditory or linguistic information at a slower rate than their typically developing peers (Cashin and Barker 2009). This auditory processing lag can cause great difficulty during traditional classroom instruction. In addition, research indicates that processing verbal and visual stimuli simultaneously may also be difficult. Visual processing, however, appears to be intact and in fact, can be a strength for individuals on the spectrum. Weak Central Coherence: Individuals with ASD may have difficulty processing incoming information in context, and instead, the specific details of an event or concept are remembered instead of the “big picture.” This piecemeal processing makes understanding abstract concepts more difficult, as information is stored in chunks without being unified by past experiences or understandings of the world. For example, when recalling a story, individuals with autism are more likely to remember only specific details of the story, perhaps names and locations, rather than the main idea of the story and how it may relate to other stories or past experiences (Hill 2004). Executive Function: “Executive function” is a term used to describe brain functions such as planning, working memory, and flexibility. These functions are often impaired in individuals with ASD, specifically the ability to plan multistep sequences of events (e.g., steps required to complete a homework project) and to demonstrate mental flexibility (e.g., shift quickly from one idea or plan to another).
Academic Supports, Table 1 Accommodations and modifications provided to students with autism
Attention and Inhibition: Individuals with ASD may have difficulty orienting, sustaining, and shifting attention to relevant targets (e.g., the teacher or appropriate topic during instruction) (Patten and Watson 2011). Students with ASD may focus on details that are not relevant, such as a pattern of light created by the blinds or the color of the teacher’s shirt, and miss the most meaningful information or content presented. In addition, individuals with ASD may have difficulty in managing their impulsive behavior (Mesibov et al. 2005). The State of the Use of Academic Supports Little is known about what types of academic supports are actually in use by students with ASD, as few researchers have investigated this issue. One source of data, however, has provided the field with a snapshot of the accommodations and modifications used by secondary students with ASD. The National Longitudinal Transition Study 2 (NLTS2) provides data on approximately 1,000 students with ASD ages 14–18 enrolled in secondary education settings. The data indicates that 91% of students with ASD receive some type of academic support or modification in their academic settings (Newman 2007). The types of supports and the percentage of students with ASD who access those supports are listed in Table 1. Additional learning supports are provided to 81% of the sample (Newman 2007), and those supports are listed below in Table 2. Finally, 57% of the population used some sort of technology aid to support their academic instruction. See Table 3.
Additional time to complete assignments More time in taking tests Alternative tests or assessments Slower-paced instruction Shorter or different assignments Modified tests Modified grading standards Tests read to student Modifications to physical aspects of the classroom
52% 52% 49% 41% 38% 33% 30% 25% 16%
Academic Supports
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Academic Supports, Table 2 Learning supports provided to students with autism Monitoring of progress by special education teacher A teacher’s aide or instructional assistant More frequent feedback Learning strategies/study skills assistance A peer tutor Self-advocacy training Tutoring by an adult A reader or interpreter
57% 55% 32% 22% 14% 13% 9% 6%
Academic Supports, Table 3 Technology aids provided to students with autism A calculator for activities not allowed other students Computer software designed for students with disabilities A computer for activities not allowed other students Communication aids Computer hardware adapted for special needs Books on tape
28% 23% 16% 16% 8% 8%
Description of Commonly Used Academic Supports with Students with ASD As practitioners gain a better understanding of the cognitive profile of the individuals with ASD that they serve, they are more likely to select meaningful and successful academic supports. Below are some of the most commonly used supports designed to match the academic content and expectations to the strengths and needs of individuals with ASD. Additional Time: Providing extra time for students with ASD to complete assignments or tests is a common academic support and is recommend for students who have auditory processing lags as described above, as well as for students who may have anxiety, a common co-occurring condition. The time constraints posed by testing protocols may prompt higher levels of anxiety, thus reducing academic success. Visual Supports: Visuals are a common academic support used by individuals with ASD. Visual supports include any concrete cue that supports verbal explanations and directions provided by teachers. These include diagrams, pictures,
objects to hold, graphic organizers, concept maps, outlines, flowcharts, checklists, and schedules. Descriptions of abstract concepts should include a hands-on and realistic explanation and application, including a visual representation. Students with ASD may benefit from audio recording class lectures and then later transcribing them or using a peer/peer tutor to assist with note-taking. Highlighting text is also a helpful visual support, as students can then clearly “see” what concepts are important. Classroom rules and expectations should also be presented to students with ASD visually to ensure their understanding. Organizational Supports: Both the instruction and environment should be organized for students with ASD. Assignments should be broken down into clear smaller steps (i.e., task analysis), and those steps may be written or visually represented clearly on a “to-do” list. Feedback and redirection from teachers should be frequent to ensure task completion. Classrooms should be well organized and free of distracters to assist in maintaining the attention of the students with ASD. Establishing a color-coded folder or filing system for the student’s desk or locker may also assist the student in competing and turning in academic assignments. Computer-Assisted Instruction: Using computers to present academic materials to students with ASD may be beneficial for several reasons, including the increased predictability, frequent feedback and reinforcement, and the limited need for social interaction; another deficit are for students with ASD. Computer-based teaching has been proven to promote achievement in math, spelling, literacy, and problem-solving. Assistive Technology: Technology can be used as an academic support in a number of other ways including an organizational tool (e.g., using a personal digital assistant to serve as a reminder or provide a to-do list), a teaching tool (e.g., using video to teach a specific academic behavior or skill), a supplement to instruction (e.g., student listens to a book on tape while the class reads it aloud), a communication tool (e.g., a nonverbal student can indicate the correct answer using a communication device), or a basic support (e.g., a calculator). Strategy Instruction: Learning strategy instruction provides step-by-step processes for students
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to follow in classroom settings and situations. For example, individuals with ASD may be taught specific strategies around test-taking, such as how to read instructions, how to respond appropriately (e.g., filling in “bubbles”), and how to reduce anxiety during test-taking. Strategy instruction can also be used to help students with ASD take notes during a lecture, complete large projects such as a term paper, and write an essay. These strategies have been used with students with learning disabilities with great success and have recently been applied to students with ASD (Songlee et al. 2008). Attention and Motivation Supports: Several supports can contribute to an increased ability to attend to and successfully complete academic tasks. A self-monitoring procedure teaches individuals with ASD to observe their own attending behavior, compare it with predetermined models of behavior (i.e., attending to task), and record if their behavior matches the desired example. Allowing students to choose the sequence in which activities are completed as well as the stimulus used in activities (e.g., choose what color marker to use) is also a proven academic support for students with ASD. Building academic activities around the special interest of a student with ASD can be helpful to increase motivation, as can allowing access to highly preferred materials after the completion of academic work. Finally, pairing nonpreferred academic tasks with preferred academic tasks has been shown to increase task completion as well. Academic Subject-Specific Supports: The academic supports described above can be applied across academic subjects. Below are supports designed specifically for the following subject areas: Writing: Writing is often difficult for individuals on the spectrum, likely due to visual-motor and coordination challenges. These may be reduced or alleviated through the use of several supplementary aids such as word processors, voice recognition software, special pencils or grips, or slant boards (Heflin and Alaimo 2007). Teachers may offer reduced writing assignments or allow students to produce outlines rather than lengthier written assignments. Students may also use a note-taker or scribe to assist in reducing the writing load. Beyond the physical difficulties of
Academic Supports
writing, the production of written text can be difficult for students with ASD for other reasons, including organizational difficulties and challenges in developing ideas. Academic supports include the use of graphic organizers, planning charts, writing prompts, a word bank, and/or a story grammar map. Reading: A number of academic supports have been identified to assist in the development of literacy skills. These include several discussed previously, including graphic organizers, multimedia programs, strategy instruction, and highly structured direct instruction (Chiang and Lin 2007). In addition, cooperative groups, one-toone instruction, interactive books, peer/classwide tutoring, and flash cards have proven to be effective academic supports in enhancing literacy skills. Reading comprehension can prove especially difficult for students on the spectrum, as broad themes, story meaning, and character motivation may be missed, though recall of specific details and facts may be intact. Math: Computational skills have generally been a strength for students with ASD; however, difficulty often arises in applying these skills to real tasks or problem-solving (Aspy and Grossman 2007). Academic supports to assist in skill development in this area include the use of practical examples with pictures or diagrams to clarify concepts, the use of visual and tactile cues such as TouchMath, use of graph paper during computation activities to help students organize their problems, increased time to complete math tasks, use of calculators and computer programs, and peer tutoring.
Future Directions Additional research is needed in the area of academic supports, including a better understanding of what supports are currently in place for elementaryaged students and how effective the supports are in increasing student engagement and academic success. Matching a support with the cognitive strengths and needs of individual students would be most effective, but additional study is required to determine how to accurately assess the academic skills of students with ASD. This is important work, though challenging, as our understanding
Accommodations
of the cognitive profile of students with ASD is changing and evolving as more sophisticated brain research is conducted, including the use of functional MRIs. Additionally, the prevalence of students with ASD appears to be increasing (Kim et al. 2011), which increases the likelihood that all teachers, both special and general education, will be serving students with ASD, thus implementing a number of academic supports. This requires additional staff training, both for in-service and preservice teachers, as staff must appropriately implement supports determined by the IEP team. Finally, the use of personal and portable technology with individuals with ASD is on the rise (e.g., iPad, iPod, personal digital assistants, communication devices). It is likely that these devices will serve as academic supports for individuals with ASD, as they can provide visual supports (e.g., graphic organizers, video clips), organizational supports (e.g., to-do lists), strategy instruction (e.g., provide step-by-step cues or directions), and motivational supports (e.g., students with ASD are often attracted to the use of technology). Further research on the efficacy of personal technology as an academic support is warranted.
41 A review of the literature. Focus on Autism and Other Developmental Disorders, 22, 259–267. de Bildt, S., Systema, D., Kraijer, A., & Minderaa, R. (2004). Prevalence of pervasive developmental disorders in children and adolescents with mental retardation. Journal of Child Psychology and Psychiatry, 46, 275–286. Ghaziuddin, M. (2000). Autism in mental retardation. Current Opinion in Psychiatry, 13, 481–484. Heflin, J., & Alaimo, D. (2007). Students with autism spectrum disorders. Upper Saddle River: Pearson. Hill, E. (2004). Executive dysfunction in autism. Trends in Cognitive Sciences, 8, 26–32. Kim, Y. S., Leventhal, B., Koh, Y. J., Fombonne, E., Laska, E., et al. (2011). Prevalence of autism spectrum disorders in a total population sample. AJP in Advance. https://doi.org/10.1176/appi.ajp.2011.10101532. Mesibov, G., Shea, V., & Schopler, E. (2005). The TEACCH approach to autism spectrum disorders. New York: Plenum Press. Newman, L. (April 2007). Facts from NLTS2: Secondary school experiences of students with autism. Menlo Park: SRI International. Available at www.nlts2.org/ fact_sheets/nlts2_fact_sheet_2007_04.pdf. Patten, E., & Watson, L. (2011). Interventions targeting attention in young children with autism. American Journal of Speech-Language Pathology, 20, 60–69. Songlee, D., Miller, S., Tincani, M., Sileo, N., & Perkins, P. (2008). Effects of a test-taking strategy instruction on high functioning adolescents with ASD. Focus on Autism and Other Developmental Disorders, 23, 217–228.
See Also ▶ Academic Skills ▶ Computer-Based Intervention Assistive Technology ▶ Individual Education Plan ▶ Individuals with Disabilities Education Act (IDEA) ▶ Modified Testing ▶ Self-management Interventions ▶ Visual Supports
References and Reading Aspy, R., & Grossman, B. (2007). The ziggurat model: A framework for designing interventions for individuals with high functioning autism and Asperger syndrome. Shawnee Mission: Autism Asperger Publishing. Cashin, A., & Barker, P. (2009). The triad of impairment in autism revisited. Journal of Child and Adolescent Psychiatric Nursing, 22, 189–193. Chiang, H., & Lin, Y. (2007). Reading comprehension instruction for students with autism spectrum disorders:
Academic Testing ▶ Educational Testing
Acallosal Syndrome ▶ Agenesis of Corpus Callosum
ACC ▶ Anterior Cingulate
Accommodations ▶ Modified Testing ▶ Special Needs
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Accommodations in Testing ▶ Modified Testing
Accuracy of the ADOS-2 in Identifying Autism Among Adults with Complex Psychiatric Conditions, The Brenna B. Maddox Penn Center for Mental Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA Psychology Department, Virginia Tech, Blacksburg, VA, USA
Definition The diagnostic accuracy of the Autism Diagnostic Observation Schedule, Second Edition (ADOS-2; Lord et al. 2012), with adults who present with serious mental illness (e.g., psychosis), chronic mental health problems, and/or multiple comorbid conditions. Most autistic adults have at least one co-occurring psychiatric condition (e.g., Buck et al. 2014), and overlapping symptoms can make diagnosis difficult. Clinicians need assessment tools that accurately distinguish autism spectrum disorder (ASD) from other psychiatric disorders in adulthood. The ADOS-2 Module 4 is considered a “gold-standard” instrument for collecting standardized and objective information about social communication skills, restricted interests, and repetitive behaviors in adults. It is a widely used, semistructured assessment tool that allows systematic evaluation of the presence of ASD symptoms. Although the ADOS-2 shows good sensitivity and specificity in university- and lab-based settings (Hus and Lord 2014; Pugliese et al. 2015), it has rarely been studied in community clinics that serve a more psychiatrically impaired population. In community clinics, ruling out psychosis is likely an important component of an ASD
Accommodations in Testing
evaluation. Although distinct in many ways, there are clear areas of symptom overlap between psychosis and ASD (Chisholm et al. 2015; King and Lord 2011). This is particularly true for the negative symptoms of psychosis (e.g., affective flattening, poverty of speech, social withdrawal), which are similar to the core social communication impairments in ASD. Three studies have shown that Module 4 may not perform well in differentiating between ASD and psychosis. Bastiaansen et al. (2011) found that the ADOS domain and total scores did not significantly differ between autistic adults and adults with schizophrenia using the original algorithm. The Module 4 only correctly classified 74% of cases, with a sensitivity of 61% and specificity of 82%. Using the same sample, de Bildt et al. (2016) applied the revised Module 4 algorithm (Hus and Lord 2014) and also found that the ADOS did not discriminate well between ASD and schizophrenia, with a sensitivity of 61% and specificity of 50%. Bastiaansen et al. (2011), however, found three ADOS items on which autistic adults scored significantly higher than adults with schizophrenia: stereotyped language, quality of social response, and overall quality of rapport. In a separate sample of adults receiving community mental health services (n ¼ 75), the ADOS-2 accurately identified all six autistic adults (Maddox et al. 2017). However, there was a high rate of false positives, with a particular limitation in accurately discriminating between ASD and psychosis. All 21 of the false positive cases had a lifetime history of psychosis symptoms. Of the 57 participants with psychosis, 37% exceeded the clinical cut-off score on the ADOS-2. Elevated ADOS-2 scores in the false positive group were driven primarily by high Social Communication domain scores, but not by high Restricted Interests/Repetitive Behaviors domain scores. This finding is likely due to the overlapping nature of the negative symptoms of psychosis and some core ASD symptoms (e.g., flat affect, limited conversation, reduced eye contact). It is important for clinicians and researchers to remember that prominent social communication difficulties are not specific to ASD, particularly in a clinically complex setting such as community
Acetylcholine: Definition
clinics, where many patients may present with impaired social communication skills. Module 4 of the ADOS-2 provides important information about current social communication skills, restricted interests, and repetitive behaviors. However, the ADOS-2 should be used with caution as a diagnostic instrument with adults, particularly when the differential diagnosis includes the possibility of psychosis.
See Also ▶ Bias in Assessment Instruments for Autism ▶ Clinical Assessment ▶ Mental Health and ASD ▶ Psychotic Symptoms in Autism ▶ Service Utilization in Autism
References and Reading Bastiaansen, J. A., Meffert, H., Hein, S., Huizinga, P., Ketelaars, C., Pijnenborg, M., . . . de Bildt, A. (2011). Diagnosing autism spectrum disorders in adults: The use of Autism Diagnostic Observation Schedule (ADOS) Module 4. Journal of Autism and Developmental Disorders, 41, 1256–1266. https://doi.org/10. 1007/s10803-010-1157-x Buck, T. R., Viskochil, J., Farley, M., Coon, H., McMahon, W. M., Morgan, J., & Bilder, D. A. (2014). Psychiatric comorbidity and medication use in adults with autism spectrum disorder. Journal of Autism and Developmental Disorders, 44, 3063–3071. https://doi.org/10.1007/ s10803-014-2170-2. Chisholm, K., Lin, A., Abu-Akel, A., & Wood, S. J. (2015). The association between autism and schizophrenia spectrum disorders: A review of eight alternate models of co-occurrence. Neuroscience and Biobehavioral Reviews, 55, 173–183. https://doi.org/10.1016/j. neubiorev.2015.04.012. de Bildt, A., Sytema, S., Meffert, H., & Bastiaansen, J. A. (2016). The autism diagnostic observation schedule, Module 4: Application of the revised algorithms in an independent, well-defined, Dutch sample (n ¼ 93). Journal of Autism and Developmental Disorders, 46, 21–30. https://doi.org/10.1007/s10803-015-2532-4. Hus, V., & Lord, C. (2014). The Autism diagnostic observation schedule, Module 4: Revised algorithm and standardized severity scores. Journal of Autism and Developmental Disorders, 44, 1996–2012. https://doi. org/10.1007/s10803-014-2080-3. King, B. H., & Lord, C. (2011). Is schizophrenia on the autism spectrum? Brain Research, 1380, 34–41. https://doi.org/10.1016/j.brainres.2010.11.031.
43 Lord, C., Rutter, M., DiLavore, P. C., Risi, S., Gotham, K., & Bishop, S. L. (2012). Autism diagnostic observation schedule (2nd ed.). Torrance: Western Psychological Services. Maddox, B. B., Brodkin, E. S., Calkins, M. E., Shea, K., Mullan, K., Hostager, J., Mandell, D. S., & Miller, J. S. (2017). The accuracy of the ADOS-2 in identifying autism among adults with complex psychiatric conditions. Journal of Autism and Developmental Disorders, 47, 2703–2709. https://doi.org/10.1007/s10803-0173188-z. Pugliese, C. E., Kenworthy, L., Hus-Bal, V., Wallace, G. L., Yerys, B. E., Maddox, B. B., White, S. W., Popal, H., Armour, A. C., Miller, J., Herrington, J. D., Schultz, R. T., Martin, A., & Anthony, L. G. (2015). Replication and comparison of the newly proposed ADOS-2, module 4 algorithm in ASD without ID: A multi-site study. Journal of Autism and Developmental Disorders, 45, 3919–3931. https://doi.org/10.1007/ s10803-015-2586-3.
Accuracy of Treatment Implementation ▶ Treatment Fidelity
Acetylcholine: Definition Karthikeyan Ardhanareeswaran Autism Program, Child Study Center, Yale School of Medicine, New Haven, CT, USA Program in Neurodevelopment and Regeneration, Yale School of Medicine, New Haven, CT, USA Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT, USA
Synonyms ACh; Cholinergic
Definition Acetylcholine (ACh) is a neurotransmitter critical in an individual’s ability to assess their surroundings and respond accordingly. More specifically,
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ACh functions to evaluate the potential reward and/or threat in a certain stimuli or environmental change and act on it. With roles in regulating attention, cognitive flexibility, social interactions, and stereotypical behaviors, ACh has been heavily implicated in autism. ASD patients show unusually sized, numbered, and structured neurons in the acetylcholine output centers of the basal forebrain as well as decreased concentrations of choline, a precursor of ACh. Low levels of choline have also been correlated with autism severity. Postmortem studies reveal a reduction of ACh receptor and receptor subunits. At the genetic level, mutations and duplications in genes encoding various ACh receptor subunits have been found in ASD patients. Furthermore, mutagenesis, inhibition, and/or deletion of various ACh receptor subunit-encoding genes as well as lesions in ACh-containing cells leads to autistic-like behaviors in rodents. Many of these disturbances in ACh neurotransmission can have direct consequences on synaptic plasticity, a process key in learning and memory. Finally, apart from its direct consequences, ACh also plays an indirect role in the modulation of the balance between excitatory and inhibitory neurons. Perturbations in this balance are hypothesized to contribute greatly to pathogenesis of autism spectrum disorders. No differences have been reported in choline acetyltransferase, involved in the formation of acetylcholine, or acetylcholinesterase, involved in the degradation of acetylcholine, activity between ASD patients and unaffected individuals.
See Also ▶ Anticholinesterase Inhibitors ▶ Donepezil: Definition
References and Reading Belmonte, M. K., Cook, E. H., Anderson, G. M., Rubenstein, J. L., Greenough, W. T., BeckelMitchener, A., . . . & Tierney, E. (2004). Autism as a disorder of neural information processing: Directions for research and targets for therapy. Molecular psychiatry, 9(7), 646–663.
Acetylcholinesterase Inhibitors Deutsch, S. I., Urbano, M. R., Neumann, S. A., Burket, J. A., & Katz, E. (2010). Cholinergic abnormalities in autism: Is there a rationale for selective nicotinic agonist interventions? Clinical Neuropharmacology, 33(3), 114. Deutsch, S. I., Schwartz, B. L., Urbano, M. R., Burket, J. A., Benson, A. D., & Herndon, A. L. (2014). Nicotinic acetylcholine receptors in autism spectrum disorders: Therapeutic implications. Karvat, G., & Kimchi, T. (2013). Acetylcholine elevation relieves cognitive rigidity and social deficiency in a mouse model of autism. Neuropsychopharmacology, 39, 831.
Acetylcholinesterase Inhibitors ▶ Anticholinesterase Inhibitors
ACh ▶ Acetylcholine: Definition
AChE-Inhibitors ▶ Anticholinesterase Inhibitors
Achievement Testing Melissa Maye Clinical Psychology, University of Massachusetts Boston, Boston, MA, USA
Definition Achievement tests are designed to assess an individual’s competencies in relation to scholastic material that she/he has been expected to be exposed to in school, home, and community settings (Stetson et al. 2001). Achievement tests are different from intelligence tests. Achievement tests are designed to measure mastery of a specific
Achievement Testing
subject, or subjects, such as reading ability, number fluency, and scientific knowledge; whereas, intelligence tests are designed to measure both novel problem-solving abilities and stored knowledge (Stedman 2006). Typically, achievement tests are administered in the school setting, as opposed to in mental health clinics (Klin et al. 2005).
Historical Background Achievement testing has been respected as an accurate tool of academic attainment since 1914 when the Department of Superintendence of the National Education Association officially adopted a favorable view toward educational assessment (Levine 1976), another phrase for achievement testing. Achievement testing was not held in high regard until it was identified as a political tool that both sides, both educators and policymakers, could use to pursue their own interests. However, the origins of achievement testing date back to 1903 when Edward Lee Thorndike and his students developed the Comprehension, Arithmetic, Vocabulary, and Direction following test, better known as the CAVD. Thorndike believed that these four domains were four of the most important dimensions of intellect (Thorndike 1949). In addition to developing four distinct subtests to assess intellect, Thorndike developed scales for the CAVD. While Thorndike was a frontrunner in the development of the achievement test he was primarily interested in measurement of achievement as a utility to establish psychology as a science (Levine 1976). Achievement tests have come to be critical in the measurement of elementary, middle, and high school students. These tests are used in all states to assess both a student’s competency and a school’s success. Achievement testing is especially important for high school students hoping to gain entry into college. Lastly, used clinically, achievement tests are administered on a case-by-case basis to identify strengths and weaknesses for academic planning. The achievement test was revolutionized during the late 1940s and the early 1950s when Henry
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Chauncey developed the Census of Abilities. The Census of Abilities was the first test that the Educational Testing Service published, with Chauncey as the first president. Chauncey’s goal in creating the first test of achievement was to be able to assess the strengths of every member of society and to utilize these strengths in determining each person’s role in society (Lemann 2000). While this ideology would certainly be considered problematic today, the remnants of the Census of Abilities still exist in the form of the Scholastic Aptitude Test, better known as the SAT. The SAT was one of the first standardized tests to assess individual competencies in the subject areas of reading, writing, and math and significantly changed the procedure in which students are selected for admission to university. Psychologists have been aware of differences between socioeconomic status and race (which are often confounded in the US context), since the beginning of the development of these measures. However, when Alfred Binet determined that significant differences in level of academic functioning existed across different social classes, this information was used to legitimatize different educational experiences for different social classes, as opposed to calling to the need for more equitable educational experiences for children across economic background (Levine 1976). Early achievement test findings were also used to discriminate against other marginalized groups such as racial minorities and immigrants deeming them incompetent (Levine 1976). This pattern of discrimination against lower social classes and marginalized groups continued into the late 1970s, and to some extent still affects minorities and individuals of lower socioeconomic status today. For example, the effects of summer vacation reading recognition regression have been found to be significant among lowerclass students, whereas, middle class students saw improvement in this subtest following summer vacations (Cooper et al. 1996). It has been found that schooling improves achievement and that highly effective schooling raises achievement more. Until recently, achievement testing had been thought to reflect intelligence and the belief was that the influence of
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schooling was nonsignificant (Hansen et al. 2004). This new knowledge has many implications for all students, particularly those with some degree of learning difficulty. This new research indicates that quality and fit of schooling could be significant in a child’s achievement score.
Current Knowledge Two types of achievement tests are generally employed: screening for academic delays/deficits and comprehensive tests to characterize profiles of academic achievement functioning. Screening tests are brief and typically contain only one subtest, or a set of questions, for each subject covered. Comprehensive tests utilize more than one subtest for each subject area and generally cover more depth, often in the service of determining appropriate intervention services. Both screening and comprehensive achievement tests routinely assess reading, writing, and mathematics. Screening tests are generally short and easier to score. This makes them a useful tool to assess whether or not gaps exist within an individual’s educational development and prompt whether or not further comprehensive testing may be needed. The Wide Range Achievement Test-4 and the Wechsler Individual Achievement Test-Screener are two commonly used screening tests that have one subtest each of reading, math, and spelling. Comprehensive tests assess at least three subject areas typically taught in schools, include at least two different subtests from each subject area, and assess both high and lower levels of cognitive ability within each subject area (Stetson et al. 2001). A commonly used comprehensive test is the Wechsler Individual Achievement TestComprehensive. A common achievement test used with individuals with an Autism Spectrum Disorder (ASD) is the Woodcock-Johnson III Tests of Achievement. The Woodcock-Johnson III contains 23 different achievement scales or subtests. In addition to screening and comprehensive achievement tests, there are single-subject versus multiple-subject achievement tests. Singlesubject tests include several subtests designed to
Achievement Testing
explore an individual’s competency within one subject area and multiple-subject tests explore several subject areas with one or more subtest (e.g., reading, writing, and mathematics). Educators and school psychologists often use multiple-subject tests more often than singlesubject tests because they assess at least three school subjects and provide preliminary analysis of an individual’s overall level of academic achievement. In general, it is recommended that multiple-subject tests be used first in order to assess areas of strengths and weaknesses. Singlesubject tests should then be used to further assess an individual’s competency in a specific subject area (Stetson et al. 2001). Single-subject tests allow an assessor to gain a more in-depth understanding of an individual’s competency. For example, a single-subject test, such as the Woodcock Reading Mastery Tests – III, includes subtests such as letter identification, word identification, word attack, word comprehension, and passage comprehension. Singlesubject tests may be particularly useful in the development of an individualized education plan (IEP) given that they provide detailed information regarding an individual’s strengths and weaknesses in a particular subject, thus allowing for a more exact IEP. Generally, achievement tests are organized with lower-level cognitive tasks first and increase the cognitive difficulty as the task progresses. Achievement tests are organized in this way because the lower the level assessed the less reliable one can predict performance on higher-level skills. Comprehensive tests have several subtests within each subject area and therefore allow several distinct levels of cognition to be assessed, thus allowing a more accurate prediction of achievement. Screener tests, in large part due to only having one subtest per subject, test lower levels of cognition and therefore do not predict achievement as well as comprehensive tests (Stetson et al. 2001). A note on seasonal norms: achievement tests that include seasonal norms need to be paid close attention to. The difference in standard score of just 1 day can be significant in some tests (Stetson et al. 2001). Additionally, it has been found that
Achievement Testing
over summer vacation, achievement test scores tend to regress. Of the three core subjects assessed (reading, writing, and mathematics), it was found that math skills seemed to deteriorate the most (Cooper et al. 1996). When completing achievement testing with an individual who has an ASD, choosing the right achievement test should depend on the specific needs of the individual. For example, some individuals with an ASD struggle with maintaining their attention and should be administered a screening test to maximize concentrated performance (Koegel et al. 1997). Whereas, other individuals with an ASD may be able to focus for long periods of time but may have considerable gaps in knowledge and a more comprehensive test may be the more appropriate choice (Koegel et al. 1997). Tests that include visual stimuli and that do not require long verbal responses may also be most appropriate for some individuals with ASD. For example, the Peabody Individual Achievement Test – Revised (PIAT-R) touts a multiple choice format that is designed to be easy to use with individuals having severe disabilities. While the simple administration and multiple choice responses certainly make the PIAT-R a desirable choice for testing individuals with severe disability, it should be noted that this test was developed with a typical population and therefore the norms do not address the unique needs of individual special needs populations.
Future Directions While considerable gains have been made in the development of achievement tests since they were first developed in the early 1900s, it is imperative that research and development of new tests continue to create measures that represent the abilities of all individuals. When considering the development of new measures, it is important to take into consideration the needs of the groups that most often use achievement tests, aside from those used in state and nationwide testing. Additionally, future editions of achievement tests should strive to include norms for different populations. It would be especially useful, given the number of individuals
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affected, if norms for the ASD population were provided. These norms would provide helpful insight to providers and parents regarding what is typical and could be expected of children in this population over the course of their development.
See Also ▶ Educational Testing ▶ Peabody Individual Achievement Test, Revised ▶ Psychological Assessment ▶ Wechsler Preschool and Primary Scale of Intelligence ▶ Wide Range Assessment of Memory and Learning (WRAML) ▶ Woodcock-Johnson Cognitive and Achievement Batteries
References and Reading Cooper, H., Nye, B., Charlton, K., Lindsay, J., & Greathouse, S. (1996). The effects of summer vacation on achievement test scores: A narrative and metaanalytic review. Review of Educational Research, 66(3), 227–268. https://doi.org/10.3102/ 00346543066003227. Hansen, K. T., Heckman, J. J., & Mullen, K. J. (2004). The effect of schooling and ability on achievement test scores. Journal of Econmetrics, 121(1–2), 39–98. https://doi.org/10.1016/j.jeconom.2003.10.011. Klin, A., Saulnier, C. D., Tsatsanis, K. D., & Volkmar, F. R. (2005). Clinical evaluation in autism spectrum disorders: Psychological assessment within a transdisciplinary framework. In F. R. Volkmar, R. Paul, A. Klin, & D. Cohen (Eds.), Handbook of autism and pervasive developmental disorders (3rd ed.). Hoboken: Wiley. Koegel, L. K., Koegel, R. L., & Smith, A. (1997). Variables related to differences in standardized test outcomes for children with autism. Journal of Autism and Developmental Disorders, 27(3), 233–243. 0162-3257/ 97J0600-0233$12.50/0. Lemann, N. (2000). The big test. New York: Farrar, Straus and Giroux. Levine, M. (1976). The academic achievement test: Its historical context and social functions. American Psychologist, 31(3), 228–238. https://doi.org/10.1037/ 0003-066X.31.3.228. Markwardt, F. C. (1997). Peabody individual achievement test – Revised/normative update. Bloomington: Pearson Assessments. Stedman, T. L. (2006). Stedman’s medical dictionary (28th ed.). Philadelphia: Lippincott Williams & Wilkins.
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48 Stetson, R., Stetson, E. G., & Sattler, J. M. (2001). Assessment of academic achievement. In J. M. Sattler (Ed.), Assessment of children (4th ed., pp. 576–609). San Diego: Jerome M. Sattler. Thorndike, E. L. (1949). Selected writings from a connectionist’s psychology. New York: Appleton. Wechsler, D. (2009). Wechsler individual achievement test third edition (WIAT III). San Antonio: Pearson. Wilkinson, G. S., & Robertson, G. J. (2006). Wide range achievement test 4. Lutz: Psychological Assessment Resources. Woodcock, R. N. (1997). Woodcock reading mastery test – Revised/normative update. Circle Pines: American Guidance Service. Woodcock, R. W., Mather, N., & McGrew, K. S. (2007). Woodcock-Johnson III tests of cognitive abilities, normative update (NU) complete. Rolling Meadows: Houghton Mifflin Harcourt, Riverside.
Achieving a Better Life Experience Savings Account (ABLE Savings Account, ABLE Account, 529A Savings Plan, and 529A Account) Annemarie M. Kelly and Christina N. Marsack-Topolewski College of Health and Human Services, Eastern Michigan University, Ypsilanti, MI, USA
Achieving a Better Life Experience Savings Account
Plans” because the ABLE Act amends Section 529 of the US Internal Revenue Code.
History of ABLE Accounts The Stephen Beck Jr. Achieving a Better Life Experience (ABLE) Act was signed into law on December 19, 2014. The US Congress has since amended the original ABLE Act by passing other legislation including the Consolidated Appropriations Act of 2016 and Tax Cuts and Jobs Act of 2017. The Consolidated Appropriations Act allowed qualified beneficiaries to enroll in any ABLE savings program in any state, not just their state of residence (Pub. L. No 114–113 2016). The Tax Cuts and Jobs Act made three key changes to ABLE account law by: (1) increasing the yearly contribution amounts allowed from accountholders who are employed (subject to IRS rules); (2) allowing a qualified ABLE accountholder to claim a Retirement Savings Contributions Credit (Saver’s Credit); and (3) allowing funds saved in a government-sponsored college tuition savings account (“529 account”) to roll into a ABLE account (Pub. L. No. 115–9 2017).
Principles of an ABLE Account Definition An Achieving a Better Life Experience account (ABLE account) is a tax-advantaged savings account for qualified individuals with disabilities (often referred to as “accountholders” or “designated beneficiaries”) (Pub. L. No. 113–295 2014). ABLE accounts allow beneficiaries to save funds without losing their eligibility for state and federal government benefits programs such as Medicare, Medicaid, Social Security, Supplemental Security Income (SSI), and Supplemental Security Disability Income (SSDI). ABLE account funds can be used for a broad range of health, wellness, and living expenses to support the beneficiary. ABLE accounts are sometimes referred to as “529A accounts” or “529A Savings
An ABLE account can be a useful special needs planning tool for individuals with autism spectrum disorder (ASD) and their caregivers (US Senate Committee on Finance 2014). See Table 1, Four key benefits of an ABLE account. ABLE accounts were first created in the United States under the Stephen Beck, Jr. Achieving a Better Life Experience Act of 2014 (Pub. L. No. 113–295) and are governed by federal law, the laws of the individual states, and Internal Revenue Service (IRS) administrative rules. Under federal law, ABLE account savings are excluded from the accountholder’s income for the tax year (January 1 through December 31). Accountholders will face taxes and penalty fees if they deposit more than the federal gift tax limit into an ABLE account during a tax year (e.g., $15,000 in 2020 and adjusted
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Achieving a Better Life Experience Savings Account (ABLE Savings Account, ABLE Account, 529A Savings Plan, and 529A Account), Table 1 Four key benefits of an ABLE account Item number 1
Summary Tax-free savings
2
Protected eligibility for government benefits
3
Many possible uses for “qualified disability expenses”
4
Tax deduction incentives (for employed accountholders and for account contributors in certain states)
periodically by the IRS to account for inflation). Accountholders who are employed can contribute funds beyond the federal gift tax limit – these amounts depend on the accountholder’s income totals according to IRS rules (US IRS 2020b). ABLE account savings are designed to supplement the benefits from private healthcare insurance and government healthcare programs. Importantly, ABLE accounts allow beneficiaries to save funds without losing their eligibility for state and federal government benefits programs from Medicare, Medicaid, and the Social Security Administration (SSA). If ABLE accountholders maintain a total account balance of no more than $100,000, they can continue to receive care and services from their government means-tested benefits without paying additional out-of-pocket costs (US Social Security Administration 2020). ABLE account balances in excess of this threshold are considered “excess resources” and must be spent down before the accountholder can receive additional benefits that are paid for by the government. The ABLE Act specifies that an accountholder’s government benefits “shall
Benefit description Savings in an ABLE account are not considered part of the accountholder’s income for the tax year (January 1 through December 31). Savings in an ABLE account cannot cause the accountholder to terminate their eligibility for state and federal government benefits programs. ABLE account funds can be used for a broad range of costs that support the beneficiary. Qualified disability expenses include: education, housing, transportation, employment training/support, assistive technology/ services, health, healthcare services not covered by private insurance or government programs, wellness goods/services, financial planning, financial management, legal assistance, funeral/burial costs, and basic living expenses. Any person or organization can contribute to an ABLE account. Several state ABLE account programs offer tax deduction incentives to encourage friends, family, and others to contribute funds to ABLE accounts. Certain ABLE account contributions can qualify as tax deductions for ABLE accountholders who are employed.
not be terminated, but shall be suspended” if there are excess resources in his or her ABLE account (Pub. L. No. 113–295 2014). Under current laws, many individuals with serious disabilities do not qualify for ABLE accounts. To enroll in an ABLE account program in any state, individuals must have a qualified disability that occurred before age 26. Provided you satisfy the age requirement, you are automatically eligible to establish an ABLE account if you are a recipient of SSI and/or SSDI benefits. You also can become eligible to open an ABLE account if you can satisfy each of the following criteria: (a) the above-mentioned age requirement, (b) the SSA’s definition and criteria for a disability with significant functional limitations, and (c) receive a letter from a licensed physician that certifies your disability diagnosis. Any person, for-profit business, or non-profit organization can make a deposit into an ABLE account. Several states offer tax deduction incentives to encourage friends, family, and crowdsourcing initiatives to fund ABLE accounts. Certain ABLE account contributions can qualify as
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income tax deductions for ABLE accountholders who are employed (US Internal Revenue Service 2020a). Each state allows online applications to open an ABLE account. Users can monitor their ABLE savings through their ABLE program’s website and make contributions or withdrawals online. A beneficiary’s parent, legal guardian, legal conservator, or Power of Attorney agent can assist in creating an ABLE account. Beneficiaries can set up their ABLE accounts directly if they are age 18 or older and have “legally capacity” (Garner 2019). For purposes of entering into any ABLE account contractual agreement, an accountholder’s legal capacity is defined as the ability to understand the fundamental benefits, risks, and effect of entering into the agreement (Parker 2016). Individuals do not have legal capacity when a judge rules that they are entirely incapable of managing financial or personal affairs (National Council on Disability 2018). A person who lacks legal capacity is unable to fully safeguard himself or herself against harm to self, wealth, and/or property (see also Kohn et al. 2013 regarding the growing legal and medical field of “supported decision-making”).
Qualified Disability Expenses for an ABLE Account The funds in ABLE accounts can only be spent on expenses that support the accountholder (Morris et al. 2016). Under the guidelines set forth in the federal ABLE Act statute, these are called “qualified disability expenses” (QDE). Federal law defines several wide-ranging categories for QDE as follows: [A]ny expenses related to the eligible individual’s blindness or disability which are made for the benefit of an eligible individual who is the designated beneficiary [accountholder], including the following expenses: education, housing, transportation, employment training and support, assistive technology and personal support services, health, prevention and wellness, financial management and administrative services, legal fees, expenses for oversight and monitoring, funeral and burial expenses, and other expenses. . . consistent with the purposes of this section [and in support of the accountholder] (Pub. L. No. 113–295).
QDE for ABLE accounts are broadly defined and not tied to medical necessity. This is a stark departure from other federal healthcare spending requirements, such as Medicare’s payments for healthcare costs that are “reasonable and medically necessary” for the diagnosis or treatment of illness or injury (Stein and Lipschutz 2019). As a general rule, Medicare and Medicaid spending does not include care that is still considered experimental, investigative, or unproven. In contrast, the only condition for spending on QDE with an ABLE account is that the expense loosely relate back to the accountholder’s disability. Best practices for documenting QDE include keeping a Qualified Expenses Withdrawal Log (e.g., Iowa IAble 2020).
Possible Challenges with an ABLE Account When either considering or utilizing ABLE accounts, individuals should take care to weigh all potential challenges and benefits (Hershey et al. 2017a). See Table 2, Six key challenges of an ABLE Account. Users must take care to remain informed about each tax year’s annual contribution limit (set by the federal gift tax limit and IRS rules) and their maximum account balance limit (which varies according to each state’s law). Account contributions that are in excess of annual limits are subject to penalty taxes. The total account balance limit for an ABLE account is set by individual state programs and modeled after state limits for college tuition savings accounts (also referred to as “529 accounts”). In most states, the total account limit for ABLE savings is $300,000 or higher. Though individuals cannot deposit a total amount into their ABLE account over time that exceeds their account balance limit, it is impractical for most accountholders to save more than $100,000 in their ABLE accounts – ABLE accountholders with savings in excess of $100,000 receive a suspension of their government benefits. In sum, experts recommend that ABLE accountholders keep their balances at $100,000 or lower at all times to avoid the out-of-pocket costs associated with suspended government benefits. After an accountholder has passed away, most state governments will claim and collect all of the
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Achieving a Better Life Experience Savings Account (ABLE Savings Account, ABLE Account, 529A Savings Plan, and 529A Account), Table 2 Six key challenges of an ABLE account Item number 1
Summary Maximum account balance limitations
2
Annual contribution limitations
3
Limited free assistance for account management
4
Remaining funds claimed by the state government after the accountholder has passed away (in most states)
5
Most beneficial when combined with other special needs planning tools
6
Potential for financial loss (depending on the accountholder’s investment selections)
funds that remain in an ABLE account. This process is often referred to as the government’s “estate recovery.” Under these laws, a state is allowed to recoup expenses that were paid in the form of government benefits to the accountholder during his or her lifetime. Most ABLE accountholders should consider keeping large savings amounts inside a special needs trust
Challenge description The total account balance limit for an ABLE account is set by individual state programs and modeled after state limits for college tuition savings accounts (529 accounts). Many states have set the total account limit for ABLE savings at rates of $300,000 or higher. Deposits into an ABLE account cannot exceed the threshold of the federal gift tax limit during each tax year (e.g., $15,000 in 2020). Account contributions that are in excess of federal limits are subject to a penalty tax. Most state governments do not provide comprehensive helplines to provide accountholders with meaningful guidance or individualized assistance. Nationwide, state ABLE programs encourage do-it-yourself ABLE account management online. At the same time, all state ABLE contracts strongly encourage ABLE accountholders to manage their finances with a private attorney, financial planner, and/or tax advisor. In most states, all funds that remain in an ABLE account after the accountholder’s death will be claimed and collected by the state government to recoup expenses paid to the accountholder as government benefits during his or her lifetime. Finance experts recommend that, if possible, many accountholders should coordinate the spend-down of their ABLE accounts with a special needs trust (SNT) and/or Roth individual retirement account (IRA). As a best practice, accountholders should discuss their ABLE account plans with a special needs planning attorney to ensure all short- and long-term goals for the ABLE account align with the SNT and other legal instruments. ABLE accountholders assume all investment risks as well as responsibility for any federal and state tax consequences related to the account. Depending the accountholder’s investment selections, it is possible to lose all or a portion of the savings in an ABLE account. Accounts with high investment risks can be impacted very negatively by market conditions. As a best practice, accountholders should consult with a special needs financial planner and/or tax advisor before selecting an ABLE account investment structure with high financial risks of loss.
(SNT), a legal instrument which is typically shielded from government collections after the beneficiary’s death (Hershey et al. 2017b). Unlike most ABLE accounts, funds protected in an SNT can be transferred to another person or charity organization as part of a beneficiary’s estate through a Last Will and Testament (Andersen and Gary 2018).
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ABLE accounts are often most advantageous when combined with other special needs planning tools as part of a comprehensive long- and shortterm budget plan (Rephan and Groshek 2016). Finance experts recommend that, if possible, many accountholders should coordinate the spend-down of the relatively low funds in their ABLE accounts with spending from higher savings amounts in their SNT (Abbey and Hershey 2016; see also Hershey and Kelly 2019 regarding using a Roth individual retirement account (IRA) as another possible source of funding for special needs planning in coordination with an ABLE account). ABLE accountholders and their families should be aware that state governments do not provide comprehensive helplines to provide accountholders with meaningful guidance or individualized assistance. All state ABLE contracts strongly encourage ABLE accountholders to manage their finances with a private attorney, financial planner, and/or tax advisor. Hiring professional consultants can be helpful to protect ABLE account users against accidental financial losses – ABLE accountholders assume all financial investment risks related to their accounts.
Helpful Resources for State-by-State Comparisons of ABLE Programs Qualified individuals can open an account in any state with an active ABLE account program and are not limited to their current state of residency. One of the greatest initial challenges in creating an ABLE account is determining which state ABLE program to utilize. Those who are interested in learning more about ABLE accounts can consult the online resources available from the National Disability Institute’s ABLE National Resource Center (ANRC), a leading comprehensive source of independent information about each state’s ABLE program. The ANRC website includes two information research functions: a “Search by ABLE Program Features” tool (ABLE National Resource Center 2020a) and a “View All State Programs” tool (ABLE National Resource Center 2020b). These webpages allow individuals to
Achieving a Better Life Experience Savings Account
compare state ABLE program features in several areas, including: debit card availability, estate recovery rules (state government collections of funds after the accountholder’s death), and tax deduction options for in-state residents.
Key Considerations When Selecting an ABLE Account Program There are six key issues to consider when deciding which ABLE program best serves an individual beneficiary’s needs: 1. Many state ABLE programs include account fees for the following services: account maintenance, disbursement, account report printing, rollovers, and administration. Will the ABLE program fees in this state unduly burden or otherwise inconvenience the accountholder? 2. What is this state’s total account balance limit for ABLE accounts? Moreover, do these limits align with the accountholder’s short- and longterm budget goals? 3. Is the ABLE accountholder or those who contribute to an ABLE account eligible for an income tax deduction in this state? 4. Is the accountholder best served by a program that offers a debit or purchasing card to use ABLE account funds? If so, does this state ABLE program offer a card option? 5. State ABLE programs offer allow accountholders to choose between investment options with varying degrees of financial risk. Does this particular state ABLE account program offer the degree of investment risk that most benefits the accountholder? 6. How will the investment-related fees for this particular state ABLE account program impact the beneficiary? Does this state ABLE program have high investment fees that will significantly reduce the ABLE account balance? (Kelly and Hershey 2018). In addition to the above considerations, accountholders and/or their families should review state ABLE program contracts in detail, ideally with the assistance of a qualified attorney
Achieving a Better Life Experience Savings Account
and/or financial planner. As part of the ABLE enrollment process, each state program asks applicants to perform a thorough review of voluminous contracts and forms. Generally, each state ABLE program requires accountholders – or their designated agents – to sign an account disclosure agreement, which sometimes runs in excess of 100 pages (e.g., Illinois Able 2020). Applicants should be aware that ABLE account program disclosure forms have various titles depending on the state of origin, including – but not limited to – member plan, program disclosure statement, and plan disclosure statement. Some states combine their ABLE disclosures with participation agreement contracts, while others maintain separate documents.
Hiring Legal and/or Financial Professionals as a Recommended Best Practice Most ABLE savings account program agreements contain links to additional information regarding investment options, strategies, and potential risks of financial losses. Directly or indirectly, a majority of state ABLE programs encourage do-it-yourself account management, while at the same time, explicitly warning individuals against it with legal disclaimers. Consider, for example, the following broad disclaimer which is prominently placed within several state ABLE program websites and contract agreements: Before investing in any ABLE program, you should consider whether your home state offers an ABLE program that provides its taxpayers with favorable state tax or other benefits that are only available through investment in the home state’s ABLE program. You also should consult your financial, tax, or other adviser to learn more about how state-based benefits (or any limitations) would apply to your specific circumstances. You also may wish to directly contact your home state’s ABLE program, or any other ABLE program, to learn more about those plans’ features, benefits and limitations. Keep in mind that state-based benefits should be one of many appropriately weighted factors to be considered when making an investment decision (e.g. Alaska ABLE Plan: A Member of the National ABLE Alliance 2020)
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After reviewing all contract agreements and associated links, it is a recommended best practice for individuals considering an ABLE account to seek personalized, professional guidance (McGee and Ferguson 2017). If feasible, discuss specific legal, investment, and tax situations in detail with a tax advisor, attorney, and/or other financial planner.
See Also ▶ Conservatorship (Full Conservatorship and Limited Conservatorship) ▶ Guardianship ▶ Power of Attorney (Financial and Property Management) ▶ Power of Attorney for Healthcare (Durable Healthcare Power of Attorney and Medical Power of Attorney) ▶ Special Needs Planning (SNP)
References and Reading Abbey, B., & Hershey, L. (2016). Does the ABLE Act enable the wealthy and disable the poor? Journal of Financial Service Professionals, 70(2), 46–52. Retrieved from https://www.emich.edu/cob/docu ments/2016_does_the_able_act_enable_the_wealthy_ and_disable_the_poor.pdf ABLE National Resource Center. (2020a). Get started: Search by ABLE program features. https://www. ablenrc.org/state-plan-search/ ABLE National Resource Center. (2020b). Get started: View all state programs. Retrieved from https://www. ablenrc.org/select-a-state-program/ Alaska ABLE Plan: A member of the national ABLE alliance. (2020). Home. Retrieved from https:// savewithable.com/ak/home.html Andersen, R., & Gary, S. (2018). Understanding trusts and estates (6th ed.). Durham: Carolina Academic Press. Stephen Beck, Jr. Achieving a Better Life Experience Act of 2014, Pub. L. No. 113–295, 128 Stat. 4010, codified at 26 U.S.C. § 529A. Retrieved from https://www. congress.gov/bill/113th-congress/house-bill/5771/text/ enr?r¼123 Consolidated Appropriations Act of 2016, Pub. L. No 114–113, codified as amended at 26 U.S.C. § 529A. Retrieved from https://www.congress.gov/bill/114thcongress/house-bill/2029/text Garner, B. (Ed.). (2019). Incapacity. Black’s Law Dictionary. 11th ed. St. Paul: Thomson/West.
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Hershey, L., & Kelly, A. (2019). Using Roth conversions of legacy retirement plans to fund special needs planning. Journal of Financial Service Professionals, 73(2), 80–88. Retrieved from https://www.emich.edu/ cob/documents/2019_hershey_kelly.pdf Hershey, L., Kelly, A., & Abbey, B. (2017a). Disabling ABLE: Five possible pitfalls when implementing the ABLE Act. Journal of Financial Service Professionals, 71(2), 70–78. Retrieved from https://www.emich.edu/ cob/documents/2017_disabling_able.pdf Hershey, L., Kelly, A., & Abbey, B. (2017b). Enabling ABLE: Five potential positives for implementing the ABLE Act. Journal of Financial Service Professionals, 71(2), 60–68. Retrieved from https://www.emich.edu/ cob/documents/2017_enabling_able.pdf Illinois Able. (2020). Plan disclosure documents. Retrieved from https://cdn.unite529.com/jcdn/files/ UABLE/pdfs/il-programdescription.pdf Iowa IAble. (2020). Qualified expenses withdrawal log. Retrieved from https://www.iable.gov/resources/ qualified-expenses-withdrawal-log Kelly, A., & Hershey, L. (2018). A 50 state review of ABLE Act 529A accounts. Journal of Financial Service Professionals, 72(2), 69–84. Retrieved from https://www.emich.edu/cob/documents/a_50_state_ review_of_able_act_ccounts.pdf Kohn, N., Blumenthal, J., & Campbell, A. (2013). Supported decision-making: A viable alternative to guardianship. Penn State Law Review, 117(4), 1111. http://www.pennstatelawreview.org/117/4%20Final/4Kohn%20et%20al.%20(final)%20(rev2).pdf McGee, C., & Ferguson, G. (2017). A primer on ABLE accounts. University of Richmond Law Review, 52, 149–180. Retrieved from http://lawreview.richmond. edu/files/2017/11/McGee-521A.pdf Morris, M., Rodriguez, C., & Blanck, P. (2016). ABLE accounts: A down payment on freedom. Inclusion, 4(1), 21–29. https://doi.org/10.1352/23266988-4.1.21. National Council on Disability. (2018). Beyond guardianship: Toward alternatives that promote greater self-determination (pp. 145–152). Retrieved from https://ncd.gov/publications/2018/ beyond-guardianship-toward-alternatives Parker, M. (2016). Getting the balance right: Conceptual considerations concerning legal capacity and supported decision-making. Journal of Bioethical Inquiry, 13(3), 381–393. https://doi.org/10.1007/s11673-016-9727-z. Rephan, D. A., & Groshek, J. (2016). ABLE Act accounts: Achieving a better life experience for individuals with disabilities with tax-preferred savings (and the old reliable special and supplemental needs trust). Mitchell Hamline Law Review, 42, 963. Retrieved from https://open.mitchellhamline.edu/cgi/viewcontent.cgi? article¼1034&context¼mhlr Stein, J., & Lipschutz, D. (2019). Surmounting barriers to Medicare-covered care: The Center for Medicare Advocacy offers advocacy suggestions for accessing medically necessary care via Medicare. Generations,
43, 4. Retrieved from https://www.jstor.org/stable/ 26908290 Tax Cuts and Jobs Act of 2017, Pub. L. No. 115–97, codified as amended at 26 U.S.C. § 529A. Retrieved from https://www.congress.gov/bill/115thcongress/house-bill/1/text U.S. Internal Revenue Service. (2020a). ABLE accounts – Tax benefit for people with disabilities. Retrieved from https://www.irs.gov/government-entities/federalstate-local-governments/able-accounts-tax-benefit-forpeople-with-disabilities U.S. Internal Revenue Service. (2020b). Instructions for forms 1099-QA and 5498-QA: Distributions from ABLE accounts and ABLE account contribution information. Retrieved from https://www.irs.gov/ instructions/i1099qa U.S. Senate Committee on Finance. (2014, July 23). Saving for an uncertain future: How the ABLE Act can help people with disabilities and their families. Hearing No. 113–598. Retrieved from https://www. finance.senate.gov/imo/media/doc/937721.pdf U.S. Social Security Administration. (2020). Spotlight on achieving a better life experience (ABLE) accounts. Retrieved from https://www.ssa.gov/ssi/ spotlights/spot-able.html
Achieving Academic Independence in Middle School-Outpatient (AIMS-O) Leanne Tamm and Amie Duncan Cincinnati Children’s Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, OH, USA
Definition The Achieving Academic Independence in Middle School-Outpatient (AIMS-O) intervention involves teaching academic executive functioning (EF) skills using behavioral management (e.g., reinforcement, behavioral contract, first-then language, etc.) principles to promote increased independence related to academics. Sessions follow a consistent routine of (1) real-world practice review (discussion of previously taught skill and how it was used at home); (2) teaching component (PowerPoint, video clips, hands-on activities, etc.) that focuses on teens and parents learning key
Achieving Academic Independence in Middle School-Outpatient (AIMS-O)
academic EF skills (e.g., creating a homework system, study cards); and (3) in-session practice of the newly taught concepts/strategies (e.g., parents and teens work together to create a behavioral contract/agreement, study cards, etc.) with coaching from the psychologist. Teens are assigned real-world practice tasks each session that are designed to lead to further mastery of newly taught skills through practice at home with support (as needed) from their parents between sessions. Currently, the seven 90-minute AIMS-O sessions include (1) education related to EF, (2) problem solving, (3) behavioral contracting related to use of academic EF skills, (4) organization and time management skills, (5 and 6) study skills including study cards, memory strategies such as acrostics and acronyms, summarizing skills, and graphic organizers, and (7) planning for use of skills in different settings (e.g., school), school collaboration, and teen initiative. AIMS-O is taught by a clinical psychologist and co-facilitator (e.g., clinical psychology graduate student) and is attended by parents and teens. Parents play a critical role as a coach for their teens to support the acquisition and mastery of key academic EF skills at home. AIMS-O is intended for middle school teens with Autism Spectrum Disorders (ASD) without intellectual disability (ID) who are in the general education setting.
Historical Background Youth with autism spectrum disorders (ASD) frequently experience significant academic problems in a variety of domains (Whitby and Mancil 2009). Although higher cognitive abilities are associated with better academic performance, youth who have ASD without an intellectual disability (i.e., average IQ) may still struggle academically (Keen et al. 2016). Specific academic challenges in high functioning ASD may include writing (e.g., organizing content), reading comprehension (e.g., understanding how individual details contribute to a greater lesson, taking the perspectives of others), and math problem solving (Keen et al. 2016). While some of these academic
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struggles stem from key features of ASD (e.g., social-communication deficits, narrowly defined interests, and concrete/literal thinking), they are also strongly linked to deficits in executive functioning (EF) such as organization, time management, prioritization, and initiation (Pennington and Ozonoff 1996). EF skills are critical for academic success. Students must be able to initiate tasks, perform multistep sequences of events, reflect, reason, plan, and prioritize (e.g., complete different tasks for several subjects on time), sustain performance and complete tasks, be flexible in their thinking (e.g., select the learning strategy appropriate for each context), and monitor their performance (e.g., manage progress and check for mistakes; (Best et al. 2009, 2011; Endedijk et al. 2011; Fisher and Happe 2005). However, 35–70% of teens with ASD without an intellectual disability present with EF deficits (Blijd-Hoogewys et al. 2014; Pennington and Ozonoff 1996) including deficits in planning, flexibility, shifting set, metacognition (awareness of own thought processes), and monitoring their own behavior (Hill 2004). Common challenges include difficulties getting started on tasks, managing distractions, planning for studying, multitasking, keeping materials organized, and prioritizing tasks. Parents of teens with ASD also report difficulties getting their child to start school work independently (Endedijk et al. 2011; Hampshire et al. 2014). As a result, teens with ASD and their parents may struggle to acquire and manage critical academic behaviors (e.g., material organization, tracking assignments, homework completion, effectively studying, and breaking down large assignments) and experience significant homework issues (e.g., misunderstanding assignments) that are associated with EF deficits. Persistent EF deficits are clear predictors of poor academic performance (Best et al. 2009, 2011; Fisher and Happe 2005) and poor outcomes in ASD (Clark et al. 2010). There is a clear need for interventions targeting academic EF skills, including planning, organization, time management, and study skills, that lead to more successful outcomes in ASD. Yet, according to the National
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Achieving Academic Independence in Middle School-Outpatient (AIMS-O)
Research Council and National Autism Center, there are currently no evidence-based interventions targeting academic EF skills for teens with ASD despite similar interventions already existing for children with similar EF deficits (e.g., attention-deficit/hyperactivity disorder or ADHD). More specifically, to date, there are no randomized clinical trials demonstrating the efficacy of EF interventions for middle school teens with ASD. The majority of research with children with ASD has focused on the younger ages, with less attention to the transition issues and challenges they face as adolescents (Wong et al. 2015). This is problematic since the inclusion of students with disabilities into general education classrooms is a substantiated transition best practice (Schall et al. 2012). In fact, general education placement of students with ASD has increased at a rate faster than all other disability categories (Whitby 2013). Further, as rates of inclusion for middle school students with high functioning ASD increase, teachers and para-educators do not typically have the support and training to implement evidence-based treatment to meet their needs (Kurth and Mastergeorge 2010). Specifically, most individualized education programs (IEPs) for students with high functioning ASD enrolled in inclusive education settings are more likely to focus on academic progress, but little research has been conducted on how to develop environments and utilize strategies and supports to facilitate academic success (Kurth and Mastergeorge 2010). Given the prevalence of EF deficits coexisting through the transition to middle school, it is unsurprising that students with ASD have significant academic problems in middle school (Adreon and Stella 2001; Mullins and Irvin 2000). In fact, during middle school years, the academic performance of teens with high functioning ASD is approximately 2–3 years below their typical peers (Wagner et al. 2003).
Current Knowledge AIMS-O is currently still in development. Focus groups with parents, teens with ASD, teachers,
and school administrators confirm that an intervention targeting academic EFs is needed for middle school teens with ASD without an ID. Themes that emerged from parent and teen groups (Tamm et al. 2019) include: 1. Executive Functioning Is a Barrier to Academic Success: Both parents and teens agreed that the teens had difficulty with remembering to either do assigned homework or turn in completed homework, which then leads to missing assignments and negatively affects grades. Organization was another prominent challenge noted by both teens and parents. Parents also reported that they provide varying levels of support to help their teen stay organized. Most teens required verbal reminders from teachers to complete assignments. Teens and parents reported that difficulties paying attention also affect school performance. Teens related that they have difficulty listening to others, which gets them in trouble at school and at home. Parents agreed that their teens have difficulty listening and focusing, and they must monitor their teens to ensure tasks are completed. When tasks require sustained attention (e.g., listen to a class lecture), teens often become overwhelmed or frustrated and “shut down,” which then leads to poor performance. Parents noted that teens had difficulties with multitasking. If teens were expected to both pay attention and take notes, only one task would be completed. Additionally, teens reported difficulties at school and home related to a lack of inhibition and self-control (e.g., blurting inappropriate things out in class, working on homework when they should be listening to the teacher). Parents reported that teens respond best to established routines such as a specific time to complete homework. 2. Expectations for Independence and Socialization Make Middle School Particularly Challenging: Parents reported that middle school is particularly challenging for their teens. As expectations for independence in the school environment increase, teens with ASD struggle due to their difficulties with organization and planning ahead. Teens’ difficulties with
Achieving Academic Independence in Middle School-Outpatient (AIMS-O)
abstract thinking also interfere with academic success in middle school. Once concepts become more obscure and abstract, teens struggle and take a longer time to complete assignments, which may lead to them avoiding similar tasks in the future. Further, limited abstract thinking causes tension between parents and teens. Unlike other teens, parents reported that teens with ASD often fail to understand that grades are important for future achievements. This inability to connect academic performance with future life aspirations may make teens less apt to excel in school. 3. Teens with ASD Experience Individual Academic Challenges: Each teen and parent reported that their teen had different academic challenges. While one teen might have difficulties with English, another would not have problems with English but would report problems with math. Across the groups, teens had difficulties with language arts, math, social studies, science, English, and spelling. Similarly, teens and/or their parents reported using a variety of study strategies including web study, teacher resources, repetitive fact review, reading over textbooks and notes, creating flash cards, and online study programs and apps such as Quizlet. 4. High Need for Parent Involvement: More than 80% of parents reported helping teens pack their backpacks and stay organized, as well as helping with homework including determining assignments, helping with completion, and closely monitoring homework. More than 60% reported helping with test studying. All teens and parents reported the use of external rewards to motivate teens to complete work and study for tests. Although formal behavioral contracts were not common, parents and teens reported use of verbal contracts that required teens to perform academic tasks prior to receiving some reward. 5. Challenges with Academic Accommodations: A major theme for parents was that their teens were not receiving proper academic accommodations in middle school. Parents felt teens were either given too many, too few, or inappropriate accommodations. The majority of
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parents also expressed dissatisfaction with the IEP team at their teen’s school stating that if their teen had high grades, then s/he would often not receive assistance with EF deficits (e.g., organization, planning, and prioritizing). Parents reported high levels of frustration as the IEP team would often ignore parents’ concerns and sometimes refuse to meet with them. As a result, some parents changed their teen’s school in order to receive additional academic supports. 6. Critical Role for Teachers: Parents and teens reported that the level and quality of teacher support had a significant impact on these teens’ academic success. Teens desired additional help and resources from teachers including homework reminders, study tools, and more in-depth explanations of some assignments. Parents reported that teens had more difficulties when teachers had negative attitudes. These attitudes may have developed because not all teachers are equipped to manage teens with ASD, may not understand how EF deficits affect academic performance, and may benefit from learning to promote the skills taught in the group intervention (e.g., organization, problem solving, and study cards). In contrast, parents noted that when teachers were calm, patient, creative, and persistent, teens were more successful. Overall, parents reported a lack of consistent communication with teachers. They expressed disappointment that teachers sometimes ignored their advice about responding to their teen’s behavior. Parents felt that open communication among all involved in their child’s education (teachers, parents, aides, IEP teams) was necessary for success in middle school. A blend of in-person and electronic communication strategies was preferred. A small “proof of concept” trial showed that AIMS-O is possible to deliver, results in meaningful improvements, and is acceptable to parents and teens with ASD (Tamm et al. 2019). For example, attendance was excellent across the seven sessions (100%). Parents rated clinically significant improvements for the teen on the
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Achieving Academic Independence in Middle School-Outpatient (AIMS-O)
Children’s Organizational Skills Scale (Abikoff and Gallagher 2009) and Homework Problem Checklist (Anesko et al. 1987). On consumer satisfaction ratings, parents gave high ratings for the effectiveness of the group, the content of each session, the effectiveness of the PowerPoint slides, worksheets, visuals, and instructors. However, parents gave lower ratings for how well they thought their teen had understood the content. Teens themselves had generally low ratings for how well they understood their challenges with attention and EF, how to use the study strategies, how to use study cards, how to summarize, how to use time management strategies, and how to create an effective homework system and routine. Given that both teens and parents reported difficulties with teen comprehension of content, additional adaptation of AIMS-O is warranted.
Future Directions With funding support from the National Institutes of Health, work is ongoing to further adapt AIMS-O for use in the outpatient clinical setting. Based on feedback from expert consultants and key stakeholders, the AIMS-O content and teaching approach is being modified and adapted to address core deficits in ASD. Examples of strategies that will be used to enhance the accessibility of concepts include: use of visual supports (e.g., using graphic organizers as a method of summarizing content), a self-assessment to assist in understanding EF strengths and deficits, use of technology (e.g., videos to teach core components, use of apps for studying and summarizing), worksheets combined with multiple choice lists for new concepts, multiple opportunities for repetition and practice, and hands-on activities to increase engagement while building skills. Also, as schools are increasingly turning to technology (e.g., Blackboard, Power School, Google Classroom, DropBox), strategies for organizing electronic content will be critical. Given the themes identified by parents and teens related to school, an adaptation of AIMS-O for administration in the school setting is warranted. Such work is also underway supported by funding from the Institute of Education Sciences.
See Also ▶ Academic Skills ▶ Family-Centered Care, Second Edition ▶ Functional Life Skills ▶ High-Functioning Autism (HFA) ▶ Qualitative Versus Quantitative Approaches
References and Reading Abikoff, H., & Gallagher, R. (2009). The children‘s organizational skills scales technical manual. North Tonawanda: Multihealth Systems Inc.. Adreon, D., & Stella, J. (2001). Transition to middle and high school: Increasing the success of students with Asperger Syndrome. Intervention in School and Clinic, 36(5), 268–271. Anesko, K. M., Schoiock, G., Ramirez, R., & Levine, F. M. (1987). The homework problem checklist: Assessing children’s homework problems. Behavioral Assessment, 9, 179–185. Best, J. R., Miller, P. H., & Jones, L. L. (2009). Executive functions after age 5: Changes and correlates. Developmental Review, 29(3), 180–200. https://doi.org/10. 1016/j.dr.2009.05.002. Best, J. R., Miller, P. H., & Naglieri, J. A. (2011). Relations between executive function and academic achievement from ages 5 to 17 in a large, representative national sample. Learning and Individual Differences, 21(4), 327–336. https://doi.org/10.1016/j.lindif.2011.01.007. Blijd-Hoogewys, E. M., Bezemer, M. L., & van Geert, P. L. (2014). Executive functioning in children with ASD: An analysis of the BRIEF. Journal of Autism and Developmental Disorders, 44(12), 3089–3100. https:// doi.org/10.1007/s10803-014-2176-9. Clark, C. A., Pritchard, V. E., & Woodward, L. J. (2010). Preschool executive functioning abilities predict early mathematics achievement. Developmental Psychology, 46(5), 1176–1191. https://doi.org/10.1037/a0019672. Endedijk, H., Denessen, E., & Hendriks, A. W. (2011). Relationships between executive functioning and homework difficulties in students with and without autism spectrum disorder: An analysis of student- and parent-reports. Learning and Individual Differences, 21, 765–770. Fisher, N., & Happe, F. (2005). A training study of theory of mind and executive function in children with autistic spectrum disorders. Journal of Autism and Developmental Disorders, 35(6), 757–771. https://doi.org/10. 1007/s10803-005-0022-9. Hampshire, P. K., Butera, G. D., & Dustin, T. J. (2014). Promoting homework independence for students with autism spectrum disorders. Intervention in School and Clinic, 49(5), 290–297. https://doi.org/10.1177/ 1053451213513955. Hill, E. L. (2004). Executive dysfunction in autism. Trends in Cognitive Sciences, 8(1), 26–32.
Acquired Autism Keen, D., Webster, A., & Ridley, G. (2016). How well are children with autism spectrum disorder doing academically at school? Autism, 20(3), 276–294. Kurth, J. A., & Mastergeorge, A. M. (2010). Individual education plan goals and services for adolescents with autism: Impact of age and educational setting. The Journal of Special Education, 44(3), 146–160. Mullins, E. R., & Irvin, J. L. (2000). Transition into middle school: What research says. Middle School Journal, 31(3), 57–60. Pennington, B. F., & Ozonoff, S. (1996). Executive functions and developmental psychopathology. Journal of Child Psychology and Psychiatry, 37(1), 51–87. Schall, C., Wehman, P., & McDonough, J. L. (2012). Transition from school to work for students with autism spectrum disorders: Understanding the process and achieving better outcomes. Pediatric Clinics of North America, 59(1), 189–202., xii. https://doi.org/10.1016/ j.pcl.2011.10.009. Tamm, L., Duncan, A., Vaughn, A., McDade, R., Estell, N., Birnschein, A., & Crosby, L. (2019). Academic needs in middle school: Perspectives of parents and youth with autism. Journal of Autism and Developmental Disorders. https://doi.org/10.1007/s10803-019-03995-1. Wagner, M., Marder, C., Blackorby, J., Cameto, R., Newman, L., Levine, P., & Davies-Mercier, E. (2003). The achievements of youth with disabilities during secondary school: A report from the National Longitudinal Transition Study-2. Retrieved from Menlo Park: Whitby, P. J. (2013). The Effects of “Solve It!” on the Mathematical Word Problem Solving Ability of Adolescents with Autism Spectrum Disorders. Focus on Autism and Other Developmental Disabilities, 28(2), 78–88. Whitby, P. J. S., & Mancil, G. R. (2009). Academic achievement profiles of children with high functioning autism and asperger syndrome: A review of the literature. Education and Training in Developmental Disabilities, 44(4), 551–560. Wong, C., Odom, S. L., Hume, K. A., Cox, A. W., Fettig, A., Kucharczyk, S., . . . & Schultz, T. R. (2015). Evidencebased practices for children, youth, and young adults with autism spectrum disorder: A comprehensive review. Journal of Autism and Developmental Disorders. https://doi.org/10.1007/s10803-014-2351-z.
Acquired Autism Isabelle Rapin Neurology and Pediatrics (Neurology), Albert Einstein College of Medicine, Bronx, NY, USA
Synonyms Autistic regression; Disintegrative disorder; Language/autistic regression; Regressive autism
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Definition Autism (autism spectrum disorders – ASD) typically denotes a static, behaviorally defined, developmental disorder of the immature brain, with identifiable etiologies rare and biologically treatable causes rarer still. Acquired autism implies newly acquired/progressive brain dysfunction, with multiple, mostly undefined, potential causes, presumably affecting similar brain circuitry as developmental ASD. Acquired autism requires prompt neurologic investigation and, in some cases, brain imaging, electrophysiologic, genetic, or other tests to detect potentially medically treatable causes or progressive disease. Subtypes of acquired autism (discussed in more detailed entries in the encyclopedia): 1. Language/autistic regression – Reported by 20–35% of parents, usually between 15 and 30 months. Its causes are unknown because language regression/plateau is rarely studied while in process, especially when its insidious onset is glossed over. It occasionally follows a nonspecific illness or emotional stress. Epilepsy only exceptionally plays a causative role. Regression rarely overlaps acquired epileptic aphasia (Landau-Kleffner syndrome) of preschoolers who all have seizures or epileptiform EEGs, but not autism. 2. Childhood disintegrative disorder – Exceptionally rare language/autistic/intellectual but not motor regression of all functions between ages 2 and 10 years following entirely normal earlier development. Its causes are unknown, its prognosis poor, without known medical treatment. It requires thorough neurologic investigation. 3. Rett syndrome – Generalized developmental regression in girls, mostly between 6 and 18 months when they cease progressing, head growth stagnates, irritability, hand stereotypies, and a variety of other systemic and neurologic symptoms appear. Severity varies, prognosis is poor. Most are due to mutations of the MECP2 gene. 4. Malignant epilepsies of early life – Infantile spasms with a hypsarrhythmic EEG (West syndrome) in infancy and Lennox-Gastaut syndrome in toddlers with drop and other seizure
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types and slow spike waves in the EEG are the most prevalent harbingers of acquired autism with cognitive impairment. They and others have a variety of genetic and acquired etiologies. Prognosis is guarded, but some are medically treatable; so prompt diagnosis is key. 5. Cerebellar surgery – Transitory (usually weeks) postoperative mutism with autistic features following removal of midline cerebellar tumors, mostly medulloblastomas. 6. Psychoses, drug intoxication – Catatonia may overlap with acquired autism and needs to be diagnosed because treatable. Psychotic depression, mania, and drug intoxication must be considered in unexplained acquired social withdrawal and loss of language and functional skills. Immunizations are not credible causes of autism. 7. Encephalopathies – Rarely, acute or chronic infectious, immune, metabolic, or toxic encephalopathies that involve limbic circuitry may result in an acquired autistic state. Diagnosing the causes of encephalopathies is critical because some are treatable, e.g., Hashimoto encephalitis, NMDA receptor limbic encephalitis, herpes simplex, or other infectious encephalitis.
Acquired Dysgraphia
Action Level Imitation Nicole Slade Department of Psychology, University of Massachusetts Boston, Boston, MA, USA
Definition An individual copies, or mimics (Lopes and Santos-Victor 2004), the actions of a model (Nehaniv and Dautenhahn 1998). It is considered a lower form of imitation because it is not necessary for the imitator to process the meaning of the actions. Action level imitations can range from single actions (e.g., sticking out tongue, tapping on a table, making a bunny hop) to a string of actions. The imitation is considered successful when the behavior or set of behaviors is repeated exactly as presented by the model (Nehaniv and Dautenhahn 1998). This kind of imitation is seen in human newborns as well as in nonhuman primates such as chimpanzees and apes (Byrne and Russon 1998).
References and Reading Dhossche, D. (1998). Brief report: Catatonia in autistic disorders. Journal of Autism and Developmental Disorders, 28, 329–331. Homan, K. J., Mellon, M. W., Houlihan, D., & Katusic, M. Z. (2011). Brief report: Childhood disintegrative disorder: A brief examination of eight case studies. Journal of Autism and Developmental Disorders, 41, 497–504. Offit, P. A. (2009). Autism’s false prophets: Bad science, risky medicine, and the search for a cure. New York: Columbia University Press. Riva, D., & Giorgi, C. (2000). The cerebellum contributes to higher functions during development: Evidence from a series of children surgically treated for posterior fossa tumours. Brain, 123(Pt 5), 1051–1061. Tuchman, R., Cuccaro, M., & Alessandri, M. (2010). Autism and epilepsy: Historical perspective. Brain & Development, 32, 709–718.
Acquired Dysgraphia ▶ Agraphia
See Also ▶ Action on Objects ▶ Imitation
References and Reading Byrne, R., & Russon, A. (1998). Learning by imitation: A hierarchical approach. Behavioral and Brain Sciences, 21(5), 667–721. Lopes, M., & Santos-Victor, J. (2004). Visual learning by imitation with motor representations. IEEE Transactions on Systems, Man and Cybernetics, Part B, Cybernetics, Special Issue on Learning in Computer Vision and Patter Recognition, 35(3). Retrieved February 13, 2012, from http://ieeexplore.ieee.org/xpls/abs_all. jsp?arnumber¼1430829. Nehaniv, C., & Dautenhahn, K. (1998). Mapping between dissimilar bodies: Affordances and the algebraic foundations of imitation. In Proceedings of the seventh European workshop on learning robots, Edinburgh.
Action Prediction in Autism
Action on Objects Nicole Slade Department of Psychology, University of Massachusetts Boston, Boston, MA, USA
Definition Movement of an object by another object or person. Action on object imitation trials are often used when studying imitation in children and other nonhuman primates (Tomasello et al. 1993). Some research has shown that adult humans use action on objects to stimulate and engage infants in play.
See Also ▶ Action Level Imitation
References and Reading Bard, K., & Vauclair, J. (1984). The communicative context of object manipulation in Ape and Human adultinfant Pairs. Journal of Human Evolution, 13(2), 181–190. Tomasello, M., Savage-Rumbaugh, S., & Kruger, A. (1993). Imitative learning of actions on objects by children, Chimpanzees, and enculturated Chimpanzees. Child Development, 64(6), 1688–1705.
Action Prediction in Autism Tobias Schuwerk and Markus Paulus Department Psychology, Ludwig-MaximiliansUniversität München, Munich, Germany
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interaction and involve the predictions of others’ action goals as well as the means they use to achieve their goals. In everyday life, humans constantly coordinate their actions with others. For example, having a cup of coffee at a café involves numerous joint actions, such as ordering the coffee when the waiter is attending, giving the cash and receiving the change, or holding up the cup so that the waiter can refill it with more coffee from the coffeepot. All these actions have to be sensitively attuned in order to successfully enjoy the cup of coffee without dropping money or spilling hot coffee on one’s pants. Eye movements during the observation of another individual’s action reveal that, instead of passively following the movement trajectory of this action, humans proactively anticipate the end state as well as the trajectory of an ongoing action (Gredebäck and Falck-Ytter 2015). These gaze patterns are highly similar to eye movements during the performance of one’s own actions (Flanagan and Johansson 2003). Developmental psychological research showed that infants in their first year of life already produce such anticipatory eye movements when they observe another’s action (Falck-Ytter and von Hofsten 2006). A substantial body of research suggests that action processing is altered in autism spectrum disorders (ASDs) and that this affects social interaction and communication abilities in individuals with ASD. Recently, it was proposed that impaired action prediction is a key factor that determines social interaction and communication skills in ASD. Yet, theoretical positions and empirical evidence remain controversial. This entry presents current knowledge on if and how action prediction is altered in ASD. Hence, the entry puts emphasis on more recent work on visual action anticipation (often assessed by eye tracking technology) and mentions older work on verbal action prediction only in passing.
Definition Historical Background Action prediction is the inherent social cognitive ability to anticipate how another individual’s action will unfold over time. Such projections are essential for smooth reciprocal social
Research on action prediction in ASD was mainly framed by two theoretical backgrounds. First, the theory of mind deficit hypothesis suggested that
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individuals with ASD have difficulties with the prediction of other’s actions, because they are impaired in attributing mental states, such as beliefs or desires, to others and themselves (see Frith 2012). This hypothesis is based on the premise that action predictions require the prior representation of another’s goal (e.g., the guest at the neighboring table also wants a coffee refill) and belief about that goal (e.g., the guest thinks that drawing the waiters attention to herself will get her to that goal; cf., Dennett 1989). Thus, if individuals with ASD have difficulties in attributing mental states such as goals and beliefs, they should also be impaired in predicting the corresponding action (e.g., the guest will lift her empty cup of coffee when the waiter passes). Second, the broken mirror hypothesis suggested that the mirror neuron system is dysfunctional in ASD. The mirror neuron system is a network of brain regions that presumably matches observed actions with one’s own motor system and thereby enables action imitation and interpretation (Gallese et al. 2004). It was proposed that an ontogenetically early deficit in this neuronal mechanism of mapping another’s and one’s own actions in ASD leads to impaired imitation, understanding, and prediction of other’s actions (Oberman and Ramachandran 2007). However, mounting evidence is incompatible with both hypotheses. Children and adults with ASD show typical imitation, understanding, and prediction of goal-directed actions in a variety of paradigms (e.g., Cusack et al. 2015; Falck-Ytter 2010; Marsh et al. 2015; Sebanz et al. 2005). On the other hand, studies using different paradigms suggest that in some situations individuals with ASD do have difficulties with accurate action predictions (e.g., von Hofsten et al. 2009; Vivanti et al. 2011). Together, these findings challenge the theory of mind deficit hypothesis and the broken mirror hypothesis because these frameworks cannot fully explain the nuanced characteristics of action prediction in ASD. Consequently, more refined theories that account for impaired and intact aspects of action prediction in ASD are required (Hamilton 2009).
Action Prediction in Autism
Current Knowledge Recent years have seen a surge of empirical work on various levels of action processing in ASD, ranging from the detection of biological motion to belief-based action prediction. These findings have in turn driven rapid advance in the development of new theories. In contrast to previous research (e.g., Blake et al. 2003; Klin and Jones 2008), several recent studies found no group difference between children, adolescents, and adults with ASD and typical participants in cognitive processes that are presumably required for successful action prediction (Cusack et al. 2015; Murphy et al. 2009; Saygin et al. 2010). For example, Cusack et al. (2015) reported intact detection of animacy, detection of biological motion, and discrimination of different types of actions in adolescents with ASD. Thus, deficient precursor mechanisms cannot serve as explanation for altered action prediction in ASD. Moreover, the prediction of goals and means of a simple “pick-and-place” action was found to be unaffected in 5-year-old children with ASD (Falck-Ytter 2010). The children watched an agent reaching for objects on a table and placing it into a container at the other side of the table. Just as children from the comparison group, 5-yearolds with ASD visually anticipated the end state of these actions, i.e., grasping the corresponding ball and placing it in the container. In contrast, eye movements of children with and without ASD were reactive when the objects were moving self-propelled, i.e., their gaze followed the movement. This suggests that, when observing other agents, children with ASD proactively process the agent’s goal and predict according actions. Yet, action prediction in ASD seems to reach its limits when it becomes necessary to consider another’s false beliefs about a certain situation. This was shown in explicit theory of mind tasks testing verbal action predictions (Baron-Cohen et al. 1985), and more recently using implicit theory of mind tasks, eye tracking versions of the classical explicit paradigms. In these implicit tasks, participants are familiarized with an agent’s goal to get an object by opening one of two doors. Individuals with and without ASD need only a few
Action Prediction in Autism
trials to generate predictive eye movements towards the door that is about to be opened. In the subsequent test trial, the agent falsely believes the object would be located behind door A, although it actually is either behind door B or it was removed completely from the scene. To accurately predict the agent’s action in this trial (that she will open door A), participants have to take into account that the agent’s upcoming action is based on her false belief that the object would still be behind this door. Children and adults with ASD systematically fail to correctly predict this false belief-based action (Schuwerk et al. 2015; Senju et al. 2010). However, it is important to note that, unlike previously thought, individuals with ASD are able to correctly predict the agent’s false beliefbased action in a variety of explicit theory of mind tasks (Scheeren et al. 2013). One explanation for this finding is that individuals with ASD, especially those with good intellectual and language abilities, develop compensatory strategies to pass these tasks (cf., Livingstone and Happé 2017). In sum, current mixed and partially inconclusive evidence on intact and impaired aspects of action prediction in ASD speaks against the ideas of a general action prediction deficit in ASD and that single links in the chain of action processing are broken. Individuals with ASD are in principle able to predict other’s actions. Rather, the finding that this ability is hampered in some contexts but not in others suggests that computations associated with successful action prediction work less effectively, and/or alternative cognitive routes are taken to get to an accurate action prediction (Livingstone and Happé 2017). An appealing way to explain these altered cognitive processes that affect action prediction in ASD is offered by the theoretical framework of hierarchical predictive processing. In short, this currently prominent theory holds that we do not perceive the world by the unbiased interpretation of the information conveyed by sensory systems. Rather, we have a model of how the world should look like and our brain uses it to actively and optimally predict incoming sensory information. The flow of information is bidirectional: at each level within the postulated cognitive hierarchy, downward driving predictions and upward
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transmitted sensory information are compared. The part of sensory input that cannot be explained by the prediction results in a prediction error, which is passed upward so that adjusted and more accurate predictions can be generated. This theory, describing a cognitively very efficient way of making sense of our world, is able to explain cognitive information processing in a variety of domains, ranging from vision to social cognition (Clark 2013). Related claims have been made in developmental psychology. Ruffman (2014) suggested a reduced ability for learning from statistical information in ASD. Given that action anticipation and social learning might rely on implicit statistical learning (Paulus 2014; Ruffman 2014), such a deficit could account for a variety of problems associated with ASD. Pellicano and Burr (2012) suggested that these predictions of sensory information are attenuated in ASD. Thus, perception is less biased by prior expectations about sensory information. Assuming that strong expectations help the cognitive system to reduce the complexity of sensory input, attenuated expectations result in an overburdening stream of relatively unfiltered incoming information that has to be processed. This fits well with the clinical observation of sensory sensitivities and repetitive behavior patterns. Within this framework, the latter can be viewed as a way to reduce the need to process unpredictable external information by creating expectable and consistent sensory stimulation. In the case of action processing, this means that individuals with ASD are affected in the ability to generate action predictions based on prior experience and current observations. This presumably not only affects the control of one’s own actions but also the prediction of other people’s actions (Sinha et al. 2014). Indeed, individuals with ASD show deficits in motor coordination like action preparation or action planning (Fournier et al. 2010). And also when watching interactions of others, children and adults with ASD are less likely to predict their actions (Chambon et al. 2017; von der Lühe et al. 2016; von Hofsten et al. 2009). This form of interpersonal action prediction is crucial for smooth interactive turn-taking.
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Moreover, there is evidence that even very young children with ASD show less anticipation of other’s actions. For example, Brisson et al. (2012) retrospectively analyzed home videos of spoon-feeding situations of children around 5 months of age who have been diagnosed with ASD later. In contrast to a control group, they showed less anticipatory mouth opening when the caregiver moved the spoon towards the infant’s mouth. Interestingly, typically developing infants who displayed low-anticipation rates, improved rapidly. Although an increase in accurate anticipations was also observed in infants later diagnosed with ASD, they seemed to learn more slowly from experience in such feeding situations. Also later in life, the ability to exploit past experience to generate action predictions seems to be affected in ASD. For example, adults and 10-year-old children with ASD showed altered action predictions in a task that elicited visual action anticipations of an agent who repeatedly produced one of two possible actions to get to its goal (Schuwerk et al. 2016). The participants with ASD not only showed overall lower rates of action predictions, they also profited less than the respective comparison groups from frequency information. Adults and children from the comparison groups rapidly used the observation that the agent repeatedly acted the same way to predict that, in the same situation, it will produce the same action again. However, participants with ASD showed less improvement in accurate action predictions over time, suggesting that this form of statistical learning is affected in ASD. In sum, there is growing evidence that the ability to effectively build expectations of another’s upcoming actions based on previously observed actions under the same situational constraints is impaired in ASD. This might not affect the anticipation of simple actions with only one plausible outcome (Falck-Ytter 2010), or actions that follow certain rules (e.g., building a tower with colored pieces following the rule “alternate colors”; Vivanti et al. 2011). But, when options for an action become more complex or additional social cues or beliefs are involved, accurate action prediction might become challenging (Senju et al. 2010; Vivanti et al. 2011). It is important to note
Action Prediction in Autism
that the ability to learn from experience to generate accurate action predictions is not absent in ASD (Chambon et al. 2017; Schuwerk et al. 2015). Yet, it seems that this form of learning from experience works less efficiently in individuals with ASD.
Future Directions Impaired hierarchical predictive processing is a promising theoretical account, which is able to elucidate a variety of empirical findings on altered action prediction ASD. However, more evidence is needed to firmly conclude that deficient predictive processing is at the core of observed social interaction deficits in ASD. Moreover, it is unclear whether a predictive processing deficit in action prediction sufficiently explains the entire range of symptoms of impaired social interaction and communication. In other words, is navigating the social world challenging for individuals with ASD merely because it is so unpredictable, or do other factors, for example, the motivation to engage with the social world, also play a role (Chevallier et al. 2012)? Further, if it were the case that decelerated learning from experience with past actions underlies altered action processing in ASD, this could be targeted by interventions that, for example, offer additional opportunities to learn from experience, or help to elaborate more explicit and rule-based strategies to predict other’s actions. It is up to future research to investigate if these are viable routes to modulate altered action prediction is ASD.
See Also ▶ Mirror Neuron System ▶ Social Cognition ▶ Theory of Mind
References and Reading Baron-Cohen, S., Leslie, A. M., & Frith, U. (1985). Does the autistic child have a “theory of mind”? Cognition, 21(1), 37–46. Blake, R., Turner, L. M., Smoski, M. J., Pozdol, S. L., & Stone, W. L. (2003). Visual recognition of biological
Action Prediction in Autism motion is impaired in children with autism. Psychological Science, 14(2), 151–157. Brisson, J., Warreyn, P., Serres, J., Foussier, S., & AdrienLouis, J. (2012). Motor anticipation failure in infants with autism: a retrospective analysis of feeding situations. Autism, 16(4), 420–429. Chambon, V., Farrer, C., Pacherie, E., Jacquet, P. O., Leboyer, M., & Zalla, T. (2017). Reduced sensitivity to social priors during action prediction in adults with autism spectrum disorders. Cognition, 160, 17–26. Chevallier, C., Kohls, G., Troiani, V., Brodkin, E. S., & Schultz, R. T. (2012). The social motivation theory of autism. Trends in Cognitive Sciences, 16(4), 231–239. Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Sciences, 36(3), 181–204. Cusack, J. P., Williams, J. H., & Neri, P. (2015). Action perception is intact in autism spectrum disorder. Journal of Neuroscience, 35(5), 1849–1857. Dennett, D. C. (1989). The intentional stance. Cambridge, MA: MIT press. Falck-Ytter, T. (2010). Young children with autism spectrum disorder use predictive eye movements in action observation. Biology Letters, 6(3), 375–378. Falck-Ytter, T., & von Hofsten, C. (2006). Infants predict other people’s action goals. Nature Neuroscience, 9(7), 878. Fournier, K. A., Hass, C. J., Naik, S. K., Lodha, N., & Cauraugh, J. H. (2010). Motor coordination in autism spectrum disorders: a synthesis and meta-analysis. Journal of Autism and Developmental Disorders, 40(10), 1227–1240. Frith, U. (2012). Why we need cognitive explanations of autism. The Quarterly Journal of Experimental Psychology, 65(11), 2073–2092. Gallese, V., Keysers, C., & Rizzolatti, G. (2004). A unifying view of the basis of social cognition. Trends in Cognitive Sciences, 8, 396–403. Gredebäck, G., & Falck-Ytter, T. (2015). Eye movements during action observation. Perspectives on Psychological Science, 10(5), 591–598. Hamilton, A. D. C. (2009). Research review: Goals, intentions and mental states: Challenges for theories of autism. Journal of Child Psychology and Psychiatry, 50(8), 881–892. Klin, A., & Jones, W. (2008). Altered face scanning and impaired recognition of biological motion in a 15-month-old infant with autism. Developmental Science, 11(1), 40–46. Livingston, L. A., & Happé, F. (2017). Conceptualising compensation in neurodevelopmental disorders: Reflections from autism spectrum disorder. Neuroscience & Biobehavioral Reviews, 80, 729–742. Marsh, L. E., Pearson, A., Ropar, D., & Hamilton, A. D. C. (2015). Predictive gaze during observation of irrational actions in adults with autism spectrum conditions. Journal of Autism and Developmental Disorders, 45(1), 245–261. Murphy, P., Brady, N., Fitzgerald, M., & Troje, N. F. (2009). No evidence for impaired perception of
65 biological motion in adults with autistic spectrum disorders. Neuropsychologia, 47(14), 3225–3235. Oberman, L. M., & Ramachandran, V. S. (2007). The simulating social mind: The role of the mirror neuron system and simulation in the social and communicative deficits of autism spectrum disorders. Psychological Bulletin, 133, 310–327. Paulus, M. (2014). How and why do infants imitate? An ideomotor approach to social and imitative learning in infancy (and beyond). Psychonomic Bulletin & Review, 21, 1139–1156. Pellicano, E., & Burr, D. (2012). When the world becomes ‘too real’: a Bayesian explanation of autistic perception. Trends in Cognitive Sciences, 16(10), 504–510. Ruffman, T. (2014). To belief or not belief: Children’s theory of mind. Developmental Review, 34, 265–293. Saygin, A. P., Cook, J., & Blakemore, S. J. (2010). Unaffected perceptual thresholds for biological and nonbiological form-from-motion perception in autism spectrum conditions. PloS one, 5(10), e13491. Scheeren, A. M., de Rosnay, M., Koot, H. M., & Begeer, S. (2013). Rethinking theory of mind in highfunctioning autism spectrum disorder. Journal of Child Psychology and Psychiatry, 54(6), 628–635. Schuwerk, T., Vuori, M., & Sodian, B. (2015). Implicit and explicit theory of mind reasoning in autism spectrum disorders: the impact of experience. Autism, 19(4), 459–468. Schuwerk, T., Sodian, B., & Paulus, M. (2016). Cognitive mechanisms underlying action prediction in children and adults with autism spectrum condition. Journal of Autism and Developmental Disorders, 46(12), 3623–3639. Sebanz, N., Knoblich, G., Stumpf, L., & Prinz, W. (2005). Far from action-blind: Representation of others’ actions in individuals with autism. Cognitive Neuropsychology, 22(3–4), 433–454. Senju, A., Southgate, V., Miura, Y., Matsui, T., Hasegawa, T., Tojo, Y., et al. (2010). Absence of spontaneous action anticipation by false belief attribution in children with autism spectrum disorder. Development and Psychopathology, 22(2), 353–360. Sinha, P., Kjelgaard, M. M., Gandhi, T. K., Tsourides, K., Cardinaux, A. L., Pantazis, D., et al. (2014). Autism as a disorder of prediction. Proceedings of the National Academy of Sciences, 111(42), 15,220–15,225. Vivanti, G., McCormick, C., Young, G. S., Abucayan, F., Hatt, N., Nadig, A., et al. (2011). Intact and impaired mechanisms of action understanding in autism. Developmental Psychology, 47(3), 841–856. von der Lühe, T., Manera, V., Barisic, I., Becchio, C., Vogeley, K., & Schilbach, L. (2016). Interpersonal predictive coding, not action perception, is impaired in autism. Philosophical Transactions of the Royal Society B, 371(1693), 20,150,373. von Hofsten, C., Uhlig, H., Adell, M., & Kochukhova, O. (2009). How children with autism look at events. Research in Autism Spectrum Disorders, 3(2), 556–569.
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Active-But-Odd Group Judith Gould NAS Lorna Wing Centre for Autism, Bromley, UK
Definition Active but Odd Lorna Wing and Judith Gould (1979) put forward the concept of a spectrum of autistic conditions. As part of the spectrum, they described different manifestations of social interaction. These were aloof, passive, active but odd in their interactions. Since their early work, an additional group has been included referred to as “over formal and stilted in their approach to others.” The active-but-odd group are those individuals who make spontaneous approaches to others, but in a peculiar, naïve, and one-sided way. These individuals are usually more able and they approach others on their own terms and their behavior is not modified according to the needs, interests, and responses of the person approached. Often the person seeks to indulge their special interest by talking at another person but not for the pleasure of reciprocal social interaction. Compared with the aloof and passive groups, this group has much longer vocabularies and use their language considerably more but their speech characteristically is repetitive, long winded, often pedantic with peculiar intonations. As children, many in this group have pretend play but this is usually repetitive, stereotyped, and often copied from others or DVD’s/TV. This type of play can be misinterpreted as imaginative play but on careful observation over time the quality is not representational or symbolic but is a repetitive routine. As adults, this group shows a lack of social imagination. They are unable to foresee the consequences in social and practical terms of their own and other people’s actions and to act appropriately on that knowledge. They find it difficult to learn from experience so tend to make the same mistakes repeatedly.
Active-But-Odd Group
In his paper, Asperger described some children with this pattern of social interaction (1944). It must be emphasized that there are no clear dividing lines between any of these groups. It is possible for one person to change from one type of social interaction to another or may even show different types of social interaction in different environments, with different people, in different states of health and at different ages. However, at any one time describing the type of social interaction is a helpful indicator in understanding the needs and supporting the individual.
See Also ▶ Asperger, Hans ▶ Wing, Lorna
References and Reading Asperger, H. (1944). Die ‘Autistischen Psychopathen’ im Kindesalter (Autistic psychopats in childhood). Archiv für psychiatrie und nervenkrankheiten, 117, 76–136. (in German). http://www.springerlink.com/content/ u350x0683r1g6432 Wing, L., & Gould, J. (1979). Severe impairments of social interaction and associated abnormalities in children: Epidemiology and classification. Journal of Autism and Developmental Disorders, 9, 11–29.
Activities of Daily Living ▶ Daily Living Skills
Activities of Daily Living (ADLs) ▶ Assessment of Functional Living Skills (AFLS)
Activity Schedules ▶ Daily Routines
Activity-Based Instruction
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Rationale or Underlying Theory
Activity-Based Instruction Howard Goldstein Human Development and Family Science, The Ohio State University, Columbus, OH, USA
Definition Activity-based intervention (ABI) refers to instruction that is embedded within children’s and families’ daily activities and routines. The instructional strategies vary according to child goals and needs, but the approach emphasizes child-directed contexts for instruction and the use of naturally occurring antecedents and consequences to develop functional skills.
Historical Background Diane Bricker and Juliann Woods-Cripe (1992) distinguished activity-based intervention from more traditional approaches to early intervention in the early 1990s. Procedurally, ABI is similar to intervention practices that came earlier, such as incidental language teaching (Hart and Risley 1968), environmental language intervention (MacDonald et al. 1974), embedded instruction (Neef et al. 1984), and routine-based intervention (Dunst et al. 1987). ABI emphasizes the role of parents as teachers and how to capitalize on the potential advantages of parents teaching their children with disabilities during daily activities and routines. Likewise, educators can be encouraged to embed instruction into naturally occurring, daily classroom activities. Activity-based intervention has provided a foundation for the development and evaluation of a number of related interventions that go by a number of general names, such as embedded instruction, routine-based intervention, and integrated therapy. Although there has been a recent focus on teaching intervention targets in the context of children’s daily routines in the home, there are also studies on embedding instruction in community settings as well as many studies applying ABI in daily classroom activities.
Activity-based intervention sought to improve on traditional teaching approaches in several ways. First, this approach was viewed as a way to increase the amount of instruction provided to children with disabilities by involving caregivers as teachers in contexts that would fit into everyday activities and routines. The idea was to capitalize on the natural instruction that many caregivers use with their young children and to expand upon the quantity and quality of those teaching opportunities. Second, the focus on using everyday contexts also provided an approach that would lessen the need to program generalization from contrived teaching situations into everyday contexts. This approach sought to take advantage of the child’s interest in stimuli consistently present in the natural environment and the naturally occurring reinforcers that accompany interactions around those everyday events. Third, by focusing on everyday activities, early interventionists, educators, and families might more carefully consider what objectives would be most functional for children with disabilities in present and future environments that they would naturally encounter in their everyday lives. Analyzing those natural contexts could provide insight into what typically occurring antecedents might evoke learned skills and what natural consequences might maintain or strengthen their use.
Goals and Objectives The goal of ABI is to teach functional skills in the context of daily activities. The specific objectives cut across developmental domains, such as communication, social, cognitive, adaptive or selfhelp, and motor skills.
Treatment Participants ABI has been applied to a variety of populations of individuals with developmental disabilities. The bulk of literature has come from early intervention with participants ranging from infants to school age children. Applicability to children with
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autism is obvious, especially with a focus on social and communication skills, which tend to be domains of weakness typically addressed to promote the socialization of individuals with autism in natural environments.
Treatment Procedures ABI represents a departure from practices that were clinician-directed and that took place in clinical or contrived settings. ABI embraced the idea of “natural environments” as a concept that means more than a location for service delivery. It also recognizes that learning should occur in intervention contexts that represent families’ typical and valued activities, routines, and events. Because children learn through participating in their everyday activities and meaningful experiences, ABI seeks to take advantage of these activities as intervention settings. By teaching caregivers, parents, and teachers to take advantage of these learning opportunities, intervention can be dispersed throughout the day to enhance learning and generalization for the child. Although daily routines may be similar across families, they vary in how and when they are completed. Daily activities that follow consistent, predictable sequences, that are repeated frequently, and that produce meaningful, reinforcing outcomes are especially useful for teaching functional skills. Functional skills improve the child’s ability to participate more fully and independently in their natural environments. During familiar routines, opportunities for communication, social, or other responses can be rather predictable. Thus, caregivers are often taught how to prompt and reinforce targeted responses using a range of facilitative strategies that seem appropriate for the child and the caregiver. For example, some caregivers might be taught how to wait and look expectantly to prompt a response, while others might be taught to prompt the child to ask for help before the child gets frustrated. Some caregivers may be encouraged to model targeted responses, and others may be encouraged to prompt more elaborated responses from their child. Sometimes the focus is limited to getting the caregiver to implement a facilitative strategy in one daily activity, and
Activity-Based Instruction
sometimes the focus is on getting the caregiver to generalize the use of facilitative strategies to multiple activities across the day. Woods et al. (2004), McWilliam (2010a), Dunst (2001), and their colleagues are among the investigators who have outlined taxonomies for describing daily activities. For example, Kashinath and Woods (2007) highlighted four major categories of family routines: (a) play routines (including constructive play, pretend play, physical play, and social games), (b) caregiving routines (including disability-, dressing-, hygiene, and food-related activities), preacademic routines (including reading, singing, watching electronic media (TV, computer, video), and writing or drawing), and (d) community and home routines (including community errands, home chores, arts, cultural, and recreational activities). Such frameworks can help families identify the activities that might provide ample learning opportunities for functional skill development in their child. Implementation of ABI has been characterized as child-centered and family-centered. The childcentered approach emphasizes following the child’s lead and being responsive to the child’s interests, desires, and initiations especially in educational settings. The family-centered approach to ABI requires a great deal of sensitivity on the part of early interventionists to follow the family’s lead and to form a productive partnership. It may take some time to develop a relationship with caregivers that is conducive to open information exchange, observation and discussion of teaching and learning opportunities, joint problem-solving around which routine and facilitative strategies will be most effective, and thoughtful selection of functional target behaviors that will have a meaningful effect on the child’s life. The early interventionist must be aware of the varied values, goals, and circumstances in families’ lives that must be navigated for ABI to be successfully implemented with sufficient frequency and accuracy to be effective.
Efficacy Information Reviews of naturalistic instruction approaches highlight the difficulty in summarizing the
Activity-Based Instruction
empirical support for ABI and similar interventions (Hepting and Goldstein 1996; MilagrosSantos and Lignugaris-Kraft 1997; Rule et al. 1998). That is, examples of ABI found in the literature differ quite a bit procedurally, even when called the same thing. Nevertheless, there are numerous studies that have found positive effects from implementing ABI to teach a variety of behaviors, e.g., social skills, picture naming, instruction following, and counting (PrettiFrontczak et al. 2003). The bulk of the studies summarized by Pretti-Frontczak et al. investigated ABI within classroom settings. Few of the studies compared ABI to other approaches, such as direct instruction interventions. The advantage of ABI is not necessarily seen during skill acquisition. However, better results tend to be seen in the demonstrations of the generalized use of those skills (e.g., Losardo and Bricker 1994). When teaching strategies are not embedded in activities frequently, then progress on children’s targeted objectives tends to be diminished. Milagros-Santos and Lignugaris/ Kraft (1997) provide an analysis of instructional features that are likely to affect learning of new skills. ABI also has been investigated in parent training programs (McWilliam 2010b; Woods et al. 2004). For example, Woods et al. taught caregivers to implement teaching strategies within daily routines; their toddlers with developmental disabilities learned communication skills and demonstrated generalization across routines to varying extents. This work was extended to children with autism (Kashinath et al. 2006). ABI has broad applicability to teaching a variety of skills, using a variety of intervention agents in a variety of natural contexts or activities. Although evidence indicates that ABI approaches can be effective, procedures for selecting functional goals and teaching them effectively in everyday activities are increasingly being developed. Moreover, as these treatment approaches are better refined, comparative studies will be needed to investigate whether ABI is shown to increase generalization and improve functioning in natural environments in comparison to other approaches.
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Outcome Measurement Any IEP goals that are amenable to use in natural environments could serve as outcome measures. ABI promotes the identification of functional goals that enhance the ability of the child to participate in daily activities with more meaningful involvement and independence. Thus, the outcome measures that are targeted and measured cut across developmental domains (e.g., communication, social, cognitive, adaptive or self-help, and motor skills). Most often, the occurrence of the targeted behaviors is captured through observational data collection. Sometimes, the environment is arranged to enhance the opportunities for the behavior of interest to be demonstrated.
Qualifications of Treatment Providers ABI has been implemented by a variety of individuals, typically with training provided by an early intervention professional. Parents, caregivers, general and special educators, related service personnel, and paraprofessionals have been responsible for implementing ABI.
See Also ▶ Daily Routines ▶ Early Intervention ▶ Functional Routines (FR), Teaching ▶ Home-Based Programs ▶ Natural Environment ▶ Naturalistic Interventions
References and Reading Bricker, D., & Woods-Cripe, J. (1992). An activity-based approach to early intervention. Baltimore: Paul H. Brookes. Dunst, C. J. (2001). Participation of young children with disabilities in community learning activities. In M. J. Guralnick (Ed.), Early childhood inclusion: Focus on change (pp. 307–333). Baltimore: Paul H. Brookes. Dunst, C. J., Lesko, J., Holbert, K., Wilson, L., Sharpe, K., & Liles, R. (1987). A systemic approach to infant intervention. Topics in Early Childhood Special Education, 7(2), 19–37.
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70 Dunst, C. J., Herter, S., Shields, H., & Bennis, L. (2001). Mapping community-based natural learning opportunities. Young Exceptional Children, 4(4), 16–24. Hart, B. M., & Risley, T. R. (1968). Establishing use of descriptive adjectives in the spontaneous speech of disadvantaged preschool children. Journal of Applied Behavior Analysis, 1, 109–120. Hepting, N. H., & Goldstein, H. (1996). What’s natural about naturalistic language intervention? Journal of Early Intervention, 20(3), 249–264. Kashinath, S., Woods, J., & Goldstein, H. (2006). Enhancing generalized teaching strategy use in daily routines by parents of children with autism. Journal of Speech, Language, and Hearing Research, 49(3), 466–485. Losardo, A., & Bricker, D. D. (1994). Activity-based intervention and direct instruction: A comparison study. American Journal of Mental Retardation, 98, 744–765. MacDonald, J. D., Blott, J. P., Gordon, K., Spiegel, B., & Hartmann, M. (1974). An experimental parent-assisted treatment program for preschool language-delayed children. The Journal of Speech and Hearing Disorders, 39, 395–415. McWilliam, R. A. (2010a). Routines-based early intervention: Supporting young children and their families. Baltimore: Paul H. Brookes. McWilliam, R. A. (Ed.). (2010b). Working with families of young children with special needs. New York: Guilford. Milagros-Santos, R., & Lignugaris-Kraft, B. (1997). Integrating research on effective instruction with instruction in the natural environment for young children with disabilities. Exceptionality, 7(2), 97–129. Neef, N. A., Walters, J., & Egel, A. L. (1984). Establishing generative yes/no responses in developmentally disabled children. Journal of Applied Behavior Analysis, 17, 453–460. Pretti-Frontczak, K., & Bricker, D. (2004). An activitybased approach to early intervention (3rd ed.). Baltimore: Paul H. Brookes. Pretti-Frontczak, K. L., Barr, D. M., Macy, M., & Carter, A. (2003). Research and resources relate to activitybased intervention, embedded learning opportunities, and routines-based instruction: An annotated bibliography. Topics in Early Childhood Special Education, 23(1), 29–39. Rakap, S., & Parlak-Rakap, A. (2011). Effectiveness of embedded instruction in early childhood special education: A literature review. European Early Childhood Education Research Journal, 19(1), 79–96. Rule, S., Losardo, A., Dinnebeil, L., Kaiser, A., & Rowland, C. (1998). Translating research on naturalistic instruction into practice. Journal of Early Intervention, 21, 283–293. Schwartz, I. S., Billingsley, F. F., & McBride, B. M. (1998). Including children with Autism in inclusive preschools: Strategies that work. Young Exceptional Children, 1(2), 19–26.
Acuity Woods, J. J., & Kashinath, S. (2007). Expanding opportunities for social communication into daily routines. Early Childhood Services, 1(2), 137–154. Woods, J. J., Kashinath, S., & Goldstein, H. (2004). Effects of embedding caregiver-implemented teaching strategies in daily routines on children’s communication outcomes. Journal of Early Intervention, 26(3), 175–193.
Acuity Armando Bertone McGill University, Montreal, QC, Canada
Definition Given that detailed or locally oriented perception is a central tenet of visual cognition in autism (Behrmann et al. 2006; Dakin and Frith 2005; Mottron et al. 2006), several studies have systematically assessed the spatial resolution of vision in autism by measuring visual acuity (VA). VA is generally defined as the ability to perceive targets such as optotypes, letters, or numbers of a specific size at a given distance. For example, “normal” Snellen VA, often referred to as 20/20 vision, is a clinical term that reflects a person’s ability to recognize a target (i.e., letter E) from 20 ft away when its defining spatial features (i.e., spacing of lines composing an E target) are separated by a visual angle of 1 arc minute. Several studies have assessed VA in ASD using a variety of clinical screening charts. For the most part, VA has been demonstrated to be unremarkable in ASD when assessed with either the Crowded LogMAR test (Milne et al. 2009), chart and/or computer-based Landolt-C optotype paradigms (de Jonge et al. 2007; Keita et al. 2010; Tavassoli et al. 2011; but see Ashwin, Ashwin, Rhydderch, Howells, and Baron-Cohen (2009) with replies from Bach and Dakin (2009)), or Snellen-type visual charts (Falkmer et al. 2011). These demonstrations of unaffected visual acuity in ASD suggest that detailed or locally oriented visual perception in autism is not of peripheral or ocular origin.
Adapin
A more direct method of assessing the spatial resolution of the visual system is to measure contrast sensitivity as a function of spatial frequency, thus defining a contrast sensitivity function (CSF) that describes the variation of sensitivity over a range of spatial frequencies (defined by cycles per degree or cpd) from detailed (or high-spatial frequency) to less-detailed (or lower spatial frequency) information. Surprisingly, relatively few direct assessments of contrast sensitivity are available for ASD. de Jonge et al. (2007) assessed contrast sensitivity using the Vistech contrast sensitivity chart, which included spatial frequency gratings of 3, 6, 12, and 18 cpd. Albeit nonsignificant, their ASD group demonstrated increased sensitivity from the mid- to high-spatial frequencies. This trend was consistent with the electrophysiological findings of Jemel et al. (2010), who demonstrated that mid- and high-frequency gratings elicited similar brain responses in their ASD group only (responses segregated in control group), suggesting a response bias toward detailed or high-spatial frequency information. However, in the only published behavioral assessment of contrast sensitivity function (CSF) in ASD to date, Koh, Milne, and Dobkins (2010) demonstrated unremarkable visual acuity, peak spatial frequency, peak contrast sensitivity, and contrast sensitivity at a low-spatial frequency in a small group of participants with ASD.
References and Reading Ashwin, E., Ashwin, C., Rhydderch, D., Howells, J., & Baron-Cohen, S. (2009). Eagle-eyed visual acuity: An experimental investigation of enhanced perception in autism. Biological Psychiatry, 65, 17–21. Bach, M., & Dakin, S. C. (2009). Regarding “Eagle-eyed visual acuity: An experimental investigation of enhanced perception in autism”. Biological Psychiatry, 66, e19–e20. author reply e23–14. Behrmann, M., Thomas, C., & Humphreys, K. (2006). Seeing it differently: Visual processing in autism. Trends in Cognitive Sciences, 10(6), 258–264. Crewther, D. P., & Sutherland, A. (2009). The more he looked inside, the more piglet wasn’t there: Is autism really blessed with visual hyperacuity? Biological Psychiatry, 66, e21–e22. author reply e23–24. Dakin, S., & Frith, U. (2005). Vagaries of visual perception in autism. Neuron, 48(3), 497–507.
71 de Jonge, M. V., Kemner, C., de Haan, E. H., Coppens, J. E., van den Berg, T. J., & van Engeland, H. (2007). Visual information processing in high-functioning individuals with autism spectrum disorders and their parents. Neuropsychology, 21, 65–73. Falkmer, M., Stuart, G. W., Danielsson, H., Bram, S., Lönebrink, M., & Falkmer, T. (2011). Visual acuity in adults with Asperger’s syndrome: No evidence for “eagle-eyed” vision. Biological Psychiatry, 70, 812–816. Jemel, B., Mimeault, D., Saint-Amour, D., Hosein, A., & Mottron, L. (2010). VEP contrast sensitivity responses reveal reduced functional segregation of mid and high filters of visual channels in autism. Journal of Vision, 10(6), 13. Keita, L., Mottron, L., & Bertone, A. (2010). Far visual acuity is unremarkable in autism: Do we need to focus on crowding? Autism Research, 3, 333–341. Koh, H. C., Milne, E., & Dobkins, K. (2010). Spatial contrast sensitivity in adolescents with autism spectrum disorders. Journal of Autism and Developmental Disorders, 40, 978–987. Milne, E., Griffiths, H., Buckley, D., & Scope, A. (2009). Vision in children and adolescents with autistic spectrum disorder: Evidence for reduced convergence. Journal of Autism and Developmental Disorders, 39, 965–975. Mottron, L., Dawson, M., Soulieres, I., Hubert, B., & Burack, J. (2006). Enhanced perceptual functioning in autism: An update, and eight principles of autistic perception. Journal of Autism and Developmental Disorders, 36(1), 27–43. Tavassoli, T., Latham, K., Bach, M., Dakin, S. C., & Baron-Cohen, S. (2011). Psychophysical measures of visual acuity in autism spectrum conditions. Vision Research, 51, 1778–1780.
Adapin Karthikeyan Ardhanareeswaran Autism Program, Child Study Center, Yale School of Medicine, New Haven, CT, USA Program in Neurodevelopment and Regeneration, Yale School of Medicine, New Haven, CT, USA Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT, USA
Synonyms Doxepin; Silenor; Sinequan; Zonalon
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Adaptive Behavior
Definition
individuals with autism spectrum disorder. Doxepin has been FDA-approved for the treatment of insomnia.
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See Also ▶ Antidepressants ▶ Serotonin Reuptake Inhibitors (SRIs)
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References and Reading N C19H22ClNO 315.83708 g/mol H Cl
Usually used in the treatment of depression and/or anxiety, Adapin is a member of a class of drugs known as tricyclic antidepressants (TCAs). TCAs work by blocking noradrenaline and serotonin reuptake by presynaptic neurons, resulting in a greater availability and accessibility of these neurotransmitters to the postsynaptic neuron. Long-term administration can result in an increase in sensitivity and number of adrenergic and serotonergic receptors on the postsynaptic neuron. Serotonin is an inhibitory neurotransmitter that is heavily involved in mood and emotion regulation and has been found to be elevated in the whole blood and platelets of ASD patients. However, the use of TCAs in children and adolescents has been fairly limited due to its narrow therapeutic index and high toxicity profile. TCAs have low receptor specificity and, in addition to their reuptake inhibitory effects, exert antagonistic effects on histamine H2, serotonin 5-HT2, α1-adrenergic, and muscarinic acetylcholine receptors. This results in a large array of side effects including, but not limited to, nausea, drowsiness, weakness, dry mouth, changes in appetite or weight gain, constipation, blurred vision, increased sweating, and decreased sexual drive. Evidence for on-target effects and the reduction of autistic symptoms is limited and conflicting. Accordingly, a Cochrane review in 2012 did not recommend the use of tricyclic antidepressants for the treatment of
Hurwitz, R., Blackmore, R., Hazell, P., Williams, K., & Woolfenden, S. (2012). Tricyclic antidepressants for autism spectrum disorders (ASD) in children and adolescents. Cochrane Database of Systematic Reviews, 3, CD008372.
Adaptive Behavior Arlette Cassidy The Gengras Center, University of Saint Joseph, West Hartford, CT, USA
Synonyms Functional life skills
Definition The American Association on Mental Retardation (AAMR 2002) defines adaptive behavior as “the collection of conceptual, social, and practical skills that have been learned by people in order to function in everyday lives.” Adaptive behavior is best understood as the degree to which individuals are able to function and maintain themselves independently and meet cultural expectations for personal and social responsibility at various ages. As such, adaptive behavior involves the person’s physical skills, cognitive ability, affect, motivation, culture, socioeconomic status, family, and environment. Persons with autism spectrum disorders often demonstrate a discrepancy between intellectual potential and
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consistently displayed adaptive skills. Assessing adaptive behavior can include standardized adaptive behavior scales, observation, interview, or review of anecdotal records. Some commonly used ratings include the Vineland Adaptive Behavior Scales, Second Edition; Scales of Independent Behavior – Revised (SIB-R); Adaptive Behavior Assessment System – Second Edition (ABAS-II); and the Battelle Developmental Inventory, Second Edition (BDI-2).
Sattler, J. M., & Hoge, R. D. (2006). Assessment of children: Behavioral, social, and clinical foundations. San Diego: Jerome M. Sattler. Shea, V., & Mesibov, G. B. (2005). Adolescents and adults with autism. In F. R. Volkmar, R. Paul, A. Klin, & D. Cohen (Eds.), Handbook of autism and pervasive developmental disorders, volume one: Diagnosis, development, neurobiology, and behavior. Hoboken: Wiley. Sparrow, S. S., Cicchetti, D. V., & Balla, D. A. (2005). Vineland adaptive behavior scales (2nd ed.). Circle Pines: American Guidance Service.
See Also ▶ Age Appropriate ▶ Age Equivalents ▶ Daily Living Skills ▶ Developmental Delay ▶ Developmental Milestones ▶ Functional Life Skills ▶ Self-Help Skills
Adaptive Behavior Assessment System, Second Edition Sarah A. O. Gray and Alice S. Carter Department of Psychology, University of Massachusetts Boston, Boston, MA, USA
Synonyms References and Reading ABAS-II; ABAS, Second Edition American Association on Mental Retardation. (2002). Mental retardation: Definition, classification, and systems of support (10th ed.). Washington, DC: Author. Anderson, S. R., Jablonski, A. L., Thomeer, M. L., & Knapp, V. M. (2007). Self-help skills for people with autism. Bethesda: Woodbine House. Carter, A. S., Gillham, J. E., Sparrow, S. S., & Volkmar, F. R. (1996). Adaptive behavior in autism. Mental Retardation, 5, 945–960. Chawarska, K., & Volkmar, F. R. (2005). Autism in infancy and early childhood. In F. R. Volkmar, R. Paul, A. Klin, & D. Cohen (Eds.), Handbook of autism and pervasive developmental disorders, volume one: Diagnosis, development, neurobiology, and behavior. Hoboken: Wiley. Harrison, P., & Oakland, T. (2003). Adaptive behavior assessment system (2nd ed.). San Antonio: The Psychological Corporation. National Research Council. (2001). Educating children with autism. (C. Lord & J.P. McGee, Eds.). Committee on Educational Interventions for Children with Autism. Division of Behavioral and Social Sciences and Education. Washington, DC: National Academy Press. Openden, D., Whalen, C., Cernich, S., & Vaupel, M. (2009). Generalization and autism spectrum disorders. In C. Whalen (Ed.), Real life, real progress for children with autism spectrum disorders (pp. 1–18). Baltimore: Brookes.
Description The Adaptive Behavior Assessment System is a reliable, valid, and norms-based questionnaire assessment of adaptive behavior, or the personal and social skills necessary for everyday independent living. Because children and adults with autism spectrum disorders often struggle with practical independent functioning and effective interactions with others, the assessment of adaptive behavior is a crucial part of a comprehensive assessment of individuals on the spectrum. Now in its second edition, the ABAS-II can be used for individuals across the life span, with norm referenced scores available for ages 0–89. Like other assessments of adaptive behavior, this assessment can be used with individuals with autism spectrum disorders to determine how an individual is responding to day-to-day demands compared to others his/her age, to develop treatment and training goals, to determine eligibility
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for services and Social Security benefits, and to assess the capability of adults to live independently. The test may also be used to assess adaptive behavior in individuals with other impairments, including intellectual disability, learning difficulties, or ADHD. The test is published by the Psychological Corporation, and the authors are Patti Harrison & Thomas Oakland. The ABAS-II assesses three general areas of adaptive behavior: Conceptual, Social, and Practical. These domains were selected according to guidelines of the American Association of Intellectual Disabilities. These three domain areas are divided into ten specific adaptive skill areas, organized according to the specifications of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV), which is published by the American Psychiatric Association and provides standard criteria for the classification of mental disorders. These ten skill areas are Communication (e.g., “speaks clearly), Community Use (e.g., “finds the restroom in public places”), Functional Academics (e.g., “tells time correctly, using a watch or a clock with hands”), Health and Safety (e.g., “carries scissors safely”), Home or School Living (e.g., “sweeps the floor”), Leisure (e.g., “invites others home for fun activity”), Self-Care (e.g., “washes hands with soap”), Self-Direction (e.g., “controls temper when disagreeing with friends”), Social (e.g., “says ‘please’ when asking for something”), Work (e.g., “performs tasks at work neatly”). The Work skills area is optional. Communication, Functional Academics, and SelfDirection areas are a part of the Conceptual domain; Social and Leisure skill areas are a part of the Social domain; and Self-Care, Home or School Living, Community Use, Health and Safety, and Work are a part of the Practical domain. The Motor skills area is not a part of any domain score. The ABAS-II is available in a five forms, all which assess the same areas of adaptive functioning. Parents of children aged 5–21 may use a rating form; a new form for parents of children aged 0–5 was developed for the second edition. There is also a teacher rating form for individuals aged 5–21, as well as a teacher/day care form for children aged 2–5 (also new to the second edition). Finally, there is an adult form for individuals
aged 16–89, with which adult individuals can report on their own adaptive behavior. Forms include between 193 and 241 items, and items are rated on a 4-point scale, with 0 ¼ is not able, 1 ¼ never when needed, 2 ¼ sometimes when needed, and 2 ¼ always or almost always when needed. An additional category is “check if you guessed,” which helps examiners determine how much confidence to place in responses. The questionnaire takes approximately 15–20 min to complete and around 5 min to score. Scoring assistance software can aid with the speed and accuracy. A limited amount of research with the ABASII in individuals with autism confirms patterns of adaptive behavior deficits similar to those observed with other assessments of adaptive behavior. For example, in a sample of 40 individuals with high-functioning autism and 30 typically developing controls, individuals with autism demonstrated lower general adaptive composites, as well as specific deficits in social skills. The general adaptive composite was negatively associated with autism symptomatology (Kenworthy et al. 2010).
Historical Background The AdaptiveBehavior Assessment System, Second Edition, is a revision and a downward extension of an earlier first-edition version of the test by the same authors, the Adaptive Behavior Assessment System, published just 3 years prior in 2000. The update was in response to the 2002 AAMR guidelines that suggested looking within conceptual, social, and practical domains of adaptive behavior. The ABAS-II added domain scores for these three areas. Whereas the first edition was available only for school-aged children and adults, the ABAS-II has two new Infant/Preschool forms to allow for administration to parents of children aged 0–5.
Psychometric Data The ABAS-II provides scores based on agerelated norms, based on a standardization sample
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that drew from the US Census data from 1999 to 2000. Thirty-one age groups were assessed for each form, with at least 100 participants per group. In addition to normative samples, the standardization included 20 clinical samples, including a clinical sample for autism. However, these clinical samples were small and not randomly selected, so no autism-specific norms exist for the ABAS-II. A General Adaptive Composite, with a mean of 100 and a standard deviation of 15, is yielded as an overall measure of an individual’s adaptive skills. In the second edition, domain composite scores, also with means of 100 and a standard deviation of 15, are yielded. Skill area standard scores have a mean of 10 and a standard deviation of 3. Confidence intervals and descriptive classifications are also provided. Finally, for individuals up to 22 years, age-based percentile ranks and age equivalencies are yielded. The GAC has a lowest possible score of 40, and the GAC ceiling for 0–5 is 160, and for adults and children over 8 it is 120. In addition to scaled scores, information about relative strengths and weaknesses by skill area as well as base rates in the standardization sample are provided. On the school-aged parent and teacher data from the standardization sample, girls scored significantly higher than boys on the General Adaptive Composite, and this gender effect was stronger in teachers; however, gender accounted for only a small amount of variance (.6% and 2.7%). These gender differences are consistent with some other adaptive behavior tests (e.g., the Adaptive Behavior Inventory for Children, which showed similar patterns), though not all measures of adaptive behavior (e.g., the Vineland Adaptive Behavior Scales does not demonstrates sex differences). Effects of race were also observed in the standardization sample, with white children scoring higher than Latino children. Again, an ethnicity main effect has been observed in some but not all other assessments of adaptive behavior. Given that adaptive behavior is defined according to the cultural norms and expectations regarding independent behavior and social functioning, sensitivity to cultural context is a critical part of the sensitive assessment of adaptive behavior.
The ABAS-II has shown very strong reliability. Most skill areas have internal consistency of .90 or higher. In studies examining test-retest reliability over a 2-week period, General Adaptive Composite correlations were near or above .90 for all versions of the ABAS-II. The test also demonstrates adequate validity. Factor analysis supports both the three-factor model and the GAC factor. The factor model is similar for boys and girls (Wei et al. 2008). Comparisons to other adaptive behavior measures, such as the Vineland, show correlations ranging between .70 and .84, demonstrating concurrent validity. Clinical validity studies have also suggested that the ABAS-II is highly sensitive when differentiating clinical and nonclinical samples. Correlations with the Wechsler Intelligence Scale for Children-Third Edition, the Wechsler Adult Intelligence Scale-Third Edition, and the Wechsler Abbreviated Scale of Intelligence were medium-sized, confirming that intelligence and adaptive functioning are inter-related but distinct constructs. No predictive validity studies are known. Items were selected from an original pool of 1500 generated items, from which a third to a half were used in standardization sampling. The test has been criticized for requiring a high level of reading comprehension for some items (seventh grade) and for its relatively low ceiling (120) (Sattler 2002).
Clinical Uses Adaptive skills generate opportunities for independence and meaningful social interaction. Given that core deficits in social and communication skills are at the heart of a diagnosis of Autism Spectrum Disorder, measurement of the adaptive skills that children and adults are using – and where they may need remediation – is a key component of assessment and treatment planning for individuals with ASDs. Indeed, some conceptualizations of developmental disabilities suggest more emphasis be placed on adaptive skills than on IQ, as adaptive skills are modifiable and capture real-world implementation, whereas intellectual ability does not necessarily capture the skills an individual is using in a day-to-day context
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(Schalock 1999). Individuals with autism, particularly high-functioning ones, typically have a profile that includes adaptive skill levels that are lower than intelligence levels. The ABAS-II provides a categorical and agenormed assessment of individuals’ adaptive skills, which can be used to guide treatment planning. The ABAS-II can also be used to generate a profile of an individual’s adaptive skills, so that areas of relative strength and weakness can be better understood. For example, if an individual is shown to demonstrate deficits in a skill area (e.g., Social), then specific behavioral interventions can be built around the specific deficits documented in testing. Assessing adaptive behaviors across a range of settings (e.g., home and school) can also provide information about the generalization of skills. Moreover, using a measure like the ABAS-II over time can document progress in adaptive skills or capture students’ response to intervention, a critical component of special education service planning. The ABAS-II can also be used to determine eligibility for services, such as Social Security and special education services under the Individuals with Disabilities Education Act. A documented deficit in adaptive behavior is necessary for a diagnosis of intellectual disability, which often co-occurs with ASDs. Investigating profiles of adaptive skill strengths and weaknesses using the ABAS can also be helpful in differential diagnosis, as children and adults on the spectrum tend to have particular adaptive skill deficits in social and communication areas of adaptive skills.
See Also
Adaptive Behavior Predicting Postschool Outcomes Kristin Dell’Armo The Ohio State University Nisonger Center – UCEDD, Columbus, OH, USA
Definition
▶ Maladaptive Behavior ▶ Vineland Adaptive Behavior Scales (VABS)
References and Reading Harrison, P. L., & Oakland, assessment system. San Corporation. Harrison, P. L., & Oakland, assessment system (2nd chological Corporation.
Kenworthy, L., Case, L., Harms, M. B., Martin, A., & Wallace, G. L. (2010). Adaptive behavior ratings correlate with symptomatology and IQ among individuals with high-functioning autism spectrum disorders. Journal of Autism and Developmental Disorders, 40(4), 416–423. Oakland, T., & Algina, J. (2011). Adaptive behavior assessment system-II parent/primary caregiver form: Ages 0–5: Its factor structure and other implications for practice. Journal of Applied School Psychology, 27(2), 103. Oakland, T., & Harrison, P. L. (Eds.). (2008). Adaptive behavior assessment system II: Clinical use and interpretation. San Diego: Academic. Ozonoff, S., Goodlin-Jones, B. L., & Solomon, M. (2005). Evidence-based assessment of autism spectrum disorders in children and adolescents. Journal of Clinical Child and Adolescent Psychology, 34(3), 523–540. Rust, J., & Wallace, M. (2004). Book review: Adaptive behavior assessment system – Second edition. Journal of Psychoeducational Assessment, 22, 367–373. Sattler, J. M. (2002). Assessment of children: Behavioral and clinical applications. San Diego: Author. Schalock, R. L. (Ed.). (1999). Adaptive behavior and its measurement in the field of mental retardation. Washington, DC: American Association on Mental Retardation. Wei, Y., Oakland, T., Algina, J., & MacLean, W. E. (2008). Multigroup confirmatory factor analysis for the adaptive behavior assessment system-II parent form, ages 5–21. American Journal on Mental Retardation, 113(3), 178–186.
T. (2000). Adaptive behavior Antonio: The Psychological T. (2003). Adaptive behavior ed.). San Antonio: The Psy-
Adaptive Behavior The construct of adaptive behavior has been defined by both the American Association on Intellectual and Developmental Disabilities (AAIDD) and the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) as the collection of conceptual, social, and practical skills that are learned and performed by people in their everyday lives. These three domains of adaptive behavior – conceptual, social, and practical skills – have been
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consistently identified through factor analytic work on adaptive behavior (Tassé et al. 2012). Conceptual skills refer to language abilities, reading and writing, numbers, time, and money concepts. Social skills include interpersonal skills, friendships, social participation, and social problem-solving. Practical skills encompass selfcare, activities of daily living, health and safety, ability to use transportation, etc. Postschool Outcomes Researchers have struggled over the years to define what it means for a person with autism to be successful in adulthood (Henninger and Taylor 2013). As adults with autism have increasingly moved out of institutions and into the community, the criteria for positive outcomes have shifted from merely avoiding life in an institution to independently functioning within the community. Three key areas of postschool outcomes are regularly discussed in the literature on disabilities at large, including ASD: postsecondary education, employment, and independent living. In their systematic review of evidence-based predictors of successful postschool outcomes, the National Technical Assistance Center on Transition (NTACT) identified these three areas as the critical outcomes of interest for students with disabilities (Test et al. 2009). Postsecondary education participation is defined as enrollment in any vocational or technical school, 2-year or community college, or 4-year college. Competitive employment – defined as a job at which a majority of the other workers do not have a disability and the person with a disability is earning at least minimum wage – is generally considered the gold standard for employment among young adults with ASD. Residential independence refers to living on one’s own or with a roommate, partner, or spouse (i.e., not with caregivers or paid staff, although support from these individuals may be provided). In the literature specific to autism spectrum disorder (ASD), social relationships are often considered another important component of postschool outcomes, including having at least one meaningful friendship with a person in the same age group who is not a paid staff member (Henninger and Taylor 2013).
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Historical Background Transition to Adulthood for Youth with ASD The transition from secondary school to adulthood is a growing area of concern within the ASD community. In fact, many researchers describe this period of time as a “services cliff” because of the steep decline in services that are available after a student with ASD graduates high school (Roux et al. 2015). It has been welldocumented that adults with ASD have poor outcomes compared to their peers without disabilities and peers with other types of disabilities. Data from the National Longitudinal Transition Study-2 (NLTS2) – a large, nationally representative, 10-year longitudinal study of transition-aged youth receiving special education services – is commonly used to summarize the postschool outcomes of youth with ASD and other kinds of disabilities (Newman et al. 2011; Roux et al. 2015). These data show that among young adults with autism ages 23–26, just 44% attended any type of postsecondary education, less than all other disability types except intellectual disability and multiple disabilities. Seventeen percent lived independently, which was lower than all other disability types except multiple disabilities. Twenty-four percent had no socialization with peers, either in person or on a phone call, in the past year, which was lower than all other disabilities studied. Just 63% of the sample with ASD had worked for pay since leaving high school, which was lower than all other disability types except multiple disabilities. Additionally, the majority of people with ASD who had a paid job were working part-time jobs for approximately minimum wage. In many cases, it took 2 years or more to find a job after graduating high school. Findings also show that young adults with ASD have difficulty maintaining a job relative to their peers with other kinds of disabilities. These negative outcomes exist even among adults with ASD who have average or above-average intelligence. Despite average cognitive abilities, they are struggling to engage in typical adult activities. Based on these outcomes, it appears these young adults do not have the skills or supports they need to succeed in their adult lives. Therefore, it is crucial to understand the factors that predict a successful transition.
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Adaptive Behavior Profiles in ASD Research indicates that adaptive skills are likely a key factor in the successful transition to adulthood for youth with ASD. Children and adults with ASD have unique adaptive behavior profiles. It is relatively common for people with ASD to have “scatter” in their skills, meaning that scores on some domains are much higher than others. Research consistently finds that people with ASD have the lowest scores in socialization, as measured by the Vineland Adaptive Behavior Scales (e.g., Kanne et al. 2011). However, some studies have described an “autism profile” in which daily living skills are an area of relative strength, while others find that daily living skills are a relative weakness and communication is the greatest strength. Regardless of the relative strengths and weaknesses found in particular papers, it is important to remember that they are just that: relative. It is well-established that scores on all domains of adaptive behavior for people with ASD are low relative to typically developing peers. In fact, this pattern of low adaptive behavior scores in people with ASD is a robust finding that exists independent of cognitive functioning. There is a well-documented gap between IQ and adaptive behavior scores in this population. Adaptive functioning often lags behind cognitive level, especially among those with average or aboveaverage IQ (Alvares et al. 2019; Kraper et al. 2017), such that even people with ASD who have high IQ scores often have significantly impaired adaptive behavior. This gap has been shown to emerge as early as 12–36 months of age and widen throughout childhood development and into adolescence (Bradshaw et al. 2019; Kanne et al. 2011; Klin et al. 2007), with gaps as large as 2 standard deviations in some studies (Klin et al. 2007). The gap between IQ and adaptive behavior is largest in people with ASD who are older and who have higher IQs (Kanne et al. 2011). In fact, some studies have found that this gap only exists in those with average intelligence and have found adaptive skills commensurate with mental age in those with intellectual disability (Kanne et al. 2011). While much of the research on adaptive behavior in ASD has been done on children, some
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studies have documented plateaus or declines in adaptive behavior scores through adolescence (Klin et al. 2007; Meyer et al. 2018), where continued improvements are expected. It is important to note that this does not suggest adolescents with ASD are losing adaptive skills, but rather that their rate of progress is slowing down relative to their same-aged peers without ASD. Importantly, age is not associated with either ASD symptom severity or cognitive abilities, so these declines in skill development are found only with regard to adaptive behavior. Therefore, deficits in functional skills are decreasing despite these other two variables remaining the same (Kanne et al. 2011). This trend is worrisome and suggests the need for increased intervention on adaptive skills specifically as children with ASD enter adolescence.
Current Knowledge In order to improve transition practices and promote positive outcomes for young adults with ASD, it is necessary to understand the factors that influence these outcomes. In the broader disability literature, there is evidence that adaptive behavior skills predict postschool outcomes. For instance, in their systematic review of transition literature across all federal special education disability categories, Test et al. (2009) found that students with more self-care and daily living skills were more likely to participate in all three postschool outcomes of interest: postsecondary education, independent living, and employment. They also found that students with better social skills had improved outcomes in postsecondary education attendance and employment. In a sample of participants with intellectual disability, Dell’Armo and Tassé (2019) found that adaptive behavior variables (social skills, academic performance, and functional skills) were more predictive of postschool outcomes than parent expectations or demographic factors such as race, household income, and parent education level. In a sample with ASD, adaptive behavior – particularly the Daily Living Skills subscale of the Vineland Adaptive Behavior Scales – was most closely correlated with positive adult outcomes
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(Farley et al. 2009). Taken together, all of this research suggests that adaptive behavior is likely an important variable in predicting postschool outcomes in ASD, much like in the broader disability population. Unfortunately, there is relatively little research investigating the effects of adaptive behavior on postschool outcomes. Review papers on predictors of outcomes (e.g., Kirby et al. 2016) have identified relatively few adaptive behavior variables being studied. Much of the research that does exist uses data from the National Longitudinal Transition Study-2 (NLTS). These data are beneficial for studying postschool outcomes as they are longitudinal data that were collected over a period of 10 years. Participants were enrolled in high school at the start of the study and had left high school by the end of the 10 years. Therefore, the data allows researchers to not only describe postschool outcomes but to investigate variables from earlier time points that may be associated with those outcomes. However, a limitation of NLTS2 data is that a standardized adaptive behavior measure was not administered to participants. Therefore, researchers interested in studying adaptive behavior must attempt to use existing variables as a proxy for adaptive behavior (e.g., Dell’Armo and Tassé 2019). Despite this shortcoming, the longitudinal nature of the dataset makes it one of the best available datasets for exploring variables related to postschool outcomes. Secondary analyses using NLTS2 data have provided further insight into how adaptive behavior predicts postschool outcomes. In a sample of students with autism, Chiang et al. (2012) found that academic performance – which would fall under the conceptual domain of adaptive behavior – was a significant predictor of enrollment in postsecondary education. Similarly, Wei et al. (2013) found that young adults with ASD who had higher levels of basic conceptual skills (i.e., telling time, counting change, reading and understanding signs) were more likely to enroll in postsecondary education. These same basic conceptual skills, along with conversation ability, have also been associated with social participation outcomes. Orsmond
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et al. (2013) found that young adults with ASD who had lower levels of all these skills were less likely to see friends, be called by friends, and be invited to activities (i.e., were more likely to be socially isolated). In terms of employment, Roux et al. (2013) found greater levels of these same basic conceptual skills were associated with greater odds of paid employment, as were greater levels of conversational ability. Another study determined that social skills were a significant predictor of employment after leaving high school (Chiang et al. 2013). Although studies of adult outcomes do not typically measure psychiatric symptoms, they are important in this population because research suggests that people with ASD experience co-occurring mental health conditions at much greater rates than in the general population (Simonoff et al. 2008). In the only study investigating this topic, Kraper et al. (2017) found that the size of the gap between IQ and adaptive behavior was significantly related to comorbid psychopathology, such that those with greater discrepancies between IQ and adaptive behavior scores were more likely to have another mental health diagnosis like anxiety or depression. While more research is needed to replicate this finding, it appears that adaptive behavior may also impact psychiatric outcomes and psychological wellbeing.
Future Directions It should be clear from the information presented that an increased focus on adaptive behavior skills for youth with ASD as they transition to adulthood is warranted. The research that exists on adaptive behavior profiles in this age group suggests that adaptive behavior, and not IQ, is most predictive of postschool outcomes. There are countless examples of young adults with ASD who are of average or above-average intelligence, but adaptive behavior deficits prevent them from living independently, keeping a job, or otherwise succeeding in their adult lives. Future research should put more focus on adaptive behavior for people with ASD in this age group. It would be
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useful to know more about typical adaptive behavior profiles (including common areas of relative strength or weakness, as current findings are somewhat conflicting) as well as their longitudinal trajectory (i.e., how do adaptive skills change as youth with ASD age out of school and enter young adulthood?). In addition, these questions should be studied separately in people with ASD who have comorbid ID and those who do not, as prior research suggests that adaptive behavior profiles and outcomes may differ for these two groups. Despite the importance of adaptive behavior, relatively little work has been done examining the factors that predict or influence adaptive skills (Kraper et al. 2017). A better understanding of the development of adaptive behavior skills is warranted, with the hope that this would lead to increased knowledge of how to improve adaptive skills in people with ASD. Increased focus should be placed on improving adaptive behavior skills, both in research and in clinical practice. Adaptive behavior skills, in particular those that relate to adult outcomes, should be a target of intervention for all youth with ASD as they transition to adulthood. However, it is still unclear exactly which adaptive skills are most related to adult outcomes. Very little research has specifically investigated how adaptive behavior relates to postschool outcomes, and virtually none of the current research investigating predictors of postschool success use standardized, comprehensive measures of adaptive behavior, as most comes from the same NLTS2 dataset. More research should be conducted to identify which adaptive skills are the most important, as well as develop effective interventions for improving these skills.
See Also ▶ Factors Affecting Outcomes ▶ Functional Life Skills ▶ Outcome Studies ▶ Quality of Life For Transition-Age Youth with ASD
Adaptive Behavior Predicting Postschool Outcomes
References and Reading Alvares, G. A., Bebbington, K., Cleary, D., Evans, K., Glasson, E. J., Maybery, M. T., Pillar, S., Uljarević, M., Varcin, K., Wray, J., & Whitehouse, A. J. (2019). The misnomer of ‘high functioning autism’: Intelligence is an imprecise predictor of functional abilities at diagnosis. Autism. Advance online publication. https://doi.org/10.1177/ 1362361319852831. Bradshaw, J., Gillespie, S., Klaiman, C., Klin, A., & Saulnier, C. (2019). Early emergence of discrepancy in adaptive behavior and cognitive skills in toddlers with autism spectrum disorder. Autism, 23(6), 1485–1496. Chiang, H. M., Cheung, Y. K., Hickson, L., Xiang, R., & Tsai, L. Y. (2012). Predictive factors of participation in postsecondary education for high school leavers with autism. Journal of Autism and Developmental Disorders, 42(5), 685–696. https://doi.org/10.1007/ s10803-011-1297-7. Chiang, H. M., Cheung, Y. K., Li, H., & Tsai, L. Y. (2013). Factors associated with participation in employment for high school leavers with autism. Journal of Autism and Developmental Disorders, 43(8), 1832–1842. https://doi.org/10.1007/s10803-012-1734-2. Dell’Armo, K., & Tassé, M. J. (2019). The role of adaptive behavior and parent expectations in predicting post-school outcomes for young adults with intellectual disability. Journal of Autism and Developmental Disorders, 49, 1638–1651. https://doi.org/10.1007/ s10803-018-3857-6. Farley, M. A., McMahon, W. M., Fombonne, E., Jenson, W. R., Miller, J., Gardner, M., et al. (2009). Twenty-year outcome for individuals with autism and average or near-average cognitive abilities. Autism Research, 2, 109–118. Henninger, N. A., & Taylor, J. L. (2013). Outcomes in adults with autism spectrum disorders: A historical perspective. Autism, 17, 103–116. Kanne, S. M., Gerber, A. J., Quirmbach, L. M., Sparrow, S. S., Cicchetti, D. V., & Saulnier, C. A. (2011). The role of adaptive behavior in autism spectrum disorders: Implications for functional outcome. Journal of Autism and Developmental Disorders, 41(8), 1007–1018. Kirby, A. V., Baranek, G. T., & Fox, L. (2016). Longitudinal predictors of outcomes for adults with autism spectrum disorder: Systematic review. Occupation, Participation, and Health, 36(2), 55–64. Klin, A., Saulnier, C. A., Sparrow, S. S., Cicchetti, D. V., Volkmar, F. R., & Lord, C. (2007). Social and communication abilities and disabilities in higher functioning individuals with autism spectrum disorders: The Vineland and the ADOS. Journal of Autism and Developmental Disorders, 37(4), 748–759. Kraper, C. K., Kenworthy, L., Popal, H., Martin, A., & Wallace, G. L. (2017). The gap between adaptive behavior and intelligence in autism persists into
Adaptive Behavior Scales young adulthood and is linked to psychiatric co-morbidities. Journal of Autism and Developmental Disorders, 47, 3007–3017. https://doi.org/10.1007/ s10803-017-3213-2. Meyer, A. T., Powell, P. S., Butera, N., Klinger, M. R., & Klinger, L. G. (2018). Brief report: Developmental trajectories of adaptive behavior in children and adolescents with ASD. Journal of Autism and Developmental Disorders, 48(8), 2870–2878. Newman, L., Wagner, M., Knokey, A.-M., Marder, C., Nagle, K., Shaver, D., Wei, X., with Cameto, R., Contreras, E., Ferguson, K., Greene, S., and Schwarting, M. (2011). The post-high school outcomes of young adults with disabilities up to 8 years after high school. A report from the national longitudinal transition study-2 (NLTS2) (NCSER 2011-3005). Menlo Park: SRI International. Orsmond, G. I., Shattuck, P. T., Cooper, B. P., Sterzing, P. R., & Anderson, K. A. (2013). Social participation among young adults with an autism spectrum disorder. Journal of Autism and Developmental Disorders, 43, 2710–2719. Roux, A. M., Shattuck, P. T., Cooper, B. P., Anderson, K. A., Wagner, M., & Narendorf, S. C. (2013). Postsecondary employment experiences among young adults with an autism spectrum disorder. Journal of the American Academy of Child and Adolescent Psychiatry, 52(9), 931–939. Roux, A. M., Shattuck, P. T., Rast, J. E., Rava, J. A., & Anderson, K. A. (2015). National autism indicators report: Transition into young adulthood. Philadelphia: Life Course Outcomes Research Program, A.J. Drexel Autism Institute, Drexel University. Simonoff, E., Pickles, A., Charman, T., Chandler, S., Loucas, T., & Baird, G. (2008). Psychiatric disorders in children with autism spectrum disorders: Prevalence, comorbidity, and associated factors in a populationderived sample. Journal of the American Academy of Child & Adolescent Psychiatry, 47(8), 921–929. Tassé, M. J., Schalock, R. L., Balboni, G., Bersani, H., Borthwick-Duffy, S. A., Spreat, S., Thissen, D., Widaman, K. F., & Zhang, D. (2012). The construct of adaptive behavior: Its conceptualization, measurement, and use in the field of intellectual disability. American Journal on Intellectual and Developmental Disabilities, 4, 291–303. https://doi.org/10.1352/19447558-117.4.291. Test, D. W., Mazzotti, V. L., Mustian, A. L., Fowler, C. H., Kortering, L., & Kohler, P. (2009). Evidence-based secondary transition predictors for improving postschool outcomes for students with disabilities. Career Development for Exceptional Individuals, 32(3), 160–181. https://doi.org/10.1177/0885728809346960. Wei, X., Jennifer, W. Y., Shattuck, P., McCracken, M., & Blackorby, J. (2013). Science, technology, engineering, and mathematics (STEM) participation among college students with an autism spectrum disorder. Journal of Autism and Developmental Disorders, 43(7), 1539–1546.
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Adaptive Behavior Scales Marisa O’Boyle Clinical Psychology, University of Massachusetts Boston, Boston, MA, USA
Definition Adaptive behavior scales can provide information about children’s communication, socialization, and other everyday behavior relative to their age (Demchak and Drinkwater 1998; Gillham et al. 2000). Adaptive behavior scales are different from intelligence tests in that they measure what a child does in the real world versus what a child is capable of in a structured testing situation (Volkmar 2003). The most widely used adaptive behavior scale is the Vineland Adaptive Behavior Scales (Sparrow et al. 1984, 2005); which is a semi-structured interview with parents or caregivers to assess capacities for self-sufficiency in various areas, including communication, daily living, and social, as well as for young children, motor skills.
Historical Background Beginning with the first descriptions of mental retardation in the 1800s, limits to or an inability to adapt to the demands of everyday life, that is, adaptive behavior, was emphasized as a main descriptor of people considered to have mental retardation (Bothwick-Duffy 2007). Adaptive behavior scales were developed to identify behavior deficits needing treatment in people who were already known to have a disability. Eventually, assessment of adaptive behavior became used for the purposes of diagnosis and eligibility for special services (Bothwick-Duffy 2007). Although social factors are currently viewed as a central defining characteristic of the autistic syndrome, earlier research did not systematically evaluate social dysfunction in autistic individuals; therefore, the utility of a well-standardized, normative
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assessment instrument for documenting autistic social dysfunction in terms of daily adaptive functioning became clear (Volkmar et al. 1987). Literature describing adaptive deficits in autism emerged with Sparrow et al. (1984) and Volkmar et al. (1987) in the 1980s. The assessment of adaptive behavior in individuals with autism along with standardized measures of intellectual functioning was developed to determine whether or not to assign a diagnosis of mental retardation or intellectual disability as well as to distinguish between Autism Spectrum Disorders and other intellectual and developmental disabilities (Carter et al. 1998). Multiple assessments were created to measure these adaptive skills. The Behavior Inventory for Rating Development (BIRD) was designed to assess types and levels of adaptive behaviors. The BIRD is classified into several subscales of adaptive behavior; Cognitive Development, Self-Help, Physical Development, Social Behavior, and Self-Control (Sparrow and Cicchetti 1984). Other measures of adaptive behavior include the Comprehensive Test of Adaptive Behavior (Adams 1984); Scales of Independent Behavior (Bruinicks et al. 1984); and the Adaptive Behavior Inventory (Brown and Leigh 1986). The more widely used measure, the Vineland Adaptive Behavior Scales-Survey Form evaluates children’s personal and social sufficiency in a semi-structured interview with a primary caregiver (Sparrow et al. 1984). This instrument assesses four areas of adaptive behavior: Communication, Daily Living Skills, Socialization, and Motor Skills (Carter et al. 1998; Sparrow et al. 1984).
Current Knowledge Adaptive skills include whatever capacities an individual possesses to function within their everyday environment, encompassing selfsufficiency as well as social competence (Demchak and Drinkwater 1998; Paul et al. 2004). These skills are particularly important in individuals with autism and related conditions because they contribute the most to an individual’s ability to function successfully and
Adaptive Behavior Scales
independently in the world (Liss et al. 2001). Adaptive behavior, or children’s ability to take care of themselves and get along with others, is an extremely important aspect of multidimensional assessment and interventions for preschool and school-aged children as well as for adolescents and adults. Adaptive behavior assessment is useful for diagnosing possible disabilities and developmental problems of preschoolers, which then can lead to planning effective home, family, and school programs (Harrison and Raineri 2007). Given that adaptive behavior is modifiable, it can lead to planning effective home, family, school, community, and vocational planning through the life span. As noted, the most widely used measurement of adaptive behavior is the Vineland Adaptive Behavior Scales, which are broken down into four scales. The Communication scale refers to skills required for receptive, expressive, and written language; Daily Living Skills scale includes the practical skills needed to take care of oneself and contribute to a household and community; Socialization scale includes skills needed to get along with others, regulate emotions and behavior, as well as skills involved in leisure activities such as play; and finally the Motor Skills scale, comprising both fine and gross motor items, which is typically assessed in individuals below the age of 6 years or when significant difficulty in motor development is suspected. Additionally, the Vineland also contains a Maladaptive Behavior Domain, which assesses the presence of problematic behaviors that interfere with an individual’s functioning. The Maladaptive Behavior Domain can be administered to children aged 5 and older and includes both behaviors that are common in early development but are less common as children get older and more serious behaviors that are of concern throughout development (Carter et al. 1998; Sparrow et al. 1984). Further explanation of these scales is as follows: the Communication scale includes expressive, which is what an individual says, while receptive is what an individual understands, and written is what an individual reads and writes. The Daily Living scale includes personal information such as how an individual eats, dresses, and practices personal hygiene, as
Adaptive Behavior Scales
well as domestic information such as what household tasks an individual performs, and finally community, such as how an individual uses time, money, the telephone, and job skills. The Socialization scale includes interpersonal information such as how an individual interacts with others, as well as play and leisure, such as how an individual plays and uses leisure time, as well as coping, or how an individual shows responsibility and sensitivity to others (Paul et al. 2004). Volkmar and colleagues evaluated the ability of the Vineland Adaptive Behavior Scales to diagnose autism by looking at multiple regression equations to predict expected socialization and communication skills on the basis of age, parent education, and sex of the child (Volkmar et al. 1993). While deficits in both communication and socialization are characteristic of the disorder, individuals with autism tend to evidence greater impairment in socialization relative to both communication and daily living skills (Carter et al. 1996). Children with autism display significantly poorer daily living skills and more serious maladaptive behaviors than those with other developmental disorders (Gillham et al. 2000). Multiple studies have confirmed that the Vineland Adaptive Behavior Scales (Sparrow et al. 1984), is a well-standardized, semi-structured instrument for assessing adaptive behavior. Gillham et al. (2000) reported that autism could be differentiated from both PDD-NOS and nonautistic developmental disorder (DD) with the Socialization and Daily Living scales of the Vineland Adaptive Behavior Scales (Paul et al. 2004; Sparrow et al. 1984). Children with PDD-NOS, when compared with those with autism, differ only in very specific areas, primarily the use of expressive language for communication – particularly syntax and pragmatics – and the areas of adaptive function on which these skills have a direct effect, such as phone use, manners in conversation, and using language to identify and initiate interaction with others (Paul et al. 2004). Studies have compared the Vineland Adaptive Behavior Scales with other Adaptive Behavior measures and found significant between score correlations (Villa et al. 2010). An international study that compared the Scales of Independent
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Behavior (SIB) and the revised Vineland Adaptive Behavior Scales revealed one similar significant factor, demonstrating personal independence for both tests. The summary scores of both tests were found to correlate moderately with IQ as well as with the extent of integration children achieved in their subsequent school placement (Roberts et al. 1993). There are state and local differences in the adoption of specific criteria for deficits in adaptive behavior. However, the development of instruments that provide national norms such as the Comprehensive Test of Adaptive Behavior (Adams 1984) and Vineland Adaptive Behavior Scales (Sparrow et al. 1984) have enabled more normalized and quantifiable guidelines that could be widely used (Carter et al. 1998). Adaptive behavior scales have multiple implications for clinical practice, including assessment, diagnosis, and treatment planning. In contrast to intellectual functioning, adaptive behavior is modifiable (Carter et al. 1998). For all individuals, however, cognitive functioning will set some constraints on the level of adaptive functioning that can be achieved. The adaptive behavior scales are a crucial component of a developmental and diagnostic assessment for children with autism and potential comorbid intellectual disability. Additionally, determining strengths and weaknesses in everyday skills has important implications for intervention planning and family support (Perry et al. 2009) and can inform recommendations for educational and psychotherapeutic interventions for high- and low-functioning individuals (Carter et al. 1996). Adaptive behavior scales have been applied to instructional program planning for disabled preschool and school-aged children, adolescents, and adults (Demchak and Drinkwater 1998). Additionally, the assessment of adaptive behavior can be used as an outcome measure to document the efficacy of intervention programs (Carter et al. 1998).
Future Directions While considerable gains have been made in the development of adaptive behavior scales,
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continued research into their generalizability and cultural sensitivity is imperative, as with all measures. The impact of adaptive behavior scales is widely felt, as they are integral to the diagnosis of intellectual disability and have become a key component of assessment and intervention planning for individuals with autism spectrum disorders. Therefore, it is critical that individuals designing intervention programs set attainable goals across domains of adaptive functioning to lead to increased self-efficacy for all involved (Carter et al. 1998). Further research could explore the connections between outcomes of adaptive behavior scales and successful intervention, providing further guidance for practitioners who design these intervention goals. The importance of intensive intervention in the area of adaptive behavior, particularly for children with autism spectrum disorders, remains clear and continued research into successful interventions is necessary.
See Also ▶ Adaptive Behavior Assessment System, Second Edition ▶ Intellectual Disability ▶ Maladaptive Behavior ▶ Mental Retardation ▶ Vineland Adaptive Behavior Scales (VABS)
References and Reading Adams, G. L. (1984). Comprehensive test of adaptive behavior. San Antonio: Psychological Corporation. Bothwick-Duffy, S. (2007). Adaptive behavior. In J. Jacobson, J. Mulick, & J. Rojahn (Eds.), Handbook of intellectual and developmental disabilities (pp. 279–293). New York: Springer. Brown, L., & Leigh, J. E. (1986). Adaptive behavior scale. Austin: PRO-ED. Bruinicks, R. H., Woodcock, R. W., Weatherman, R. F., & Hill, B. K. (1984). Scales of independent behavior. Allen: DLM Teaching Resources. Carter, A., Gillham, J., Sparrow, S., & Volkmar, F. (1996). Adaptive behavior in autism. Child and Adolescent Pscyhiatric Clinics of North America, 5(4), 945–961.
Adaptive Behavior Scales Carter, A., Volkmar, F., Sparrow, S., Wang, J., Lord, C., Dawson, G., Fombonne, E., Loveland, K., Mesibov, G., & Schopler, E. (1998). The Vineland adaptive behavior scales: Supplementary norms for individuals with autism. Journal of Autism and Developmental Disorders, 28(4), 287. Demchak, M., & Drinkwater, S. (1998). Assessing adaptive behavior. In V. Booney (Ed.), Psychological assessment of children: Best practices for school and clinical settings (2nd ed., pp. 297–322). Hoboken: Wiley. Gillham, J. E., Carter, A. S., Volkmar, F. R., & Sparrow, S. S. (2000). Toward a developmental operational definition of autism. Journal of Autism and Developmental Disorders, 30(4), 269. Harrison, P., & Raineri, G. (2007). Adaptive behavior assessment for preschool children. In B. A. Bruce & R. J. Nagle (Eds.), Psychoeducational assessment of preschool children (4th ed., pp. 195–218). Mahwah: Lawrence Erlbaum Associates. Liss, M., Harel, B., Fein, D., Allen, D., Dunn, M., Feinstein, C., Morris, R., Waterhouse, L., & Rapin, I. (2001). Predictors and correlates of adaptive functioning in children with developmental disorders. Journal of Autism and Developmental Disorders, 31, 219–230. Paul, R., Miles, S., Cicchetti, D., Sparrow, S., Klin, A., Volkmar, F., Coflin, M., & Booker, S. (2004). Adaptive behavior in autism and pervasive developmental disorder- not otherwise specified: Microanalysis of scores on the Vineland adaptive behavior scales. Journal of Autism and Developmental Disorders, 34(2), 223. Perry, A., Flanagan, H., Geier, J. D., & Freeman, N. L. (2009). Brief report: The Vineland adaptive behavior scales in young children with autism spectrum disorders at different cognitive levels. Journal of Autism and Developmental Disorders, 39, 1066–1078. Roberts, C., McCoy, M., Reidy, D., & Crucitti, F. (1993). A comparison of methods of assessing adaptive behavior in pre-school children with developmental disabilities. Australia & New Zealand Journal of Developmental Disabilities, 18(4), 261–272. Sparrow, S. S., & Cicchetti, D. V. (1984). The behavior inventory for rating development (BIRD): Assessment of reliability and factorial validity. Applied Research in Mental Retardation, 5(2), 219–231. Sparrow, S. S., Balla, D., & Cicchetti, D. (1984). Vineland adaptive behavior scales (expanded form). Circle Pines: American Guidance Service. Sparrow, S. S., Cicchetti, D., & Balla, D. (2005). A revision of the Vineland adaptive behavior scales: Survey/caregiver form. Circle Pines: American Guidance Service. Villa, S., Micheli, E., Villa, L., Pastore, V., Crippa, A., & Molteni, M. (2010). Further empirical data on the psychoeducational profile- revised (PEP-R): Reliability and validation with the Vineland adaptive behavior scales. Journal of Autism and Developmental Disorders, 40, 334–341.
ADHD Rating Scale Volkmar, F. (2003). Adaptive skills. Journal of Autism and Developmental Disorders, 33(1), 3. Volkmar, F., Sparrow, S., Goudreau, D., & Cicchetti, D. (1987). Social deficits in autism: An operational approach using the Vineland adaptive behavior scales. Journal of the American Academy of Child & Adolescent Psychiatry, 26(2), 156–161. Volkmar, F., Carter, A., Sparrow, S., & Cicchetti, D. (1993). Quantifying social development in autism. Journal of the American Academy of Child Psychiatry, 32(3), 627–632.
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ADHD Rating Scale Benjamin E. Yerys Center for Autism Research, Children’s Hospital of Philadelphia, Philadelphia, PA, USA Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
Synonyms
▶ Assessment of Functional Living Skills (AFLS)
ADHD¼Attention deficit/hyperactivity disorder; ASD¼Autism spectrum disorder
ADD
Description
▶ Attention Deficit/Hyperactivity Disorder
The ADHD rating scale is an 18-question informant report that screens for symptoms of ADHD in children and teenagers from 5 to 17 years of age (DuPaul et al. 1998, 2016a). This questionnaire has four forms that are separated by setting and age group. There is one form for caregivers (parents and legal guardians) to complete for children (ages 5–10) and another for adolescents (ages 11–17), and there are parallel age forms for teachers. Each question on the ADHD rating scale asks about the presence and frequency of ADHD symptoms. Nine items ask about behaviors related to inattention (“easily distracted”), and nine questions about hyperactive and impulsive behaviors (“interrupts others”). Frequency is rated on a fourpoint scale from “never” to “sometimes” to “often” to “very often.” The ADHD rating scale, fifth edition, incorporates questions about how these symptoms cause problems at home or school in six areas: (1) getting along with family members/school professionals, (2) getting along with other children, (3) completing or returning homework, (4) performing academically in school, (5) controlling behavior in school, and (6) feeling good about himself/herself. The ADHD rating scale provides symptom counts and percentile scores relative to a community sample of parents
Adderall ▶ D-Amphetamine ▶ Dexedrine ▶ Dextroamphetamine
Adding ▶ Reinforcement
Addison-Schilder Disease ▶ Adrenoleukodystrophy
ADHD ▶ Attention Deficit/Hyperactivity Disorder
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and teachers that were recruited from all regions of the United States.
Historical Background The ADHD rating scale has been used as an evaluation tool for ADHD for almost 20 years. The ADHD rating scale fourth edition was created with an explicit goal of matching parent and teacher reports of ADHD symptoms to the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM-IV; American Psychiatric Association 1994) and the text revised version (DSM-IV-TR; American Psychiatric Association 2000). While the DSM-IV and DSM-IV-TR prohibited a co-occurring diagnosis of ADHD when a diagnosis of autism spectrum disorder (ASD) was made, scientific papers studied the treatment of ADHD symptoms in ASD (Aman et al. 2008; Handen et al. 2000; Posey et al. 2007; Research Units of Pediatric Psychopharmacology 2005), as well as the influence of ADHD symptoms on the clinical presentation of ASD (Corbett and Constantine 2006; Corbett et al. 2009; Gadow et al. 2006; Yerys et al. 2009, 2011) using the ADHD rating scale and other comparable measures. The ADHD rating scale fifth edition (DuPaul et al. 2016a) integrated additional questions about impairment due to increased recognition that both symptom and symptom-related impairments are critical for making a diagnosis (Power et al. 2015).
Psychometric Data The ADHD rating scale – fifth edition – has excellent reliability and validity metrics (DuPaul et al. 2016a,b). The normative data for the ADHD rating scale fifth edition was collected on 2079 children from 2079 parents and guardians and 2140 students from 1070 teachers (samples did not overlap). Parent and guardian incomes ranged from 0.89), child’s and rater’s gender (alphas >0.90), ethnicity (alphas >0.90), and assessment language (English vs. Spanish; alphas >0.88). There was acceptable test-retest reliability over a 6-week period for parent ratings on child and adolescent forms for inattention (r ¼ 0.80 and 0.70, respectively) and hyperactivity/impulsivity (r ¼ 0.83 and 0.61, respectively). Test-retest reliability was also acceptable over a 6-week period for teacher ratings on child and adolescent forms for inattention (r ¼ 0.91 and 0.85, respectively) and hyperactivity/impulsivity (r ¼ 0.90 and 0.77, respectively). The impairment ratings introduced in the fifth edition showed better test-retest reliability for both raters in the child form (correlations range from 0.62 to 0.85) than in adolescents (correlations range from 0.06 to 0.81). Criterion validity was also assessed in comparison to the Connors rating scales (Conners 2008), and validity coefficients were acceptable (correlations ranged from 0.81 to 0.89). The ADHD rating scale – fifth edition – also showed excellent factorial validity with the 2-factor structure (inattention and hyperactivity/impulsivity) being the optimum structure. All critical goodness-of-fit statistics were met (comparative fit index and Tucker-Lewis index >0.91 and root mean square error of approximation R. In a traditional classical conditioning arrangement, a neutral stimulus (e.g., a flashing light) that has no previous history of being paired with the occurrence of the reflex (e.g., an eye blink) is presented along with a stimulus that elicits the reflex (e.g., a puff of air). The stimulus that elicits the reflex response is known as the unconditioned stimulus as it does not require a learning history to elicit the reflex or unconditioned response. After repeated pairings of the neutral stimulus with the unconditioned
Behaviorism
stimulus, presentation of the neutral stimulus will come to elicit the unconditioned response without presenting the unconditioned stimulus. For this example, presenting the flashing light prior to the puff of air over multiple trials will eventually lead to the flashing light eliciting eye blinking without presenting the puff of air. This arrangement is represented as: Neutral stimulus ! unconditioned stimulus ! unconditioned response: With continued pairing of the neutral stimulus and the unconditioned stimulus, the neutral stimulus, now a conditioned stimulus, comes to control the occurrence of the unconditioned response, now called a conditioned response. This arrangement is represented as: Conditioned stimulus ! conditioned response: When the conditioned stimulus is presented, the response follows as if the unconditioned stimulus had been presented. In this instance, behavior is elicited, that is, behavior is caused by the occurrence of an external stimulus. Continued presentation of the conditioned stimulus without the presentation of the unconditioned stimulus will gradually lead to reductions in the conditioned response. This process is termed extinction. Operant conditioning occurs when a behavior comes under the control of consequences that follow it. The operant conditioning paradigm can be described in the three-term contingency: Antecedent ! Behavior ! Consequence: An antecedent is a stimulus event that precedes the occurrence of behavior where as a consequence is a stimulus event that follows the occurrence of the behavior. A behavior is anything an organism does and results in a change in the environment. During operant conditioning, an organism’s behavior is subject to consequences that lead to increases (reinforcement) or decreases (punishment) in the future occurrence of that behavior. Along with these increases, antecedent stimulus events come to serve as discriminative
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stimuli for the likelihood of reinforcement. That is, environmental events signal the availability of reinforcement if the organism engages in a particular repertoire of behavior. Skinner’s work on shaping is instrumental to the development of learned repertoires of behavior. Shaping involves reinforcement of closer and closer approximations to the target behavior. For example, a rat in an operant chamber may be required to push a lever to access food (a reinforcer). As the rat moves about the cage and orients to the lever, a click is followed by the delivery of the reinforcer. As the rat begins to orient toward the lever more frequently, reinforcement is delivered and then withheld. This withholding is called extinction. Extinction leads to variability in responding where the rat may now touch the lever which would be followed by reinforcer delivery. This process continues until the rat reliably presses the lever. Shaping, extinction, and schedules of reinforcement serve as the basis for our understanding of the development of behavioral repertoires. Molecular Versus Molar Behaviorism The contrast of molar and molecular behaviorism represents the focus of attention in a functional analysis. Those who support a molecular view of behaviorism support looking at the moment to moment changes in behavior and analyze the direct antecedents to and consequences of those behaviors. This is a view that is in line with Skinner’s analyses of behavior in his basic experimental work. A molar perspective looks at behavior over time and views behavior in the context of other, longer sequences (chains) of behavior. That is, when describing an event, one needs to observe the behavior to completion as opposed to a moment in time. Lever pressing is best understood as the duration of engaging in lever pressing and not in the instant where the lever is pressed. The molar view contrasts with the molecular view in terms of how responses are strengthened. The molecular view focuses on increases in response rates as an indicator of response strength. In contrast, the molar view focuses on increased allocation of responding to one or another behavior in a choice paradigm. That is, all behavior requires
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choices between responses and the selection of one behavior over another is a function of reinforcement. There is ongoing discussion among behavior analysts as to which perspective best explains behavioral phenomena. Applications Experimental Analysis of Behavior – The experimental analysis of behavior has a primary focus on basic research, that is, research on human and nonhuman organisms whose purpose is to develop greater understanding of behavioral principles. This, in turn, enhances our understanding of conditions that reliably predict their occurrence. The experimental analysis of behavior is responsible for our understanding of reinforcement, schedules of reinforcement, and their impact on behavior, punishment, discriminative stimuli, and choice. Basic behavioral principles demonstrated in laboratory settings serve as the basis for procedures used in applied settings. Applied Behavior Analysis – Applied behavior analysis focuses on the application of behavioral principles to socially important behavior (Baer et al. 1968). Applied behavior analysis limits its scope of focus to the improvement of socially important behavior. This is not a limitation of applied behavior analysis, but rather, the need to focus on those behaviors brought to our attention as needing improvement. Methods for assessing the environmental variables that control behavior are consistent between the experimental analysis of behavior and applied behavior analysis. Applied behavior analysis practices include application of reinforcement contingencies, stimulus control procedures, shaping, chaining, and task analysis and are applied to various populations and areas of practice. Applied behavior analysts have formed an accrediting body and established criteria for university coursework and supervision that leads to certification as a board-certified behavior analyst.
Behaviorist Theory
and application of these principles to socially significant behaviors. Extensions to complex human behaviors continue to fields such as pharmacology, neuroscience, performance management, gun safety, interventions for addiction and gambling, and treatment for individuals with neurodevelopmental disorders including autism spectrum disorders.
See Also ▶ Applied Behavior Analysis (ABA) ▶ Classical Conditioning ▶ Functional Analysis ▶ Operant Conditioning ▶ Punishment ▶ Reinforcement
References and Reading Baer, D., Wolf, M., & Risley, T. (1968). Some current dimensions of applied behavior analysis. Journal of Applied Behavior Analysis, 1, 91–97. Baum, W. (2005). Understanding behaviorism. Malden: Blackwell Publishing. Cooper, J. O., Heron, J., & Heward, W. H. (2007). Applied behavior analysis (2nd ed.). New York: Pearson. Malone, J. C. (2004). Modern molar behaviorism and theoretical behaviorism: Religion and science. Journal of the Experimental Analysis of Behavior, 82, 95–102. Skinner, B. F. (1974). About behaviorism. New York: Knopf.
Behaviorist Theory Susan A. Mason Services for Students with Autism Spectrum Disorders, Montgomery County Public Schools, Silver Spring, MD, USA
Definition Future Directions Behaviorists continue to evaluate and extend our understanding of the basic principles of behavior
Behaviorism is widely used to refer to the philosophy of a science of behavior. More specifically, within the field of psychology, behaviorism
Behaviorist Theory
explains responses of humans and other animals only in relation to environmental stimuli and observable and measurable responses to those stimuli. There are various forms of behaviorism: structuralism; behaviorism that uses cognition as causal factors (e.g., cognitive behavior modification); social learning theory, in addition to methodological behaviorism; and radical behaviorism. In his text, About Behaviorism, B. F. Skinner (1974) wrote: “Behaviorism is not the science of human behavior, it is the philosophy of that science” (Cooper et al. 2007).
Historical Background Prior to the introduction of behavioral science, the field of psychology consisted of the study of states of mind and mental processes. There are four historical building blocks of behaviorism: classical conditioning as presented by Pavlov, Thorndike’s law of effect, Watson’s experiments with human conditioning, and Skinner’s conceptualization of operant conditioning. The development of behaviorism is largely attributed to John B. Watson who wrote a seminal article in 1913 in which he argued that psychology should be viewed as a purely objective experimental branch of natural science. As such, the goal should be to study the prediction and control of behavior through direct observation of the relationship of environmental stimuli and resulting evoked responses. This relationship became known as the stimulus-response (S-R) paradigm, and Watson proposed that it could be used to predict and control human behavior in a way that would allow practitioners to improve performance in areas such as education, business, and law. Although Watson later made exaggerated claims about the ability to predict and control human behavior, he is recognized for providing a strong case that the study of behavior as a natural science is on par with physical and biological sciences (Cooper et al. 2007, p. 9). The premise that the study of behavior is a science was further expanded upon in a work by B.F. Skinner who was interested in providing scientific accounts of all behavior. Skinner’s publication of The
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Behavior of Organisms (1938, 1966) summarized his laboratory research and gave rise to two kinds of behavior, respondent and operant. Respondent behavior is behavior that is elicited by a stimulus; it is reflexive and essentially involuntary. Operant behavior is behavior that is influenced by stimulus changes (consequences) that follow the behavior. Skinner argued that the uniqueness of operant behavior warranted its own field of study (see also ▶ “Behavior Analysis”). Skinner conducted thousands of laboratory investigations that allowed him to systematically study functional relationships of antecedent stimuli, responses, and reinforcement of those responses in a controlled environment. Skinner’s methodology resulted in the foundation of behavior analysis as we know it today.
Current Knowledge Behaviorism has evolved into many areas of study. It is most widely represented in the disciplines of experimental analysis of behavior and applied behavior analysis. Within the field of applied behavior analysis, methods of behaviorism have been used to study and affect services in the areas of verbal behavior, public safety, organizational behavior, education, special education, habit reversal, behavioral medicine, cognitive behavior modification, and therapy including derived relational responding as it is related to relational frame theory and acceptance and commitment therapy, social learning theory, functional analysis and assessment of behaviors, and more. The foundation of behaviorism continues as a philosophy of a science of behavior.
Future Directions As previously noted, behaviorism has been the underpinning for both experimental analysis of behavior and applied behavior analysis. As such, its methodology can be used to study many branches of behavior as long as the behaviors can be operationally defined and observable. As noted by Pear and Eldridge (1984, p. 459),
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“Several alternatives to the operant respondent framework have been proposed, but there is no indication that any of these currently has comparable organizing power. Until such a paradigm is put forth, therefore, we see modification of the operant respondent framework, rather than its elimination, as the more fruitful approach.”
Bell-Shaped Curve Structuralism. Retrieved from http://web.mst.edu/ ~psyworld/structuralism.htm?pagewanted¼all Sulzer-Azaroff, B., & Mayer, G. R. (1977). Applying behavior analysis procedures with children and youth. New York: Holt Rinehart and Winston. Sulzer-Azaroff, B., & Mayer, G. R. (1991). Behavior analysis for lasting change. New York: Holt, Rinehart and Winston. Vargas, J. S. (2009). Behavior analysis for effective teaching. New York: Routledge.
See Also ▶ Behavior Analysis ▶ Behavior Modification ▶ Behaviorism
Bell-Shaped Curve ▶ Normal Curve
References and Reading Cognitive behavior modification. Texas guide for effective teaching cognitive behavior modification. Retrieved from http://cdd.unm.edu/swan/autism_course/mod ules/behavior/cbm/index.htm Cooper, J. O., Heron, T. E., & Heward, W. L. (2007). Applied behavior analysis (2nd ed.). New Jersey: Pearson Merrill Prentice Hall. Dymond, S., & Roche, B. (Eds.). (2013). Advances in relational frame theory: Research and application. Oakland: New Harbinger Publications, Inc. Harris, R., & Hayes, S. C. (2009). ACT made simple: An easy-to-read primer on acceptance commitment therapy. Oakland: New Harbinger Press, Inc. Hernandez, P., & Ikkanda, Z. (2011). Applied behavior analysis: Behavior management of children with autism spectrum disorders in dental environments. The Journal of the American Dental Association, 143(3), 281–287. Methodological behaviorism. Retrieved from www.ucm. es/info/psi/docs/journal/v6_n2_2003/art133.pdf Pear, J. J., & Eldridge, G. D. (1984). The operantrespondent distinction: Future directions. Journal of Experimental Analysis of Behavior, 42(3), 453–467. https://www.questia.com/library/psychology/other-typesof-psychology/behaviorism Radical behaviorism. Retrieved from www.behaviorology. org/pdf/PhilPaperOriginsBk.pdf Redd, W. H., Porterfield, A. L., & Andersen, B. L. (1979). Behavior modification. New York: Random House. Rehfeldt, R. A., Barnes-Holmes, Y., & Hayes, S. C. (2009). Derived relational responding applications for learners with autism and other developmental disabilities. Oakland: New Harbinger Publications, Inc.. Skinner, B. F. (1974). About behaviorism. New York: Knopf. Social learning theory. Retrieved from http://teachnet.edb. utexas.edu/~Lynda_abbot/Social.html
Benadryl ▶ Diphenhydramine HCl
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Bender Visual-Motor Gestalt Test II
Benadryl ® Children’s Allergy Perfect Measure™ ▶ Diphenhydramine
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Benchmark ▶ Criterion
Benchmark Data ®
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▶ Normative Data
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Benchmarks Benadryl ® Children’s DyeFree Allergy [OTC] ▶ Diphenhydramine
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Bender ▶ Bender Visual-Motor Gestalt Test II
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Bender Visual-Motor Gestalt Test II Mikle South and Jessica Palilla Departments of Psychology and Neuroscience, Brigham Young University, Provo, UT, USA
Synonyms Bender; Bender-Gestalt II; BG-II
Benadryl ® Itch Stopping [OTC] ▶ Diphenhydramine
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Description The Bender Visual Gestalt II testing kit includes 16 stimulus cards that are separated into two tests. These stimulus cards include an improved version of the original nine designs and new cards that were constructed to be more fitting for the age range covered by the test. All of the stimulus cards have been mechanically drawn to increase the clarity of the design.
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The administration of the Bender-Gestalt II is considered to be user-friendly and relatively easy. It occurs in two phases: the copy phase and the recall phase. During the copy phase, the examinee is presented with the age-appropriate stimulus cards one at a time and instructed to copy each design onto a blank, white sheet of paper using a No. 2 pencil. In the recall phase, the examinee is instructed to draw as many of the designs as they can from memory onto a new sheet of paper. While there are no time limits for any of the designs or phases, the examiner should begin timing immediately following the presentation of the first design, in order to keep track of the amount of time needed for the examinee to complete each separate design. The examiner should also pay attention to behavioral and physical characteristics of client. Such observation can help determine if poor reproductions of a design are the result of impaired motor or perception abilities. To score the Bender-Gestalt II, a new Global Scoring System has been outlined. This scoring system evaluates the examinee reproduction of designs at the copy and recall phases and rates the quality on a five-point scale. A score of 0 is given to designs that have no resemblance to the design or are the product of random drawing or scribbling. A score of 4 is given to those designs that are nearly perfect in their resemblance to the design. This scoring system is considered to be fairly simple as specific examples of the Global Scoring System are provided in the manual. However, it requires rigid adherence to the scoring examples and much stricter than previous scoring methods.
Historical Background The Bender Visual Motor Gestalt Test was first published in 1938 by the American Orthopsychiatric Association under the title of “A Visual Motor Gestalt Test and Its Clinical Use.” It evolved from Max Wertheimer’s early studies of a Gestalt theory of perception. Lauretta Bender adapted nine of Wertheimer’s designs and put them on cards in order to understand the
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gestalt experiences of psychiatric patients. Specifically, the test was designed as a screening measure to test the ability of the perceptual system to organize visual stimuli into configural wholes, as a screening measure for neuropsychological damage. It quickly grew in popularity because it was brief and fairly simple to score and administer. Since its original development, the test has undergone many revisions that have largely focused on changes in interpretation and scoring procedures. Awide variety of scoring procedures have been developed over the years using the original Bender-Gestalt Test. Among the most notable are the Koppitz’s Developmental Bender Scoring System, published in 1964 as The Bender-Gestalt Test for Young Children, and Max Hutt’s Scoring System. Under Koppitz scoring system, 30 discrete errors are scored if present, with each design ranging from 2 to 4 possible errors. This scoring procedure was designed to measure neuropsychological impairment and the developmental maturation of children. Hutt’s Scoring System, on the other hand, was designed to use the BenderGestalt Test as a projective personality assessment for adults. It scored tests based on the frequency and severity with which an examinee deviated from protocol. Koppitz’s original scoring system was adapted after her death by Cecil Reynolds (2007). Notable psychometric problems with the original version limit interpretation of data from studies utilizing that test. Several studies in the earliest history of autism research utilized the original version of the Bender, but in the context of psychometric problems with the test as well as the lack of standardized diagnostic criteria for autism, these studies are not considered relevant. The test was included in Norcross et al. (2006) list of “Discredited Psychological Treatments and Tests” based on ratings by a large expert panel, either for use in screening neuropsychological impairment or personality function. This presumably referred to the original version of the test and its uses, rather than to the revised BenderGestalt II. The second edition of the Bender Visual-Motor Gestalt Test was published in 2003. This new
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edition is a product of many years of analysis with the first edition of the test, as well as modern research in the fields of psychological testing and test construction. This comprehensive revision added four easier items and three harder items in order to increase the measurement scale. In other words, it lowered the “floor” of the test and created a higher “ceiling” so as to better describe those individuals who score on the extremes of the spectrum.
Psychometric Data The Bender-Gestalt II was normed from a stratified, random sampling of 4000 subjects that comparatively matched US census data from the year 2000. T-sores, percentile ranks, confidence intervals, and classification labels are available for subjects ages 4 to 85+ years. The psychometric properties of the test are fairly strong. Interrater reliability is reported at a range of .83 to .84 for the copy phase and .94 to .97 for the recall phase. A validity of .91 was found using split-half procedures. Over a 2–3 week interval, test-retest reliability is between .80 and .88 for the copy phase and .80 to .86 for the recall phase. Construct validity for the Bender-Gestalt II has been supported by moderate correlations to other measures. For example, it has moderate correlation of .65 with the Beery-Buktenica Developmental Test of Visual-Motor Integration and a correlation of .75 with the Perceptual Organization factor on the WISC-III.
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considered a screening device as it is limited to severe forms of brain damage. Allen and Decker (2008) found significant differences to indicate impaired performance, after controlling for IQ, in a moderately sized sample of children (mean age ¼ 11) diagnosed with attention-deficit/hyperactivity disorder compared to a healthy comparison group, suggesting possible utility as a measure of function in other disorders autism. Effect sizes were very small, however. One study (Volker et al. 2010) has used the Bender Gestalt II to analyze the visual-motor skills of individuals with autism spectrum disorders. In demographically matched subsamples of ASD and healthy children (mean age ¼ 9.7; n ¼ 27 for each group), and after statistical control for IQ, a high-functioning autism spectrum disorder group scored lower than the comparison group on the two tests most sensitive to motor function (the copy and supplemental motor scales). There appears to be substantial justification for continued investigation of atypical motor function in autism. A recent review by Dowd et al. (2010) notes the potential utility of motor function as (a) a diagnostic marker of autism, (b) an endophenotype of autism, and (c) a marker of severity of impairment, including social-communicative impairment. The BenderGestalt II could be used in future studies to characterize basic motor deficits and possibly higher order problems with visuomotor planning and organization.
See Also Clinical Uses The Bender Gestalt II is designed to assess the visual-motor integration abilities of children and adults from 4 to 85+ years of age. It is also designed to be used as a test of motor memory in children and adults ages 5 to 85+. It has been used to identify brain dysfunction in children and adults, and discern emotional problems in children. Generally, if the Bender-Gestalt II is being used to assess for brain damage, it should be
▶ Bruininks-Oseretsky Test of Motor Proficiency
References and Reading Allen, R. A., & Decker, S. L. (2008). Utility of the bender visual-motor gestalt test-second edition in the assessment of attention-deficit/hyperactivity disorder. Perceptual and Motor Skills, 107, 663–675. Brannigan, G. G., & Decker, S. L. (2003). Bender visualmotor gestalt test (2nd ed.). Itasca: Riverside Publishing.
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682 Brannigan, G. G., Decker, S. L., & Madsen, D. H. (2004). Innovative features of the Bender-Gestalt II and expanded guidelines for the use of the Global Scoring System (Bender visual-motor gestalt test, Second Edition Assessment Service Bulletin No.1). Itasca: Riverside Publishing. Dowd, A. M., Rinehart, N. J., & McGinley, J. (2010). Motor function in children with autism: Why is this relevant to psychologists? Clinical Psychologist, 14, 90–96. Norcross, J. C., Koocher, G. P., & Garofalo, A. (2006). Discredited psychological treatments and tests: A Delphi poll. Professional Psychology: Research and Practice, 37, 515–522. Reynolds, C. R. (2007). Koppitz-2: The Koppitz developmental scoring system for the Bender-Gestalt Test. Austin: Pro-Ed. Volker, M. A., Lopata, C., Vujnovic, R. K., Smerbeck, A. M., Toomey, J. A., Rodgers, J. D., et al. (2010). Comparison of the Bender Gestalt-II and the VMI-V in samples of typical children and children with highfunctioning autism spectrum disorders. Journal of Psychoeducational Assessment, 28, 187–200.
Bender, Lauretta Fred R. Volkmar Child Study Center, Irving B. Harris Professor of Child Psychiatry, Pediatrics and Psychology, Yale Child Study Center, School of Medicine, Yale University, New Haven, CT, USA
Bender, Lauretta
Major Honors and Awards In 1955, Dr. Bender was the recipient of the Adolf Meyer Memorial Award from the American Psychiatric Association for her work on severe psychiatric disturbance in children.
Landmark Clinical, Scientific, and Professional Contributions Loretta Bender was an early pioneer in the study of learning disabilities and severe psychiatric disturbance in children. Highly active at the professional level both as a clinician and researcher, she was involved in development of various approaches to treatment and to theories of childhood psychopathology. The Bender-Gestalt test remains in use today. Her view of learning disabilities was based on a theory related to discrepancies in areas of maturation, and she emphasized the confluence of various problems in children with learning problems that reflected their common origins. She also worked in the area of language difficulty and conducted some of the early work on reading disability. Her work was conducted at a time when childhood schizophrenia/childhood psychosis was used to describe all severe neuropsychiatric disturbance, i.e., before the distinction of autism as a distinctive diagnostic category was made.
Name and Degrees Lauretta Bender MD • B.A. (1922) University of Chicago • M.A. (1923) University of Chicago • M.D. (1926) State of University of Iowa
Major Appointments (Institution, Location, Dates) Bender held positions at the Hospital of the University of Chicago, the Boston Psychopathic Hospital, the University of Amsterdam, the Johns Hopkins University Hospital, and Bellevue Hospital in New York, as well as at the University of Maryland.
Short Biography A native of Butte, Montana, Lauretta Bender coped with a significant learning difficulty but persevered to become the valedictorian of her high school class. She received her B.A. (1922) and M.A. (1923) from the University of Chicago. She received an M.D. from the State of University of Iowa (1926). Bender held positions at the Hospital of the University of Chicago, the Boston Psychopathic Hospital, the University of Amsterdam, the Johns Hopkins University Hospital, and Bellevue Hospital in New York as well as at the University of Maryland. Bender was active in many ways at the professional level. She was Director of Research of the new Children’s Unit
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at Creedmoor State Hospital in the 1950s and while there conducted much of her work with severely impaired children. Her first husband, the psychiatrist Paul Schuler (1886–1940), tragically died after a few years of marriage. She married Henry B. Parkes, a professor at New York University, in 1954.
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the third of three ingredients critical to the creation of a trust: (1) property, usually money, placed in a trust administered by (2) a trustee for the benefit of (3) a beneficiary (restatement). A trust cannot exist without a beneficiary (Bogert, 121). On occasion, American courts refer to the beneficiary by the French phrase cestui que trust. A trust may have multiple beneficiaries. The trust documents dictate when and how much of the trust property a beneficiary will receive.
▶ Bender Visual-Motor Gestalt Test II
References and Reading Bender, L. (1969). A longitudinal study of schizophrenic children with autism. Hospital & Community Psychiatry, 20(8), 230–237. Bender, L. (1971). Alpha and omega of childhood schizophrenia. Journal of Autism and Childhood Schizophrenia, 1(2), 115–118. Bender, L. (1973). The life course of children with schizophrenia. American Journal of Psychiatry, 130(7), 783–786. Bender, L. (1974). The family patterns of 100 schizophrenic children observed at Bellevue, 1935–1952. Journal of Autism and Childhood Schizophrenia, 4(4), 279–292.
Who May Be a Beneficiary Any legal entity, including individuals or corporations, may be a beneficiary (Bogert, 125). But, only the entities intended by the creator of the trust, or settlor, to benefit from the trust may be a beneficiary. The settlor may be a beneficiary, and even the trustee may be a beneficiary as long as he or she is not the sole trustee. Many trusts have multiple beneficiaries who can be named individually or can be designated as a “class,” such as all of the children of a particular person.
John W. Thomas Independent Educational Consultant, Durham, NC, USA Quinnipiac University School of Law, Hamden, CT, USA
Rights of a Beneficiary A beneficiary’s interest in a trust varies according to the type of trust created (Dietz). In a “fixed trust” in which the benefits are spelled out precisely by the trust documents, the beneficiary has an ownership interest in the trust proceeds. If the trust is a “discretionary trust,” meaning that the trustee has discretion as to when and how much of the trust property to give to the beneficiary, a beneficiary’s interest is subject to the determination of the trustee. A beneficiary may refuse the benefits of the trust by disclaiming her right to them. The disclaimer may be implied by conduct “inconsistent with a trust for his” (Bogert, 170) ASD-related issues.
Definition
See Also
Basic Definition A beneficiary is a person for whose benefit property is placed in trust. The beneficiary is
▶ Discretionary Trust ▶ Support Trust ▶ Trust
Bender-Gestalt II ▶ Bender Visual-Motor Gestalt Test II
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References and Reading Bogert, G. T. (1987). Trusts (6th ed.). St. Paul: West Publishing. Bogert, G. T., & Bogert, G. B. (1987). The law of trusts and trustees (1987). St. Paul: West Publishing. Garner, B. A. (Ed.). (2009). Black’s law dictionary (9th ed.). St. Paul: West Publishing. Laura Dietz, L., Lindsley, W., Martin, L., Payne, A., Shampo, J., & Surette, E. C. (1998–2011). Trusts (American Jurisprudence). St. Paul: West Publishing.
Benhaven Residential Services Cynthia Beesley and Linda Rumanoff Simonson Benhaven, Inc., North Haven, CT, USA
Definition Benhaven is an agency that provides educational, day program, and residential supports to adults and adolescents with autism and developmental disabilities in New Haven, Connecticut, and surrounding towns. The residential services division of the program, which is the topic of discussion in this entry, entails a variety of programs including in-home consultation, family respite, professional parent settings, and group homes. In each setting, the emphasis is on individualized programming and is driven by the principles of positive behavioral support (Koegel et al. 1996; Lavigna and Donnellan 1986; Smull and Harrison 1992; Structured Teaching 2010).
Historical Background Benhaven’s residential services developed in response to the overwhelming need for such services in the early 1970s. Amy Lettick, the director and founder of Benhaven School and the mother of an autistic son, Ben, recognized that need. She believed that in order for many children with autism and their families to function optimally, the continuity and consistency of 24-h programming and care was necessary. In addition, during
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the late 1960s and early 1970s, quality educational programs were few and far between, so the development of a residential program allowed Benhaven to accept many students in need. Benhaven School was established in 1967, followed in 1972 by the purchase and development of one home, the beginning of Benhaven’s residential program. With many more applicants looking for both school and residential support, more homes were opened, and by 1991, a total of 7 homes served 34 residents. In 1990, Benhaven introduced its Shared Living Program. This program gives people the opportunity to live in a typical home with the skilled support of licensed families and individuals. In 1996, the Individual and Family Support Services Program was created to provide support to children and families in their own home. Benhaven has had a long-standing supportive relationship with the Yale Child Study Center, particularly with Dr. Fred Volkmar and the late Dr. Donald Cohen. In the mid-1980s and early 1990s, under the direction of its (now retired) Executive Director, Larry Wood, Benhaven underwent a major shift in its understanding of state-of-the-art approaches to teaching and supporting people with autism. Benhaven’s administrative, teaching, and managerial staff received extensive training in structured teaching practices, functional behavioral support, and team building, conducted by expert leaders in the field, such as Dr. Gary LaVigna and Dr. Anne Donnellan. This training changed how Benhaven provides services to its program participants and training to its staff. This rigorous approach to staying informed about best practices in the field of autism has continued at Benhaven throughout the years. Under the leadership of Benhaven’s new Executive Director, Kathryn DuPree, plans for further residential development to meet the growing need for adult residential services are underway.
Rationale or Underlying Theory The rationale for Benhaven’s approach to residential services is that, based on experience,
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individuals with autism have a unique set of learning characteristics that respond well to a visual and structured approach to teaching. Positive behavioral intervention is an established approach that begins with understanding the factors that drive behavior through a process of functional analysis of problem behavior (Fox et al. 2000; O’Neill et al. 1997). When functions of behavior can be identified, an individualized behavior support plan is designed and implemented. The majority of interventions that make up the plan are preventative and positive in nature. They are aimed at maximizing a comfortable physical and social environment for the individual, recognizing and preventing triggers to challenging behavior, and teaching alternative, appropriate means of communicating needs and wishes. The plan also includes carefully planned interventions for managing challenging behavior when it does occur. This approach requires having knowledgeable, educated, and well-trained leadership as well as direct support staff and also requires that supporters’ professional development plans include goals to establish positive relationships with the residents in their care. The most important aspect of the relationship between support provider and the person receiving support is respect. Respect is the cornerstone upon which all support services should be rendered. The roles and tasks assumed by the provider of support will not be as effective if this foundation of respect is missing. The support provider’s respect should not have to be earned. It must be there unconditionally. Part of the challenge of this work is learning how to provide choices, honor preferences, and respect the person’s individuality while satisfying the professional responsibilities associated with helping the person stay safe and healthy, helping the person learn, helping the person gain new experiences, and helping the person avoid disappointment or embarrassment. Responding to this challenge is what makes the work more of an art than a science. Balancing the role of supporting what the person wants with the temptation to control because “teacher knows best” can be frustrating, and supporters may find themselves too far in one direction or the other. Approaching the
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work from the foundation of respect allows continual assessment of that balance while struggling to carry out multiple and sometimes conflicting roles. Constantly striving to learn and do better is part of the job.
Goals and Objectives The approaches described here are intended to address issues that exist for people with autism as a result of the inability to communicate or relate to the social world in a typical way (American Psychiatric Association 2000). Challenging behavior is a frequent problem in autism and can be highly interfering to a person’s relationship with one’s family, to relationships with peers and teachers, to learning, and to one’s ability to develop functional life skills and experience a broad range of opportunities. Challenging behavior is most often the result of a person’s inability to communicate needs, desires, discomfort, and refusal, and many of the other things a person typically resolves through the use of spoken or symbolic language. Many people with autism, due to the social delay inherent in the disability from infancy, have not learned the vital process of getting needs met through social interactions with others (Volkmar and Wiesner 2009). In the absence of language or another way to express one’s needs, maladaptive behavior evolves as an often successful means to quickly and effectively make choices, refuse to participate, or obtain something desired. Other characteristics of autism, for example, distractibility, or need for sameness, can interfere with learning and compound a cognitive delay. Heightened senses in many people with autism create other barriers to participation. For example, a sensitivity to noises may limit the physical and social environments in which a person may be comfortable, in this way narrowing a person’s experiences and learning opportunities. Additionally, each person with autism has a unique set of characteristics and a distinctive learning style. Given all these factors, supporting and teaching people with autism in a residential setting requires a multilayered approach.
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One objective in this process is the thoughtful establishment of an environment that makes sense for an individual, taking into consideration both the physical aspects of a setting, such as the amount and type of space that are important, and the social environment, involving the number of others living and working in the setting. Another extremely important factor is the establishment of a means of communication for each individual, including the teaching of the skills necessary to utilize that communication method. A behavior support plan is a vital component of a residential program, which guides instructors in the prevention and response to whatever challenging behaviors may exist, while teaching appropriate alternatives to behavior. Skill development is another objective of the program. Functional skills are taught in the context of caring for one’s home and oneself while living with cognitive, communication, and behavioral challenges. Skills to participate in the community, including recreational as well as functional and employment settings, are also essential. Other goals of the program include establishing and/or building a person’s relationship with his or her family and friends and attending to a person’s health and medication needs. In short, the responsibility is to teach skills and provide opportunities to empower and equip residents with a range of appropriate choices and productive control that will enhance their quality of life.
Treatment Participants Those who are most likely to benefit from treatment at Benhaven’s residential program are individuals whose primary diagnosis fits the DSM-V diagnostic criteria for autism spectrum disorder and others with cognitive delays and behavioral symptoms similar to those experienced in autism. This is the case because treatment is designed to address the specific learning characteristics of people with autism. However, the strategies described here could be beneficial to others experiencing learning or behavioral challenges, since the primary characteristics involve individualized and structured teaching. While Benhaven’s residential program
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currently serves adolescents and adults, the treatment procedures are suitable for people with developmental disabilities of any age. Many of the program participants have additional psychiatric diagnoses that are addressed through consultation with outside providers. Because the treatment emphasis is on autism and symptoms of other developmental delay, those with other primary psychiatric diagnoses without cognitive delay would not be well suited to treatment at Benhaven’s residential program.
Treatment Procedures All treatments begin with the process of conducting assessments, which is the first step to designing an individualized program for a person with autism. Functional Behavioral Assessment and Behavior Support Plan Development A functional behavioral assessment is a means of identifying the important factors that contribute to the existence of challenging behavior in a person with autism. This process involves interviewing the team of people supporting the individual to determine what factors probably cause and maintain the target behavior(s). This process identifies likely functions of that behavior and tests these hypotheses through observation of the individual in his or her life settings to determine whether the identified functions are supported. Once functions of behavior are established, a series of strategies are developed in the form of an individualized behavior support plan. This plan lays out components to address the target behavior(s) from different angles. Preventative strategies are designed to predict and prevent behavior from happening through management of antecedent settings and events. For example, it might direct instructors to give a person a “heads up” that a difficult transition is coming. Rather than interrupting the person relaxing with a magazine and announcing “It’s time to do your laundry,” a situation that is likely to cause a target behavior to occur, the instructor may be directed to provide the individual with a verbal and visual countdown. The instructor will
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verbally alert the person about the change in activity at 1-min intervals and will use a visual timer to more concretely help the person recognize the passage of time. The behavior support plan also contains teaching and coping strategies. Coping strategies are means for a person to handle the challenges of the environment or demands in the daily schedule. For example, rather than striking out in frustration when the environment is too noisy or overwhelming, a person may be taught to simply leave the area for a quieter environment within his home. Or, a person may be taught to utilize calming techniques, such as music or deep breathing, when he senses himself becoming overwhelmed. He may also be taught a means to communicate his need, for example, saying “it’s too loud” as a means to ask an instructor to help find a quieter place. Teaching strategies teach a person to engage in alternative actions to the target behavior. These alternatives are meant to be positive and equally efficient ways to get the same needs met. When negative behavior is the result of inability to communicate in a functionally appropriate manner, teaching strategies will involve teaching a person a means to communicate something he was not able to initiate on his own before, such as a need, a desire to change activities or environment, or to refuse something. Teaching strategies also involve teaching a person skills to accomplish things that may have only been accomplished before through behavior, for example, learning to ask for a break rather than hitting the instructor to indicate frustration with the activity. Teaching strategies can also be a means to help someone predict events and have a sense of order in life through the use of visual schedules and scripts. This will be described further below. Finally, behavior support plans contain strategies for providing consequences to challenging behavior and for responding to challenging behavior if and when it does occur. The term “consequence” refers to the events that occur immediately following the occurrence of a behavior, whether positive or negative. The way instructors respond to the behavior has a major impact on whether the behavior is strengthened or weakened. The goal, of course, is to strengthen positive behaviors and
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weaken negative ones, so the strategies are designed to do just that. The consequence for engaging in a positive behavior, such as utilizing communication to ask for help, is to provide the help and offer praise for the communication. Done consistently, this will strengthen the communication response. Done inconsistently, for example, by saying, “I’m glad you asked for help, but I think you can do that on your own,” will result in a weak learning and erratic use of the skill. Concurrently, the response to negative behavior must be clearly designated and consistently applied. If the function of screaming at the instructor is to end the activity, then responding to screaming by ending the activity will maintain that behavior. Rather, the plan might designate that breaks are initially built in after very short work periods, preempting the person from becoming frustrated and acting out. The resident is shown how to ask for a break, perhaps by sign, picture, or words, and then is guided to do that before any frustration sets in. He is immediately rewarded with his break for engaging in the positive behavior. Screaming would be ignored, while the person was helped to utilize communication to ask for a break. Reactive strategies are designed to manage a crisis situation and direct instructors in how to respond when challenging behavior does occur. These response strategies are not intended to teach the person any new skills. The teaching comes from all the prior components – antecedent, teaching, coping, and consequence strategies. Reactive strategies are an important part of the plan because it is desirable and necessary to train instructors in proper, safe response techniques rather than rely on improvisation in the heat of the moment. Techniques that have proven to be the most effective and safe for that individual are carefully designed and trained. Reactive strategies often include some kind of debriefing for the individual and for the instructors to help everyone get back on track once the incident is over. Skills Assessment and Individualized Program Development Another important component of teaching individuals with autism in a residential setting is the creation of an individualized learning plan.
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While each person’s program is specifically tailored to his or her specific learning needs, there are some learning style characteristics that many people with autism share and which form the basis for a learning program for a person living in a residential setting. Examples of some broad learning characteristics in autism include difficulties with changes in routines and schedules, distractibility, trouble organizing and filtering out relevant information, difficulty with auditory processing, and poor understanding of social cues. It is important to design a learning environment and individualized program that takes these characteristics into consideration while also factoring in the unique learning style of the individual himself. Components of a residential program that utilize a structured approach to teaching include the strong use of visual supports. Visual supports are those environmental and teaching tools that rely primarily on the visual rather than auditory modality to help teach a person with autism. Visual supports are used to help organize the environment, for example, by labeling drawers and cabinets with pictures and/or words, by arranging furniture and other items to create work and recreational spaces, by color coding and using other visual cues, and by storing and arranging materials in clear plastic containers or designated closets so the contents can be accessed easily. Visual supports are also used to help a person organize, understand, and predict his routine. These take the form, for example, of picture schedules, written checklists, or pictorial or written scripts. One type of visual support that is well suited to a learner with autism is the use of a “first . . . then” map, where a person who is anxious for or motivated to engage in a specific activity can see from pictures or words that once the less preferred task is over, the preferred activity can take place: • “First clean your bedroom, then play video games.” A consequence chain is another visual tool that helps a learner see and predict the course of events: • “It’s sunny outside: I can go swimming. • It’s raining out: I cannot go swimming: I will go to the gym.”
Benhaven Residential Services
There are dozens of tools and variations that can be created to visually assist a learner to understand his routine, including the use of modeling, pointing, sign language, photographs, objects, video, and even handheld devices and applications. Lastly, visual strategies are very widely used when teaching skill-building activities. Unlike typical teaching approaches done through presentation of verbal material, visual strategies present information and provide direction more concretely. The spoken word is transitory and fleeting in nature and relies heavily on strong auditory processing skills, whereas visual tools are tangible and enduring. Some examples of visual instructional tools are picture recipes that lay out directions for preparing a meal or picture checklists for grocery shopping. The method of displaying the visual tools is based on the individual’s strengths. For some, laying the pictures out sequentially on one long strip is effective; for another, gathering them in a photo album which the learner turns as he completes each step is more suitable. Picture strips may be posted on a wall to assist a person through a step-by-step process, such as brushing one’s teeth. Cards or pictures can be kept in a wallet which the learner can pull out as needed. Visual tools are also useful in assisting learners to make choices. Giving the learner two or more concrete visual items to choose from increases the likelihood the person will select the truly preferred item, rather than simply repeating the last item heard, which often happens when people are verbally asked to make a choice. The teaching method most commonly and effectively used at Benhaven is “errorless learning,” a system of teaching a person skills through the use of carefully designed and implemented prompts (Etzel and LeBlanc 1979; McDuff et al. 2001). Skills are broken down into a series of steps, called a “task analysis,” and instructors provide prompting designed to help the person complete each step before making an error. Prompts are planfully faded as the learner gains independence at each step. Instructors are trained to provide prompts that are best suited to that individual’s needs. A person with strong memory and
Benhaven Residential Services
visual skills may be successful following a series of pointing prompts – the instructor points to each step in the sequence and the learner responds by engaging in the step. To make a sandwich, for example, the instructor points to the loaf of bread, the learner takes out two slices of bread; the instructor points to the filling, the learner places the filling on the bread; etc. Another learner with greater needs may be more successful with physical prompts, where the instructor guides the person’s hands to take out the bread and place it on the plate, take the filling and place it on the bread, etc. Other types of prompts include modeling, gesturing, signing, and, of course, visual supports like photos, pictures, or written instructions. Verbal prompts are not widely recommended for people with autism as they rely too heavily on auditory processing skills and, unlike the other types of prompts, are not easily conducive to fading.
Efficacy Information The methods described here, including functional behavioral assessment, positive behavioral intervention, structured teaching, and errorless learning, are all widely used in the field of education for people with developmental disabilities and are accepted as effective methods to teach positive behavior and functional skills to those on the autism spectrum (Connecticut State Department of Education 2005). Success of these strategies in a residential setting relies heavily on individualized program planning, consistency of implementation by well-trained and caring direct support staff, and strong oversight of routines and practices by knowledgeable administrative personnel.
Outcome Measurement Treatment outcomes are measured using a variety of tools. Progress toward reducing negative behaviors and increasing adaptive ones are measured through behavioral data charts that track frequency, duration, and intensity of behavior.
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Observational and anecdotal reports are commonly used to track the circumstances under which a behavior occurred. Positive behavior is tracked as well, for example, the frequency with which a person engages in a new, positive behavior or skill and under what circumstances. Mood and behavior may also be assessed via rating scales, for example, by rating a person’s affect against a certain set of observable and describable attributes. Data is turned into graphs and charts so that it may be more easily reviewed and analyzed by team members. Progress in skill acquisition is measured in a variety of ways, for example, by recording the number and type of intervention a person needs to perform the steps of a given activity. Progress may be measured in terms of moving from more severe to minimal prompts (prompt fading), by the reduction of the number of prompts or corrections or by tracking the rate at which steps are performed without intervention. Less quantitative methods of assessing progress are “quality of life” measures. Does the resident’s behavior allow him to engage in activities that bring him pleasure and satisfaction, for example, eating out at restaurants, going to the movies, going to parties, and taking a vacation? Can a resident who formerly needed one-to-one supervision when out in the community now go out with others? Can a resident who used to sit in the back seat of a vehicle because of safety concerns now sit up front with the driver if he prefers? The answers to questions like these provide another equally important and valid measure of treatment outcome.
Qualifications of Treatment Providers Benhaven’s residential program is licensed by the Connecticut Department of Developmental Disabilities and must adhere to that agency’s licensing requirements, including requirements in the area of staff training. While not required, many management and administrative staff hold licensures and degrees in areas such as special education, psychology, sociology, and social work. Direct line personnel are not required to hold
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specific credentials but receive extensive and ongoing training in autism and teaching methods and philosophies as well as other areas vital for supporting people with disabilities, for example, CPR, first aid, medication administration, and health and safety. What distinguishes Benhaven’s treatment procedures from others is not so much the treatments themselves (which are widely accepted and established approaches and strategies in the field of autism) but, rather, other factors that impact the efficacy of treatment. First of all, program plans are individualized in reality, not just on paper. One of Benhaven’s primary treatment philosophies is that there must be an adequate number of welltrained staff to carry out these programs so that residents receive individualized training for skill acquisition and maintenance. For a residential program, Benhaven manages to maintain a remarkably high staff ratio, for example, four staff to six residents during programming time in some settings and in one setting two staff to two residents. Strategies for teaching skills and addressing behavior problems are developed to best meet the needs and suit the learning style of the individual; “one size fits all” is never the approach. Treatment is also enhanced by the relative longevity and retention of skilled and motivated staff at all levels. Benhaven’s residential administration has been in place for between 30 and 40 years; senior management of the seven homes averages over 18 years of employment at Benhaven. Advanced direct support staff constitute over 80% of the total supporters, and the average employment among all direct support staff is well over 5–10 years. In addition, and perhaps most importantly, staff receive ongoing training in groups, in team meetings, and as individuals. Staff performance is monitored through formal and informal observation, with feedback and follow-up provided. All staff, full- and part-time, participate in a staff development process that provides people with an opportunity to receive performance feedback on a quarterly basis. Formal staff evaluations are conducted annually, at which time performance goals are established and reviewed.
Benhaven Residential Services
References and Reading American Psychiatric Association. (2000). Diagnostic and statistical manual (4th ed., Text Rev.). Washington, DC: APA Press. Connecticut State Department of Education, Division of Teaching and Learning Programs and Services, Bureau of Special Education. (2005). Guidelines for identification and education of children and youth with autism. Hartford: Connecticut State Department of Education. Etzel, B. C., & LeBlanc, J. M. (1979). The simplest treatment alternative: The law of parsimony applied to choosing appropriate instructional control and errorless-learning procedures for the difficult-to-teach child. Journal of Autism and Developmental Disorders, 9(4), 361–382. Fox, L., Dunlap, G., & Buschbacher, P. (2000). Understanding and intervening with children’s challenging behavior: A comprehensive approach. In A. Wetherby & B. Prizant (Eds.), Autism spectrum disorders: A transactional developmental perspective (Vol. 9, pp. 109–141). Baltimore: Paul H. Brookes. Goldstein, H. (2002). Communication intervention for children with autism: A review of treatment efficacy. Journal of Autism and Developmental Disorders, 32(5), 373–396. Koegel, L., Koegel, R., & Dunlap, G. (1996). Positive behavioral support: Including people with difficult behavior in the community. Baltimore: Paul H. Brookes. LaVigna, G. W., & Donnellan, A. M. (1986). Alternatives to punishment: Solving behavior problems with nonaversive strategies. New York: Irvington. Maurice, C., Green, G., & Luce, S. (Eds.). (1996). Behavioral intervention for young children with autism: A manual for parents and professionals. Austin: Pro-Ed. McDuff, G. S., Krantz, P. J., & McClannahan, L. E. (2001). Prompts and prompt-fading strategies for people with autism. In C. Maurice, G. Green, & R. M. Foxx (Eds.), Making a difference: Behavioral intervention for autism (pp. 37–50). Austin: Pro-Ed. Mount, B. (1995). Capacity works: Finding windows for change using personal futures planning. New York: Graphic Futures. O’Neill, R. E., Horner, R. H., Albin, R. W., Sprague, J. R., Storey, K., & Newton, J. S. (1997). Functional assessment and program development for problem behavior (2nd ed.). Pacific Grove: Brooks/Coles Publishing Company. Prizant, B., Wetherby, A., & Rydell, P. (2000). Communication intervention issues for young children with autism spectrum disorders. In A. Wetherby & B. Prizant (Eds.), Autism spectrum disorders: A transactional developmental perspective (Vol. 9, pp. 109–141). Baltimore: Paul H. Brookes. Smull, M., & Harrison, S. B. (1992). Supporting people with severe reputations in the community: Reviewing essential lifestyle plans. Alexandria: NASMRPD.
Beta-Adrenergic Blockers Structured Teaching. (2010). Retrieved 12 June 2010 from http://www.teacch.com. Volkmar, F. R., & Wiesner, L. A. (2009). A practical guide to autism: What every parent, family member, and teacher needs to know. Hoboken: Wiley. Zionts, P., & Simpson, R.L. (1992/2000). Autism: Information and resources for professionals and parents (2nd ed., p. 3/5). Austin: Pro-Ed.
Benzodiazepine ▶ Lorazepam ▶ Xanax (Alprazolam)
Benzodiazepines ▶ Sedative Hypnotic Drugs
Beta-Adrenergic Antagonist ▶ Beta-Adrenergic Blockers
Beta-Adrenergic Blockers Jonathan Kopel Texas Tech University Health Sciences Center (TTUHSC), Lubbock, TX, USA
Synonyms Beta-adrenergic antagonist
Definition Beta blockers remain essential pharmacological agents in the management and treatment of cardiovascular and endocrine conditions. With little risk of dependence and high target selectivity, recent clinical trials have investigated the efficacy of beta blockers alongside current treatment guidelines for intellectual disabilities and psychiatric conditions
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where first-line therapies have failed. Current research suggests that beta blockers may alleviate the motor and psychiatric symptoms present in intellectual disabilities through CNS or peripheral blockade of sympathetic hyperactivity (Connor et al. 1997). As such, beta blockers may act as a potential adjuvant therapy towards the management and treatment of autism spectrum disorders (ASD). Among autistic patients, 30% exhibit anxiety, irritability, and self-injury comorbidities, which previous pharmacological studies targeted for treatment. Recent studies showed beta blockers, particularly propranolol, reduced these comorbidities, sparking new interest examining beta blockers effects on the language and cognitive deficits in ASD (SagarOuriaghli et al. 2018). Propranolol is a nonselective beta blocker used extensively to treat test and performance anxiety and cardiovascular disease. Unlike other nonselective beta blockers, such as nadolol, propranolol is a lipophilic beta blocker that blocks both the central nervous system and peripheral β-adrenergic receptors allowing for further modulation of cognitive functions (Beversdorf et al. 2002). Furthermore, propranolol is welltolerated by children and exhibits less unwanted side effects compared to other pharmaceutical agents (Deepmala and Agrawal 2014). Many ASD patients show a decrease connectivity between brain regions involved in language, social, and motor skills (Koshino et al. 2005). Previous studies using propranolol showed ASD children exhibited less anxiety and more social and adaptive behaviors (Ratey et al. 1987). Further analysis demonstrated propranolol’s efficacy improving word fluency, conversation problem solving, and conversational reciprocity in ASD patients (Zamzow et al. 2016). In addition, ASD patients show deficits in phonological processing during social and motor tasks (Schmidt et al. 2008). A functional magnetic resonance imaging (fMRI) of 10 autistic patients showed an increase in neuronal networks with propranolol administration during phonological tasks compared to nadolol (Narayanan et al. 2010). Although social and cognitive deficits remain the focus of autism research, hypersexuality remains a prevalent comorbidity among ASD patients leading to social embarrassment and
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repercussions with limited pharmacological interventions available (Deepmala and Agrawal 2014). However, a recent case report of an adolescent ASD patient demonstrated decreased hypersexual behavior upon administration of propranolol (Deepmala and Agrawal 2014). Although the mechanism behind the observed decreased hypersexual behavior remains unknown, current literature suggest that propranolol decreases testosterone and antagonizes serotonin receptors in the brain (Rosen et al. 1988). Overall, these findings encourage future investigations comparing the efficacy of different beta blockers in managing ASD and the mechanism behind their effects.
See Also ▶ Autonomic Nervous System ▶ Irritability in Autism ▶ Learning Disability
References and Reading Beversdorf, D. Q., White, D. M., Chever, D. C., Hughes, J. D., & Bornstein, R. A. (2002). Central β-adrenergic modulation of cognitive flexibility. Neuroreport, 13(18), 2505–2507. https://doi.org/10.10 97/00001756-200212200-00025. Connor, D. F., Ozbayrak, K. R., Benjamin, S., Ma, Y., & Fletcher, K. E. (1997). A pilot study of Nadolol for overt aggression in developmentally delayed individuals. Journal of the American Academy of Child & Adolescent Psychiatry, 36(6), 826–834. https://doi.org/10.1097/00004583-199706000-00021. Deepmala, & Agrawal, M. (2014). Use of propranolol for hypersexual behavior in an adolescent with autism. Annals of Pharmacotherapy, 48(10), 1385–1388. https://doi.org/10.1177/1060028014541630. Koshino, H., Carpenter, P. A., Minshew, N. J., Cherkassky, V. L., Keller, T. A., & Just, M. A. (2005). Functional connectivity in an fMRI working memory task in high-functioning autism. NeuroImage, 24(3), 810–821. https://doi.org/10.1016/ j.neuroimage.2004.09.028. Narayanan, A., White, C. A., Saklayen, S., Scaduto, M. J., Carpenter, A. L., Abduljalil, A., . . . Beversdorf, D. Q. (2010). Effect of propranolol on functional connectivity in autism Spectrum disorder – A pilot study. Brain Imaging and Behavior, 4(2), 189–197. https://doi.org/ 10.1007/s11682-010-9098-8. Ratey, J. J., Bemporad, J., Sorgi, P., Bick, P., Polakoff, S., O’Driscoll, G., & Mikkelsen, E. (1987). Brief report:
Open trial effects of beta-blockers on speech and social behaviors in 8 autistic adults. Journal of Autism and Developmental Disorders, 17(3), 439–446. https://doi.org/10.1007/bf01487073. Rosen, R. C., Kostis, J. B., & Jekelis, A. W. (1988). Betablocker effects on sexual function in normal males. Archives of Sexual Behavior, 17(3), 241–255. https://doi.org/10.1007/bf01541742. Sagar-Ouriaghli, I., Lievesley, K., & Santosh, P. J. (2018). Propranolol for treating emotional, behavioural, autonomic dysregulation in children and adolescents with autism spectrum disorders. Journal of Psychopharmacology, 32(6), 641–653. https://doi.org/10.117 7/0269881118756245. Schmidt, G. L., Kimel, L. K., Winterrowd, E., Pennington, B. F., Hepburn, S. L., & Rojas, D. C. (2008). Impairments in phonological processing and nonverbal intellectual function in parents of children with autism. Journal of Clinical and Experimental Neuropsychology, 30(5), 557–567. https://doi.org/10.1 080/13803390701551225. Zamzow, R. M., Ferguson, B. J., Stichter, J. P., Porges, E. C., Ragsdale, A. S., Lewis, M. L., & Beversdorf, D. Q. (2016). Effects of propranolol on conversational reciprocity in autism spectrum disorder: A pilot, double-blind, single-dose psychopharmacological challenge study. Psychopharmacology, 233(7), 1171–1178. https://doi.org/10.1007/s002 13-015-4199-0.
Beta-Alanyl-L-Histidine ▶ Carnosine
Bettelheim, Bruno Fred R. Volkmar Child Study Center, Irving B. Harris Professor of Child Psychiatry, Pediatrics and Psychology, Yale Child Study Center, School of Medicine, Yale University, New Haven, CT, USA
Short Biography A highly controversial figure in the history of autism, Dr. Bettelheim was born in Austria and trained in Art History. His work in history led him to the study of psychology. He became a refugee from the Nazis and moved to the United States.
Better OutcOmes and Successful Transitions for Autism (BOOST-A) Program, The
He eventually moved to Chicago where he became a professor at the University of Chicago (teaching there from 1944 to 1973). He had some psychoanalytic training in Vienna and served, in Chicago, as the Director of the University of Chicago’s Sonia Shankman Orthogenic School – a center for treatment of severely disturbed children. He made many claims for successful treatment but did so within the context of claiming that parents were involved in the pathogenesis of autism (a theory now long discredited). His early work on the topic was widely cited, although it is not clear exactly how many children with autism he actually saw. The diagnoses of autism in his patients have also been questioned. His popularization of the concept of the “refrigerator mother” traumatized a generation of parents who were told they were responsible for their child’s autism. Questions were raised about possible plagiarism in his scholarly writing and the validity of his work.
References and Reading Bettelheim, B. (1950). Love is not enough: The treatment of emotionally disturbed children. Glencoe: Free Press. Bettelheim, B. (1959). Joey: A mechanical boy. Scientific American, 200, 117–126. Pollak, R. (1997). The creation of Dr. B: A biography of Bruno Bettelheim (Hardcover). New York: Touchstone. Sutton, N. (1996). Bettelheim: A life and legacy. New York: Basic Books.
Better OutcOmes and Successful Transitions for Autism (BOOST-A) Program, The Megan Hatfield, Marita Falkmer and Marina Ciccarelli School of Occupational Therapy, Social Work and Speech Pathology, Curtin University, Perth, WA, Australia
Definition The Better OutcOmes & Successful Transitions for Autism (BOOST-ATM) is an autism-specific,
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web-based program that aims to prepare adolescents on the autism spectrum for the transition from high school to further education, training, or employment. The BOOST-ATM supports adolescents on the autism spectrum to develop career pathways and set goals that enhance their employment-readiness skills. The program consists of four modules, as outlined in Fig. 1. Module 1, About Me, supports career awareness by engaging the adolescent in the following six activities: • “Interests”: The adolescent completes a questionnaire and is provided with a summary of their key areas of interest related to employment. • “Strengths”: The adolescent reflects on their areas of strength in the fields of technology, science, physical activities, and arts. • “Work Preferences”: The adolescent considers the types of work environments they might prefer. They explore factors such as sensory input (e.g., noise and movement), social interactions, and task routines. • “Training after School”: The adolescent states their preferred post-school training pathway. For example, on-the-job training or an apprenticeship; a vocational training center; or tertiary education at university or college. • “My skills”: The adolescent explores their current level of performance in activities of daily living, and their participation in community activities. • “Learning Styles”: The adolescent describes their preferred learning style, which may be one or more of the following: reading, writing, hearing, seeing, or doing. Module 2, My Team, supports the adolescent and their caregiver to identify people who might be best placed to participate in their transition planning, asking them to come together to form a team. In addition, this module provides potential strategies for the adolescent to contribute as actively as possible to the team meetings in a way that they feel comfortable with, as this has been shown to promote increased self-determination (Hendricks and Wehman 2009; Martin and Williams-Diehm 2013).
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Better OutcOmes and Successful Transitions for Autism (BOOST-A) Program, The, Fig. 1 Description of the four modules of the program
Module 3, First Meeting, involves a team meeting that keeps the adolescent at the center of all decision-making. The program provides recommendations for job pathways that leverage the adolescent’s strengths. The team decides on a few pathways on which to develop goals. For each goal, the team identifies the actions needed, people responsible, and timeframes for completion. Goals are suggested by the program and encourage the adolescent to gain real-life experiences; e.g., acquiring a part-time job, doing a work experience, finding a mentor, getting more information about potential careers, and developing life skills. The fourth module, My Progress, is completed at subsequent team meetings. The team reflects on the progress toward each goal, and amends and updates the goals and job pathways, as needed. This module encourages the development of resilience through positive reflection, guiding the adolescent to reframe challenging experiences as opportunities for development, rather than failures. Since the program is designed to build self-determination, the adolescent is encouraged to increase their level of involvement at each subsequent meeting. The aim is that the adolescent will eventually lead the meetings and experience a greater sense of control over their plans for the future.
Historical Background Adolescents on the autism spectrum have numerous strengths; however, many have
difficulty transitioning to post-school employment or higher education (Australian Bureau of Statistics 2012). Furthermore, many people on the autism spectrum are often underemployed or in positions that do not reflect their education level or abilities (Hendricks 2010; Müller et al. 2003). There is increasing recognition of the many strengths people on the autism spectrum bring to the workplace, including low absenteeism, reliability, and excellent recall for information on topics related to their special interests (Mottron 2011; Hillier et al. 2007; Hagner and Cooney 2005). Transition planning enhances post-school outcomes in adolescents with disabilities (King et al. 2005; Wei et al. 2016), but most existing transition planning programs are not autismspecific and may not meet the needs of adolescents on the autism spectrum. Therefore, the current program was developed specifically for adolescents on the autism spectrum to improve transition planning outcomes for this group.
Rationale or Underlying Theory The program was developed based on three frameworks: self-determination theory, a strengths-based approach, and a technologybased approach. Adolescents with high selfdetermination are more likely to have post-school employment (Test et al. 2009). The strengthsbased approach capitalizes on an individual’s expertise and special interests to enhance performance and promote changes in self-determination
Better OutcOmes and Successful Transitions for Autism (BOOST-A) Program, The
(Patten Koenig and Hough Williams 2017). Technology-based interventions are effective in improving outcomes for people on the autism spectrum in areas such as communication, social skills, and emotional recognition (Grynszpan et al. 2014).
Goals and Objectives Two pilot studies (Pilots A and B) were conducted to determine the feasibility and acceptability of the program (Hatfield et al. 2017b). The effectiveness of the program was then determined in a quasi-randomized controlled trial (RCT) (Hatfield et al. 2016; Hatfield et al. 2017a). Finally, a process evaluation identified the enablers and barriers related to using the program (Hatfield et al. 2016, 2018). The process evaluation involved collecting qualitative and quantitative feedback from the intervention group participants in the RCT.
Treatment Participants Pilot A consisted of six adolescents on the autism spectrum who trialed the program with their parents and the professionals in their team. Pilot B obtained the feedback from 88 allied health professionals via an online survey. Participants in the RCT and process evaluation were 94 adolescents on the autism spectrum from Australia. The intervention group participants (n ¼ 49) received the program and the control group (n ¼ 45) participated in usual transition planning practices at their school.
Treatment Procedures Participants were screened for eligibility and allocated to groups using an alternate allocation method. Outcome measures were completed on enrolment and then 12 months post-intervention, to allow the participants to complete all modules of the program and make progress on their transition planning goals. Outcomes
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included self-determination, career planning and exploration, quality of life, environmental support, and domain-specific self-determination. Data were collected from parents and adolescents. Curtin University Human Research Ethics Committee and relevant school sectors provided ethics approval. All participants aged 18+ years provided informed consent and adolescents provided informed assent. For the RCT, normality of the data was determined using the Kolmogorov-Smirnov test. Effectiveness was determined using the independent samples t-test and/or Mann-Whitney U test. An intention-totreat approach was used, along with the last observation carried forward method.
Efficacy Information Results from the pilot studies indicated that the program was an acceptable and feasible program (Hatfield et al. 2017b). Modifications to the program were made based on the feedback from participants. Changes included the conversion to a web-based program to improve the usability of the program; a reduction in the content length of the modules; and enhanced use of visuals, such as videos and graphics. The RCT results indicated significant differences in favor of the intervention group in three areas: (i) opportunity for self-determination at home (parent report); (ii) career exploration (parent and adolescent report); and (iii) transition-specific self-determination (parent report) (Hatfield et al. 2017a). There were no significant differences between groups for the summary scores of the remaining outcomes. Results from the process evaluation indicated that the program enabled adolescents on the autism spectrum to feel empowered because of the strengths-focus of the program (Hatfield et al. 2018). The program supported parents and adolescents to overcome inertia and take action by providing a structured process to follow, and providing new insights into potential career pathways. Participants were less likely to report benefits from using the program when they did not have a “champion” in their team. The
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champion was typically a parent or professional taking charge and moving the transition planning process forward.
this study. The authors acknowledge the financial support of the Cooperative Research Centre for Living with Autism (Autism CRC), established and supported under the Australian Government Cooperative Research Centres Program (http://www.autismcrc.com.au/).
Outcome Measurement The primary outcome of the RCT was self-determination, measured by the AIR Self-Determination Scale (AIR). The four secondary outcomes included: (i) Career planning and exploration, measured by the Career Development Inventory – Australia – Short Form (CDI-A); (ii) Quality of life, measured by the Personal Wellbeing Index-School Children (PWI-SC); (iii) Environment support, measured by the Learning Climate Questionnaire (LCQ); and (iv) Domain specific self-determination, measured by the Transition Planning Objectives Scale.
Qualifications of Treatment Providers The program is appropriate for use by trusted adults who support the adolescent in transition planning including parents, educators, and allied health professionals, such as occupational therapists, psychologists, and speech pathologists. No formal training, certification, or level of qualification is required.
See Also ▶ Education ▶ Functional Life Skills ▶ Peer Mentors for Students with ASD on College Campuses ▶ Preemployment Skills for People with ASD ▶ Quality of Life for Transition-Age Youth with ASD ▶ Stepped Transition in Education Program for Students with ASD (STEPS) ▶ Team Approach Acknowledgments An Australian Postgraduate Award scholarship and funding from the Australian Federal Government and Curtin University supported
References and Reading Australian Bureau of Statistics. (2012). Autism in Australia (cat no. 4428.0). Canberra: Australian Bureau of Statistics. Viewed 5 June 2015. Retrieved from http:// www.abs.gov.au/ausstats/[email protected]/mf/4428.0 Grynszpan, O., Weiss, P., Perez-Diaz, F., & Gal, E. (2014). Innovative technology-based interventions for autism spectrum disorders: A meta-analysis. Autism, 18(4), 346–361. https://doi.org/10.1177/1362361313476767. Hagner, D., & Cooney, B. (2005). “I do that for everybody”: Supervising employees with autism. Focus on Autism and Other Developmental Disabilities, 20, 91–97. https://doi.org/10.1177/10883576050 200020501. Hatfield, M., Falkmer, M., Falkmer, T., & Ciccarelli, M. (2016). Evaluation of the effectiveness of an online transition planning program for adolescents on the autism spectrum: Trial protocol. Child and Adolescent Psychiatry and Mental Health, 10(48), 1–11. https:// doi.org/10.1186/s13034-016-0137-0. Hatfield, M., Falkmer, M., Falkmer, T., & Ciccarelli, M. (2017a). Effectiveness of the BOOST-A online transition planning program for adolescents on the autism spectrum: A quasi-randomised controlled trial. Child and Adolescent Psychiatry and Mental Health, 11(54), 1–12. https://doi.org/10.1186/s13034-017-01 91-2. Hatfield, M., Murray, N., Ciccarelli, M., Falkmer, T., & Falkmer, M. (2017b). Pilot of the BOOST-A: An online transition planning program for adolescents with autism. Australian Occupational Therapy Journal, 64(6), 448–456. https://doi.org/10.1111/ 1440-1630.12410. Hatfield, M., Falkmer, M., Falkmer, T., & Ciccarelli, M. (2018). Process evaluation of the BOOST-A transition planning program for adolescents on the autism spectrum: A strengths-based approach. Journal of Autism and Developmental Disorders, 48(2), 377–388. https:// doi.org/10.1007/s10803-017-3317-8. Hendricks, D. (2010). Employment and adults with autism spectrum disorders: Challenges and strategies for success. Journal of Vocational Rehabilitation, 32(2), 125–134. https://doi.org/10.3233/JVR-2010-0502. Hendricks, D., & Wehman, P. (2009). Transition from school to adulthood for youth with autism spectrum disorders: Review and recommendations. Focus on Autism and Other Developmental Disabilities, 24, 77–88. https://doi.org/10.1177/1088357608329827. Hillier, A., Campbell, H., Mastriani, K., Izzo, M., Kool-Tucker, A., Cherry, L., & Beversdorf, D. (2007).
Bias in Assessment Instruments for Autism Two-year evaluation of a vocational support program for adults on the autism spectrum. Career Development and Transition for Exceptional Individuals, 30(1), 35–47. https://doi.org/10.1177/ 08857288070300010501. King, G., Baldwin, P., Currie, M., & Evans, J. (2005). Planning successful transitions from school to adult roles for youth with disabilities. Children’s Health Care, 34, 195–216. https://doi.org/10.1207/s15326888 chc3403_3. Martin, J., & Williams-Diehm, K. (2013). Student engagement and leadership of the transition planning process. Career Development and Transition for Exceptional Individuals, 36, 43–50. https://doi.org/10. 1177/2165143413476545. Mottron, L. (2011). Changing perceptions: The power of autism. Nature, 479(7371), 33–35. https://doi.org/ 10.1038/479033a. Müller, E., Schuler, A., Burton, B., & Yates, G. (2003). Meeting the vocational support needs of individuals with Asperger syndrome and other autism spectrum disabilities. Journal of Vocational Rehabilitation, 18(3), 163–175. http://content.iospress.com.dbgw.lis. curtin.edu.au/articles/journal-of-vocational-rehabilita tion/jvr00193. Patten Koenig, K., & Hough Williams, L. (2017). Characterization and utilization of preferred interests: A survey of adults on the autism spectrum. Occupational Therapy in Mental Health, Early view, 1–12. https://doi.org/10.1080/0164212X.2016.124887 7. Test, D., Mazzotti, V., Mustian, A., Fowler, C., Kortering, L., & Kohler, P. (2009). Evidence-based secondary transition predictors for improving postschool outcomes for students with disabilities. Career Development for Exceptional Individuals, 32, 160–181. https://doi.org/10.1177/0885728809346960. Wei, X., Wagner, M., Hudson, L., Yu, J., & Javitz, H. (2016). The effect of transition planning participation and goal-setting on college enrollment among youth with autism spectrum disorders. Remedial and Special Education, 37(1), 3–14. https:// doi.org/10.1177/0741932515581495.
BG-II ▶ Bender Visual-Motor Gestalt Test II
Bias ▶ Measurement Error
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Bias in Assessment Instruments for Autism Roald A. Øien1,2 and Anders Nordahl-Hansen3,4 1 Department of Psychology/Department of Education, UIT – The Arctic University of Norway, University of Tromsø, Tromsø, Norway 2 Yale Child Study Center, Yale Autism Program, Yale University School of Medicine, New Haven, CT, USA 3 Department of Special Needs Education, UiO University of Oslo, Oslo, Norway 4 Faculty of Education, Østfold University College, Halden, Norway
Introduction Standardized diagnostic tools are critical in terms of assessing Autism Spectrum Disorder (ASD). There is an increasing interest in how culture, sex, social economic status, and other factors affect the performance of concurrent gold standard instruments for the purpose of identifying autism spectrum disorder (ASD). While the performance of, for example, the Autism Diagnostic Observation Schedule (ADOS) (Rutter et al. 2012) and the Autism Diagnostic Interview (ADI-R) (Rutter et al. 2003) performs well in identifying individuals suspected of ASD, and especially when used to complement each other, little is known on the performance of the ADOS and the ADI-R in different cultures, ethnicities, and between sexes. Most of the instruments created for the purpose of identifying ASD are created and validated in the USA and Europe. However, they are often translated into a large range of languages for use in different cultures and ethnicities, without conducting validation studies of the instruments. As a result, it could be discussed how culture, ethnicity, sex, and the translation itself affect test performance of instruments. Even considering how instruments work between sexes are rarely examined in validation studies of instruments. In terms of parent report, it is likely to believe that culture could affect how parents regard various elements of behavior and development, and how
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parents rate their boy or girl in terms of endorsement of, for example, behaviors or development.
Current Knowledge While we do believe that some of the issues raised could potentially bias diagnostic instruments, little has been reported on ASD-specific diagnostic instruments in terms of potential biases and test performance (Volkmar et al. 2014). A recent study by Vanegas et al. (2016) revealed that the sensitivity and specificity of the ADI-R were moderate, but lower than previously reported values. However, currently we have little information on test performance from different cultures, ethnicities, taking sex also into consideration. Behavioral, developmental and temperamental differences could ultimately affect the performance of screening and diagnostic instruments (Dworzynski et al. 2012; Macari et al. 2017; Øien et al. 2017). In terms of culture, research has revealed cross-cultural prevalence differences (Elsabbagh et al. 2012), and culture is regarded to have a great impact on how ASD is perceived, diagnosed, and treated across cultures (Volkmar et al. 2014). In the western world, early diagnosis is regarded as paramount for early intervention and access to services. It is not sure to what extent, but a diagnosis of ASD could in other cultures impair the access to treatment and services. A great example of how ASD symptomatology could be affected by culture is found in eye contact. As impairment or atypicalities in eye contact is regarded as a core symptom of ASD, avoiding eye contact in some eastern cultures are regarded as appropriate and polite (Volkmar et al. 2014). Non-autism-specific research, among the Sami population of Norway (ethnic minority), revealed that disorders such as ASD and other mental health disorders were not prevalent as in the larger nonethnic majority of Norway (Nergård 2006).
Future Directions As there is a pressing need to understand how culture, ethnicity, sex, and other factors affect test performance of a range of different diagnostic
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instruments, it is necessary to focus on implementation of validation studies when diagnostic instruments are translated. While translating and back translating is regarded as necessary, translations of instruments can still incorporate wording or translations that might not be suitable for the given culture, ethnicity, or language. Future studies should also examine the performance in sex separately, aiming at exploring if there are different sensitivity and specificity for males and females, respectively. One solution for future research should be to conduct meta-analyses of test performance of the various diagnostic instruments across cultures, ethnicities, and social economic statuses, taking sex into consideration. This could show if the diagnostic instruments perform differently in different conditions, and it could also indicate in which areas the discrepancies arise from. Increasing knowledge on how to identify ASD in third world countries and cultures is of highest importance, and current diagnostic instruments are often too expensive for many cultures and countries. Focusing on the development of community-based identification of ASD in areas with limited resources and utilizing less comprehensive, but culture-sensitive instruments could potentially decrease differences in prevalence and differences across cultures.
References and Reading Dworzynski, K., Ronald, A., Bolton, P., & Happé, F. (2012). How different are girls and boys above and below the diagnostic threshold for autism spectrum disorders? Journal of the American Academy of Child and Adolescent Psychiatry, 51, 788. Elsabbagh, M., Divan, G., Koh, Y.-J., Kim, Y. S., Kauchali, S., Marcín, C., et al. (2012). Global prevalence of autism and other pervasive developmental disorders. Autism Research, 5(3), 160–179. https:// doi.org/10.1002/aur.239 Macari, S. L., Koller, J., Campbell, D. J., & Chawarska, K. (2017). Temperamental markers in toddlers with autism spectrum disorder. Journal of Child Psychology and Psychiatry, 58(7), 819–828. https://doi.org/10.1111/ jcpp.12710 Nergård, J.-I. (2006). Den levende erfaring. Oslo: Cappelen. Øien, R. A., Hart, L., Schjølberg, S., Wall, C. A., Kim, E. S., Nordahl-Hansen, A., et al. (2017). Parentendorsed sex differences in toddlers with and without
Bilingualism and Language Development in Children with Autism Spectrum Disorders ASD: Utilizing the M-CHAT. Journal of Autism and Developmental Disorders, 47(1), 126–134. https://doi. org/10.1007/s10803-016-2945-8 Rutter, M., Le Couteur, A., & Lord, C. (2003). Autism diagnostic interview-revised (Vol. 29, p. 30). Los Angeles: Western Psychological Services. Rutter, M., DiLavore, P. C., Risi, S., Gotham, K., & Bishop, S. (2012). Autism diagnostic observation schedule, (ADOS-2). Torrance: Western Psychological. Vanegas, S. B., Magaña, S., Morales, M., & McNamara, E. (2016). Clinical validity of the ADI-R in a US-based Latino population. Journal of Autism and Developmental Disorders, 46(5), 1623–1635. https://doi.org/10. 1007/s10803-015-2690-4 Volkmar, F. R., Paul, R., Rogers, S. J., & Pelphrey, K. A. (2014). Handbook of autism and pervasive developmental disorders. Hoboken: Wiley.
BIG-2 ▶ CNTN4: Contactin 4
Bilingualism and Language Development in Children with Autism Spectrum Disorders Ana Maria Gonzalez-Barrero1 and Aparna Nadig2 1 Department of Psychology, Concordia University, Montreal, QC, Canada 2 School of Communication Sciences and Disorders, McGill University, Montreal, QC, Canada
Definition Bilingually exposed children with autism spectrum disorders (ASD) are those who are exposed to two languages from early ages. These children are typically being raised in bilingual families and/or bilingual communities. Bilingualism is a multidimensional characteristic that is influenced by multiple factors such as sociolinguistic context (e.g., majority or minority language), age of acquisition, and amount of exposure or usage (for children) or proficiency (for older children and adults), among others (Surrain and Luk 2017). Additionally, socioeconomic differences
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are linked to bilingual status in some contexts. These factors need to be kept in mind when interpreting and comparing research findings (Kay-Raining Bird et al. 2016).
B Historical Background Parents of children with ASD are commonly advised to use only one language when interacting with their children. This often stems from beliefs that bilingualism might be too challenging or confusing for the child and hinder his/her language development (Yu 2013). However, evidence does not support this claim (for reviews see Drysdale et al. 2015; Lund et al. 2017). Research with children under age 6 has repeatedly shown no additional language delays caused by bilingual exposure. Importantly, in the long term, bilingualism can provide social and vocational advantages. For children from bilingual families and communities, it also provides opportunities to maintain familial bonds (Yu 2013) and rich social and linguistic input (Hudry et al. 2018). In situations of highly proficient bilingualism, it may even provide advantages in some executive function skills (Gonzalez-Barrero and Nadig 2017, 2019b; Nadig and Gonzalez-Barrero 2019).
Current Knowledge Bilingual Language Development in Toddlers and Preschoolers Research with toddlers and preschool bilingual children with ASD has shown that these children reach language milestones, such as first words and onset of first sentences, at a similar age relative to their monolingual peers with ASD (Hambly and Fombonne 2012; Ohashi et al. 2012; ValicentiMcDermott et al. 2013). Furthermore, their early vocabulary and communication skills develop at a similar rate to that of children with ASD exposed to only one language (Dai et al. 2018; Ohashi et al. 2012; Petersen et al. 2012; Reetzke et al. 2015). Most studies on bilingualism and ASD including children of this age have relied primarily on parent report, which is a valid measure to use early in
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development (e.g., Hambly and Fombonne 2012; Reetzke et al. 2015). However, there is a lack of studies examining the language development of school-age bilingual children with ASD. It is important to extend investigations to this older age group as more sophisticated language skills are developed during the school years when language is used as a tool for learning. Bilingual Language Development at School Age Only a few studies have directly examined the language skills of school-age bilingual children with ASD (Gonzalez-Barrero and Nadig 2018, 2019a; Meir and Novogrodsky 2019). Importantly, unlike early development, at school age it is well established that typically developing monolinguals outperform bilinguals on standardized language tests administered in one language (Bialystok et al. 2010), which is linked to bilinguals’ language exposure being split between languages (Thordardottir 2011). How do bilingual children with autism, without intellectual disability, fare at school age? Gonzalez-Barrero and Nadig (2019a) assessed the vocabulary and grammatical skills of 13 bilingual and 13 monolingual school-aged children with ASD (age range 5–10 years). Children were recruited in Montreal, Quebec, Canada, a multicultural city where the use of French and English is a common practice and both languages are official languages of the country. Children were speakers of French, English, Spanish, or the combination of two of these languages. To qualify as bilingual in this study, children had to meet a rigorous three-step criterion including amount of exposure above 20% to two languages, proficiency judgments from parents, as well as completion of tasks in both languages. Bilinguals and monolinguals did not differ with respect to chronological age, nonverbal IQ, maternal education, dominant language, or autism symptoms, and a similar percentage of children in each group had language impairment. Standardized tests of vocabulary and grammar were administered to compare the performance of the bilingual children with ASD relative to their monolingual peers with ASD. Results concerning receptive vocabulary
skills in the bilingual children’s dominant language showed a trend where monolingual children exhibited higher scores relative to bilinguals. Yet, most of the bilingual children with ASD performed within the average range of the test (within 1 SD above or below the test mean). Results from the expressive grammatical test mirrored those found for vocabulary skills; there were no significant differences between the bilinguals’ dominant language scores and those of the monolingual group, although there was a tendency in the bilingual group to score below the average range on this measure. These findings demonstrate the same patterns found in typically developing bilinguals (Bialystok et al. 2010) and highlight the key role that amount of language exposure plays in child language abilities, in autism (Gonzalez-Barrero and Nadig 2018) as in typical development. Hoang et al. (2018) elicited short narratives in a picture sequencing task from a subsample with similar characteristics to the participants just described (n ¼ 20, including 5 bilinguals and 5 monolinguals with ASD). As reported in Gonzalez-Barrero and Nadig (2019a), monolinguals had higher receptive vocabulary scores than bilinguals. Yet, when using language functionally to tell a narrative, bilinguals produced significantly more utterances than monolinguals. With respect to narrative skills, bilinguals and monolinguals did not differ in macrostructure (e.g., sequencing of events and coherence), microstructure (e.g., use of referential terms and conjunctions), or elaborations (e.g., sound effects, character speech). Baldimtsi et al. (2016) used a similar study design to examine narrative production in Greek-speaking bilinguals and monolinguals with and without autism and found that bilingual children with ASD outperformed monolinguals with ASD in some narrative skills. Though preliminary given very small sample sizes, these converging findings suggest that lower standardized language test scores in bilingual children with autism do not reflect a reduced ability to use that language functionally. This body of work provides information to clinicians working with this population as it demonstrates that many children with ASD can
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become bilingual when adequate language exposure is provided (Gonzalez-Barrero and Nadig 2018). Additionally, in contrast to language development during the early years, it is expected that at school age bilinguals will underperform relative to their monolingual peers on standardized language tests, which calls for caution when assessing bilingual children with ASD using monolingual norms (Thordardottir 2015).
Future Directions With the increasing rate of bilingualism around the world (Surrain and Luk 2017), more research is needed to better understand and describe the language development trajectories of children with ASD being raised in bilingual families and societies. Although there is a growing interest in bilingualism and autism, and some studies have recently been conducted examining the narrative and syntactic abilities in this population (e.g., Hoang et al. 2018; Meir and Novogrodsky 2019), more studies are needed in language domains such as phonology and pragmatics, as well as comparing different contexts of bilingualism. A review indicated that many practitioners would like to support bilingualism in children with developmental disabilities when relevant for families but that the lack of bilingual specialized services and educational opportunities is a major obstacle (Marinova-Todd et al. 2016). Baker et al. (2018) offer a potential solution through dual immersion educational programs for children with autism, and with respect to how a child’s heritage language can be incorporated in therapy, see ▶ “Heritage Language Use for Intervention in Autism”. The development of bilingual services and research evaluating them is a critical area for future work. In sum, the available evidence suggests that bilingualism is a possible outcome for children with ASD and that bilingually exposed children with ASD show globally similar language development trajectories to their monolingual peers with ASD. Thus, parents of bilingual children should be encouraged to use the language in
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which they can better interact with their child, which could facilitate the delivery of parentimplemented interventions (Lim et al. 2019) and allow parents to provide quality input that supports language development.
See Also ▶ Communicative Acquisition in ASD ▶ Heritage Language Use for Intervention in Autism ▶ Theories of Language Development
References and Reading Baker, D., Roberson, A., & Kim, H. (2018). Autism and dual immersion: Sorting through the questions. Advances in Autism, 4, 174–183. Baldimtsi, E., Peristeri, E., Tsimpli, I. M., & Nicolopoulou, A. (2016). Bilingual children with high functioning autism spectrum disorder: Evidence from oral narratives and non-verbal executive function tasks. In J. Scott & D. Waughtal (Eds.), Proceedings of the 40th Annual Boston University Conference on Language Development (pp. 18–31). Somerville: Cascadilla Press. Bialystok, E., Luk, G., Peets, K. F., & Yang, S. (2010). Receptive vocabulary differences in monolingual and bilingual children. Bilingualism: Language and Cognition, 13(4), 525–531. Bird, E. K. R., Genesee, F., & Verhoeven, L. (2016). Bilingualism in children with developmental disorders: A narrative review. Journal of Communication Disorders, 63, 1–14. Dai, Y. G., Burke, J. D., Naigles, L., Eigsti, I. M., & Fein, D. A. (2018). Language abilities in monolingual-and bilingual-exposed children with autism or other developmental disorders. Research in Autism Spectrum Disorders, 55, 38–49. Drysdale, H., van der Meer, L., & Kagohara, D. (2015). Children with autism spectrum disorder from bilingual families: A systematic review. Review Journal of Autism and Developmental Disorders, 2, 26–38. Gonzalez-Barrero, A. M., & Nadig, A. (2017). Verbal fluency in bilingual children with autism Spectrum disorders. Linguistic Approaches to Bilingualism, 7, 460–475. Gonzalez-Barrero, A. M., & Nadig, A. (2018). Bilingual children with autism spectrum disorders: The impact of amount of language exposure on vocabulary and morphological skills at school age. Autism Research, 11, 1667–1678. Gonzalez-Barrero, A. M., & Nadig, A. (2019a). Brief report: Vocabulary and grammatical skills of bilingual children with autism spectrum disorders at school age.
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702 Journal of Autism and Developmental Disorders, 49, 3888–3897. Gonzalez-Barrero, A. M., & Nadig, A. S. (2019b). Can bilingualism mitigate set-shifting difficulties in children with autism spectrum disorders? Child Development, 90, 1043–1060. Hambly, C., & Fombonne, E. (2012). The impact of bilingual environments on language development in children with autism spectrum disorders. Journal of Autism and Developmental Disorders, 42, 1342–1352. Hoang, H., Gonzalez-Barrero, A. M., & Nadig, A. (2018). Narrative skills of bilingual children with autism spectrum disorder. Discours. Revue de linguistique, psycholinguistique et informatique. A Journal of Linguistics, Psycholinguistics and Computational Linguistics, (23), 1–33. Hudry, K., Rumney, L., Pitt, N., Barbaro, J., & Vivanti, G. (2018). Interaction behaviors of bilingual parents with their young children with autism spectrum disorder. Journal of Clinical Child & Adolescent Psychology, 47(sup1), S321–S328. Lim, N., O’Reilly, M. F., Sigafoos, J., Ledbetter-Cho, K., & Lancioni, G. E. (2019). Should heritage languages be incorporated into interventions for bilingual individuals with neurodevelopmental disorders? A systematic review. Journal of Autism and Developmental Disorders, 49, 887–912. Lund, E. M., Kohlmeier, T. L., & Durán, L. K. (2017). Comparative language development in bilingual and monolingual children with autism spectrum disorder: A systematic review. Journal of Early Intervention, 39, 106–124. Marinova-Todd, S. H., Colozzo, P., Mirenda, P., Stahl, H., Bird, E. K. R., Parkington, K., . . . & Genesee, F. (2016). Professional practices and opinions about services available to bilingual children with developmental disabilities: An international study. Journal of Communication Disorders, 63, 47–62. Meir, N., & Novogrodsky, R. (2019). Syntactic abilities and verbal memory in monolingual and bilingual children with high functioning autism (HFA). First Language. https://doi.org/10.1177/0142723719849981. Nadig, A. S., & Gonzalez-Barrero, A. M. (2019). Proficient bilingualism may alleviate some executive function difficulties in children with autism spectrum disorders. In L. Spradlin, I. Sekerina, & V. Valian (Eds.), Bilingualism, executive function, and beyond. Questions and insights (pp. 337–353). Amsterdam: John Benjamins. Ohashi, J. K., Mirenda, P., Marinova-Todd, S., Hambly, C., Fombonne, E., Szatmari, P., . . . & Volden, J. (2012). Comparing early language development in monolingual-and bilingual-exposed young children with autism spectrum disorders. Research in Autism Spectrum Disorders, 6, 890–897. Petersen, J. M., Marinova-Todd, S. H., & Mirenda, P. (2012). Brief report: An exploratory study of lexical skills in bilingual children with autism spectrum disorder. Journal of Autism and Developmental Disorders, 42, 1499–1503.
Biological Motion Reetzke, R., Zou, X., Sheng, L., & Katsos, N. (2015). Communicative development in bilingually exposed Chinese children with autism spectrum disorders. Journal of Speech, Language, and Hearing Research, 58, 813–825. Surrain, S., & Luk, G. (2017). Describing bilinguals: A systematic review of labels and descriptions used in the literature between 2005–2015. Bilingualism: Language and Cognition, 22, 401–415. Thordardottir, E. (2011). The relationship between bilingual exposure and vocabulary development. International Journal of Bilingualism, 15(4), 426–445. Thordardottir, E. (2015). Proposed diagnostic procedures for use in bilingual and cross-linguistic contexts. In S. Armon-Lotem, J. de Jong, & N. Meir (Eds.), Assessing multilingual children: Disentangling bilingualism from language impairment (pp. 331–358). Bristol: Multilingual Matters. Valicenti-McDermott, M., Tarshis, N., Schouls, M., Galdston, M., Hottinger, K., Seijo, R., Shulman, L., & Shinnar, S. (2013). Language differences between monolingual English and bilingual English-Spanish young children with autism spectrum disorders. Journal of Child Neurology, 28, 945–948. Yu, B. (2013). Issues in bilingualism and heritage language maintenance: Perspectives of minority-language mothers of children with autism spectrum disorders. American Journal of Speech-Language Pathology, 22, 10–24.
Biological Motion Martha D. Kaiser Child Neuroscience Laboratory, Yale Child Study Center, New Haven, CT, USA
Synonyms Human or animal motion
Definition Biological motion refers to the movements of humans or animals including eye, face, and full body motion. Typical observers exhibit robust sensitivity to biological motion cues provided by other people. However, disrupted sensitivity to biological motion, at the behavioral and neural level, is emerging as a hallmark of autism
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spectrum disorders (ASD). The lack of tuning to such socially relevant information may reflect some of the pathognomic social deficits associated with ASD.
References and Reading Annaz, D., Cambell, R., Coleman, M., Milne, E., & Swettenham, J. (2011). Young children with autism spectrum disorder do not preferentially attend to biological motion. Journal of Autism and Developmental Disorders. https://doi.org/10.1007/s10803-011-1256-3. Blake, R., & Shiffrar, M. (2007). Perception of human motion. Annual Review of Psychology, 58, 47–73. Kaiser, M. D., & Shiffrar, M. (2009). The visual perception of motion by observers with autism spectrum disorder: A review and synthesis. Psychonomic Bulletin & Review, 16(5), 761–777. Kaiser, M. D., Hudac, C. M., Shultz, S., Lee, S.-M., Cheung, C., Berkena, A. M., et al. (2010). Neural signatures of autism. Proceedings of the National Academy of Sciences, 107(49), 21223–21228. Klin, A., Lin, D., Gorrindo, P., Ramsay, G., & Jones, W. (2009). Two-year-olds with autism orient to nonsocial contingencies rather than biological motion. Nature, 459, 257–261.
Biomarker Research in Autism Spectrum Disorder Talena C. Day and James C. McPartland School of Medicine, Child Study Center, Yale University, New Haven, CT, USA
Definition A biological marker (biomarker) is defined as a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention (Biomarkers Definitions Working 2001). Although the term biomarker is often associated with biological processes, many data modalities can be used to objectively measure relevant processes, such as genes, metabolites, brain structure and function, and overt behaviors. In fields like oncology, biomarkers have been categorized into
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meaningful subtypes, and similar efforts to classify biomarkers in neuropsychiatry are in progress (Davis et al. 2015; McPartland 2016). Through meaningful subtypes, biomarkers can serve a multitude of purposes in autism spectrum disorder (ASD) research and treatment. Diagnostic biomarkers are discrete, and objective measures of whether or not an individual has ASD and their identification have been a long-standing objective in the field. Diagnostic biomarkers should not overlap with other conditions, reflected in the sensitivity and specificity of biomarkers (Davis et al. 2015). Screening biomarkers measure diagnostic risk status and potentially measure processes prior to observable behavioral symptoms. Screening biomarkers would allow for intensive early intervention for individuals which have been shown to improve the prognosis of ASD (Dawson et al. 2010; Estes et al. 2015; Lovaas 1987; Smith et al. 2000). Stratification biomarkers determine meaningful subgroups of individuals to predict or evaluate treatment (Loth et al. 2016a). Stratification biomarkers, for example, may indicate a group of children with ASD who may be likely to respond to a specific treatment. Early efficacy biomarkers indicate whether a treatment is altering targeted symptoms or the processes underlying those symptoms (McPartland 2016). In autism, an early efficacy biomarker may reveal symptomology changes from a treatment before they are observable in clinical observation or caregiver report, the current measures used in clinical trials of ASD. Target engagement biomarkers measure whether an intervention is affecting the intended process (Zhao et al. 2015). For example, in autism, a target engagement biomarker might assist in determining whether a specific medication is affecting neural activity in the intended brain region. These biomarker subtypes strive to improve the understanding of autism and foster precisionmedicine approaches in the diagnosis and treatment of autism (Varcin and Nelson 2016). Determining which biomarker subtype is under research will guide study designs and impact future applicability. Nevertheless, before the
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field can reliably utilize biomarkers, key challenges in the identification of biomarkers must be addressed in future study designs.
Historical Background Biomarker identification and validation in ASD, a condition characterized by two domains of core symptoms, including impairments in social communication and presence of restricted and repetitive behaviors (American Psychiatric Association 2013), hold promise in subtyping the heterogeneous neurodevelopmental disorder and guiding personalized treatments. In other biomedical fields, such as oncology, biomarkers have been successfully implemented to inform personalized treatments as well as measure processes involved in the diagnosis, prognosis, and prevention of various cancers (Nalejska et al. 2014). For example, treatments for different forms of cancer are based on specific genetic mutations (Kalia 2015). Despite significant biomarker discovery efforts in ASD, biomarkers are not yet readily applied in the treatment and diagnosis of ASD. Clinical judgment of observable behaviors remains the primary clinical tool applied in ASD. The diagnostic criteria, as outlined in the Diagnostic and Statistical Manual of Mental Disorders, fifth edition, consist of a checklist of behaviors in which a tally of symptoms across domains constitutes as a diagnosis (American Psychiatric Association 2013). The strongest and most reliable diagnostic tools include a parent interview (Lord et al. 1994) and play-based observational assessment (Lord et al. 2012). Furthermore, clinicians determine the best course of treatment and prognoses based on the results of these standardized behavioral assessments and subjective evaluation. Outcomes, from the individual level to multisite clinical trials, are often reported by parent and teacher report or behavioral observation. The applications of these methods have significantly advanced autism, used interchangeably with ASD, research, and these clinical tools can be administered reliably. Nevertheless, the nature of these methods remains remarkably similar to the methods used by Asperger (1944) and Kanner (1943) decades ago.
Biomarker Research in Autism Spectrum Disorder
Biomarker discovery for autism remains in its infancy. The first steps in identifying potential biomarkers are operationalizing the definition for the term “biomarker” and describing various subtypes. Challenges to biomarker research must be highlighted so that future research endeavors can determine the best course of action to address them in study designs. Currently, national and international studies are underway and address many of these challenges to biomarker research, and studies with large cohorts show promising signs of biomarker discovery in ASD. Future studies must consider the objectivity, sensitivity, and scalability of the methodologies used to measure biomarkers to ensure a large public health impact.
Current Knowledge Specificity of Biomarkers It is well-established that autism is an extremely heterogeneous disorder containing a large variation in core diagnostic features and associated characteristics, such as cognitive and language ability. Therefore, the search for biomarkers of ASD may prove elusive for potential underlying etiologies. Instead, a more effective approach may be to research potential biomarkers in relation to specific functional processes or symptom indices, as outlined by the Research Domain Criteria approach (http://www.nimh.nih.gov/rese arch-priorities/rdoc/), rather than in relation to diagnostic status. Many features present in autism are found in other disorders; for example, repetitive and restricted behaviors are present in obsessive compulsive disorder, and social dysfunction is present in schizophrenia spectrum disorders. Often, treatment goals are centered around variation in a specific area as opposed to diagnostic status more broadly. Biomarkers that measure changes in specific functional domains may prove extremely useful in these cases as opposed to biomarkers measuring diagnostic status. Individual Versus Composite Biomarkers Generally, biomarker studies in ASD and other neurodevelopmental disorders focus on utilizing
Biomarker Research in Autism Spectrum Disorder
a specific measure as a biomarker. For example, a study may focus on activation in a specific brain region associated with social behavior. Yet, the complexity of biological processes that give rise to social behavior, as well as the potential for compensatory mechanisms within these processes, and the well-known heterogeneity in autism complicate the potential utility of a single biomarker. It may be necessary to develop biomarkers reflecting multiple measures. An individual’s profile as measured through a composite from multiple biomarkers, either within one data modality or across data modalities, may provide more relevant information for prognoses and treatment compared to a single biomarker. Developmental Considerations Autism is a developmental disorder wherein early-life symptoms influence experience as well as experience-expectant biological processes (Dawson et al. 2005). Different biological systems may have varying developmental trajectories. Therefore, in individuals with ASD, the patterns of brain activity represent an interplay between early-occurring neural atypicalities and subsequent developmental sequelae. Consequently, biomarker studies conducted at different developmental points may result in disparate findings. A key factor in biomarker discovery in autism will be understanding biomarkers across development. To address this factor, biomarker research must be conducted with sufficiently large samples to analyze developmental effects or performed in tightly developmentally constrained studies. Differences At the Group and Individual Level In autism research, and neurodevelopmental disorders more generally, studies report findings as differences in means between groups of individuals. These findings often reflect a shift in the distribution of values between the groups on a biomarker parameter. Thus, there is little information about the biomarker at the individual level unless the value of the biomarker is true for every individual with ASD. Advancing translational goals of biomarkers requires designing studies to evaluate effects at the individual level or individual variation on specific characteristics. Through
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these study designs, biomarkers may, at the individual level, provide relevant information. Current Efforts in Biomarker Discovery While significant challenges to biomarker discovery in ASD are present, many are addressed through studies developed by collaborative consortia. Researchers are working together to develop protocols for multiple data modalities to identify biomarkers in autism and disseminate these methodologies. Through large cohorts or longitudinal designs, oftentimes both, these studies address many of the challenges to biomarker research in ASD and offer the first opportunities to review biomarkers in autism with sufficiently large sample sizes. The largest ongoing effort toward autism biomarker measure development is the European Autism Interventions – A Multicenter Study for Developing New Medications (EU-AIMS) Longitudinal European Autism Project (LEAP). The multisite, multidisciplinary study aims to discover stratification biomarkers for ASD. Throughout the study, participants are characterized through their symptom profile, comorbidities, quality of life, adaptive functioning, neurocognitive profile, brain structure and function, biochemical biomarkers, prenatal environmental risk factors, and genomics (Loth et al. 2016b). For this study, validation of biomarkers will be carried out similar to other biomedical fields in which biomarkers are used for specific clinical practice (Lee et al. 2006). Subgroups will be divided through a priori definitions or through data-driven approaches. The protocols will be shared with international research groups to determine reproducibility (Loth et al. 2017). The Autism Biomarkers Consortium for Clinical Trials (ABC-CT) is a US-based multisite study working in collaboration with EU-AIMS for similar purposes. The ABC-CT is dedicated to utilizing objective approaches to develop reliable measures of social-communicative behaviors in children with autism (www.asdbiomarkers. org). The longitudinal study collects electrophysiological, eye-tracking, and video-tracking data as well as comprehensive characterization of individuals through parent interviews, behavioral
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observation, and parent questionnaires at three points over a 6-month period. Additionally, DNA samples are collected from the participants with autism and their biological parents for future genetic analysis. Ultimately, the study is designed to create an infrastructure which will readily transfer into research and treatment areas, such as clinical trials, with tools that will allow for objective and predictive measurements of how individuals with ASD will respond to treatments. In contrast to these studies oriented toward biomarkers for use in clinical trials, the Infant Brain Imaging Study (IBIS) Network seeks to develop screening and diagnostic biomarkers. The study collects longitudinal MRI data at ages 6, 12, and 24 months from infants who have an older sibling with autism thus have an increased risk for developing autism themselves (Shen et al. 2017). It has already delivered promising results, as elevated extra-axial cerebrospinal fluid (EA-CSF) at 6 months of age predicted the diagnosis of toddlers at 24 months of age where the infants with most severe autistic behaviors at 24 months had the highest EA-CSF volume. This converged with findings from an earlier study with EA-CSF (Shen et al. 2013). The longitudinal study design deepened the developmental understanding of EA-CSF in typical and atypical development, and the automated segmentation algorithm used has the potential to translate to many settings (Pelphrey 2017). The consortia address many of the challenges to biomarker identification in autism through their study designs. By establishing multisite studies, biomarker measures can be evaluated with sufficiently large cohorts, developmental effects can be examined through large cohorts and longitudinal design, and the feasibility of translating these measures to a larger scale can be assessed.
Biomarker Research in Autism Spectrum Disorder
on the translational impact of the technologies used to measure these biomarkers. In order to implement biomarker research goals, the biomarker measures must be objective, sensitive, economical, and scalable. Here, electrophysiology is used an example addressing the aforementioned criteria. Applicability Methods that are useful in a large functional range, such as individuals with intellectual disability, and developmental range, as early as infancy, are well suited for future biomarker research. Electrophysiology requires minimal instructions with a straightforward application. Generally, the participant is only required to tolerate sensors on their skin. Artifacts due to movement during a session recording are specific to trials which can be removed from the data or corrected to preserve the integrity of the recording. Objectivity Methodological rigor in defining biomarker parameters when compared to other methodologies like behavioral methods will be critical for future research. Consistent data collection across multiple locations is possible through identical equipment and experimental paradigms without the need for developing clinician reliability.
Future Directions
Sensitivity Electrophysiological measures, for example, can potentially measure processes relevant to biomarker discovery more sensitively than behavioral methods. These recordings may delineate processes that either may never be present in behavior or not present in behavior yet. Many of the features that characterize the ASD diagnosis are not observable until the second year of life; therefore, methods that sensitively index these processes may elucidate atypical processes before overt behaviors are displayed.
The efforts in biomarker research are moving toward earlier identification and precision-based treatments of ASD while simultaneously deepening our understanding of the disorder. The success of proposed biomarkers, however, is dependent
Cost and Scalability Electrophysiological methods are an extremely cost-effective way to measure biological processes; thus prohibitive cost is not a factor compared to other biological assays. Large-scale
Biomarker Research in Autism Spectrum Disorder
implementation with electrophysiological recordings would readily translate into the current healthcare system since these facilities already exist. Psychophysiological methods offer both low cost and widespread availability necessary to utilize biomarkers on a public health level.
Conclusion Biomarker research has made considerable strides in the last decade. Long-term investments have been made in the hopes of discovering translatable, practicable biomarkers. As the field of biomarker research moves forward, researchers must ascertain the appropriate biomarkers to pursue and whether it is more advantageous to examine processes dimensionally or examine diagnoses categorically. While there are many challenges to biomarker research in autism, identifying and addressing these challenges will increase the ability to discover applicable biomarkers. Large-scale efforts such as the EU-AIMS LEAP and ABC-CT demonstrate collaborative efforts that should have the power to detect modest effect sizes and promise to disseminate the methodologies used to translate these potential biomarkers to other settings. Due to the current efforts in the field, biomarkers may soon be informing individualized treatment plans and implemented in multisite clinical trials and deepening our understanding of autism.
See Also ▶ Diagnosis and Classification ▶ Longitudinal Research in Autism ▶ Neural Signatures of Treatment Response ▶ Studies to Advance Autism Research and Treatment
References and Reading American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders: DSM-5. Washington, DC: American Psychiatric Association.
707 Asperger, H. (1944). Die “Autistischen Psychopathen” im Kindesalter. Archive fur psychiatrie und Nervenkrankheiten, 117(1), 76–136. Biomarkers Definitions Working Group. (2001). Biomarkers and surrogate endpoints: Preferred definitions and conceptual framework. Clinical Pharmacology and Therapeutics, 69(3), 89–95. https://doi.org/10. 1067/mcp.2001.113989. Davis, J., Maes, M., Andreazza, A., McGrath, J. J., Tye, S. J., & Berk, M. (2015). Towards a classification of biomarkers of neuropsychiatric disease: From encompass to compass. Molecular Psychiatry, 20(2), 152–153. https://doi.org/10.1038/mp.2014.139. Dawson, G., Webb, S. J., & McPartland, J. (2005). Understanding the nature of face processing impairment in autism: Insights from behavioral and electrophysiological studies. Developmental Neuropsychology, 27(3), 403–424. https://doi.org/10.1207/s15326942dn 2703_6. Dawson, G., Rogers, S., Munson, J., Smith, M., Winter, J., Greenson, J., . . . Varley, J. (2010). Randomized, controlled trial of an intervention for toddlers with autism: The early start Denver model. Pediatrics, 125(1), e17– e23. Estes, A., Munson, J., Rogers, S. J., Greenson, J., Winter, J., & Dawson, G. (2015). Long-term outcomes of early intervention in 6-year-old children with autism spectrum disorder. Journal of the American Academy of Child and Adolescent Psychiatry, 54(7), 580–587. https://doi.org/10.1016/j.jaac.2015.04.005. Kalia, M. (2015). Biomarkers for personalized oncology: Recent advances and future challenges. Metabolism, 64(3), S16–S21. https://doi.org/10.1016/j.metabol. 2014.10.027. Kanner, L. (1943). Autistic disturbances of affective contact. Nervous Child, 2(3), 217–250. Lee, J. W., Devanarayan, V., Barrett, Y. C., Weiner, R., Allinson, J., Fountain, S., . . . Wagner, J. A. (2006). Fitfor-purpose method development and validation for successful biomarker measurement. Pharmaceutical Research, 23(2), 312–328. https://doi.org/10.1007/ s11095-005-9045-3. Lord, C., Rutter, M., & Le Couteur, A. (1994). Autism diagnostic interview-revised: A revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. Journal of Autism and Developmental Disorders, 24(5), 659–685. Lord, C., Rutter, M., DiLavore, P., Risi, S., Gotham, K., & Bishop, S. (2012). Autism diagnostic observation schedule–2nd edition (ADOS-2). Los Angeles: Western Psychological Corporation. Loth, E., Murphy, D. G., & Spooren, W. (2016a). Defining precision medicine approaches to autism spectrum disorders: Concepts and challenges. Frontiers in Psychiatry, 7, 188. https://doi.org/10.3389/fpsyt.2016. 00188. Loth, E., Spooren, W., Ham, L. M., Isaac, M. B., AuricheBenichou, C., Banaschewski, T., . . . Murphy, D. G. (2016b). Identification and validation of biomarkers
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708 for autism spectrum disorders. Nature Reviews: Drug Discovery, 15(1), 70–73. https://doi.org/10.1038/nrd. 2015.7. Loth, E., Charman, T., Mason, L., Tillmann, J., Jones, E. J. H., Wooldridge, C., . . . Buitelaar, J. K. (2017). The EU-AIMS longitudinal European autism project (LEAP): Design and methodologies to identify and validate stratification biomarkers for autism spectrum disorders. Molecular Autism, 8, 24. https://doi. org/10.1186/s13229-017-0146-8. Lovaas, O. I. (1987). Behavioral treatment and normal educational and intellectual functioning in young autistic children. Journal of Consulting and Clinical Psychology, 55(1), 3. McPartland, J. C. (2016). Considerations in biomarker development for neurodevelopmental disorders. Current Opinion in Neurology, 29(2), 118–122. Nalejska, E., Maczynska, E., & Lewandowska, M. A. (2014). Prognostic and predictive biomarkers: Tools in personalized oncology. Molecular Diagnosis & Therapy, 18(3), 273–284. https://doi.org/10.1007/ s40291-013-0077-9. Pelphrey, K. (2017). Charting a course for autism biomarkers. Biological Psychiatry, 82(3), 155–156. https://doi.org/10.1016/j.biopsych.2017.06.002. Shen, M. D., Nordahl, C. W., Young, G. S., WoottonGorges, S. L., Lee, A., Liston, S. E., . . . Amaral, D. G. (2013). Early brain enlargement and elevated extraaxial fluid in infants who develop autism spectrum disorder. Brain, 136(Pt 9), 2825–2835. https://doi.org/ 10.1093/brain/awt166. Shen, M. D., Kim, S. H., McKinstry, R. C., Gu, H., Hazlett, H. C., Nordahl, C. W., . . . Gu, H. (2017). Increased extra-axial cerebrospinal fluid in high-risk infants who later develop autism. Biological Psychiatry, 82(3), 186–193. https://doi.org/10.1016/j.biopsy ch.2017.02.1095. Smith, T., Groen, A. D., & Wynn, J. W. (2000). Randomized trial of intensive early intervention for children with pervasive developmental disorder. American Journal of Mental Retardation, 105(4), 269–285. https://doi.org/10.1352/0895-8017(2000)1052.0.co;2. Varcin, K. J., & Nelson, C. A. (2016). A developmental neuroscience approach to the search for biomarkers in autism spectrum disorder. Current Opinion in Neurology, 29(2), 123–129. https://doi.org/10.1097/WCO. 0000000000000298. Zhao, X., Modur, V., Carayannopoulos, L. N., & Laterza, O. F. (2015). Biomarkers in pharmaceutical research. Clinical Chemistry, 61(11), 1343–1353. https://doi.org/10.1373/clinchem.2014.231712.
Biomedical Engineer ▶ Rehabilitation Engineer
Biomedical Engineer
Birth Complications Jan Rutger Van der Gaag Department of Psychiatry and Karakter University Center for Child and Adolescent Psychiatry, Radboud University Medical Centre, Utrecht, Netherlands Stradina University of Riga, Riga, Latvia
Definition Birth is a crucial event in life. The transition from the uterine status to the outside world is a very stressful occasion for parents but also for the newborn child. In retrospect, many parents will impute developmental deviances to a poor start in life. Thus, birth and perinatal complications and the first week of the neonate have been the focus of many research aimed to determine if factors involved with birth and start of life play a role in the etiology of autism.
Definitions • Birth: the transition from intrauterine life to life outside. This includes the vaginal pathway or the extraction through a so-called caesarian operation. • Complications: any deviance from normal physiology around the birth (perinatal period). • Perinatal period: an interval extending from the 28th week of gestation until the 28th day after birth. • Apgar score: simple repetitive method introduced by Virginia Apgar in 1952 to assess the health of a newborn baby – appearance (skin color), pulse (heart rate), grimace (reflex irritability), activity (muscle tone), and respiration. The five criteria are given marks ranging from 0 to 2. 0 is absent of highly disordered and 2 is fair and normal. Thus, the scale ranges from 0 to 10. It is mostly scored 5 and 10 min after birth. 7–10 is considered normal, 4–7 fairly low, and under 3 critically low.
Birth Complications
Historical Background Direct injuries following forcipes extractions or acute termination can cause massive damage to the brain of a neonate, with palsy and severe developmental hazards as consequences. These dramatic circumstances have not been related to the emergence in later life of any form of psychopathology. But over the years, subtle deviances at birth (low Apgar scores, respiratory distress, hypoglycemia, or hyperbilirubinemia after 5–7 days) have been associated with developmental disorders such as attention deficit hyperactivity disorders of autism. When taking the developmental history in parents of children with developmental disorders, one is often struck by the emphasis put on perinatal hazards. These retrospective recollections are not always reliable. Yet they illustrate how much value is given to the condition of the child just after birth as a potential cause of later disorders. Thus, methodologically retrospective date must be distrusted. In this item, the evidence for associations between birth complications and autism will be reviewed from methodologically sound studies.
Current Knowledge Gardener et al. (2011) carried out a comprehensive meta-analysis to evaluate the perinatal and neonatal risk factors for autism. After a PubMed, Embase, and PsycINFO search, 60 methodologically sound studies (out of 124 published until 2007) could be retained for a thorough metaanalysis of the possible causal relationship between the occurrence between perinatal and neonatal complications and autism. Since then, only five studies were published to date, implying that the Gardener et al. meta-analysis gives a good summary of the current knowledge with regard to peri- and neonatal factors that are associated with an increased risk for autism. This formulation is of great importance because peri- and neonatal factors appear to be by no means specific for any kind of psychopathology, thus pointing towards a multicausal heterogeneity already hypothesized by Bolton et al. (1997).
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But the results from series of well-conducted studies clearly show that there are factors that have no association with autism and others that show a positive association with the occurrence of autism later in life: Factors that show no association with autism are the following: premature rupture of membranes, delayed labor, loss of amnionic fluid on the day before delivery, analgesia during labor, green [meconium holding] amnionic fluid, acidosis (pH < 7.2 in the umbilical cord, shoulder dystocia, near-dead baby, “blue baby,” hypoglycemia, hypocalcemia, infantile vomiting, intracranial hemorrhage, macrocephaly, abnormal fetal cardiac activity, assisted vaginal delivery, postterm birth, a high birth weight, and incubator use! Finally, neither preterm birth nor Cesarean delivery reached statistical significance. Factors that do show a significant increase of the risk for autism are the following: abnormal presentation in general, beech presentation, umbilical cord complications (prolapse, cord wrapping around the neck), multiple birth, (very) low birth weight, small for gestational age, fetal distress, Apgar scores low after 5 min, birth injury or trauma, congenital malformations, meconium aspiration, neonatal anemia, ABO or rhesus incompatibility, and hyperbilirubinemia. There are also two factors that are not related directly to the condition of the child but enhance the risk for autism and those are: maternal bleeding and season of birth (with two high-risk periods, namely, children born in March and in the late summer (August and September)). Yet, from the discussions around birth complications as a risk factor for autism, it appears clearly that they cannot be perceived independently from earlier prenatal factors. Factors such as advanced age of both mother and father, but also an autistic condition in the offspring as a result of genetic and early embryo-environment interplay early in gestation (viral infections, drugs, etc.), suggest that the birth complications are more the result of prenatal factors that are the cause of autism later on. According to the classic Bolton et al. (1997) study on the “shared risk hypothesis,” this shared risk hypothesis is also supported by the Zwaigenbaum et al. studies
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(2002) that show more composite prenatal, perinatal, and neonatal adversity among both affected children and unaffected siblings in families with a high loading for the broader autism phenotype.
Future Directions In order to fully understand the impact as a risk factor of birth complications on the increased risk for autism, longitudinal studies, starting well before birth like the ABC study in Norway of Generation R, are needed in order to get a better understanding of the interplay between family genetic-embryonic development-birth hazards and neonatal stress on the risk factors for autism.
References and Reading Bilder, D., Pinborough-Zimmerman, J., Miller, J., & McMahon, W. (2009). Prenatal, perinatal, and neonatal factors associated with autism spectrum disorders. Pediatrics, 123(5), 1293–1300. Bolton, P. F., Murphy, M., Macdonald, H., Whitlock, B., Pickles, A., & Rutter, M. (1997). Obstetric complications in autism: Consequences or causes of the condition? Journal of the American Academy of Child and Adolescent Psychiatry, 36(2), 272–281. Brimacombe, M., Ming, X., & Lamendola, M. (2006). Prenatal and birth complications in autism. Maternal and Child Health Journal, 11(1), 73–79. Burstyn, I., Sithole, F., & Zwaigenbaum, L. (2011a). Autism spectrum disorders, maternal characteristics and obstetric complications among singletons born in Alberta, Canada. Chronic Diseases in Canada, 30(4), 125–134. Burstyn, I., Wang, X., Yasui, Y., Sithole, F., & Zwaigenbaum, L. (2011b). Autism spectrum disorders and fetal hypoxia in a population-based cohort: Accounting for missing exposures via estimationmaximization algorithm. BMC Medical Research Methodology, 11, 2. Cederlund, M., & Gillberg, C. (2004). One hundred males with Asperger syndrome: A clinical study of background and associated factors. Developmental Medicine and Child Neurology, 46(10), 652–660. Croen, L. A., Yoshida, C. K., Odouli, R., & Newman, T. B. (2005). Neonatal hyperbilirubinemia and risk of autism spectrum disorders. Pediatrics, 115(2), e135–e138. Gardener, H., Spiegelman, D., & Buka, S. L. (2009). Prenatal risk factors for autism: Comprehensive meta-
Birth Complications analysis. The British Journal of Psychiatry, 195(1), 7–14. Gardener, H., Spiegelman, D., & Buka, S. L. (2011). Perinatal and neonatal risk factors for autism: A comprehensive meta-analysis. Pediatrics, 128(2), 344–355. Glasson, E. J., Bower, C., Petterson, B., de Klerk, N., Chaney, G., & Hallmayer, J. F. (2004). Perinatal factors and the development of autism: A population study. Archives of General Psychiatry, 61(6), 618–627. Haglund, N. G., & Källén, K. B. (2011). Risk factors for autism and Asperger syndrome. Perinatal factors and migration. Autism, 15(2), 163–183. Hultman, C. M., Sparén, P., & Cnattingius, S. (2002). Perinatal risk factors for infantile autism. Epidemiology, 13(4), 417–423. Juul-Dam, N., Townsend, J., & Courchesne, E. (2001). Prenatal, perinatal, and neonatal factors in autism, pervasive developmental disorder-not otherwise specified, and the general population. Pediatrics, 107(4), E63. Kern, J. K. (2003). Purkinje cell vulnerability and autism: A possible etiological connection. Brain & Development, 25(6), 377–382. Kolevzon, A., Gross, R., & Reichenberg, A. (2007). Prenatal and perinatal risk factors for autism: A review and integration of findings. Archives of Pediatrics & Adolescent Medicine, 161(4), 326–333. Lampi, K. M., Banerjee, P. N., Gissler, M., Hinkka-YliSalomäki, S., Huttunen, J., Kulmala, U., et al. (2011). Finnish prenatal study of autism and autism spectrum disorders (FIPS-A): Overview and design. Journal of Autism and Developmental Disorders, 41(8), 1090–1096. Lyall, K., Pauls, D. L., Spiegelman, D., Ascherio, A., & Santangelo, S. L. (2012). Pregnancy complications and obstetric suboptimality in association with autism spectrum disorders in children of the nurses’ health study II. Autism Research, 5(1), 21–30. Maimburg, R. D., & Vaeth, M. (2006). Perinatal risk factors and infantile autism. Acta Psychiatrica Scandinavica, 114(4), 257–264. Maimburg, R. D., Vaeth, M., Schendel, D. E., Bech, B. H., Olsen, J., & Thorsen, P. (2008). Neonatal jaundice: A risk factor for infantile autism? Paediatric and Perinatal Epidemiology, 22(6), 562–568. Simon, E. N. (2004). Autism as a birth defect. Birth Defects Research. Part A, Clinical and Molecular Teratology, 70(6), 416; 15211712. Sivberg, B. (2003). Parents’ detection of early signs in their children having an autistic spectrum disorder. Journal of Pediatric Nursing, 18(6), 433–439. Stein, D., Weizman, A., Ring, A., & Barak, Y. (2006). Obstetric complications in individuals diagnosed with autism and in healthy controls. Comprehensive Psychiatry, 47(1), 69–75. Stevens, M. C., Fein, D. H., & Waterhouse, L. H. (2000). Season of birth effects in autism. Journal of Clinical and Experimental Neuropsychology, 22(3), 399–407.
Birth Rank Effect Sugie, Y., Sugie, H., Fukuda, T., & Ito, M. (2005). Neonatal factors in infants with autistic disorder and typically developing infants. Autism, 9(5), 487–494. Taylor, E. (2011). Antecedents of ADHD: A historical account of diagnostic concepts. Attention Deficit and Hyperactivity Disorders, 3(2), 69–75. Wilkerson, D. S., Volpe, A. G., Dean, R. S., & Titus, J. B. (2002). Perinatal complications aspredictors of infantile autism. International Journal of Neuroscience, 112(9), 1085–1098. Yeates-Frederikx, M. H., Nijman, H., Logher, E., & Merckelbach, H. L. (2000). Birth patterns in mentally retarded autistic patients. Journal of Autism and Developmental Disorders, 30(3), 257–262. Zhang, X., Lv, C. C., Tian, J., Miao, R. J., Xi, W., HertzPicciotto, I., et al. (2010). Prenatal and perinatal risk factors for autism in China. Journal of Autism and Developmental Disorders, 40(11), 1311–1321. Erratum in: Journal of Autism and Developmental Disorders, 40(11). Zwaigenbaum, L., Szatmari, P., Jones, M. B., Bryson, S. E., MacLean, J. E., Mahoney, W. J., et al. (2002). Pregnancy and birth complications in autism and liability to the broader autism phenotype. Journal of the American Academy of Child and Adolescent Psychiatry, 41(5), 572–579.
Birth Order ▶ Birth Order Effects in Autism
Birth Order Effects in Autism Tychele N. Turner University of Washington, Seattle, WA, USA
Synonyms Birth order; Birth rank effect
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effects have been identified in a few studies. One type of birth order effect that has been observed and replicated is a lower nonverbal IQ score in the second child with autism in the family (Lord 1992; Spiker et al. 2001). Another study found that there was an effect of birth order on multiple aspects of autism including repetitive behaviors, phrase speech, social communication, and nonverbal communication (Reichenberg et al. 2007). Finally, in a more recent study general birth order effects were seen where middle births in multiplex families and later births in simplex families were more likely to develop autism (Turner et al. 2011). There are a number of potential reasons for birth order effects, and these include demographic factors such as stoppage in a family after the first child with autism is born, as well as biological factors including paternal age effects, maternal age effects, maternal-fetal genotype incompatibilities, and potential epigenetic effects. The discovery of birth order effects can help guide researchers in their examination of potential risk factors for autism.
References and Reading Lord, C. (1992). Birth order effects on nonverbal IQ in families with multiple incidence of autism or pervasive developmental disorder. Journal of Autism and Developmental Disorders, 22(4), 663–666. Reichenberg, A., Smith, C., Schmeidler, J., & Silverman, J. M. (2007). Birth order effects on autism symptom domains. Psychiatry Research, 150(2), 199–204. https://doi.org/10.1016/j.psychres.2004.09.012. Spiker, D., Lotspeich, L. J., Dimiceli, S., Szatmari, P., Myers, R. M., & Risch, N. (2001). Birth order effects on nonverbal IQ scores in autism multiplex families. Journal of Autism and Developmental Disorders, 31(5), 449–460. Turner, T., Pihur, V., & Chakravarti, A. (2011). Quantifying and modeling birth order effects in autism. PLoS One, 6(10), e26418. https://doi.org/10.1371/journal. pone.0026418.
Definition Birth order relates to the order in which a child is born to a set of parents and birth order effects refer to differences seen between individuals that are not of the same birth order. In autism, birth order
Birth Rank Effect ▶ Birth Order Effects in Autism
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Birth-to-Three ▶ Early Intervention
BITSEA ▶ Brief Infant-Toddler Social and Emotional Assessment (BITSEA)
BITSEA-ASD Subscales ▶ Brief Infant-Toddler Social and Emotional Assessment (BITSEA)
Blindness Therese R. Welch School of Medicine and Dentistry, University of Rochester Medical Center, Rochester, NY, USA
Definition The relationship of blindness and autism spectrum disorders (ASD) is complex, and, as a group of prominent researchers had aptly noted, it has been a continuing source of interest and perplexity for researchers and clinicians (Hobson et al. 1999). Central to examinations of the relationship is a collection of behaviors regarded as characteristic of children who are blind, in particular children who are congenitally blind, and children who have profound visual impairment. The collection includes stereotyped and ritualistic behaviors, pronounced limitations of social and communicative competence, delayed and limited symbolic play and language, delayed use and reversals of personal pronouns, echolalia and speech limitations, and difficulties with abstract thinking (Gense and Gense 2005; Perez-Pereira and Conti-Ramsden 1999).
Birth-to-Three
Many of these behaviors also are considered to be characteristic of sighted children who have ASD. Because of the seeming commonality of certain behaviors of children who are blind and children who have ASD, researchers and clinicians have been challenged in understanding the nature of the apparent similarities (Fazzi et al. 2007; Hobson and Lee 2010; Parr et al. 2010). Essentially, the question is whether such behaviors, when demonstrated by blind children, indicate ASD. Blindness literature can offer other hypotheses for what are viewed as autistic-like behaviors in blind children: Careful consideration should be given to sensory and social deprivation, early medical complications, limited motor or physical activities, lack of ability to imitate, lack of a variety of activities, self-regulation, and others (Huebner 1986; McHugh and Lieberman 2003; Warren 1984). The preeminent challenge for both researchers and clinicians may well be definitively identifying ASD in individuals who have significant vision loss. A major hurdle is the lack of appropriate diagnostic screening and assessment tools for this population. Standards, such as the Autism Diagnostic Observation Schedule (ADOS: Lord et al. 1999, 2001, 2002, 2008) and Childhood Autism Rating Scale (CARS: Schopler et al. 1988; Schopler and Van Bourgondien 2010), which are designed for use with sighted individuals, are heavily weighted with visually based tasks. In assessing blind subjects, some researchers have made modifications to these tools, claiming their clinical utility, although the validity of these modifications has not been tested (Jutley-Neilson et al. 2013; Williams et al. 2014). The absence of measures designed for use with children who have significant visual impairments compounds the difficulties of differentiating autistic-like behaviors from ASD in blind children.
Historical Background In the field of blindness, the classic writings of Selma Fraiberg are most often recognized as the first reports of autistic-like behaviors in blind
Blindness
children. In 1964, Fraiberg and Freedman published their observations of a group of blind children, stating that nearly a third of the children had “ego deviation.” Keeler (1958), however, is credited with the earliest report of such behaviors in his study of young children with retrolental fibroplasia (otherwise known as retinopathy of prematurity). Several later studies continued investigation of an association between ASD and specific ophthalmological disorders or certain genetic disorders associated with a significant visual impairment. The most frequently researched diagnoses have included Leber’s congenital amaurosis, optic nerve hypoplasia, septooptic nerve dysplasia, CHARGE syndrome, and retinopathy of prematurity (Bahar et al. 2003; Chase 1972; Ek et al. 1998; Smith et al. 2005). Researchers, however, are not in consensus regarding the association between a specific ophthalmological diagnosis and ASD (Fraiberg 1977; Hobson et al. 1999; Mukaddes et al. 2007). Some investigators indicated that congenital blindness predisposed a child to ASD (Brown et al. 1997). As researchers sought a better understanding of the relationship between blindness and ASD, the scope of investigations broadened. The degree of vision impairment in relation to the manifestation of autistic-like features or ASD in children who are blind has been a focus of investigators. Some researchers have claimed that more severe vision loss, especially loss of the ability to distinguish forms, increases the likelihood of autistic-like behaviors (Cass et al. 1994). Cognitive impairment reflected in low IQ scores, as well as other additional disabilities, has also been associated with autistic-like behaviors and ASD in blind children (Brown et al. 1997). Other studies have examined the roles of sensory deprivation and related environmental factors in contributing to the presence of the autisticlike behaviors and ASD. Some investigators have taken a functional perspective of these behaviors, countering that in most cases, such behaviors are adaptive responses to vision loss (Cass 1998; Mottron and Burack 2001). A later review of studies reported that there were no consistent results regarding the relationship and specific types of ophthalmological diagnoses, the severity
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of vision impairment, and the role of associated disabilities (Mukaddes et al. 2007). Tied to the complexities of identification of ASD in blind children have been questions of prevalence. Reported prevalence rates have varied greatly. The US Centers for Disease Control and Prevention (CDC) conducted surveillance of children with visual impairment in metropolitan Atlanta, Georgia, and determined that approximately 6–7% of the children had co-occurring ASD (Kancherla et al. 2013). Studies have reported up to 30 times greater prevalence of ASD in blind children than in sighted children (Cass et al. 1994; Hobson et al. 1999; Jure et al. 2016).
Current Knowledge A recent review of 11 studies published from 2000 to 2015 focused on the similarity between visual impairment and autistic traits (Buchart et al. 2017). Findings suggested that the presence of autistic traits, such as limited communication and social interaction, together with repetitive and restrictive behavior does not necessarily warrant a diagnosis of ASD. The authors specifically noted that sample sizes were small, and measures used to diagnose ASD have not been systematically tested on a broader visually impaired population. [It is important to recognize, however, the low prevalence rate of childhood blindness. According to a US survey, it is the least prevalent, 0.13%, of all developmental disabilities (Boyle et al. 2011)]. The authors also expressed concern that some studies had adapted standardized autism diagnostic measures, thus undermining their validity and reliability. Debates have continued regarding the nature of autistic-like behaviors in children with significant vision loss. Andrews and Wyver (2005) have held that blind children who also display such behaviors should be viewed as having specific features rather than being on the autism spectrum. The authors questioned whether these behaviors of blind children are characteristic of true ASD or a different developmental pathway. Hobson and Lee (2010) also referred to a distinctive
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underlying pathway for blind children’s features that seem characteristic of ASD. They, in fact, termed the features as being quasi-autistic and suggested the clinicians hold back from the designation of ASD. Williams et al. (2014) reported that some behaviors suggestive of ASD in sighted children do not distinguish children with ASD and significant vision loss from children without ASD and significant vision loss. Researchers have given particular attention to young blind children who exhibit autistic-like behaviors. The recent study of Williams et al. (2014) included parent reports based on a modified version of the Autism Diagnostic Interview, Revised (ADI-R: Rutter et al. 2003). Several parents of young blind children noted significant differences in their children’s autistic-like symptoms before and after the age of 5 years. They reported that as their children developed and became comfortable exploring their environments, their social and communicative behaviors increased greatly, while their repetitive behaviors decreased proportionally. Similarly, a much earlier study of congenitally blind children found that various stereotypic behaviors occur during the first and second years of life, but many decrease from the age of 3 years onward (Troster et al. 1991). The findings also appear consistent with research by Hobson and Lee (2010), who reassessed nine congenitally blind children and seven sighted comparison children, all of whom had met diagnostic criteria for ASD, 8 years earlier. The results of reassessment determined that only one of the blind children met criteria for ASD, although all seven sighted children continued to meet the criteria. Thus, the message to clinicians is to use caution in diagnosing ASD in very young children with vision impairment, as the symptoms experienced by some children may improve significantly as they develop further (Williams et al. 2014). Differentiating between autistic-like behaviors related to blindness and those essential to a diagnosis of ASD is particularly challenging, especially if clinicians have limited experience with blind children. When considering children with significant vision loss, there is risk of misinterpreting the basis of a child’s behaviors if the
Blindness
child is not provided appropriate modes and opportunities to develop and demonstrate competence.
Future Directions Clearly, efforts must be directed toward the development of screening and assessment tools for identification of ASD in individuals with significant vision loss. Current diagnostic screening and assessment tools are heavily weighted with visually based features and tasks, having been designed for use with sighted children. The components of tools for use with individuals who have significant visual loss need to be grounded in modalities other than solely visual. Researchers and clinician will need to devise alternative equivalents for criteria that are basic to current measures, such as eye contact, directed gaze, and joint attention. The development of such measures is in very preliminary stages. A key concern is the lack of information regarding the progression of social development in the very heterogeneous population of individuals with significant vision loss. Much is yet to be learned. Normative data on social development in children with visual impairment are necessary to best inform diagnostic criteria for ASD in this population. Directly tied to identification of ASD in children who are blind or who have significant visual impairment is the need for appropriate teaching methods, tools, and strategies for this population. It has not been established whether the current instructional interventions used with sighted children who have ASD are the most appropriate for children with significant vision loss, nor whether the various means can be adapted to be such. The question merits investigation, with the ultimate goal of developing strategies, tools, and materials to address the specific learning needs of this population. Through the review of studies regarding blindness and ASD, it is readily apparent that efforts have been focused nearly exclusively on children, especially young children. A near void exists regarding information about ASD in blind adults. A broader, life course perspective is called for in future studies.
Blindness
See Also ▶ Chess, Stella ▶ Rubella
References and Reading Andrews, R., & Wyver, S. (2005). Autistic tendencies: Are there different pathways for blindness and autism spectrum disorder? British Journal of Visual Impairment, 23(2), 52–57. Bahar, C., Brody, J., McCann, M. E., Mendiola, R. M., & Slot, G. (2003). A multidisciplinary approach to educating preschool children with optic nerve hypoplasia and septo-optic nerve dysplasia. RE View, 35(1), 15–21. Boyle, C. A., Boulet, S., Schieve, L. A., Cohen, R. A., Blumberg, S. J., Yeargin-Allsopp, M., et al. (2011). Trends in the prevalence of developmental disabilities in US children, 1997–2008. Pediatrics, 127(6), 1034–1042. Brown, R., Hobson, R. P., & Lee, A. (1997). Are there “autistic-like” features in congenitally blind children? Journal of Child Psychology and Psychiatry, 38(6), 693–703. Buchart, M., Long, J. J., Brown, M., McMillan, A., Bain, J., & Karatzias, T. (2017). Autism and visual impairment: A review of the literature. Review Journal of Autism and Developmental Disorders, 4, 118–131. Cass, H. (1998). Visual impairment and autism: Current questions and future research. Autism, 2(2), 117–138. Cass, H., Sonkensen, P. M., & McCohachie, H. R. (1994). Developmental setback in severe visual impairment. Archives of Disease in Childhood, 70, 192–196. Chase, J. B. (1972). Retrolental fibroplasia and autistic symptomology. New York: American Foundation for the Blind. Chess, S. (1971). Autism in children with congenital rubella. Journal of Autism and Childhood Schizophrenia, 1, 33–47. Dale, N., & Salt, A. (2008). Social identity, autism and visual impairment in the early years. British Journal of Visual Impairment, 26(2), 135–146. de Vaan, G., Vervloed, M., Peters-Scheffer, N., van Gent, T., Knoors, H., & Verhoeven, L. (2016). Behavioural assessment of autism spectrum disorders in people with multiple disabilities. Journal of Intellectual Disability Research, 60(2), 101–112. de Verdier, K., Fernell, E., & Ek, U. (2019). Blindness and autism: Parents’ perspectives on diagnostic challenges, support needs and support provision. Journal of Autism and Developmental Disorders, 49, 1–10. Ek, U., Fernell, E., Jacobson, L., & Gilberg, C. (1998). Relation between blindness due to retinopathy of prematurity and autism spectrum disorders: A populationbased study. Developmental Medicine and Child Neurology, 40, 297–301.
715 Ek, U., Fernell, E., Jacobson, L., & Gilberg, C. (2005). Cognitive and behavioural characteristics in blind children with bilateral optic nerve hypoplasia. Acta Paediatrica, 94, 1421–1426. Fazzi, E., Rossi, M., Signorini, S., Rossi, G., Bianchi, P. E., & Lanzi, G. (2007). Leber’s congenital amaurosis: Is there an autistic component? Developmental Medicine and Child Neurology, 49, 503–507. Fraiberg, S. (1977). Insights for the blind. New York: Basic Books. Fraiberg, S., & Freedman, D. (1964). Studies in the ego development of the congenitally blind. The Psychoanalytic Study of the Child, 19, 113–169. Gense, M. H., & Gense, D. J. (2005). Autism spectrum disorders and visual impairment: Meeting students’ learning needs. New York: AFB Press. Gense, M. H., & Gense, D. J. (2011). Autism spectrum disorders and visual impairment are here to stay: Using an expanded core curriculum to implement a comprehensive program of instruction. Journal of Visual Impairment & Blindness, 105(6), 329–334. Hobson, R. P., & Lee, A. (2010). Reversible autism among congenitally blind children? A controlled follow-up study. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 51(11), 1235–1241. Hobson, R. P., Lee, A., & Brown, R. (1999). Autism and congenital blindness. Journal of Autism and Developmental Disorders, 29(1), 45–56. Huebner, K. M. (1986). Social skills. In G. T. Scholl (Ed.), Foundations of education for blind and visually handicapped children and youth (pp. 341–362). New York: American Foundation for the Blind. Jure, R., Pogonza, R., & Rapin, I. (2016). Autism spectrum disorders (ASD) in blind children: Very high prevalence, potentially better outlook. Journal of Autism and Developmental Disorders, 46(3), 749–759. Jutley-Neilson, J., Harris, G., & Kirk, J. (2013). The identification and measurement of autistic features in children with septo-optic dysplasia, optic nerve hypoplasia and isolated hypopituitarism. Research in Developmental Disabilites, 34(12), 4310–4318. Kancherla, V., Van Naarden Braun, K., & Yeargin-Allsopp, M. (2013). Childhood vison impairment, hearing loss and co-occurring autism spectrum disorder. Disability and Health Journal, 6(4), 333–342. Keeler, W. R. (1958). Autistic patterns and defective communication in blind children with retrolental fibroplasia. In P. H. Hoch & J. Zubin (Eds.), Psychopathology of communication (pp. 64–83). New York: Grune & Stratton. Kiani, R., Bhaumik, S., Tyrer, F., Bankart, J., Miller, H., Cooper, S. A., & Brugha, T. S. (2019). The relationship between symptoms of autism spectrum disorder and visual impairment among adults with intellectual disability. Autism Research: Official Journal of the International Society for Autism Research, 12(9), 1411–1422. Lord, C., Rutter, M., DeLavore, P. C., & Risi, S. (1999, 2001, 2002, 2008). Autism diagnostic observation
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716 schedule. Los Angeles: Western Psychological Services. McHugh, B. E., & Lieberman, L. J. (2003). The impact of developmental factors on incidence of stereotypic socking among children with visual impairments. Journal of Visual Impairment and Blindness, 97(8), 453–474. Mottron, L., & Burack, J. A. (2001). Enhanced perceptual functioning in the development of autism. In J. A. Burack, T. Charman, N. Yirmiya, & P. R. Zelaso (Eds.), The development of autism (pp. 131–148). Mahwah: Lawrence Erlbaum Associates. Mukaddes, N. M., Kilincasin, A., Kuckyazici, G., Sevketoglu, T., & Tuncer, S. (2007). Autism in visually impaired individuals. Psychiatry and Clinical Neurosciences, 61(1), 39–44. Parr, J. R., Dale, N. J., Shaffer, L. M., & Salt, A. T. (2010). Social communication difficulties and autism spectrum disorder in young children with optic nerve hypoplasia and/or septo-optic dysplasia. Developmental Medicine and Child Neurology, 52(10), 917–921. Perez-Pereira, M., & Conti-Ramsden, G. (1999). Language development and social interaction in blind children. East Sussex: Psychology Press. Pring, L. (Ed.). (2005). Autism and blindness: Research and reflections. London: Whurr Publishers. Rutter, M., LeCouteur, A., & Lord, C. (2003). Autism diagnostic interview-revised. Los Angeles: Western Psychological Services. Schopler, E., & Van Bourgondien, M. E. (2010). The childhood autism rating scale (2nd ed.). Los Angeles: Western Psychological Services. Schopler, E., Reichler, R., & Rochen Renner, B. (1988). The childhood autism rating scale. Los Angeles: Western Psychological Services. Smith, I. M., Nichols, S. L., Issekutz, K., & Blake, K. (2005). Behavioral profiles and symptoms of autism in CHARGE syndrome. American Journal of Medical Genetics, 133A, 248–256. Troster, H., Brambring, M., & Beelman, A. (1991). The age dependence of stereotyped behaviours in blind infants and preschoolers. Child: Care, Health and Development, 17, 137–157. Warren, D. H. (1984). Blindness and early childhood development (2nd ed.). New York: American Foundation for the Blind. Williams, M. E., Fink, C., Zamora, I., & Borchert, M. (2014). Autism assessment in children with optic nerve hypoplasia and other vision impairments. Developmental Medicine and Neurology, 56(1), 66–72.
Blitz-Nick-Salaam Kra¨mpfe ▶ Infantile Spasms/West Syndrome
Block Design Subtest Timothy Soto and Cate Kraper Clinical Psychology, University of Massachusetts Boston, Boston, MA, USA
Definition Block design is a subtest that is administered as part of several of the Wechsler Intelligence tests, including the Wechsler Preschool and Primary Scale of Intelligence (WPPSI, the Wechsler Intelligence Scale for Children-fourth edition (WISC-IV; Wechsler 2003) and the Wechsler Adult Intelligence Scale-fourth edition (WAISIV; Wechsler 2008). This subtest is included in the calculation of Performance IQ. It is primarily a measure of visual-spatial and organizational processing abilities, as well as nonverbal problem-solving skills. Because it is a timed task, it is also influenced by fine motor skills. The individual is presented with identical blocks with surfaces of solid red, surfaces of solid white, and surfaces that are half red and half white. Using an increasing number of these blocks, the individual is required to replicate a pattern that the test administrator presents to them – first as a physical model, and then as a two-dimensional picture. The number of blocks required to match the presented models increases and the patterns become increasingly difficult to visually dissect into components. Individuals who do well on this subtest tend to have an aptitude for perceiving spatial patterns and for flexible problem solving; performance is also aided by the ability to work quickly. Conversely, one factor that may hinder an individual’s performance on block design is the presence of high anxiety or perfectionistic tendencies (Hopko et al. 2005), as these can lead to an overly cautious approach that causes the individual to finish after the time limit. Poor performance may also be related to a number of factors that affect an individual’s ability to perceive spatial patterns, manipulate objects, or integrate visual and spatial information. Of note, individuals
Blood-Oxygen-Level-Dependent (BOLD) Signal
with autism spectrum disorders have been observed to show superior performance on the block design task (Shah and Frith 1993). This relative strength is described by the hypothesis of the Weak Central Coherence Theory, which suggests individuals with autism have difficulty seeing the “big picture,” and instead may perceive parts of the whole with more relative skill than individuals without autism (Happe and Frith 2006). While not captured in the final score of block design, it is clinically useful to observe how an individual approaches this task. One such behavior that can be informative to the test administrator includes the above-mentioned perfectionistic tendency, or alternatively, the tendency to be impulsive or careless. An individual’s persistence may also be noted, as well as whether the individual tends to approach the pattern in a piecemeal fashion, or in a more global fashion.
See Also ▶ Perceptual Organization Index (POI) ▶ Weak Central Coherence ▶ Wechsler Preschool and Primary Scale of Intelligence
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Blood-Oxygen-Level Dependence ▶ Blood-Oxygen-Level-Dependent (BOLD) Signal
Blood-Oxygen-LevelDependent (BOLD) Contrast ▶ Event-Related Functional Magnetic Resonance Imaging (MRI)
Blood-Oxygen-LevelDependent (BOLD) Signal Kevin A. Pelphrey Harris Child Study Center, Yale University School of Medicine, New Haven, CT, USA
Synonyms Blood-oxygen-level dependence
References and Reading Happe, F., & Frith, U. (2006). The weak central coherence account: Detail-focused cognitive style in autism spectrum disorders. Journal of Autism and Developmental Disorders, 36, 5–25. Hopko, D. R., Crittendon, J. A., Grant, E., & Wilson, S. A. (2005). The impact of anxiety on performance IQ. Anxiety, Stress, & Coping: An International Journal, 18, 17–35. Shah, A., & Frith, U. (1993). Why do autistic individuals show superior performance on the block design task? Journal of Child Psychology and Psychiatry, 34, 1351–1364. Wechsler, D. (2002). The Wechsler preschool and primary scale of intelligence (3rd ed.). San Antonio: The Psychological Corporation. Wechsler, D. (2003). Wechsler intelligence scale for children (4th ed.). San Antonio: The Psychological Corporation. Wechsler, D. (2008). Wechsler adult intelligence scale (4th ed.). San Antonio: Pearson.
Definition Blood-oxygen-level-dependent (BOLD) signal is the magnetic resonance imaging (MRI) contrast of blood deoxyhemoglobin. Seiji Ogawa and his colleagues first discovered this intrinsic contrast mechanism in 1990. Neurons do not store internal reserves of glucose and oxygen, which are essential to their proper function. Increases in neuronal activity, typically in response to a demand for information processing, require more glucose and oxygen to be rapidly delivered via the blood stream. Via this hemodynamic response, blood releases glucose and oxygen to active neurons at a faster rate relative to inactive neurons. This results in a surplus of oxyhemoglobin localized
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to the active area, giving rise to a measureable change in the local ration of oxy- to deoxyhemoglobin, thus providing a localizable marker of activity for MRI.
See Also ▶ Event-Related Functional Magnetic Resonance Imaging (MRI)
References and Reading Belliveau, J. W., Kennedy, D. N., McKinstry, R. C., Buchbinder, B. R., Weisskoff, R. M., Cohen, M. S., Vevea, J. M., Brady, T. J., & Rosen, B. R. (1991). Functional mapping of the human visual cortex by magnetic resonance imaging. Science, 254, 716–719. Kwong, K. W., Belliveau, J. W., Chesler, D. A., Goldberg, I. E., Weisskoff, R. M., Poncelet, B. P., Kennedy, D. N., Hoppel, B. E., Cohen, M. S., Turner, R., Cheng, H., Brady, T. J., & Rosen, B. R. (1992). Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. Proceedings of the National Academy of Sciences, 89, 5951–5955. Ogawa, S., Lee, T. M., Nayak, A. S., & Glynn, P. (1990). Oxygenation-sensitive contrast in magnetic resonance image of rodent brain at high magnetic fields. Magnetic Resonance in Medicine, 14, 68–78.
Blu¨m Study ▶ CM-AT
Board Certified Associate Behavior Analyst Mary Jane Weiss and Samantha Russo Institute for Behavioral Studies, Endicott College, Beverly, MA, USA
Synonyms BCaBA; BCBA; BCBA-D
Definition The Behavior Analyst Certification Board®, Inc. (BACB®) credentials practitioners at four levels. The different categories denote varied depths of training and levels of independence in practice. Individuals who apply to become Board Certified Behavior Analysts® (BCBA®) must possess at least a master’s degree, have 225 classroom hours of specific graduate-level coursework, meet supervised experience requirements, and pass the BCBA examination. In order to use the Board Certified Behavior Analyst - Doctoral (BCBA-D) designation, a BCBA must possess an acceptable doctoral degree and meet other criteria. As of 2015, applicants for the BCBA credential will need to have completed 270 h of specific coursework. Persons who apply to become Board Certified Assistant Behavior Analysts ® (BCaBA ®) must have at least a bachelor’s degree, have 135 classroom hours of specific coursework, meet supervised experience requirements, and pass the BCaBA examination. The BCaBA must have 1000 h of supervised independent field or 670 practicum hours or 500 intensive practicum hours. Once certified, BCaBAs must be supervised by a BCBA; this supervision requirement includes supervision for 5% of hours for the first 1000 h post certification and then ongoing supervision for at least 2% of hours following the initial 1000 post certification hours. A recently added credential, Registered Behavior Technician, has created a professional level for behavior technicians. Individuals with this credential must practice under a BCBA, BCaBA, or FL-CBA. In order to obtain the RBT credential, the individual must have a high school diploma, complete 40 h of training, pass a competency exam, and demonstrate competency across a task list. Experience and training requirements at all levels of certification are rigorous and ensure that certificants meet minimal competence levels in their knowledge and abilities. BACB certificants must accumulate continuing education credit and recertify over 3 years to maintain their credential. In addition, certificants must annually confirm that they remain in compliance with the
Body Movements, Imitation of
BACB’s standards, including ethical guidelines and disciplinary standards. Because certification requirements periodically change as standards are increased, readers are encouraged to consult (www.bacb.com) for updated information.
Body Movements, Imitation of Giacomo Vivanti A.J. Drexel Autism Institute, Drexel University, Philadelphia, PA, USA
Synonyms Gestural imitation; Imitation of intransitive actions; Imitation of nonmeaningful gestures
Definition Imitation of body movements involves copying acts that do not include the use of objects, do not lead to an end state, do not carry a specific meaning, and can only be described in terms of changes of limb postures in space (e.g., a hand moving across a forehead). Current models of imitation suggest that imitation of body movements is supported by mechanisms that partially differ from those underlying the imitation of actions that carry a semantic meaning (e.g., opening a container or waving goodbye). While imitation of body movement is supported by a “direct visuospatial route” in which the visual input is directly mapped into a motor output, imitation of actions that carry a semantic meaning is achieved via a “semantic route” in which previous knowledge on the meaning of the action can be recruited (Tessari and Rumiati 2004). Given that the familiarity with the demonstrator’s goals and means cannot be exploited in this type of imitative task, imitation of body movement is considered to provide a rigorous methodology by which to assess “true imitation” in human and comparative research. Early signs of the ability to imitate body movements are reported to be present since early
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infancy, and mutual imitation games between child and caregiver, involving affective mirroring and copying of body movements, are observed throughout infancy and toddlerhood across cultures. These early reciprocal exchanges are thought to promote social bonding and to provide a foundation for social cognitive development (Meltzoff 2002; Stern 1985). Difficulties in imitating body movements in individuals with autism are reported in many studies that used different stimuli, coding systems, and comparison groups (including different clinical populations) and across a wide range of IQ, language levels, and chronological ages (see Edwards 2014; Rogers and Williams 2006). Differences in the way individuals with autism imitate body movements include (1) reduced frequency of spontaneous imitation and (2) diminished accuracy of imitative performance. While autism-specific deficits are documented in several imitative tasks, imitation of body movements appears to be more impaired than imitation of actions carrying a semantic meaning in this population (Vivanti and Hamilton 2014; Williams et al. 2004). Various explanations have been hypothesized to account for these difficulties in autism, including abnormalities in visual attention, a primary deficit in the perception-action mapping implemented by the mirror neuron system, a reduced motivation to imitate, and a primary deficit in motor execution. However, none of this explanation is supported by unequivocal evidence. Since children with autism have difficulties in many of the neurocognitive processes involved in imitation of body movements, including visual attention to the demonstration, social motivation, motor planning, and executive processes, it is likely that a heterogeneous vulnerability in the components of the imitative process, rather than a single cause, affects the ability to imitate body movements in individuals with autism.
See Also ▶ Apraxia ▶ Imitation ▶ Mirror Neuron System ▶ Motor Planning
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References and Reading
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Edwards, L. A. (2014). A meta-analysis of imitation abilities in individuals with autism spectrum disorders. Autism Research, 7(3), 363–380. Meltzoff, A. N. (2002). Elements of a developmental theory of imitation. In A. N. Meltzoff & W. Prinz (Eds.), The imitative mind: Development, evolution, and brain bases (pp. 19–41). Cambridge, England: Cambridge University Press. Rogers, S. J., & Williams, J. H. G. (2006). Imitation in autism: Findings and controversies. In S. J. Rogers & J. H. G. Williams (Eds.), Imitation and the social mind: Autism and typical development (pp. 277–309). New York: Guilford. Stern, D. (1985). The interpersonal world of the infant. New York: Basic Books. Tessari, A., & Rumiati, R. I. (2004). The strategic control of multiple routes in imitation of actions. Journal of Experimental Psychology. Human Perception and Performance, 30, 1107–1116. Vivanti, G., & Hamilton, A. F. (2014). Imitation in autism spectrum disorders. In F. Volkmar, R. Paul, & S. Rogers (Eds.), The handbook of autism and developmental disorders (pp. 278–301). New York: Wiley. Williams, J. H. G., Whiten, A., & Singh, T. (2004). A systematic review of action imitation in autistic spectrum disorder. Journal of Autism and Developmental Disorders, 34, 285–299.
IQ scores below average but above an intellectual disability (ID). People with BIF may have adaptive functioning deficits similar to those experienced by people with mild intellectual disabilities (ID) but are often denied critical services that are available to people with ID diagnoses because their IQs scores are too high (Peltopuro et al. 2014). Adults with borderline intellectual functioning, with or without autism, often require supports similar to those beneficial to people with mild ID. Comprehensive case management is a valuable and effective method of supporting adults with borderline intellectual functioning. Comprehensive case management involves a primary support professional or team of professionals working collaboratively to help individuals set, pursue, and attain goals for health, safety, employment, socialization, and independence. Case management often begins with addressing concrete or urgent needs, such as for housing, healthcare, or employment, and expands to promote socialemotional wellbeing and personal fulfilment. It involves coordination of care across many service systems and domains and is highly individualized.
Bogus Therapy
Historical Background
▶ Pseudoscience
The history of borderline intellectual functioning as an official diagnosis is one of progressively declining prominence and usefulness (Wieland and Zitman 2016). In the DSM-II, what is referred to here as borderline intellectual functioning was called borderline mental retardation. In the DSMIII, borderline mental retardation (i.e., BIF) was removed from mental retardation and reclassified as a V code, leaving a whole population of individuals without a suitable diagnosis. It remained a V code in the DSM-IV-TR and was defined using an IQ score range of 71–84. In the DSM-5, the IQ range was removed, leaving it with little definition and limited usefulness as a classification. The DSM-5 leaves gray area in the diagnosis of intellectual disability as well, by emphasizing a person’s adaptive functioning and the diagnosing clinician’s judgement over score in making the diagnosis. In practice, this means that some
Borderline Intellectual Functioning and Comprehensive Case Management Cara G. Streit Threshold Program, Lesley University, Cambridge, MA, USA
Definition Borderline intellectual functioning (BIF) is a little-known and little-utilized classification that has historically described individuals who have
Borderline Intellectual Functioning and Comprehensive Case Management
children and adults whose IQ scores are over 70 (who may have previously been described as having BIF) may now receive ID diagnoses, while others may not. This gray area is particularly concerning considering the difficulty of accurately measuring IQ in people with autism (Rao et al. 2015). The IQ score range of 71–84 is between 1 and 2 standard deviations below the mean of the standardized distribution of scores. Because the scores fit a normal curve, as much as 13.6% of people fall into this IQ range. The incidence of BIF is nearly twice as high among people with autism. According to the Centers for Disease Control’s 2012 prevalence data, 24.5% of children with autism have IQ scores in the range of 71–84, compared with 31.6% in the range of an intellectual disability and 43.9% with average or above average IQ (Christensen et al. 2016). While they may have a diagnosis of autism without intellectual disability, according to the DSM-5, this system of classification does not capture their borderline intellectual functioning unless it is specifically noted with a V code by the diagnosing clinician. Children with BIF are known to be at increased risk for persistent mental health issues (Jankowska 2016), poor social functioning drug abuse (Gigi et al. 2014), poor parenting, and school adjustment issues (Jankowska et al. 2014), making it critically important that BIF be recognized.
Rationale or Underlying Theory While some children and adults with borderline intellectual functioning may have only mild adaptive functioning deficits, or even none at all, others experience deficits comparable to those experienced by individuals with mild intellectual disabilities. A review of the literature on BIF shows that, compared with peers who have average IQ scores, people with BIF have lower performance on tests of cognitive and academic skills, hold jobs that are lower-skilled and lowerpaid, have poorer executive functioning and abstract reasoning, have slower processing speeds, and are more likely to be incarcerated or
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charged with crimes, have mental health problems, or display antisocial behaviors (Peltopuro et al. 2014). On many of these measures, people with BIF fall between people with specific learning disabilities and those with mild intellectual disabilities. Authors of the review conclude that people with BIF may be in a “worse situation” than adults with MID (mild intellectual disability) or with SLDs (specific learning disabilities) (p. 438). They explain that “because the problems with BIF are not as visible as those in MID and not as specific as those in SLDs, they often go unrecognized and, consequently, no support is offered.” In the United States, a diagnosis of an ID is required to receive certain critical supports, services, and government entitlements; people with BIF are denied services that could dramatically improve their life circumstances, all because of their slightly higher IQ scores. Children and adults with a diagnosis of autism who have IQ scores in this range may be able to qualify for some supports and services based on their autism diagnosis, even without an accompanying diagnosis of ID. However, this is not the case in all places or for all services. In the United States, children with disabilities are guaranteed a free and appropriate public education. A child who fits the, albeit vague, classification for borderline intellectual functioning (but not intellectual disability) may have another diagnosis, like autism, a specific learning disability, or ADHD, that can qualify them for public school services and accommodations. As an adult, these diagnoses do not guarantee comprehensive state or federal services. Shattuck et al. (2012) note that while research on services and interventions for children with autism has become more robust in sync with the increasing prevalence of autism diagnoses, research on adult services has been slow to follow. It is clear that autism can be impactful across the lifespan and that adults with autism have unmet service needs in multiple life domains (Turcotte et al. 2016). This is particularly true for individuals with BIF or ID, and appropriate services are less available to adults with BIF than to adults with ID. The services that do exist tend to be siloed; for example, an adult with autism and
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BIF can access employment supports from their state vocational rehabilitation agency but may need another agency to provide assistance in managing an apartment in order live independently. Adult service systems are generally not designed to work seamlessly together to support the needs of each individual accessing them, and an adult with BIF may not even qualify for services from all the individual systems that would be relevant for their goals. Those they do qualify for can be difficult for someone with a cognitive impairment to access and utilize effectively. Filling out paperwork, getting to appointments on time, and finding an unfamiliar service location can all be significant challenges for someone with borderline intellectual functioning. Case managers can assist people with BIF find and use the services they need and coordinate care across services. For adults with intellectual disabilities, case management may be provided by a state developmental disability agency and/or the staff of a residential program and it may or may not be comprehensive. For adults who do not live in staffed residences and who do not qualify for services from a state developmental disability agency due to relatively higher IQ scores, as is the case for most adults with borderline intellectual functioning, comprehensive case management must come from another source. Adults with autism and borderline intellectual functioning, or their families and supporters, should check with their state developmental disability agency to find out whether they qualify for case management from the state even with IQ scores higher than those required for a diagnosis of an intellectual disability. If not, this type of case management may be offered privately by individuals or by disability service organizations, either for profit or not for profit.
Goals and Objectives The goal of comprehensive case management is to support adults in setting, pursuing, attaining, and maintaining goals of their own choosing and to
promote their overall health, safety, happiness, and well-being. Typically, comprehensive case management is most useful when an individual has multiple or complex goals, or has struggled with setting or attaining goals in the past. It may also be particularly useful for people with a dual diagnosis or a special health care need. Goals may include: • Finding employment • Living independently • Making friends and learning how to manage relationships • Managing adult responsibilities, like keeping up an apartment and organizing important paperwork • Learning how to navigate public transportation • Improved budgeting and money management • Securing and/or managing entitlements and benefits (health insurance, Social Security payments, affordable housing, etc.) • Attaining a higher level of education • Coordinating health care services • Planning for life after the death of parents or caretakers • Any other goal that could be supported by a case manager Goals should be set by the client, with the support of the case manager and of other people who are involved in life-planning with the client (e.g., family members, mental health clinicians, doctors, and educators).
Treatment Participants Ideal candidates for comprehensive case management are adults with borderline intellectual functioning who require support in one or more areas in order to achieve the level of independence, community participation, and social engagement they desire. Adults with a range of adaptive functioning skills can benefit from case management, and the more significant the person’s adaptive functioning deficits, the more intensive and comprehensive the case
Borderline Intellectual Functioning and Comprehensive Case Management
management needs to be. Adults of any age can benefit from comprehensive case management and ideal services will follow an individual through their lifespan. Clients of comprehensive case management may have a range of diagnoses in addition to their below average IQ scores. They may have autism, cerebral palsy, a brain injury, special health care needs, or genetic disorders, to name a few. Conversely, they may have no official diagnosis, but a history of educational and adaptive functioning deficits with no known etiology, other than an IQ score in the borderline range.
Treatment Procedures While this section is titled “Treatment Procedures” for consistency within the publication, comprehensive case management should not be considered treatment. Rather, it is a system of support and communication that comes together underneath an individual in order to help them rise up to their potential. The professional coordinating an individual’s case management must foster a culture of collaboration with the individual, their family members, and their other providers that supports their self-determination, bolsters their independence, and encourages them to make their own choices. Comprehensive case management might entail weekly meetings with a client, or may be more or less frequent depending on what the client wants and needs. Some clients may only need relatively short-term intervention, for example, help finding new health insurance when they can no longer be on a family member’s, or may need long-term services if their goals are more complicated. A client with complex goals, fewer state, federal, and nonprofit supports, less family support, or multiple diagnoses may need long-term case management that helps them manage issues and pursue goals in multiple domains and settings throughout their lives. Services should always be highly individualized in their frequency, duration, and in the level of hands-on assistance provided by the case manager.
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Organizations offering comprehensive case management should be prepared to follow clients throughout their lives; this reduces the number of transitions for the individual, helps with building provider/client rapport, and streamlines long-term planning efforts and the preparation for later life. Within organizations, providers who are leaving should take care to transition clients thoughtfully to new case managers. Services must be fully accessible to each individual client. Some common accommodations include: • Emailing or calling the client to remind them of appointments • Communicating by the client’s preferred method (email, phone, text, instant messaging, etc.) • Providing physically accessible meeting spaces • Travel training to help the client learn how to get to and from the case manager’s office or service location • Communicating directly with family members, with the client’s permission • Converting any documents the client needs into accessible formats Case managers should refer their clients out for specialized services beyond their expertise or licensure, such as psychotherapy and medical care, but should coordinate the connection between the client and the specialist to whatever degree the client requires; this may mean setting up a first appointment together, showing the individual how to get to the specialist, or even traveling to a first appointment with the client if necessary and desired by the individual. Of course, the case manager must receive permission from the client when communicating with any other parties about their case management or other coordinated services. As much as possible, clients should be guided in advocating for themselves with family members and providers, and case managers should avoid speaking for them except when clients request it and when it is
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Borderline Intellectual Functioning and Comprehensive Case Management
absolutely necessary for clear communication. Instead, case managers can help clients develop scripts for communicating their needs to others, make phone calls together on speaker phone, and attend meetings together, if such support is necessary.
deintensified or even phased out over time as the client develops the skills to manage a wider range of responsibilities on their own.
Efficacy Information
Providers will typically have a background in human services, social work, mental health counselling, special education, vocational rehabilitation, or a related field. Providers need broad knowledge of the educational and adult service systems available to people with disabilities. Depending on the organization through which case management is offered, additional licensure or education may be required.
There is no body of literature specifically on the efficacy of comprehensive case management in improving outcomes for adults with BIF. However, data from the Lesley University Threshold Program, which serves young adults with BIF, suggests that 2 years of case management in the context of a college-based postsecondary program may contribute to positive employment, social, and independent living outcomes (Lesley University 2015). Graduates of the Threshold program have a paid employment rate of 79% and work an average of 31 hours per week. Threshold program alumni may choose to continue participating in comprehensive case management throughout their lives, provided by the program. The efficacy of comprehensive case management varies depending on the experience, persistence, and flexibility of the case manager, the motivation of the client, the complexity of the goals being pursued, and the level of other supports available to the client. A highly efficacious case manager will ensure that communication with the client is through a method comfortable and convenient to the client, will be quickly responsive to the client, and will reach out to the client if there is any concern that they need additional supports in order to effectively utilize the case management.
Outcome Measurement Individual outcomes should be measured by setting goals for case management, directed by the client, and checking-in on those goals together at predetermined times or as necessary. For many clients, comprehensive case management can be
Qualifications of Treatment Providers
See Also ▶ DSM-5 ▶ DSM-5 and Autism Spectrum Disorder ▶ Higher Education Opportunity Act of 2008 (HEOA) ▶ Learning Disability
References and Reading Christensen, D. L., Baio, J., Braun, K. V. N., Bilder, D., Charles, J., Constantino, J. N., et al. (2016). Prevalence and characteristics of autism spectrum disorder among children aged 8 years — Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2012. Morbility and Mortality Weekly Report Surveillance Summaries, 65(3), 1–23. http://dx.doi. org/10.15585/mmwr.ss6503a1. Gigi, K., Werbeloff, N., Goldberg, S., Portuguese, S., Reichenberg, A., Fruchter, E., & Weiser, M. (2014). European Neuropsychopharmacology, 24(11), 1793–1797. Jankowska, A. (2016). Towards a framework for psychological resilience in children and adolescents with borderline intellectual functioning. Polish Psychological Bulletin, 47(3), 289–299. Jankowska, A., Takagi, A., Bagdanowicz, M., & Jonak, J. (2014). Parenting style and locus of control, motivation, and school adaptation among students with borderline intellectual functioning. Current Issues in Personality Psychology, 2(4), 251–266. Lesley University (2015). Annual Threshold alumni survey: Executive summary. Lesley University.
Brain Connectivity Theories of Autism Peltopuro, M., Ahonen, T., Kaartinen, J., Seppala, H., & Narhi, V. (2014). Borderline intellectual functioning: A systematic literature review. Intellectual and Developmental Disabilities, 52(6), 419–443. Rao, P., Raman, V., Thomas, T., & Ashok, M. (2015). IQ in autism: Is there an alternative global cognitive index? Indian Journal of Psychological Medicine, 37, 48. Shattuck, P., Roux, A., Hudson, L., Taylor, J., Maenner, M., & Trani, J. (2012). Services for adults with an autism spectrum disorder. Canadian Journal of Psychiatry, 57(5), 284–291. Turcotte, P., Mathew, M., Shea, L. L., Brusilovskiy, E., & Nonnemacher, S. L. (2016). Service needs across the lifespan for individuals with autism. Journal of Autism and Developmental Disorders, 46, 2480–2489. Wieland, J., & Zitman, F. G. (2016). It is time to bring borderline intellectual functioning back into the main fold of classification systems. British Journal of Psychiatry Bulletin, 40, 204–206.
BOS ▶ Behavior Observation Scale
BOT-2 ▶ Bruininks-Oseretsky Test of Motor Proficiency
BOTMP ▶ Bruininks-Oseretsky Test of Motor Proficiency
Bound Morphemes ▶ Speech Morphology
Brachmann-de Lange Syndrome ▶ Cornelia de Lange Syndrome
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Brain Connectivity Theories of Autism John P. Hegarty1, Antonio Y. Hardan1 and RalphAxel Müller2 1 Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA 2 Department of Psychology, San Diego State University, San Diego, CA, USA
Synonyms Functional connectivity; Functional integration; Network coherence
Definition Brain connectivity refers to both structural connections between distinct regions of the brain as well as coordinated functional activity within networks of different brain regions, which may or may not share direct structural connections. Structural and functional connectivity in the brain are interrelated in that altered structural connections can affect functional coordination within brain networks and altered functional activity can affect structural connections via adaptive changes from synaptic pruning and dendritic arborization. Although there is extensive evidence to support altered structural connectivity in the brain in individuals with autism (for a specific example, see ▶ “Corpus Callosum Abnormalities in Autism”), most brain connectivity theories of autism spectrum disorder (ASD) focus on differences in functional connectivity (FC), defined as “temporal correlations between spatially remote neurophysiological events” (Friston et al. 1993). In this regard, FC is most often measured by comparing correlations between brain regions for fluctuations of the blood oxygen-level-dependent (BOLD) response from fMRI data (see Functional Magnetic Resonance Imaging) that is collected during passive rest (no stimulus) or during the completion of a cognitive processing task. Accumulating
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evidence from this line of research suggests that the development of typical FC patterns in the brain are altered in individuals with ASD. For instance, early investigations reported general patterns of global hypoconnectivity (underconnectivity) in individuals with ASD (Just et al. 2004), but subsequent studies have indicated more complex patterns of both hypo- and hyperconnectivity across different brain regions and networks. As such, general patterns of global (long-range) hypoconnectivity coupled with local (short-range) hyperconnectivity have also been proposed (Wass 2011). However, these theories oversimplify much more complex alterations of brain connectivity in individuals with ASD, especially regarding the development of FC over time. Models of optimized brain organization exhibit robust FC between neighboring brain regions with some additional long-range connections to more distant regions in order to minimize the metabolic cost of information processing. Consistent with this model, maturational changes of brain networks typically involve functional segregation between noncontributing anatomical neighbors with concurrent integration of more distant brain regions that contribute to the processing of domain-specific information (Fair et al. 2009). Conceptualized within this developmental framework, emerging theories of brain connectivity in ASD suggest that information processing is affected by disruption of the maturation and adaptive development of functional integration within as well as segregation between brain networks (Rudie et al. 2013). More recent research into disruptions of brain connectivity in ASD have reported some promising results, but the well-known heterogeneity in the etiology, neurobiology, and symptomatology of ASD across individuals suggests that there is most likely not a unique or defining brain connectivity pattern. It is also not yet clear whether FC differences contribute directly to the pathogenesis of the disorder or only emerge as secondary features associated with altered cognitive and behavioral performance. Overall, brain connectivity appears to be altered in individuals with ASD with patterns of both under-connectivity and
Brain Connectivity Theories of Autism
over-connectivity depending on the brain region or network that is being evaluated (Abbott et al. 2016). Such alterations may reduce functional network integration to affect cognitive and behavioral processing in some of the core and comorbid domains. However, the substantial variability in reports of FC alterations across individuals coupled with methodological concerns regarding fMRI data processing (Müller et al. 2011) presents major considerations and limitations for previous FC studies in ASD. Brain connectivity will need to be evaluated in larger samples with sufficient power to subgroup individuals with ASD. These subgroups should be compared to individuals with other neuropsychiatric disorders as well as typically developing controls in order to determine whether any ASD-specific alterations in brain connectivity actually exist and elucidate their potential relationship with differences in the pathogenesis of the disorder.
See Also ▶ Corpus Callosum Abnormalities in Autism ▶ Functional Magnetic Resonance Imaging ▶ Neural Signatures of Treatment Response
References and Reading Abbott, A. E., Nair, A., Keown, C. L., Datko, M., Jahedi, A., Fishman, I., & Müller, R.-A. (2016). Patterns of atypical functional connectivity and behavioral links in autism differ between default, salience, and executive networks. Cerebral Cortex, 26(10), 4034–4045. https:// doi.org/10.1093/cercor/bhv191. Fair, D. A., Cohen, A. L., Power, J. D., Dosenbach, N. U. F., Church, J. A., Miezin, F. M., et al. (2009). Functional brain networks develop from a “local to distributed” organization. PLoS Computational Biology, 5(5), e1000381. Friston, K. J., Frith, C. D., Liddle, P. F., & Frackowiak, R. S. J. (1993). Functional connectivity: The principalcomponent analysis of large (PET) data sets. Journal of Cerebral Blood Flow & Metabolism, 13(1), 5–14. https://doi.org/10.1038/jcbfm.1993.4. Just, M. A., Cherkassky, V. L., Keller, T. A., & Minshew, N. J. (2004). Cortical activation and synchronization during sentence comprehension in high-functioning autism: Evidence of underconnectivity. Brain, 127(8), 1811–1821.
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Müller, R. A., Shih, P., Keehn, B., Deyoe, J. R., Leyden, K. M., & Shukla, D. K. (2011). Underconnected, but how? A survey of functional connectivity MRI studies in autism spectrum disorders. Cerebral Cortex, 21, 2233–2243. Rudie, J. D., Brown, J. A., Beck-Pancer, D., Hernandez, L. M., Dennis, E. L., Thompson, P. M., et al. (2013). Altered functional and structural brain network organization in autism. NeuroImage: Clinical, 2(0), 79–94. https://doi.org/10.1016/j.nicl.2012.11.006. Wass, S. (2011). Distortions and disconnections: Disrupted brain connectivity in autism. Brain and Cognition, 75(1), 18–28.
traditional tests of hearing sensitivity, and therefore, the ABR may be completed to establish hearing sensitivity.
Brainstem Audiometry
References and Reading
Jennifer McCullagh Department of Communication Disorders, Southern Connecticut State University, New Haven, CT, USA
Hall, J. (1992). Handbook of auditory evoked responses. Needham Heights: Allyn & Bacon. Rosenblum, S. M., Arick, J. R., Krug, D. A., Stubbs, E. G., Young, N. B., & Pelson, R. O. (1980). Auditory brainstem evoked responses in autistic children. Journal of Autism and Developmental Disorders, 10, 215–225. Rosenhall, U., Nordin, V., Sandstrom, M., Ahlsen, G., & Gillberg, C. (1999). Autism and hearing loss. Journal of Autism and Developmental Disorders, 29(5), 349–357. Skoff, B. F., Mirsky, A. F., & Turner, D. (1980). Prolonged brainstem transmission time in autism. Psychiatry Research, 2, 157–166. Skoff, B. F., Fein, D., McNally, B., Lucci, D., HumesBartlo, M., & Waterhouse, L. (1986). Brainstem auditory evoked potentials in autism. Psychophysiology, 23, 462.
Synonyms Auditory Brainstem Response (ABR)
Definition Brainstem audiometry, sometimes called a brainstem auditory evoked response (BAER) or an auditory brainstem response (ABR), is an electrophysiologic test that assesses the auditory system through the low brainstem. This test can assess hearing sensitivity in individuals who cannot respond to traditional testing; thus, it is often used in newborn hearing screenings and in populations that are nonverbal. The ABR is completed by placing recording electrodes on the individual’s head and ears and placing earphones in their ears. Responses are elicited using click and tonal stimuli which are delivered through the earphones. Five waveforms are typically present in the ABR (waves I, II, III, IV, and V); however, wave V is the waveform used for threshold testing. Individuals with autism spectrum disorders might not be able to consistently respond to
B See Also ▶ Auditory Acuity ▶ Auditory Brainstem Response (ABR) ▶ Brainstem Auditory Evoked Potentials ▶ Hearing
Brainstem Auditory Evoked Potentials Kirsten O’Hearn Laboratory of Neurocognitive Development, Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
Synonyms Auditory brainstem response, ABR; Brainstem auditory evoked response, BAER
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Definition Brainstem Auditory Evoked Potentials (BAEP) are low-amplitude electrical voltage potentials in the brain that are evoked by a sound, often a click, and recorded using electrodes on the scalp (i.e., electroencephalography or EEG). Since the potentials typically have an amplitude around 1 mV, hundreds of trials are averaged together to provide data with adequate signal to noise ratio. BAEPs have been used as a tool to examine brainstem integrity and hearing ability clinically since the 1970s, helping to diagnose tumors and other diseases such as multiple sclerosis. BAEPs are particularly useful with patients who are difficult to test by traditional audiometry, where feedback is required, because of compromised levels of consciousness, limited communication, or behavioral noncompliance. BAEPs are also useful for detecting subtle changes that may be clinically relevant, and localizing the deficits. The BAEP reflects the function of the auditory nerve (eighth nerve), cochlear nucleus, superior olive, and inferior colliculus, measuring the time it takes an aural stimulus to travel through the auditory pathway in the brainstem. They are thought to measure action potentials and postsynaptic activity propagating along the auditory nerve, and to other regions along the auditory pathway. When measured in response to a brief stimulus (typically a click) in the ear canal via an inserted earphone or headphone, the elicited waveform response is detected by surface electrodes placed at the base of the scalp and the ear lobes. The BAEP generally includes seven waves, but it is the initial five waves that have been the most extensively studied and are useful for clinical applications. These five waves occur within 6 or 7 ms and are labeled waves I through V. Abnormalities in specific waves are informative, as they are thought to localize the differences to particular parts of the auditory pathway. Wave I is believed to reflect the distal auditory nerve on the ipsilateral side; wave II, proximal auditory nerve on the ipsilateral side; wave III, ipsilateral cochlear nucleus; wave IV, superior olivary nucleus and adjacent brainstem regions bilaterally; and wave V, distal lateral lemniscus and inferior colliculus on the contralateral
Brainstem Auditory Evoked Potentials
side. Interpretation of the BAEP is routinely done by examining the latency and length of these waves and the interpeak intervals between them (IPI), also known as interpeak latencies (IPL). The amplitudes are less frequently interpreted because they are more variable across individuals, and thought to be less reliable indices of dysfunction. The IPIs are considered particularly important, reflecting the conduction times in the auditory pathway through the brainstem (i.e., auditory nerve then cochlear nerve, cochlear nucleus, and lateral lemniscus). Therefore, longer IPIs are thought to indicate impaired function, possibly related to the number, synchronicity, or integrity of the neurons firing in these regions. The BAEP is relatively adult-like by 18 months of age, though wave V may continue to mature until 3 or 4 years of age. They differ in females and males, with a slightly shorter latency and higher amplitude in females. More recently, the BAEP has been examined in response to a tone (instead of a click), which is thought to measure dysfunction more specific to the cochlear regions.
See Also ▶ Auditory Brainstem Response, ABR ▶ Auditory Potentials ▶ Brainstem Auditory Evoked Response, BAER ▶ Evoked Potentials ▶ Visual Evoked Potential (VEP) ▶ Visual/Somatosensory Cognitive Potentials
References and Reading Legatt, A. D., Arezzo, J. C., & Vaughan, H. G., Jr. (1988). The anatomic and physiologic bases of brain stem auditory evoked potentials. Neurologic Clinics, 6(4), 681–704. Moore, J. K., & Lithicum, F., Jr. (2007). The human auditory system: A timeline of development. International Journal of Audiology, 46(9), 460–478. Starr, A., & Anchor, L. J. (1975). Auditory brain stem responses in neurological disease. Archives of Neurology, 32, 761–768. Stone, J. L., Calderon-Arnulphi, M., Watson, K. S., Patel, K., Mander, N. S., Suss, N., et al. (2009). Brainstem auditory evoked potentials–a review and modified studies in healthy subjects. Journal of Clinical Neurophysiology, 26(3), 167–175.
Brainstem Auditory Evoked Responses in Autism (BAERs)
Brainstem Auditory Evoked Response (BAER) ▶ Auditory Brainstem Response (ABR)
Brainstem Auditory Evoked Response, BAER ▶ Brainstem Auditory Evoked Potentials
Brainstem Auditory Evoked Responses in Autism (BAERs) Kirsten O’Hearn Laboratory of Neurocognitive Development, Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
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aural stimulus moves from more distal regions of the auditory nerve to the more proximal regions. Examining the length of the waves and the latencies between them (the interpeak intervals: IPIs, also known as interpeak latencies IPL) provides insight into whether there is dysfunction along the auditory pathway through the brainstem and, potentially, helps to localize that dysfunction. Waves I, III, and V have been particularly well characterized. Wave I is thought to be generated peripherally, at the auditory or cochlear nerve; wave III at the cochlear nuclei; and wave V at the lateral lemniscus. These signals go from the ipsilateral side (waves I to III in the auditory nerve, cochlear nucleus, and superior olive) to bilateral brainstem regions (wave IV) to contralateral regions (wave V in the lateral lemniscus and inferior colliculus). The wave structure develops an adult-like architecture in the first few years of life, with maturation starting in more peripheral regions (with waves I and III maturing in the first year) and moving to more central regions (with wave V maturing at 3–4 years of age; FujikawaBrooks et al. 2010; Moore and Linthicum 2007).
Definition Historical Background BAERs (brainstem auditory evoked responses; also referred to as brainstem auditory evoked potential, BAEPs, and auditory brainstem response, ABR) measure the electrical voltage potentials in the proximal auditory pathway in response to a noise. This is done via electrodes on the scalp and earlobe (see also definition: ▶ “Brainstem Auditory Evoked Potentials”). The noise is most frequently a click, but tones and other sounds have also been used (e.g., Russo et al. 2008). BAERs are thought to reflect the function of the auditory pathway through the brainstem, providing insight into both the level of hearing and the integrity of brainstem function in a given individual. When the noise is a click, BAEPs produce seven waves of activity. The first five of these – labeled waves I through V – have been well characterized, with wave V followed by a negative dip (Stone et al. 2009). These initial five waves occur within about 7 ms. The waves are thought to reflect activation progressing as the
Since sensory modulation is disrupted in ASD, with both under- and over-reactivity to sounds, early theories posited that auditory brainstem function might be affected in ASD (Ornitz et al. 1985; modified in Ornitz 1987). To empirically study this possibility, BAERs were used, examining the integrity of this region and the claim of atypical brainstem function in ASD. Early work on autism in the 1970s and early 1980s was promising, suggesting that there may be abnormalities in BAERs in individuals with ASD. A problem, however, was that what aspect of BAERs actually differed in ASD was not consistent across studies (Klin 1993). In addition, BAERs do not require attention or consciousness, making them useful for testing special populations; however, this fact also led to a very heterogeneous sample being tested in many of these early studies. Some of the participants had known neurological conditions (Klin 1993; Minshew 1991), and in some
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studies, many individuals had hearing loss (e.g., Taylor et al. 1982), which create an obvious confound when interpreting these studies. Gender has been shown to affect BAERs, with shorter latencies in women. Therefore, gender also has to be considered since a greater proportion of women in the control group could lead to spurious group differences. Indeed, the conclusion that BAERs were abnormal in ASD was disputed in the mid1980s by work suggesting that the differences reported in the early studies reflected participant characteristics other than ASD (e.g., other neurological disorder, intellectual disability). Courchesne et al. (1985) tested a cohort of highfunctioning individuals with ASD, with wellmatched controls, and found no differences in the group with ASD. Once the issues discussed above were taken into account – and the reliability of the measures, as methods were still improving – several reviews argued that differences in individuals with ASD were not evident (Minshew 1991) or less likely (Klin 1993). Klin (1993) pointed out that, while BAERS did not provide convincing evidence of brainstem dysfunction in ASD, they did suggest that peripheral hearing loss might be common in ASD and such hearing loss would be important clinically when treating those with ASD. Tables listing the results and the samples used in these earlier studies are included in Klin (1993) and Wong and Wong (1991).
Current Knowledge More work has led to further inconsistencies in the data, though several important themes have emerged. In all studies, differences in the BAERs of those with ASD are evident in a subset of participants with ASD and, in some cases, their first-degree relatives (Maziade et al. 2000). This indicates that, while abnormal BAERs are not causal, they may reflect a subgroup which would be important to identify clinically (Nagy and Loveland 2002). Thus, there is still potential for abnormal BAERs to be a biomarker for at least a subset of individuals with ASD, providing insight into the disorder. In addition, what is atypical in the BAERs of the individuals with ASD has
Brainstem Auditory Evoked Responses in Autism (BAERs)
differed both within and across studies, suggesting that there may be multiple ways to disrupt the auditory pathway through the brainstem. These disruptions generally present as prolongations of the waves or IPIs, when they are evident. Nagy and colleagues argue that some of these disruptions may be specific to ASD (e.g., prolongation of waves III to V; on the basis of Bachevalier 1996), while others might be evident in a number of disorders (e.g., speech impairment, ADHD: prolongation of waves I to III) and are potentially related to differences in language acquisition (Nagy and Loveland 2002). In general, it is not clear whether even the differences that have been identified in ASD are specific to this disorder. However, these differences do not generalize to all developmental disorders. While individuals with Down’s syndrome also display abnormal BAERS, the atypical patterns are distinct from those in autism (Sersen et al. 1990). Finally, abnormalities may have implications clinically, as recent work suggests that there may be some experience-dependent plasticity in the BAER wave pattern that is sensitive to auditory training (Chandrasekaran and Kraus 2010; Skoe and Kraus 2010; see Russo et al. 2010 for training in ASD). The studies in recent years have shown a prolongation of either the wave itself or – relatedly – the IPI (Gillberg et al. 1983; Kwon et al. 2007; Maziade et al. 2000; Rosenhall et al. 2003; Tanguay et al. 1982; Tas et al. 2007; Wong and Wong 1991), though a few early studies indicated a shortening of waves (see Table 1 in Rosenhall et al. 2003 for a summary of earlier studies). Other conditions, such as Down’s syndrome, may tend to exhibit shorter IPIs (Sersen et al. 1990). This longer latency is evident in a subset of those with ASD, generally not more than about 50% of the sample. Which wave (I, III, or V) or IPI the group differences are evident differs between studies; however, wave V appears to be most often affected, especially in the left (L) ear. (See Table 1 for a summary of recent results since 2000 to click tones in BAERs.) This may reflect a more general slowing of auditory processing that differs across this heterogeneous population. This pattern is also evident in many earlier studies.
Brainstem Auditory Evoked Responses in Autism (BAERs) Brainstem Auditory Evoked Responses in Autism (BAERs), Table 1 Recent literature on BAERs in ASD in response to clicks (see Russo et al. 2008; Tharpe et al.
Study Tas et al. (2007)
Rosenhall et al. (2003)
FujikawaBrooks et al. (2010)
Maziade et al. (2000)
Magliaro et al. (2010)
Kwon et al. (2007)
N N ¼ 30 ASD N ¼ 15 controls M age ¼ 3 year N ¼ 101 ASD with normal hearing N ¼ 59 controls M age ¼ 8 year N ¼ 20 ASD N ¼ 20 controls M age ¼ 10 year N ¼ 73 ASD, 251 relatives N ¼ 521 controls M age ¼ 7 year N ¼ 16 ASD N ¼ 25 controls M age ¼ 12 year N ¼ 71 ASD (22 autism) N ¼ 50 controls M age ¼ 3 year
731 2006 for recent evidence of differences in BAERs to other sounds, but not to clicks) Prolongation? Latencies I, III, V None
Prolongation? IPIs I–III, III–V III–V
I, V
III–V
V in L ear
None
Not reported
Longer I–III and I–V in ASD and first-degree relatives especially siblings
Many more F in control group (16 vs. 1)
III, V
I–III, I–V
May have had other medical issues, 2 cases with brainstem abnormalities
V in L ear
I–V, III–V bilateral in larger more heterogeneous ASD group only
Potential confounds? Individuals with ASD sedated controls not
Controls slightly older than individuals with ASD
Skoff and colleagues reported prolonged III–V IPIs in the L ear in 33% of their sample (1980). Thivierge et al. found that 80% of their populations had longer I–V and III–V IPIs (1990). Wong and Wong (1991) reported increased latencies of wave V, and I–III, III–V, and I–V IPIs, in sedated individuals with autistic “features,” but not in those with intellectual disability. Later studies (summarized in Table 1) reported longer IPIs I–III in both individuals
with ASD and their first-degree family members (Maziade et al. 2000). However, 52% of the families with ASD had normal BAEPs in everyone in the family. Rosenhall et al. (2003) reported that 58% of children with ASD had longer latencies in waves I and V and IPI in III–V. This study included a large sample, but a portion of the sample had hearing loss. Kwon et al. (2007) reported longer I–V and III–V and wave V in large group of those on the spectrum (ASD)
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(N ¼ 71), but not in those with autism defined more strictly (N ¼ 22). The take-home message from Kwon and colleagues was that ASD might have a lot of physiological overlap with central auditory processing disorder (CAPD), on the basis of the ABR results, and that this comorbidity might have clinical implications. In contrast to these positive results, several studies have reported no difference between groups to click stimuli (Courchesne et al. 1985; Rumsey 1984; Tharpe et al. 2006). Most of the studies do not have a wellmatched control group (but see Courchesne et al. 1985), although many of the recent ones do a test for hearing impairment before including participants in the results. In addition, since BAERs are thought to be relatively resistant to age, function level, or other potential confounds such as the effects of sedation, these differences may generally not affect the results or do so only subtly. However, in these studies, there are still issues with the control groups. One such issue is gender. Since females have shorter IPIs, including too many in the control group could bias the IPIs to be shorter in controls and therefore appear longer in ASD. For instance, Magliaro et al. (2010) found prolongation in III and V and IPIs I–III and I–V, but this study included a substantial proportion of females in the control group. Recent studies have attempted to control for gender (Russo et al. 2008), since there are almost always a few more females in the control group, and have found differences. Another issue is that a number of subjects with serious hearing loss and ASD have been identified across studies (Rosenhall et al. 2003; Tas et al. 2007). This is an important issue clinically, as it may not be immediately evident in children with ASD that they have hearing loss (Klin 1993). So, while this emphasizes the importance of examining hearing in those with ASD, it also presents confounds in the available data. For instance, Tas (2007) reported a longer III–V bilaterally in young children with ASD. However, five children were identified as having hearing loss, and, while the three with severe loss were excluded, the two with mild hearing loss were not. This study also brings up the issue of using quite
Brainstem Auditory Evoked Responses in Autism (BAERs)
young children, around 2 years old (see also Kwon et al. 2007; Wong and Wong 1991). While the BAER architecture is relatively mature by 18 months of age, there is some evidence that wave V continues to mature until around 3 or 4 years old. While age was approximately matched in many of the studies, differential development across groups may still be influencing the results. Several studies have examined the BAER response to sounds other than clicks, and these results suggest that group differences might be more likely with sounds other than with the traditional click response. Russo et al. (2008) examined pitch encoding. They found that 20% of children on the autism spectrum had difficulty with pitch, while none showed abnormal BAERs to click sounds, but this result was not correlated with language outcome. The ASD group had more boys and lower IQ, but the results did not change when these issues were controlled statistically. Tharpe et al. (2006) found differences in the BAER when the stimulus was a pure tone, but not when it was a click. This difference was evident in 11 of 22 individuals with ASD. Fujiwaka-Brooks and colleagues (2010) included more clicks per second (61–91 instead of 11–25 used typically), a stressor that is known to lead to longer latencies typically, especially in wave V. These investigators found differences in left ear only, with a trend for latency of wave I and significant results of wave V. They also report a negative correlation between the latency of wave V and verbal IQ, suggesting a relationship between this wave and language skill. About half the sample showed the difference in the L ear for wave V. This group points out the importance of testing from both ears, as some studies have only tested the right ear.
Future Directions These studies indicate that BAERs may be abnormal in ASD, but this is unlikely to reflect important information about etiology across the spectrum. These abnormal BAERs may reflect disrupted auditory processing, possibly deep in
Brainstem Auditory Evoked Responses in Autism (BAERs)
the brainstem. There is not convincing evidence that it is specific to ASD. However, that differences are evident for only a subset of participants with ASD might prove useful for identifying subgroups of ASD. In addition, differences in the developmental pattern in ASD have not been studied but may be enlightening. While TD individuals may show little change in the BAERs after age 4 or due to intellectual disability, this pattern may not be true of those with ASD. Such developmental differences could help explain the discrepancy between the findings of Courchesne et al. (1985) with a high-functioning set of adults with ASD and well-matched controls and the more recent work that generally focuses on children, often very young ones (Kwon et al. 2007; Tas et al. 2007; Wong and Wong 1991). In addition, recent studies have begun to identify plasticity in the BAER in the auditory pathway in the brainstem when training takes place (Chandrasekaran and Kraus 2010; Skoe and Kraus 2010; see Russo et al. 2010 for studies in ASD), and high-functioning individuals with ASD may be able to compensate for their social and communication issues and engage further through language. This plasticity may help to explain the variability in the BAER differences in ASD, in addition to other differences across the spectrum, as well as help to inform potential interventions.
See Also ▶ Auditory Brainstem Response, ABR ▶ Auditory Potentials ▶ Brainstem Auditory Evoked Response, BAER ▶ Evoked Potentials ▶ Visual Evoked Potential (VEP) ▶ Visual/Somatosensory Cognitive Potentials
References and Reading Bachevalier, J. (1996). Brief report: Medial temporal lobe and autism: A putative animal model in primates. Journal of Autism and Developmental Disorders, 26(2), 217–220.
733 Chandrasekaran, B., & Kraus, N. (2010). The scalprecorded brainstem responses to speech: Neural origins and plasticity. Psychophysiology, 47(2), 236–246. Courchesne, E., Courchesne, R. Y., Hicks, G., & Lincoln, A. J. (1985). Functioning of the brain-stem auditory pathway in non-retarded autistic individuals. Electroencephalography and Clinical Neurophysiology, 61(6), 491–501. Fujikawa-Brooks, S., Isenberg, A. L., Osann, K., Spence, M. A., & Gage, N. M. (2010). The effect of rate stress on the auditory brainstem response in Autism: A preliminary report. International Journal of Audiology, 49, 129–140. https://doi.org/10.3109/ 14992020903289790. Gillberg, C., Rosenhall, U., & Johansson, E. (1983). Auditory brainstem responses in childhood psychosis. Journal of Autism and Developmental Disorders, 13(2), 181–195. Klin, A. (1993). Auditory brainstem responses in autism: brainstem dysfunction or peripheral hearing loss. Journal of Autism and Developmental Disorders, 23, 15–35. Kwon, S., Jungmi, K., Choe, B., Ko, C., & Park, S. (2007). Electrophysiologic assessment of central auditory processing by auditory brainstem responses in children with Autism spectrum disorders. Journal of Korean Medical Science, 22, 656–659. Magliaro, F. C., Scheuer, C. I., Assumpcao, F. B., & Matas, C. G. (2010). Study of auditory evoked potentials in Autism. Pro-Fono Revista de Atualizacao Cientifica, 22, 31–37. Maziade, M., Merette, C., Cayer, M., Roy, M., Szatmari, P., Cote, R., et al. (2000). Prolongation of brainstem auditory – Evoked responses in Autistic probands and their unaffected relatives. Archives of General Psychiatry, 57, 1077–1083. Minshew, N. J. (1991). Indices of neural function in Autism: Clinical and biological implications. Pediatrics, 87, 774–780. Moore, J. K., & Linthicum, F. H. (2007). The human auditory system: A time-line of development. International Journal of Audiology, 46(9), 460–478. Nagy, E., & Loveland, K. A. (2002). Prolonged brainstem auditory evoked potentials: and autism specific or autism-nonspecific marker. Archives of General Psychiatry, 59(3), 288–290. Ornitz, E. M., Atwell, C. W., Kaplan, A. R., & Westlake, J. R. (1985). Brain-stem dysfunction in autism. Results of vestibular stimulation. Archives of General Psychiatry, 42(10), 1018–1025. Rosenhall, U., Nordin, V., Brantberg, K., & Gillgerg, C. (2003). Autism and auditory brain stem responses. Ear and Hearing, 24, 206–214. https://doi.org/10. 1097/01.AUD.0000069326.11466.7E. Rumsey, J. M. (1984). Auditory brainstem responses in pervasive developmental disorders. Biological Psychiatry, 19(10), 1403–1418. Russo, N. M., Skoe, E., Trommer, B., Nicol, T., Zecker, S., Brdlow, A., et al. (2008). Deficient brainstem encoding
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734 of pitch in children with Autism spectrum disorders. Clinical Neurophysiology, 119, 1720–1731. https://doi. org/10.1016/j.clinph.2008.01.108. Russo, N., Zecker, S., Trommer, B., Chen, J., & Kraus, N. (2009). Effects of background noise on cortical encoding of speech in Autism spectrum disorders. Journal of Autism and Developmental Disorders, 39, 1185–1186. Russo, N. M., Hornickel, J., Nicol, T., Zecker, S., & Kraus, N. (2010). Biological changes in auditory function following training in children with Autism spectrum disorders. Behavioral and Brain Function, 16, 6–60. https://doi.org/10.1186/1744-9081-6-60. Sersen, E. A., Heaney, G., Clausen, J., Belser, R., & Rainbow, S. (1990). Brainstem auditory-evoked responses with and without sedation in Autism and Down’s syndrome. Biological Psychiatry, 27, 834–840. Skoe, E., & Kraus, N. (2010). Auditory brain stem response to complex sounds: A tutorial. Ear and Hearing, 31, 302–324. Stone, J. L., Calderon-Arnulphi, M., Watson, K. S., Patel, K., Mander, N. S., Suss, N., et al. (2009). Brainstem auditory evoked potentials – a review and modified studies in healthy subjects. Journal of Clinical Neurophysiology, 26, 167–175. Tanguay, P. E., Edwards, R. M., Buchwald, J., Schwafel, J., & Allen, V. (1982). Auditory brainstem evoked responses in autistic children. Archives of General Psychiatry, 39(2), 174–180. Tas, A., Yagiz, R., Tas, M., Esme, M., Uzun, C., & Karasalihoglu, A. R. (2007). Evaluation of hearing in children with Autism by using TEOAE and ABR. Autism, 11, 73–79. https://doi.org/10.1177/ 1362361307070908. Taylor, M. J., Rosenblatt, B., & Linschoten, L. (1982). Auditory brainstem response abnormalities in Autistic children. Canadian Journal of Neurological Sciences, 9, 429–433. Tharpe, A. M., Bess, F. H., Sladen, D. P., Schissel, H., Couch, S., & Schery, T. (2006). Auditory characteristics of children with Autism. Ear and Hearing, 27, 430–441. Tzounopoulos, T., & Kraus, N. (2009). Learning to encode timing: Mechanisms of plasticity in the auditory brainstem. Neuron, 62, 463–469. Wong, V., & Wong, S. N. (1991). Brainstem auditory evoked potential study in children with Autistic disorder. Journal of Autism and Developmental Disorders, 21, 329–340.
Brainstem Evoked Response (BER) ▶ Evoked Potentials
Brainstem Evoked Response (BER)
Brazil and Autism Helena Brentani, Guilherme Vanoni Polanczyk and Euripedes Constantino Miguel Department of Psychiatry, Faculty of Medicine, University of Sao Paulo, Sao Paulo, Brazil
Services and Social Policies In Brazil, Autism Spectrum Disorder (ASD) is considerably understudied. A single epidemiological study has been conducted, assessing children from one city in the southeast. Results indicated an estimated prevalence of 27.2/10,000, which is believed to be an underestimation due to methodological issues (Paula et al. 2011). If the international prevalence 1% is true in Brazil, approximately 1.5–2 million Brazilian children are affected by ASD. The National Health System (SUS), still in constant construction, also has shortcomings. Although a national system, it is organized in different ways around the country. The SUS recommends that children with autism have comprehensive care of their needs. Health care occurs primarily in units of ambulatory medical care (AMAS) and psychosocial care centers (CAPS). The Brazilian health system is composed by the primary care for simple demands (Basic Health Units – UBS, Health Nuclei for Family Support – NASF, and Family Health Units – USF), secondary care (CAPS – Outpatient Mental Health) for more difficult cases without need of hospitalization, and tertiary care for hospitalizations and emergency room needs (General Hospitals). It is sure that this network needs to be improved: the CAPS only recently started to assist children with autism in a more regular way. There are some welfare agency in Brazil that plays an important role in the diligence of autism patients and families, such as ABRA – Associação Brasileira de Autismo brings together corporations related to autism in Brazil and represents the interests of Autism patients in different national councils such as the health, social, and the rights of the
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individual council; AMA – Associação de Amigos do Autista; and the APAE – Associação de Pais e Amigos dos Excepcionais, and they have an established center in most Brazilian states. They offer different activities for autism patients related to socialization, day care activities, and communication; AUMA – Associação dos Amigos da Criança Autista – since 1990 has the major objective of developing educational programs of social adaptation for autism patients and families. In São Paulo, the richest state in the country, the health system is designed in a way that the Basic Health Units (UBS), teams of the Family Health Strategy (family physicians, nurses, and dentists), with support from the Family Assistance Center (NASF) are the first place where cases arrive to make diagnosis and also less-severity cases are kept for treatment. The instruments for early identification and diagnosis should be present in established practice in these centers, which unfortunately does not happen. The CAPS is responsible for the establishment of a therapeutic project and is the reference for the UBS (secondary care, in day hospital care scheme, more intensive, with intermediate function between the outpatient and inpatient) and when faced with cases of high complexity, CAPS can trigger other places in the network, as services belonging to the university or references to specific service for ASD. São Paulo is the unique state that has a specific CAPS for adults with autism. Regarding education, children with autism should be included in regular education (inclusive education), which should provide suitable conditions for integration and development. Teachers and school staff should receive adequate training to work with children with autism. In cases that regular education is not possible given the intensity of symptoms and difficulty of the student to adapt, the option is a special school. Getting a place in special school is very difficult, and parents often have to appeal to justice to have their rights guaranteed. In Brazil, people with autism have all the rights provided for in specific laws for people with disabilities as well as international standards signed by Brazil, such as United Nation Convention on the Rights of
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Persons with Disabilities. Moreover, children and adolescents also have all the rights stated in the Statute of Children and Adolescents and the elderly, i.e., over 60 years old, have also the rights of the elderly. People with autism have also the special protection of Federal Law, which ensures proper treatment in public and private health facilities for the specific pathology they have. If the person with autism is demonstrably in need, he/she is entitled to a free pass in state and interstate transportation. The law, establishing the National Policy on Protection of Rights for Persons with Autism Spectrum Disorder, was published in the Official Newspaper in Brazil in December 2012 giving support and emphasis on rights and proper treatment, as well as access to education and to vocational teaching, housing, labor market, and social security and welfare. In cases of proven need, the person with autism spectrum disorder, included in common classes in regular education, will be entitled to have a specialized companion during the activities done in the school. Among the points set out in the Law is community participation in the formulation of public policies for people with autism, in addition to implementation, monitoring, and evaluation of the person with autism. In addition to these duties, the benefit of greater importance to the disabled person and therefore to person with autism is the Continuous Cash Benefit, a social assistance benefit which was regulated by the Organic Law of Social Assistance – LOAS. To get the benefit, the family income must be less than one fourth of the minimum wage, and proof of disability and level of temporary or permanent disability for independent life and work must be attested by medical and social expertise of the INSS. In the next section, we will explore diagnosis and treatment options for autism with an emphasis on research performed in Brazil and the Brazilian limitations.
Instruments for Diagnosis and Standard Scales in Brazil The official diagnosis of ASD in Brazil follows the ICD-10 criteria (WHO 1993), performed by
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Brazil and Autism, Table 1 Translated and validated instruments for screening and diagnosis of Autism Spectrum Disorders Description of instruments The Autistic Traits of Evaluation Scale (ATA)
Autism Behavior Checklist (ABC)
Childhood Autism Rating Scale (CARS)
Autism Screening Questionnaire (ASQ)
Authors Proposal Age of administration Reliability Validation for Brazil Authors Proposal Age of administration Reliability Validation for Brazil Authors Proposal Age of administration Reliability Validation for Brazil Authors Proposal Age of administration Reliability
Autism Diagnostic Interview (ADI-R)
Validation for Brazil Authors Proposal Age of administration Reliability Validation for Brazil
Ballabriga et al. 1994 Scale of screening based on observation Over 2 years Favors tracking the evolution of the disease Assumpção et al. 1999 Krug et al. 1980 Direct observation and interview with parents and caregivers for screening Over 18 months Identifies autism in both clinical and educational contexts Marteleto and Pedromônico 2005 Schopler et al. 1986 Assessment scale for observation of behavior for screening Over 24 months High degree of internal consistency and reliability Pereira and Wagner 2008 Berument et al. 1999 Self-administration questionnaire for parents and caregivers for screening Over 6 years Favors the large-scale use for screening of suspected cases of autism Sato et al. 2009 Lord et al. 1994 Semi-structured interviews with parents or guardians for diagnosis and research on autism Over 18 months Validated and reliable instrument for diagnosis of (GDD** TGD) for ASD in preschool aged Becker et al. 2012
interviews held with parents and caregivers and by clinical observation of the child. To assist in the diagnostic process, several scales and interviews were validated to Portuguese. Table 1 shows the translated and validated scales and instruments for use in Brazil: The ATA (Escala d’Avaluació dels Trests Autistes), ABC (Autism Behavior Checklist), M-CHAT (Modified Checklist for Autism in
Toddlers), and the ASQ (Autism Screening Questionnaire) are used for screening, while the CARS (Childhood Autism Rating Scale) and ADI-R (Autism Diagnostic Interview-reviewed) are used for diagnosis. The ATA (Autistic Traits of Evaluation Scale) was the first scale to be translated and adapted in Brazil by Assumpção Jr. et al. The translation of
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the ABC made by Marteleto and Pedromônico in 2005 added the direct observation of the child as well as interviews with parents and caregivers. The M-CHAT (Modified scale for screening autism) was translated and adapted to Portuguese in Brazil by Mirella Fiuza Losapio and Milena Pereira Pondé 2008 creating the possibility of early screening, which could be done in public health at the primary care level. The questionnaire for children older than 6 years has been translated and validated in Brazil by Sato et al. in 2009. But, in Brazil there is still no study comparing the use of these scales in our social reality, and it would be important to establish protocols for screening. The CARS is a scale that helps identify children with autism and it distinguishes from children with developmental impairment and without autism. Its importance is to differentiate mildmoderate autism from severe the CARS has two versions. One observational and one held with parents or guardians. The scale for parents was translated and validated in 2007 in Brazil (Riesgo and Wagner 2008). The ADI has been translated into 11 languages, and it is cited as the gold standard for diagnosis of autism. In Brazil, the translation and validation of the ADI-R (Becker et al. 2012) has recently been published. It is important to note the CARS alone does not indicate diagnosis (Riesgo and Wagner 2008), it must be used together with the DSM IV diagnostic criteria for autism. Santos et al. (2012) concluded that the CARS fails in the diagnosis of some cases of autism, while the ABC may result in overdiagnosis, and it is best to combine the use of both. Corroborating studies show that instruments used in isolation may not be sufficient, and it is important to use at least one questionnaire with parents and an observation scale with the kid. The administration of the ADI-R is complex and lengthy requiring trained professionals to such situations that are nonexistent in our society, even the little time availability of the ADI-R for use in Brazil. In an attempt to use instruments already validated and translated and applied in Brazilian clinical practice, Duarte et al. (2003) examined the validity of the CBCL/4-18 (Child Behavior
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Checklist) for the identification of autism spectrum disorders in the Brazilian population. The CBCL was validated for the administration to the Brazilian population by Bordin et al. (1995). As Albores-Gallo et al. (2008) pointed out, the CBCL is an important instrument to assess the most frequent comorbidities in autism spectrum disorders, such as attention problems, depression, and anxiety, and not automatically valid for diagnosis. Some Brazilian researchers have been working in the development of national scales for fast and easy administration for early detection of autistic symptoms (children younger than 18 months). The Clinical Indicators of Risk for Child Development (IRDI) was developed in Brazil, and it has the ability to detect a trend of occurrence of problems expressed in the first 18 months of life that extends throughout the development of the child at least until the sixth year, generating impact on the quality of life of the child (Kupfer et al. 2010). They are currently working on the validation of the IRDI for large-scale administration in Brazil, in partnership with the Brazilian government. Braido (2006) reported different behaviors and properties of behaviors (e.g., response latency) that already showed signs of delay in the development of babies (1 year old) who were lately diagnosed with autism. Following this line of research to identify behaviors, Bagaiolo et al. (2010) used the description of the child development from 0 to 3 years in order to analyze home videos of a baby who was lately diagnosed with autism (diagnosed with 3 years of age). Different failures in aspects of child development, before 3 years of age, were analyzed for association with autism risk. This work is still in progress requiring replication. Regarded as the gold standard for diagnosis, the Autism Diagnostic Observation Scale – ADOS (Lord et al. 1994, 2000), ADOS-G has been already translated (personal communication by Maria Clara Pacifico, Cristiane S de Paula, and Guiomar Oliveira 2012) but has not yet been published or validated. The limitation of trained human resources for its administration also prevents the use of the ADOS-G in clinical practice.
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Neuropsychological and Speech Pathology Evaluation In Brazil, several instruments are used for neuropsychological and speech pathology assessment, and there is no standardized protocol. The use of Wechsler Intelligence Scale for Children-III, Wechsler Adult Intelligence Scale-III, and ADAPTATIVE BEHAVIOR SCALES OF VINELAND are recommended for assessment for autism in Brazil. From the standpoint of speech pathology, adapted scales and instruments to the Brazilian society are ABFW tests – Child language test in the areas of phonology, vocabulary, fluency, and pragmatics (Andrade et al. 2004); PPVT– Peabody Picture Vocabulary Test (Capovilla and Capovilla 1997); Language Exam (Braz and Pellicciotti 1988); the Evaluation of Symbolic Maturity (Befi-Lopes et al. 2000); and the language development evaluation (Menezes 2004).
Genetic Evaluation in Brazilian Clinical Practice In terms of familial and genetic studies conducted in Brazil, we emphasized the study by Mecca et al. (2011) that aimed to track the occurrence of signs and symptoms of ASD in siblings of individuals with this diagnosis. The data indicate higher rates than those reported in the literature (2–6 %) and approach the findings that report 10 % of familial recurrence in dizygotic twins. This result provides evidences of possible neurogenetic factors to explain the occurrence of ASD in relatives of studied probands and emphasizes the need to make the tracking of this disorder in children who are not only evaluated but also their brothers. Brazil is beginning to contribute to genetic studies in the field of ASD and has adequate technology to conduct them. In the last 5 years (2008–2013), more than 11 genetic studies in patients with ASD were made in Brazil, most of which were case studies and some association studies, and case–control or family studies (simplex or multiplex) (Kuczynski et al. 2009; Griesi-Oliveira et al. 2012; Mulatinho et al. 2012;
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Baruffi et al. 2012; Griesi-Oliveira et al. 2012; Christofolini et al. 2012; Longo et al. 2009; Castro et al. 2010; Orabona et al. 2009; Campos Junior et al. 2011). Although the Brazilian researches have made some contributions in the genetics of ASD, there is no panel for gene diagnosis in the Brazilian population yet, and the genetic evaluation is not part of the systematic evaluations in private clinics or public ones.
Overview of Current Treatments and Research Related to Treatment Strategies In Brazil, it is recommended to follow the interventions followed by ABA (Applied Behavior Analysis), TEACCH (Treatment and Education of Autistic and related Communication handicapped Children), and PECS (Picture Exchange Communication System) in order to maximize language acquisition and improve communication skills. All the AMA – Associação de Amigos do Autista – in most of Brazilian regions offer different activities mainly based on TEACCH and PECS. Due to the high cost of professional training and the difficulty of administering such interventions in Brazil, it is needed that the research related to modifications of these therapies being either in time, performance in groups, or frequency. Below we present examples of work groups of Brazilian researchers studying single pre-evaluations for appropriateness of inclusion of children at different levels of PECS, time of administration of it as well as the speech pathology therapies in individual or in pairs. The PECS is a simple and portable equipment, especially produced to ensure that communication with autistic children occurs in spite of their speech difficulties, but previous studies (Flippin et al. (2010) reported that many failures in the PECS training may be due to the fact that the child has not acquired all conditional discrimination training required. So before you start training the PECS, it is important to assess whether the individual already has such discrimination. In 2003, Guilhardi compared the performance of participants in ABLA (Assessment of Basic
Brazil and Autism
Learning Abilities) and conditional discrimination training and equivalence testing. The author aimed to investigate whether the ABLA test results could predict the performance of children with typical development and atypical development (autism, cerebral palsy, congenital syndromes, etc.) in tasks involving the same type of stimulus. Guilhardi (2003) concluded that the predictive power of the ABLA for training equivalence does not happen to all participants and also there were no differences in predictive ability between types of conditional discrimination tests. In Brazil, Godoi et al. (2008) made a study of two cases to investigate the effectiveness of PECS in terms of assessing the amount of training necessary to acquire the skills involved in functional communication exchanging pictures. The authors measured also some side effects of PECS training, i.e., the interference of training on the frequency and appropriate verbalizations and about learning other behaviors specific to each participant. Parallel to the PECS training, additional training for other behaviors was done. These additional training involved differential reinforcement and fading out physical tips. As a side effect of this training, Godoi et al. (2008) pointed out the increasing frequency of verbalizations of both groups of participants. Furthermore, according to the authors, the structured and concrete features of the training contributed to the increased frequency of specific behaviors of each participant and independence in an everyday activity. Aiming to understand even more specifically the effects of picture exchange communication on language acquisition, Guilhardi (2009) investigated the functional independence of tacts and mands between verbal responses based on selection of stimuli (PECS). According to the author, the results showed functional independence between operant tacts and mands with verbal responses based on the selection of stimuli. Cardoso and Fernandes (2006) and Fernandes et al. (2008) did studies with children and adolescents with psychiatric diagnosis within the autistic spectrum at the beginning of the process of speech pathology therapy and they were divided into three groups (individual language
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therapy, child in a group with a coordinator, child in a group without a coordinator) according to the therapeutic intervention received for a period of 6 months; the results indicated no statistically significant differences between the groups; however, the group with most progress during the specific period of differentiated intervention was the one where the individuals were treated with a coordinator. The most interesting was that in none of the groups decrease in the levels of improvement obtained after a period of 6 months was observed, and in some situations, the number of individuals with improvement increased after this period. The results of this study reinforce the adequacy of procedures for determining the individual profile of abilities and disabilities of each individual as a basis for definitions regarding to the adopted intervention model. In terms of pharmacological treatments, not all medications are available in the SUS and there are restrictive conditions according to diagnosis. For example, the majority of atypical antipsychotics are only offered to individuals with the diagnosis of schizophrenia. Besides, we do not have proper clinical trials performed in our country to examine the use of medications in autism. Despite methodological limitations, the study of Novaes et al. (2008) concluded that pharmacological interventions with second-generation antipsychotics seemed useful for the control of behavioral disorders such as psychomotor agitation and aggressive behavior in a sample of Brazilian patients with autistic spectrum disorders corroborating worldwide studies in the area.
Priorities and Future Directions A systematic review of the Brazilian scientific literature on ASD showed a significant increase in scientific production in this subject over the last 2 years (Teixeira et al. 2010). On the other hand, it showed that most publications are not focused on subjects that can contribute significantly to the improvement of public health relating to autism in Brazil. Most publications make references to intervention studies without controls and small
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convenience samples, and the use of validated diagnostic and neuropsychosocial instruments are still required. Another intriguing finding is the extreme concentration of scientific production in only two regions of the country. Researchers from São Paulo and Rio Grande do Sul are the first authors in 90 % of papers published between 2002 and 2009 (Teixeira et al. 2010). In a recent editorial, De Paula et al. 2011 list the barriers and difficulties as well as the priority areas for investment in autism in Brazil. Some identified challenges include lack of specific funding to support autism research, lack of national multicenter projects, lack of trained researchers and clinicians in various disciplines, lack of robust scientific studies, and lack of campaigns to increase knowledge and understanding by the general public and professionals in education and health. The priorities identified for response to the challenges encountered are by (De Paula et al. 2011). 1. Research areas – to build capacity of research in various disciplines through programs of research training and training of clinicians, including diagnostic skills and tools for early detection and interventions 2. Awareness – to raise public understanding and improve public perception of autism by making information more accessible to the public 3. Services – to build capacity for services in Brazil among professionals and in community-based settings, to establish training program for screening early detection and intervention strategies All these activities are very important to improve the understanding and assistance of autism in Brazil.
References and Reading Albores-Gallo, L., Hernandez-Guzman, L., Diaz-Pichardo, J. A., & Cortes-Hernandez, B. (2008). Dificultadesenlaevaluación y diagnóstico del autismo: Una discusión. Salud Mental [online], 31(1), 37–44. American Psychiatry Association. (1994). Diagnostic and statistical manual of mental disorders (4th ed.,
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BRI (Behavior Regulation Index) ▶ BRIEF (Behavior Rating Inventory of Executive Functions)
BRIAAC ▶ Behavior Rating Instrument for Autistic and Atypical Children (BRIAAC)
BRIEF (Behavior Rating Inventory of Executive Function) ▶ BRIEF (Behavior Rating Inventory of Executive Functions)
BRIEF (Behavior Rating Inventory of Executive Functions) Jennifer H. Foss-Feig1, Naama de la Fontaine2 and Katherine Tsatsanis2 1 Department of Psychiatry, Icahn School of Medicine at Mount Sinai Hospital, New York, NY, USA 2 Yale Child Study Center, New Haven, CT, USA
Synonyms ASD (autism spectrum disorder); BRI (Behavior Regulation Index); GEC (Global Executive Composite); MI (Metacognition Index)
BRIEF (Behavior Rating Inventory of Executive Functions)
Description The Behavior Rating Inventory of Executive Function (BRIEF) is a questionnaire that assesses executive functioning behaviors of school-aged children, ages 5–18 (Gioia et al. 2000). Executive functioning is the ability to actively engage in an assortment of interrelated processes that are responsible for guiding goaldirected cognitive, emotional, and behavioral functioning. Both parent and teacher rating forms of the BRIEF exist, providing opportunities to rate executive functioning across home and school settings. The BRIEF is appropriate for the assessment of children with a variety of developmental, neurological, psychiatric, and medical conditions. It has also been normed and applied in samples of children with autism spectrum disorder (ASD). The BRIEF consists of 86 items that cluster into eight clinical scales reflecting different aspects of executive functioning. The scales are entitled: Inhibit, Shift, Emotional Control, Initiate, Working Memory, Plan/Organize, Organization of Materials, and Monitor. Three of these scales combine to create the Behavioral Regulation Index (BRI), and five comprise the Metacognition Index (MI). All eight clinical scales of the BRIEF combine to yield the Global Executive Composite (GEC). The BRI reflects the ability to shift cognitive set and to modulate behavior and emotion by exerting appropriate inhibitory control. The BRI is comprised of the Inhibit, Shift, and Emotional Control scales. The Inhibit scale assesses the ability to resist acting on an impulse or refrain from engaging in an impulsive behavior as appropriate to the situation. The Shift scale measures the ability to respond to changing circumstantial demands by flexibly transitioning from one activity, situation, or aspect of a problem to another. The Emotional Control scale quantifies the ability to regulate emotions and display emotional reactions that are situationally and developmentally appropriate. The MI reflects the ability to cognitively manage tasks by effectively initiating, planning, organizing, and monitoring goal-oriented behaviors.
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The MI is comprised of the Initiate, Working Memory, Plan/Organize, Organization of Materials, and Monitor scales. The Initiate scale measures the ability to independently begin a task or activity, including generating ideas or problemsolving strategies. The Working Memory scale assesses the ability to hold information in mind and to mentally manipulate information for the purpose of completing a task. The capacity to effectively sustain performance and attention is also integral to items on this scale. The Plan/ Organize scale evaluates the ability to apply order to information, anticipate future events, set goals, and develop appropriate, often sequential, steps in the service of task completion. The Organization of Materials scale quantifies the child’s ability to organize his or her work and play activities, as well as environment (e.g., possessions, storage, and work spaces). The Monitor scale assesses the ability to continuously engage in work-checking behaviors in order to ensure attainment of the designated goal. The scale also measures the child’s ability to continuously evaluate how his or her behavior affects others. In addition to the primary clinical scales, the questionnaire yields two validity scales, assessing inconsistency and negativity of the reporter. The Inconsistency scale of the BRIEF consists of ten pairs of items that are strongly correlated with one another. This scale is included in order to measure rater consistency. The Negativity scale is comprised of the nine items that were least frequently rated as “often” in the normative sample. High Negativity scale scores can indicate an excessively negative perception on the part of the rater, though at times they are also seen in profiles of children with very high levels of executive dysfunction. The BRIEF is intended to be completed in a single sitting, requiring approximately 10–15 minutes. Each scale and summary index yields a T score, which have a mean of 50 and a standard deviation of 10. Scores above 65 are considered clinically elevated. Percentile scores are also provided. While the BRIEF can be administered by individuals who do not possess formal training, interpretation of scores requires relevant graduate training in the field of psychology.
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Historical Background The BRIEF was developed by neuropsychologists Gerard Gioia, PhD, Peter Isquith, PhD, Steven Guy, PhD, and Lauren Kenworthy, PhD, and published in 2000 by Psychological Assessment Resources, Inc. It was designed to measure children’s executive functioning in real-world settings, as observed by parents, teachers, and caregivers. BRIEF items were generated from clinical interviews conducted by the authors, based on common concerns elicited from parents and teachers. The initial draft of the questionnaire included 129 items for the parent form and 127 items for the teacher form. The set of items was determined to match a fourth- to fifth-grade reading level, as intended by the authors. Following initial item development, twelve neuropsychologists reviewed and categorized all items into their appropriate executive functioning domains. Principal factor analysis was then conducted to further clarify clustering of items. In the final version of the BRIEF, over 80 % of items retained had at least 75 % agreement among authors and expert raters regarding how well they fell within their designated scales. Within the BRIEF, moderate to high correlations were found between most scales. Therefore, in order to strengthen the validity of the BRIEF scale structure, factor analysis was conducted, using normative and clinical samples for both the parent and teacher forms. Based on the results of a principal factor analysis, a two-factor structure was identified. This two-factor solution accounted for 74 % of the variance in parent forms and 83 % of the variance in teacher forms. The two factors derived from this analysis, entitled the Behavior Regulation Index (BRI) and Metacognition Index (MI), subsequently have been used to capture all eight scales of the BRIEF. The BRIEF represents the first informant report measure of executive functioning available for use with children and adolescents. Strengths include the ability to measure multiple domains of executive functioning simultaneously. This feature can be contrasted with research and clinical task-based assessments,
BRIEF (Behavior Rating Inventory of Executive Functions)
which often quantify isolated abilities in distinct executive domains. By utilizing caregiver report of daily functioning, the BRIEF also claims high ecological validity, as findings reflect, and are generalizable to, executive functioning capacity in various real-life settings (Kenworthy et al. 2008). Criticisms of the BRIEF concern the validity and potential for bias inherent in any informant report measure. In addition, concerns have been raised about whether executive functions can truly be parsed into individual domains, as is suggested by the eight BRIEF scales. It has been noted that such parsing of the broader executive functioning construct is not yet well grounded in neuroimaging research regarding brain function (Denckla 2002).
Psychometric Data The BRIEF was standardized and validated in a sample of school-aged children from diverse racial, socioeconomic, and geographic backgrounds. The normative sample included 1,419 parent forms and 720 teacher forms, for children between 5 and 18 years of age, attending public and private schools in Maryland. Approximately half of the normative sample was male, and no children had a history of special education or psychiatric medication. Among parent responders, 83 % were mothers. From the normative raw data, T scores and percentile norms were developed for each of the eight BRIEF scales, the BRI and MI, and the GEC. T scores were generated using a linear transformation of raw scores. Percentiles were assigned based on the distribution of raw scores for each scale, index, and GEC. As for other behavior rating scales, BRIEF scales are positively skewed, so that the majority of cases cluster at the lower, normal end of each scale, while scores in the tail represent deviations from the norm. Several aspects of test reliability were assessed using data from the normative sample. Internal consistency quantifies the degree to which items within individual scales are consistent with each
BRIEF (Behavior Rating Inventory of Executive Functions)
other and is statistically evaluated using Cronbach’s alpha coefficient. Psychometric data indicated that across all individual scales and composite indices, in both parent and teacher forms, Cronbach’s alpha values ranged from 0.80 to 0.98. These values indicate good to excellent internal reliability within scales. Inter-rater reliability was also measured to determine consensus across parent and teacher forms completed for a subset of 296 children within the normative sample. Inter-rater reliability was found to range from 0.15 to 0.50 across the different scales and composites. These values indicate low reliability across raters; however, the authors note that low inter-rater reliability is expected given that these forms are intended to capture any existing variability in children’s behavior across different environmental settings. Inter-rater reliability was lowest for the Initiate and Organization of Materials scales; in general, parents tended to endorse more problematic functioning than did teachers. Finally, test-retest reliability was assessed in order to measure how consistent individual raters were in reporting on a child’s behavior at different but proximal time points. For the parent form, this data was computed from a subsample of 54 participants within the normative sample with an average interval of 2 weeks between questionnaire completions for a single rater. Test-retest reliability values ranged from 0.76 to 0.85 across individual scales and from 0.84 to 0.88 for the three composites, indicating good reliability. For the teacher form, test-retest reliability was computed in a subsample of 41 participants, over an average interval of 3.5 weeks. Test-retest reliability for the teacher form ranged from 0.83 to 0.92 for individual BRIEF scales and from 0.90 to 0.92 for the BRI, MI, and GEC. These values indicate good test-retest reliability for individual scales, and excellent reliability for the composites when rated twice by teachers. In their manual, the BRIEF authors also address two aspects of test validity or the extent to which the BRIEF measures what it proposes to measure. Content validity, reflects the degree to
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which the content of the test instrument itself (i.e., BRIEF items) adequately captures all aspects of the construct (i.e., executive functioning) it purports to measure. The fact that items were selected from clinical interviews and that there was strong agreement among pediatric neuropsychologists that items fit within their intended scales, serves to strengthen content validity for the BRIEF. In addition, during test development, scales were refined with item-total correlations (i.e., the extent to which an individual item correlated with the overall score for its scale). Inter-rater agreement among expert reviewers served as an external check for scale membership of each item. Construct validity speaks to the degree to which the measure adequately captures the construct that it aims to address. It can be quantified by evaluating convergence with other measures that target the same construct, as well as divergence with measures targeting dissociable constructs. Evidence for good construct validity is found in high correlations between BRIEF scales and other pertinent measures, including the ADD-IV scale (Zhang et al. 2005), and several subscales of the Child Behavior Checklist (CBCL) (Achenbach 1991). Specifically, all scales were highly correlated with the ADD-IV summary scores, which is expected given that executive functioning problems are prominent in children with diagnosed attention and behavioral disorders. In addition, there is some evidence for specificity and dissociability of individual BRIEF scales with regard to the constructs they aim to capture. For example, the BRIEF Initiate scale correlated with the Withdrawn, Anxious/Depressed, and Attention Problems scales of the CBCL, whereas the BRIEF Working Memory scale correlated only with CBCL Attention Problems scale. The BRIEF Initiate scale correlated with both the CBCL Attention Problems and Aggressive Behavior scales, whereas the BRIEF Shift and Emotional Control scales correlated with the CBCL Aggressive Behavior scale alone. Evidence of divergence with constructs unrelated to executive functioning is evident in low correlation rates
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between BRIEF scales and the CBCL Somatization scale. Convergent and divergent construct validity was also evaluated by comparing the BRIEF, the Behavior Assessment System for Children (BASC) (Reynolds 2004), and the ▶ Conners’ Parent Rating Scale.
Clinical Uses Findings pertaining to the application of the BRIEF with children and adolescents diagnosed with ASD are available in the BRIEF manual. As part of measure development, the BRIEF was administered to parents (n ¼ 26) and teachers (n ¼ 18) of children with high-functioning ASD, as well as to parents (n ¼ 18) and teachers (n ¼ 16) of typically developing children with no psychiatric diagnoses. Relative to controls, scores for children with high-functioning ASD, including Autistic Disorder, Asperger’s Disorder, and Pervasive Developmental Disorder – Not Otherwise Specified – were significantly elevated across all BRIEF scales and composite indices. Similar to findings detailed in the BRIEF manual, primary research literature also confirms that the BRIEF is sensitive to differences between children with ASD and those with no psychiatric diagnoses. Thus, researchers have reported that when comparing BRIEF scores of highfunctioning children with ASD to the standardization sample in the BRIEF manual, BRI, MI, and GEC composite scores were all clinically elevated on average (Gilotty et al. 2002; Kenworthy et al. 2005; Kenworthy et al. 2009; Winsler et al. 2007). More specifically, compared to BRIEF norms, approximately two-thirds of children with ASD scored in the clinically impaired range (i.e., T scores over 65) on the BRI, MI, and GEC (Kenworthy et al. 2005). There is also some evidence that when boys with ASD are compared to those with typical development, the pattern of generalized score elevations is most salient for the Shift scale (Mackinlay et al. 2006). Research conducted within ASD samples has revealed a link between executive functioning
BRIEF (Behavior Rating Inventory of Executive Functions)
symptom elevations on the BRIEF and greater severity in core symptoms associated with the disorder. Namely, higher scores on the BRI were related to greater impairment in Communication, Reciprocal Social Interaction, and Restricted and Repetitive Behavior domains as assessed by the most widely used diagnostic measures for autism (i.e., Autism Diagnostic Observation Schedule, Autism Diagnostic Interview – Revised) (Kenworthy et al. 2009). Likewise higher BRI scores were associated with score elevations on a measure specifically targeting repetitive behaviors (Boyd et al. 2009). Increased MI scores, in contrast, were related to greater social symptom severity only (Kenworthy et al. 2009). However, when BRIEF scores were compared to scores on a measure of adaptive functioning, MI scores were significantly correlated with impairment in both the socialization and communication domains (Gilotty et al. 2002). The clinical utility of the BRIEF for children with ASD has been examined in comparison to other clinical populations. Specifically, researchers have shown that individuals with ASD can be differentiated from others with psychiatric, learning, behavioral, and learning disorders based on their BRIEF profiles. For example, children with ASD have more elevated scores on the BRIEF than do those with reading disabilities or traumatic brain injury (Gioia et al. 2002). Some research has demonstrated similarities between the BRIEF profiles of children with ASD and those with attention-deficit/ hyperactivity disorder (ADHD) (Gioia et al. 2002; Winsler et al. 2007). However, deficits in flexibility, as indexed by the Shift scale of the BRIEF, appear to be most characteristic of children with ASD (Gioia et al. 2002). Taken together, research to date has demonstrated that the BRIEF is sensitive to behavioral impairments in regulatory and metacognitive functioning in children with ASD. The level of impairment and relation between BRIEF scores and adaptive functioning in this group underscore the importance of evaluating executive functioning when assessing and treating children with ASD. The BRIEF offers a unique tool for assessing this domain of behavior, as observed by adults
Brief Infant-Toddler Social and Emotional Assessment (BITSEA)
familiar with children’s day-to-day functioning in real-world settings.
References and Reading Achenbach, T. M. (1991). Manual for the Child Behavior Checklist/4-18 and 1991 profile (p. 288). Burlington: Department of Psychiatry, University of Vermont. Boyd, B. A., McBee, M., Holtzclaw, T., Baranek, G. T., & Bodfish, J. W. (2009). Relationships among repetitive behaviors, sensory features, and executive functions in high functioning autism. Research in Autism Spectrum Disorders, 3(4), 959–966. Denckla, M. B. (2002). The behavior rating inventory of executive function: Commentary. Child Neuropsychology, 8(4), 304–306. Gilotty, L., Kenworthy, L., Sirian, L., Black, D. O., & Wagner, A. E. (2002). Adaptive skills and executive function in autism spectrum disorders. Child Neuropsychology, 8, 241–248. Gioia, G. A., Isquith, P. K., Guy, S. C., & Kenworthy, L. (2000). Behavior rating inventory of executive function professional manual. Lutz: Psychological Assessment Resources. Gioia, G. A., Isquith, P. K., Kenworthy, L., & Barton, R. M. (2002). Profiles of everyday executive function in acquired and developmental disorders. Child Neuropsychology, 8, 121–137. Kenworthy, L. E., Black, D. O., Wallace, G. L., Ahluvalia, T., Wagner, A. E., & Sirian, L. M. (2005). Disorganization: The forgotten executive dysfunction in highfunctioning autism (HFA) spectrum disorders. Developmental Neuropsychology, 28, 809–827. Kenworthy, L., Yerys, B. E., Anthony, L. G., & Wallace, G. L. (2008). Understanding executive control in autism spectrum disorders in the lab and in the real world. Neuropsychology Review, 18, 320–338. Kenworthy, L., Black, D. O., Harrison, B., Della Rosa, A., & Wallace, G. L. (2009). Are executive control functions related to autism symptoms in high-functioning children? Child Neuropsychology, 15(5), 425–440. Mackinlay, R., Charman, T., & Karmiloff-Smith, A. (2006). High functioning children with autism spectrum disorder: A novel test of multitasking. Brain and Cognition, 61, 14–24. Reynolds, C. R. (2004). Behavior assessment system for children. Wiley. Winsler, A., Abar, B., Feder, M. A., Schunn, C. D., & Rubio, D. A. (2007). Private speech and executive functioning among high- functioning children with autistic spectrum disorders. Journal of Autism and Developmental Disorders, 37, 1617–1635. Zhang, S., Faries, D. E., Vowles, M., & Michelson, D. (2005). ADHD rating scale IV: psychometric properties from a multinational study as clinician-administered instrument. International Journal of Methods in Psychiatric Research, 14(4), 186–201.
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Brief Infant-Toddler Social and Emotional Assessment (BITSEA) Ivy Giserman Kiss and Alice S. Carter Department of Psychology, University of Massachusetts Boston, Boston, MA, USA
Synonyms BITSEA; BITSEA-ASD subscales
Description The Brief Infant-Toddler Social and Emotional Assessment (BITSEA) (Briggs-Gowan and Carter 2006) is a 42-item caregiver-report broadband screening tool developed to identify socialemotional and behavior problems and delays in the acquisition of competencies in children 11–48-months of age. The BITSEA was derived from the longer (166 item) Infant-Toddler Social and Emotional Assessment (ITSEA; Carter and Briggs-Gowan 2000; Carter et al. 2003), which provides an in-depth analysis of emerging socialemotional development and intervention guidance. The BITSEA can be completed by caregiver report in approximately 5–7 minutes and is written at a fourth to sixth grade reading level (BriggsGowan et al. 2004). Items are rated on a threepoint Likert scale, with the following anchors: not true/rarely (0), somewhat true/sometimes (1), and very true/often (2). The information gathered from the BITSEA covers a broad range of social-emotional and behavioral functioning and generates scores in internalizing, externalizing, and regulatory domains, as well as other areas that may be indicative of emerging psychopathology. Following the original scoring of the BITSEA, items are grouped into Problem and Competence domains that generate possible problem and possible delay/deviance cut-scores as well as percentiles based on national norms. Of the total 42 items, the BITSEA contains 19 specific items that are consistent with early-
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Brief Infant-Toddler Social and Emotional Assessment (BITSEA)
Brief Infant-Toddler Social and Emotional Assessment (BITSEA), Table 1 Behaviors assessed on BITSEA ASD subscalesa
a
Subscale ASD-Problems
Psychometric properties Sensitivity: 76% Specificity: 72% PPV: 68%
ASD-Competence
Sensitivity: 91% Specificity: 80% PPV: 77%
Behaviors assesseda Limited enjoyment of playful activities Unresponsive when hurt Tactile sensitivities Difficulty with transitions Repetitive play Repetitive speech Repetitive motor movements Appears unaware of surroundings Limited eye contact Avoidant of physical contact Shares successes Seeks out caregiver when upset Responds to name with eye contact Emotional/physical affection Interactive play Empathy when someone is hurt Imitation skills Use of pointing to share interests/joint attention Pretend play
Adapted from table originally published by Giserman Kiss et al. (2017)
emerging autism spectrum disorder (ASD) symptomatology (See Table 1). An important feature of the BITSEA is its inclusion of both positively and negatively worded items, relating to both problem behaviors and delayed competencies associated with ASD; thus the BITSEA ASD items make up two ASD-specific subscales: ASD-Problems and ASD-Competence. The ASD-Competence subscale consists of nine items that assess early social-emotional and social-communication competencies expected in typical development. The ASD-Problems subscale consists of ten individual items that assess social-communication impairments and repetitive and restricted behaviors commonly seen in young children with ASD. The ASD-Problems and ASD-Competence subscales are calculated and prorated if fewer than three items are missing from either scale. BITSEA ASD subscales can be scored individually or together, as an ASD-Total score; however, findings presented by Giserman Kiss et al. (2017) demonstrate that the ASDCompetence subscale is the most statistically and clinically effective of all three subscales
(see the Psychometric Data section for more information). Children with subscale scores exceeding ASD-Competence, ASD-Problem, or ASD-Total cutoffs are considered “at risk” for ASD, and further developmental assessment is strongly recommended. The BITSEA allows clinicians to efficiently identify young children who are showing early symptoms of ASD, while simultaneously screening for early-emerging non-ASD social-emotional and behavior problems. Simultaneous screening may allow for more efficient assessments in fastpaced pediatric and early education settings and eliminate providers’ discomfort regarding introducing an ASD-specific screener to families who have not raised any concerns about their children’s social-emotional development, ultimately leading to increased universal screening.
Historical Background The BITSEA was originally developed in response to the recognition of the importance of
Brief Infant-Toddler Social and Emotional Assessment (BITSEA)
early detection of and early intervention services for young children with social-emotional and behavioral deficits. While the ITSEA responded to this need and effectively identified children with early-emerging psychopathology, authors recognized the limitations of this tool, particularly regarding the time required for completion and scoring. With the recommendation for routine screening during well-child visits by the American Academy of Pediatrics (AAP 2001), as well as the introduction of managed care which resulted in shorter pediatric office visits, a more efficient questionnaire, the BITSEA, was derived from the pool of ITSEA questions. Original and replication studies of the full BITSEA demonstrated excellent test-retest reliability and good interrater agreement between parents when used in a socioeconomically and ethnically diverse community-based population (BriggsGowan et al. 2004; Kruzinga et al. 2012). Subsequent studies found strong prediction of concurrent psychiatric disorders (Briggs-Gowan et al. 2013) and good prediction to parent- and teacherreported school-aged psychopathology (BriggsGowan and Carter 2008). In the original BITSEA manual, the authors recommended that the BITSEA be used for identifying young children with ASD, based on an at risk score on the overall competence scale as well as inspection of the scores for a subset of these ASD-consistent items. The ASD item pool was expanded to included sensory over- and under-responsivity following the publication of the DSM-5 criteria (APA 2013). However, cut-scores for the ASD items were not published. The increased recognition of the benefits of early intervention services for children diagnosed with ASD (e.g., Pickles et al. 2016; Seida et al. 2009; Woods and Wetherby 2003), updated recommendations of the AAP and Centers for Disease Control for frequent developmental surveillance and screening (AAP 2006; Baio 2012), and common use of the BITSEA across research and clinical settings to identify toddlers at risk for ASD lead Giserman Kiss et al. (2017) to assess the feasibility of the BITSEA ASD subscales. The measure was tested in a sample that included young children with ASD, non-ASD
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psychopathology, and typical development. Findings optimized cut-scores for each subscale that evidenced moderate to high discriminative power for detecting children with ASD. Of the three subscales, the ASD-Competence scale proved to be the most statistically and clinically effective (see the Psychometric Data section for more information).
Psychometric Data Giserman Kiss et al. (2017) used receiveroperating characteristic (ROC) plots to determine optimal cut-scores on the BITSEA ASD subscales in a diverse sample of 512 young children (223 in the ASD group, and 289 in the nonASD group) ranging in age from 15 to 48 months. Children in the non-ASD group included those with typical development as well as non-ASD early-emerging psychopathology. With regard to the ASD-Problems subscale, analyses using the optimized cut-score revealed moderate subscale accuracy (AUC ¼ 0.81), 76% sensitivity, 72% specificity, and 68% positive predictive value (PPV). The ASD-Competence subscale optimized cut-score yielded stronger psychometric properties, as analyses evidenced high subscale accuracy (AUC ¼ 0.92), 91% sensitivity, 80% specificity, and 77% PPV. Finally, the ASD-Total subscale optimized cutscore also yielded high subscale accuracy (AUC ¼ 0.92), as well as 80% sensitivity, 79% specificity, and 77% PPV. Sensitivity and specificity rates for all three subscale optimized cutscores in the subsample of children 24 months old and younger were comparable or stronger to rates in the overall sample. Importantly, the ASD-Competence subscale outperformed the other subscales in identifying both true positives and true negatives. Post-hoc analyses explored the clinical characteristics of the children who screened positive despite not receiving an ASD diagnosis. Analyses revealed that children who screened as false positives on the ASD-Competence scale (i.e., screened positive but did not have ASD) had significantly lower nonverbal problem-
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solving, receptive language, and expressive language abilities than children that screened as true negatives (i.e., screened negative and did not have ASD). In addition, children who screened as false negatives (i.e., screened negative but had ASD) had significantly higher receptive language scores than true positives (i.e., screened positive and had ASD).
Clinical Uses It is recommended that the ASD-Competence subscale be used in pediatric primary care, early intervention, and early education settings to detect children at risk for ASD, while simultaneously assessing other areas of a child’s development. The ASD-Competence subscale demonstrated strong psychometric properties and is quick and easy for providers to score. While the ASD-Total subscale also demonstrated high sensitivity, specificity, and PPV, scoring this subscale can be more cumbersome, error-prone, and time-consuming due to the additional items and need to perform calculations. Thus, providers are strongly encouraged to use the ASD-Competence subscale in order to examine a child’s risk status for ASD, while simultaneously using the overall BITSEA to assess other social-emotional and behavioral domains, thus fulfilling the AAP and CDC’s recommendations for regular developmental surveillance and screening. If a child’s score on the ASD-Competence subscale exceeds the designated cut-score, caregivers should be counseled to seek further assessment of ASD symptoms, either through a second-stage observational screener, more in-depth conversation about the child’s functioning across settings and relationships, or a developmental evaluation. The broadband nature of the BITSEA may reduce the previously documented stress felt by caregivers during the ASD screening and diagnostic process (Siklos and Kerns 2007). A global screener that has a specific ASD subscale may give the provider additional time and avenues by which to introduce the idea of ASD. Moreover,
the use of positively as well as negatively worded questions on the BITSEA may be more acceptable to parents. Finally, given the increased risk for specific forms of psychopathology among children with ASD (e.g., anxiety, sleeping, and feeding disorders) (Ming et al. 2008), gathering information about symptoms beyond ASD may inform comprehensive intervention services, when needed. Several limitations of the BITSEA ASD subscales should be kept in mind during use. Giserman Kiss et al. (2017) reported that the ASD-Competence subscale is possibly limited by its overlap with symptoms of general intellectual or developmental disabilities or other earlyemerging psychopathology and also may be compromised by advanced language abilities. Thus, like any screening tool, the BITSEA should be used as an initial assessment and not as a diagnostic tool.
References and Reading American Academy of Pediatrics. (2001). Committee on children with disabilities. Developmental surveillance and screening of infants and young children. Pediatrics, 108, 192–196. American Academy of Pediatrics, Council on Children with Disabilities, Section on Developmental and Behavioral Pediatrics, Bright Futures Steering Committee, Medical Home Initiatives for Children with Special Needs Project Advisory Committee. (2006). Identifying infants and young children with developmental disorders in the medical home: An algorithm for developmental surveillance and screening. Pediatrics, 118, 405–420. https://doi.org/10.1542/peds.20061231. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Arlington: American Psychiatric Association. Baio, J. (2012). Prevalence of autism spectrum disorders: Autism and developmental disabilities monitoring network, 14 sites, United States, 2008. Morbidity and mortality weekly report. Surveillance summaries. Centers for Disease Control and Prevention, 61(3), 1–19. Briggs-Gowan, M. J., & Carter, A. S. (2006). Examiner’s manual for the Brief Infant-Toddler Social and Emotional Assessment (BITSEA). San Antonio: Psychological Corporation, Harcourt Press. Briggs-Gowan, M. J., & Carter, A. S. (2008). Socialemotional screening status in early childhood predicts
Brief Observation of Social Communication Change (BOSCC) elementary school outcomes. Pediatrics, 121(5), 957–962. https://doi.org/10.1542/peds.2007-1948. Briggs-Gowan, M. J., Carter, A. S., Irwin, J., Wachtel, K., & Cicchetti, D. V. (2004). The brief infant-toddler social and emotional assessment: Screening for socialemotional problems and delays in competence. Journal of Pediatric Psychology, 29(2), 143–155. Briggs-Gowan, M. J., Carter, A. S., McCarthy, K., Augustyn, M., Caronna, E., & Clark, R. (2013). Clinical validity of a brief measure of early childhood socialemotional/behavioral problems. Journal of Pediatric Psychology, 38(5), 577–587. Carter, A. S., & Briggs-Gowan, M. J. (2000). Manual of the infant-toddler social-emotional assessment. New Haven: Yale University. Carter, A. S., Briggs-Gowan, M. J., Jones, S. M., & Little, T. D. (2003). The infant-toddler social and emotional assessment: Factor structure, reliability, and validity. Journal of Abnormal Child Psychology, 51, 495–514. Giserman Kiss, I., Feldman, M. S., Sheldrick, R. C., & Carter, A. S. (2017). Developing autism screening criteria for the Brief Infant Toddler Social Emotional Assessment (BITSEA). Journal of Autism and Developmental Disorders. https://doi.org/10.1007/s10803017-3044-1. Kruizinga, I., Jansen, W., de Haan, C. L., van der Ende, J., Carter, A. S., & Raat, H. (2012). Reliability and validity of the Dutch version of the Brief Infant-Toddler Social and Emotional Assessment (BITSEA). PloS One, 7(6). https://doi.org/10.1371/journal.pone.0038762. Ming, X., Brimacombe, M., Chaaban, J., ZimmermanBier, B., & Wagner, G. C. (2008). Autism spectrum disorders: Concurrent clinical disorders. Journal of Child Neurology, 23(1), 6–13. https://doi.org/10.1177/ 0883073807307102. Pickles, A., Le Couteur, A., Leadbitter, K., Salomone, E., Cole-Fletcher, R., Tobin, H., et al. (2016). Parentmediated social communication therapy for young children with autism (PACT): Long-term follow-up of a randomised controlled trial. The Lancet, 388, 2501–2509. Seida, J., Ospina, M. B., Karkhaneh, M., Hartling, L., Smith, V., & Clark, B. (2009). Systematic reviews of psychosocial interventions for autism: An umbrella review. Developmental Medicine and Child Neurology, 51(2), 95–104. https://doi.org/10.1111/j.1469-8749. 2008.03211.x. Siklos, S., & Kerns, K. A. (2007). Assessing the diagnostic experiences of a small sample of parents of children with autism spectrum disorders. Research in Developmental Disabilities, 28(1), 9–22. https://doi.org/10. 1016/j.ridd.2005.09.003. Woods, J. J., & Wetherby, A. M. (2003). Early identification of and intervention for infants and toddlers who are at risk for autism spectrum disorder. Language, Speech, and Hearing Services in Schools, 34(3), 180–193. https://doi.org/10.1044/0161-1461(2003/015).
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Brief Observation of Social Communication Change (BOSCC) Rebecca Grzadzinski Carolina Institute for Developmental Disabilities, University of North Carolina, Chapel Hill, NC, USA
Abbreviations ADOS-2 ASD BOSCC
Autism Diagnostic Observation Schedule, second Edition Autism spectrum disorder Brief Observation of Social Communication Change
Description The Brief Observation of Social Communication Change (BOSCC; Grzadzinski et al. 2016) is a treatment response measure of autism spectrum disorder (ASD) symptoms. The BOSCC is a behavioral coding scheme that is applied to 10–12 min videotaped social/play interactions between a child and a researcher or caregiver (play partner). The coding scheme was developed by expanding items from the Autism Diagnostic Observation Schedule, second Edition (ADOS-2; Lord et al. 2012), to quantify more nuanced variation in ASD symptoms. The BOSCC has been shown to be sensitive to changes in ASD symptoms from pre- to post-treatment (Divan et al. 2018; Frost et al. 2019; Gengoux et al. 2019; Grzadzinski et al. 2016; Kim et al. 2019; Kitzerow et al. 2015; Pijl et al. 2016), was designed to be flexible to use across sites/studies and coded by trained coders who were relatively naïve to ASD. As a measure of global ASD symptom change, studies show the BOSCC is most effective at quantifying treatment response in intervention trials examining improvements across a range of ASD symptoms. A module of the BOSCC for minimally verbal (those using single words or
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Brief Observation of Social Communication Change (BOSCC)
less) toddlers or preschoolers is currently available to researchers with an applicable research study. Development of BOSCC modules for individuals with phrase to fluent speech are under development. Both the BOSCC play partner and coder can be unaware of the child’s treatment status and time point, eliminating any potential bias associated with knowledge of the intervention.
Historical Background Advances in developing efficacious interventions are limited by a lack of treatment response measures, without which researchers are hindered in evaluating intervention programs (French and Kennedy 2018). Many early interventions focus on improving core ASD symptoms (See ▶ Need for Caregiver Support for Families of Children with ASD, The, and “Intervention During the Prodromal Stages of ASD”). Often subtle in nature, quantifying changes in core ASD symptoms is difficult (Anagnostou et al. 2015). Intervention programs have used the ADOS-2 to capture changes in ASD symptoms (Dawson et al. 2010; Estes et al. 2015) with limited success – perhaps since the ADOS-2 was developed to provide a diagnosis rather than measure changes in symptoms. There are additional limitations to using the ADOS-2 as well. Reliable use of the ADOS-2 requires substantial training and expertise of ASD, as well as substantial clinician and participant time (~45–60 min), limiting its use across sites and studies. As well as the ADOS-2, studies have used measures developed specifically for their intervention, impeding comparability across studies, or have focused on parent reports of improvement that, while important, may yield subjective information since parents are often aware of the child’s treatment status (Bolte and Diehl 2013; Guastella et al. 2015). To address such limitations, the BOSCC was developed by expanding ADOS-2 items to increase sensitivity compared to a diagnostic coding scheme. The BOSCC administration is also shorter, and the coding can be completed by less experienced individuals who are unaware of the child’s treatment status and time point.
Psychometric Data The initial psychometric properties of the BOSCC were evaluated in a sample of 56 children participating in early intervention trials (Grzadzinski et al. 2016). Results of this and subsequent work indicate that the BOSCC has high inter-rater (intra-class correlation coefficients; ICCs ¼ 0.88–0.98) and test-retest (ICCs ¼ 0.79–0.90) reliabilities (Grzadzinski et al. 2016; Kim et al. 2019). These reliabilities have been further confirmed in independent samples (Frost et al. 2019; Kitzerow et al. 2015; Pijl et al. 2016). Results also showed statistically significant decreases (improvements) in BOSCC scores, with small to moderate effect sizes (effect sizes ¼ 0.37–0.60), over 6 to 8 months of intervention (Grzadzinski et al. 2016; Kim et al. 2019). These improvements aligned with improvements observed in parent reports and standardized assessments of communication skills (Grzadzinski et al. 2016; Kim et al. 2019; Mullen 1995; Sparrow et al. 2005). Independent studies have also shown significant decreases in BOSCC scores over the course of intervention (Divan et al. 2018; Frost et al. 2019; Gengoux et al. 2019; Grzadzinski et al. 2016; Kim et al. 2019; Kitzerow et al. 2015; Pijl et al. 2016). However, not all intervention trials using the BOSCC have shown significant improvements. The amount of change observed may be dependent on characteristics of the intervention, such as intensity or duration (Fletcher-Watson et al. 2016; Nordahl-Hansen et al. 2016). See Grzadzinski and Lord, 2019 for a review of the BOSCC development.
Clinical Uses The BOSCC is a treatment response measure that quantifies changes in ASD symptoms over the course of an intervention (See ▶ Need for Caregiver Support for Families of Children with ASD, The, and “Intervention During the Prodromal Stages of ASD”). The BOSCC is not currently available for clinical use though can be obtained for use by researchers with an applicable
Brief Observation of Social Communication Change (BOSCC)
research study and who have completed appropriate training. The BOSCC is appropriate for minimally verbal (single words or less) toddler/ preschoolers with ASD, and modules are currently under development for individuals with phrase to fluent speech. The BOSCC is not intended for use as an ASD screener (See “Screening for ASD and Developmental Delays in Infants and Toddlers”), diagnosis, or severity metric. The BOSCC’s ability to capture treatment response in other clinical conditions that may have overlapping symptoms with ASD (e.g., fragile X) remains an area of exploration.
References and Reading Anagnostou, E., Jones, N., Huerta, M., Halladay, A. K., Wang, P., Scahill, L., et al. (2015). Measuring social communication behaviors as a treatment endpoint in individuals with autism spectrum disorder. Autism, 19(5), 622–636. https://doi.org/10.1177/1362 361314542955. Bolte, E. E., & Diehl, J. J. (2013). Measurement tools and target symptoms/skills used to assess treatment response for individuals with autism spectrum disorder. Journal of autism and developmental disorders, 43 (11), 2491–2501. Dawson, G., Rogers, S., Munson, J., Smith, M., Winter, J., Greenson, J., et al. (2010). Randomized, controlled trial of an intervention for toddlers with autism: The early start Denver model. Pediatrics, 125(1), e17–e23. https://doi.org/10.1542/peds.2009-0958. Divan, G., Vajaratkar, V., Cardozo, P., Huzurbazar, S., Verma, M., Howarth, E., et al. (2018). The feasibility and effectiveness of PASS plus, a lay health worker delivered comprehensive intervention for autism Spectrum disorders: Pilot RCT in a rural low and middle income country setting. Autism Research. in press. Estes, A., Munson, J., Rogers, S., Greenson, J., Winter, J., & Dawson, G. (2015). Long-term outcomes of early intervention in 6-year- old children with autism spectrum disorder. Journal of the American Academy of Child and Adolescent Psychiatry, 54(7), 580–587. Fletcher-Watson, S., Petrou, A., Scott-Barrett, J., Dicks, P., Graham, C., O’Hare, A., . . . & McConachie, H. (2016). A trial of an iPad™ intervention targeting social communication skills in children with autism. Autism, 20(7), 771–782. French, L., & Kennedy, E. M. (2018). Annual research review: Early intervention for infants and young children with, or at-risk of, autism spectrum disorder: A systematic review. Journal of Child Psychology and Psychiatry, 59(4), 444–456.
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Frost, K. M., Koehn, G. N., Russell, K. M., & Ingersoll, B. (2019). Measuring child social communication across contexts: Similarities and differences across play and snack routines. Autism Research, 12(4), 636–644. Gengoux, G. W., Abrams, D. A., Schuck, R., Millan, M. E., Libove, R., Ardel, C. M., . . . & Hardan, A. Y. (2019). A Pivotal Response Treatment Package for Children With Autism Spectrum Disorder: An RCT. Pediatrics, e20190178. Grzadzinski, R., Carr, T., Colombi, C., McGuire, K., Dufek, S., Pickles, A., & Lord, C. (2016). Measuring changes in social communication behaviors: Preliminary development of the brief observation of social communication change (BOSCC). Journal of Autism and Developmental Disorders, 46(7), 2464–2479. Grzadzinski, R. & Lord, C. (2019). Commentary: Insights into the Development of the Brief Observation of Social Communication Change (BOSCC). Journal of Mental Health and Clinical Psychology. Guastella, A. J., Gray, K. M., Rinehart, N. J., Alvares, G. A., Tonge, B. J., Hickie, I. B., et al. (2015). The effects of a course of intranasal oxytocin on social behaviors in youth diagnosed with autism spectrum disorders: A randomized controlled trial. Journal of Child Psychology and Psychiatry, 56(4), 444–452. https://doi.org/10.1111/jcpp.12305. Kim, S. H., Grzadzinski, R., Martinez, K., & Lord, C. (2019). Measuring treatment response in children with autism spectrum disorder: Applications of the brief observation of social communication change to the autism diagnostic observation schedule. Autism, 23(5), 1176–1185. Kitzerow, J., Teufel, K., Wilker, C., & Freitag, C. M. (2015). Using the brief observation of social communication change (BOSCC) to measure autism-specific development. Autism Research, 9, 940–950. Lord, C., Rutter, M., DiLavore, P., Risi, S., Gotham, K., & Bishop, S. (2012). Autism diagnostic observation schedule– 2nd edition (ADOS-2). Los Angeles: Western Psychological Corporation. Mullen, E. M. (1995). Mullen scales of early learning. Circle Pines: American Guidance Service. Nordahl-Hansen, A., Fletcher-Watson, S., McConachie, H., & Kaale, A. (2016). Relations between specific and global outcome measures in a social-communication intervention for children with autism spectrum disorder. Research in Autism Spectrum Disorders, 29, 19–29. Pijl, M. K., Rommelse, N. N., Hendriks, M., De Korte, M. W., Buitelaar, J. K., & Oosterling, I. J. (2016). Does the Brief Observation of Social Communication Change help moving forward in measuring change in early autism intervention studies? Autism, 1362361316669235. Sparrow, S. S., Cicchetti, D. V., & Balla, D. A. (2005). Vineland adaptive behavior scales, (Vineland-II). Circle Pines: American Guidance Services.
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Broader Autism Phenotype
Historical Background
Broader Autism Phenotype Jeremy Parr1,2 and Ann S. Le-Couteur3 1 Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK 2 Sir James Spence Institute, Institute of Health and Society, Newcastle University, Royal Victoria Infirmary, Newcastle upon Tyne, UK 3 Institute of Health and Society, Sir James Spence Institute, Newcastle University, Royal Victoria Infirmary, Newcastle upon Tyne, UK
Definition Autism spectrum disorder (ASD) twin and family studies showed during the 1990s that the behavioral phenotype extends beyond the clinical diagnoses of autism and ASD to include related milder behaviors or personality traits in the relatives of affected individuals. These qualitatively similar ASD-related behaviors in relatives are termed the broader autism phenotype (BAP) (see Losh et al. 2011, for a review). Although several authors have reported that these symptoms and traits are continuously distributed in the general population, the term “BAP” has not been used to describe individuals with social communication difficulties from population samples (see Constantino 2011, for a review of this literature). Researchers have defined BAP characteristics using interview and questionnaire methods, neuropsychological and neurophysiological testing, and neuroimaging (Bailey and Parr 2003; Dawson et al. 2002; Losh et al. 2011). However, there is no formal definition of the BAP due to variability in approaches and research findings (see section “Historical Background”); indeed BAP is not a “diagnosis” recognized in the international diagnostic classification systems. Thus, the best working definition of the BAP would be “individuals with the BAP show behavioral characteristics and personality traits similar to, but milder than, their relative with ASD” (see Current Knowledge).
Over the last 20 years, many groups have used a variety of instruments to define various characteristics in relatives of people with ASD. A large body of literature describes the different components of the BAP proposed following studies using a range of methodologies and measures in different populations (see Bailey et al. 1998, and Losh et al. 2011, for reviews). Research shows that in keeping with ASD, impaired social communication and social emotional abilities are core features of the BAP, together with repetitive behaviors (including obsessional behaviors) and behavioral rigidity (for reviews, see Parr et al. 2011 and Losh et al. 2011). Of all the BAP traits, repetitive behaviors, rigidity, perfectionism, obsessions, and circumscribed/special interests have been the most difficult to identify and quantify. Two approaches have been taken to the investigation of these ASD-like behavioral traits. First, researchers have focused on identifying the BAP in families of one child with ASD (singleton families) and two or more people with ASD (multiplex families). Various groups have continued this research as part of the search for autism susceptibility genes (see the work of Piven, Dawson, and Parr et al. and the International Molecular Genetic Study of Autism Consortium [IMGSAC]). In addition to investigating the behavioral BAP within ASD families, researchers have focused on identifying neuropsychological components of the BAP in the relatives of people with ASD (Dawson et al. 2002). More recently, neurophysiological measurement and neuroimaging studies of relatives have been a focus (see Losh et al. 2011); the rationale for using all these approaches to BAP characterization is described by Bailey and Parr (2003). By contrast to this investigation of the relatives of people with ASD, other groups have conceptualized and measured a range of social communication and other difficulties in the context of normative trait variation in the general population hypothesizing that these traits could be included on a dimension with ASD (see Constantino 2011, for a review).
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The BAP has been of increasing interest to researchers due to its potential importance for understanding the neurobiological nature of ASD (for a review, see Lainhart and Lange 2011). From 2000, research groups began collecting data from relatives in an attempt to assist in the search for ASD susceptibility genes, assuming that the BAP indexes a “genetic risk” that may be present in one or both parents and “unaffected” siblings – thus relatives might carry ASD susceptibility genes and express an “ASDlike” phenotype (see Bailey and Parr 2003, and Losh et al. 2011). The most commonly used measures for the identification of BAP in affected families are summarized in Table 1. In keeping with ASD itself, reliable direct observation of BAP behaviors has been challenging. For this reason, most research groups have used some form of interview data, either exclusively or in combination with other measures. For most ASD molecular genetic studies, the BAP measures have been designed to dimensionalize the social communication difficulties of parents and children (e.g., quantitative trait loci studies) rather than to define an affected/unaffected categorical “cut-off score” – this means “the BAP” is less clearly defined than might be expected.
Current Knowledge Research studies have shown that relatives of people with ASD have difficulties qualitatively
similar to those seen in ASD, but milder – the individual’s profile of difficulties does not meet clinical ASD threshold. Generally, males are more commonly and more severely affected than females, and relatives from multiplex families are more affected than those from singleton families. Children and young people may have difficulties with developing and maintaining friendships and problems relating to others. Children frequently have less well-developed social play than their same-age peers. Children and adults may be considered aloof. Language and communication difficulties are common. Perfectionism, obsessions, and rigidity may be seen. Considering mental health, the BAP has been associated with affective disorder (particularly depression). Whether depression is part of the BAP or is a function of having a relative with ASD remains unknown (for a detailed review, see Losh et al. 2011). Investigation of the familial mechanisms that underpin ASD continue and, indeed, the finding that parents from multiplex and simplex ASD families show the BAP at different rates is likely to be important for our understanding of etiology. However, to date, the BAP has contributed only modestly to the understanding of the neurobiology of ASD or the identification of genetic variants (see Lainhart and Lange 2011 and Parr et al. 2011). The impact of the BAP on the functioning of affected children, young people, and adults is similar to that seen in ASD itself, but milder, and usually results in less impairment in daily life.
Broader Autism Phenotype, Table 1 Broader autism phenotype measures
Instrument Social responsiveness scale
Interview/ questionnaire/ observation Questionnaire
BAPQ
Questionnaire
BPASS
Interview and observation Interview and observation
Family history interview and impression of interviewee
Populations used Twin, singleton, multiplex, and general pop samples Singleton and multiplex families Singleton and multiplex families Singleton and multiplex families
References Constantino and Todd (2003) Hurley et al. (2007) Dawson et al. (2007) Parr et al. and the IMGSAC, in preparation
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However, BAP traits may lead to difficulties with peer interactions and marital relationships and thus potentially difficulties at home, school, and in the workplace. People with BAP have varying degrees of insight into their difficulties and the impact of their behavior for themselves and others (Losh et al. 2011; Parr et al. 2011). “BAP” is a term used in research and not usually in clinical practice. However, in clinical settings, with their knowledge of the importance of genetic factors in autism, relatives of people with ASD comment about their own ASD-related difficulties, or those of other family members. For clinicians, the challenge is how best to “classify” these difficulties shown by people who do not have ASD but who do experience some degree of social communication impairment. It is important to be able to effectively describe these difficulties for the affected individual themselves, families, and professionals; this leads to a better understanding of the person’s behaviors and the reason for them. This is likely to be particularly important for individuals who may benefit from specific intervention and resources, for example, mentoring in the workplace for adults with BAP and support from education and/or social care professionals for affected children, young people, and adults (see Parr et al. 2011; Parr and Le Couteur 2011). Finally, relatively little is known about the neurobiology or pathophysiology of the BAP. It has been hoped that better understanding the BAP will lead to improved neurobiological knowledge about ASD itself. However, in keeping with ASD, replicated neurobiological findings are scarce (comprehensively recently reviewed by Lainhart and Lange 2011). Whether BAP will play a significant role in advancing our understanding of the complexity of ASD remains to be seen (Parr et al. 2011).
Future Directions During the next decade, studies of parents and other relatives of people with ASD will continue, and this will undoubtedly expand the understanding of subclinical ASD traits. Genetics research is
Broader Autism Phenotype
likely to continue to be a major “driver” of increased knowledge about BAP, for example, there will be great interest in the extent to which the BAP is seen in the relatives of people with ASD who have an identified inherited or de novo causal variant as this will further inform our knowledge of the genetic and environmental contributions to ASD. Another exciting prospect will be the findings from studies of siblings of children with ASD (“at-risk” or “high-risk” sibling studies). These studies will provide insights into the developmental trajectories of children with ASD and those without ASD who have the BAP; both groups can be compared to siblings who develop typically and to controls. In the future, as “highrisk” siblings move toward and into adolescence and adulthood, the knowledge of how early development and subsequent characteristics relate to individual progress and outcomes will improve. Finally, one new direction for BAP research relates to intervention. There is currently great interest in whether intervention changes the developmental trajectories and outcomes for “at-risk” siblings (e.g., the study of Green and colleagues in the UK). Projects evaluating the effect of the BAP on the delivery of parentmediated early intervention for ASD have commenced. If research findings show that the BAP has a negative impact on the effectiveness of parents’ interactions with their child with ASD, identifying the most beneficial intervention strategies for BAP-affected parents will become both a research and a clinical priority to ensure better understanding and effective targeting of evidence-based interventions. For older children and adults with the BAP, interventions and treatments need to be evaluated. Researchers are, for example, beginning to investigate whether behavioral interventions such as social skills training or social stories might improve the social skills of people with BAP. Indeed, it could be argued that people with BAP might be more responsive to such interventions than individuals with a clinical diagnosis of ASD as they are less likely to have cognitive impairment, will have milder social impairment, and
Broca’s Aphasia
may well have more insight into their difficulties. Workplace interventions for people with BAP may also be of benefit – whether mentoring or other types of workplace support give adults a greater chance of working more productively with colleagues still remains to be seen.
759 Parr, J. R., & Le Couteur, A. (2011). The broader autism phenotype. In S. Boelte & J. Hallmayer (Eds.), International experts answer questions on ASD. Gottingen/ Oxford: Hogrefe. Parr, J. R., Wittemeyer, K., & Le Couteur, A. S. (2011). Commentary: The broader autism phenotype implications for research & clinical practice. In D. Amaral, D. Geschwind, & G. Dawson (Eds.), Autism spectrum disorders (pp. 521–524). New York: Oxford University Press.
See Also ▶ Autism ▶ Perfectionism ▶ Repetitive Behavior ▶ Social Communication
Broca’s Aphasia Elizabeth R. Eernisse Department of Language and Literacy, Cardinal Stritch University, Milwaukee, WI, USA
References and Reading Bailey, A., Palferman, S., Heavey, L., & Le Couteur, A. (1998). Autism: The phenotype in relatives. Journal of Autism and Developmental Disorders, 28(5), 369–392 Review. Bailey, A., & Parr, J. (2003). Implications of the broader phenotype for concepts of autism. Novartis Foundation Symposium, 251, 26–35. Constantino, J. N. (2011). Autism as a quantitative trait. In D. Amaral, D. Geschwind, & G. Dawson (Eds.), Autism spectrum disorders (pp. 510–520). New York: Oxford University Press. Constantino, J. N., & Todd, R. D. (2003). Autistic traits in the general population: A twin study. Archives of General Psychiatry, 60, 524–530. Dawson, G., Estes, A., Munson, J., Schellenberg, G., Bernier, R., & Abbott, R. J. (2007). Quantitative assessment of autism symptom-related traits in probands and parents: Broader phenotype autism symptom scale. Journal of Autism and Developmental Disorders, 37(3), 523–536. Dawson, G., Webb, S., Schellenberg, G. D., Dager, S., Friedman, S., Aylward, E., et al. (2002). Defining the broader phenotype of autism: Genetic, brain, and behavioral perspectives. Development and Psychopathology, 14(3), 581–611. Hurley, R. S., Losh, M., Parlier, M., Reznick, J. S., & Piven, J. (2007). The broad autism phenotype questionnaire. Journal of Autism and Developmental Disorders, 37(9), 1679–1690. Lainhart, J. E., & Lange, N. (2011). The biological broader autism phenotype. In D. Amaral, D. Geschwind, & G. Dawson (Eds.), Autism spectrum disorders (pp. 477–509). New York: Oxford University Press. Losh, M., Adolphs, R., & Piven, J. (2011). The broad autism phenotype. In D. Amaral, D. Geschwind, & G. Dawson (Eds.), Autism spectrum disorders (pp. 457–476). New York: Oxford University Press.
Synonyms Nonfluent aphasia
Short Description or Definition Broca’s aphasia is a language disorder that is characterized by limited, “telegraphic” spoken language output in the face of intact language comprehension skills. This condition is typically the result of damage to the left frontal lobe of the brain, often due to stroke, but may also result from traumatic brain injury or a degenerative neurological condition.
Categorization Broca’s aphasia is considered a “nonfluent” aphasia under larger aphasia classification systems due to the patient’s lack of fluent speech output.
Epidemiology Estimates of Broca’s aphasia in the larger population are largely unknown, though it has been estimated that 80,000 people develop aphasia in the United States each year.
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Natural History, Prognostic Factors, and Outcomes The prognosis for individuals who are diagnosed with Broca’s aphasia is largely dependent upon the severity of the condition. Often, people with Broca’s aphasia do not completely recover fluent spoken language skills and need to develop compensatory strategies to manage the condition. It is thought that recovery is enhanced depending upon factors such as age of onset, health, education level, and how soon treatment takes place after brain damage has occurred.
Clinical Expression and Pathophysiology Broca’s aphasia is often due to damage in the left frontal lobe of the brain, also referred to as “Broca’s area.” Patients demonstrate impaired, effortful speech/language output in the face of relatively intact comprehension skills.
Evaluation and Differential Diagnosis Please see ▶ “Aphasia.”
Treatment Treatment for Broca’s aphasia, as in other aphasias, is typically individualized and is based on the patient’s profile of strengths and needs. Formal speech-language therapy is often recommended to address functional communication in a variety of settings in which patients are expected to communicate. Therapy goals are focused on maximizing the individual’s ability to communicate effectively with peers and family members, given residual strengths. For individuals with Broca’s aphasia, treatment strategies often focus on language output and naming as well as building sentence length, building on their intact comprehension skills. In addition, computer-assisted methods are beginning to show promise in
Brodmann’s Area 4
assisting individuals with Broca’s aphasia to transmit messages. Family member and patient support groups are often a critical piece of the therapeutic process as the patient and family learn to manage the patient’s changed mode of communication. Support groups are often key to recovery. Please see ▶ “Aphasia” for a list of general treatment strategies for aphasia.
See Also ▶ Aphasia
References and Reading American Speech-Language-Hearing Association (ASHA). (2008). Incidence and prevalence of speech, voice, and language disorders in adults in the United States. Available from www.asha.org/research/reports/ speech_voice_language.htm. Retrieved 5 Jan 2011. Barresi, B., Goodglass, H., & Kaplan, E. (2001). The assessment of aphasia and related disorders. Hagerstown: Lippincott Williams & Wilkins. Chapey, R. (2008). Language intervention strategies in aphasia and related neurogenic communication disorders. Philadelphia: Wolters Kluwer/Lippincott Williams & Wilkins. Goodglass, H., Kaplan, E., & Barresi, B. (2000). Boston diagnostic aphasia examination third edition (BDAE-3) (3rd ed.). Austin: Pro-Ed. Kaplan, E., Goodglass, H., & Weintraub, S. (1983). The Boston naming test. Philadelphia: Lea and Febiger. Kent, R. D. (1994). Reference manual for communicative sciences and disorders: Speech and language. Austin: Pro-Ed. Kertesz, A. (2006). Western Aphasia battery- revised (WAB-R). Austin: Pro-Ed. Lapointe, L. L. (2004). Aphasia and related neurogenic language disorders. New York: Thieme Medical Publishers. National Institute on Deafness and Other Communication Disorders. (2008). Aphasia. Available from http:// www.nidcd.nih.gov/health/voice/aphasia.htm. Retrieved 5 Jan 2011.
Brodmann’s Area 4 ▶ Precentral Gyrus
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Brodmann’s Areas
BRS
Kartik Pattabiraman Child Study Center, Yale School of Medicine, New Haven, CT, USA Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
▶ Behavior Rating Scale (BRS)
Definition
Mikle South and Jessica Palilla Departments of Psychology and Neuroscience, Brigham Young University, Provo, UT, USA
Brodmann’s areas (BA) are regions of the cerebral cortex defined by the layer-specific organization of cells. These areas were originally annotated by German neurologist Korbinian Brodmann using basic staining techniques and microscopy in 1909. He initially divided the human cortex into 43 distinct areas. Later studies confirmed these boundaries and identified the functional significance of each area. For example, BA 44 and 45 are part of the left frontal cortex and are important for the motor aspect of speech. Several BAs have been implicated in autism including BA9 (dorsolateral prefrontal cortex), BA 24 (right anterior cingulate gyrus), and BA44 (left inferior frontal gyrus).
Bruininks-Oseretsky Test of Motor Proficiency
Synonyms BOT-2; BOTMP
Definition The original version of the Bruininks-Oseretsky Test of Motor Proficiency was abbreviated as the BOTMP. The revised second edition is usually referred to as the BOT-2.
Description See Also ▶ Cerebral Cortex ▶ Neuroanatomy
References and Reading Casanova, M. F., Buxhoeveden, D. P., Switala, A. E., & Roy, E. (2002). Minicolumnar pathology in autism. Neurology, 58(3), 428–432. Haznedar, M. M., Buchsbaum, M. S., Wei, T.-C., Hof, P. R., Cartwright, C., Bienstock, C. A., & Hollander, E. (2000). Limbic circuitry in patients with autism spectrum disorders studied with positron emission tomography and magnetic resonance imaging. American Journal of Psychiatry, 157(12), 1994–2001. Zilles, K., & Amunts, K. (2010). Centenary of Brodmann’s map – conception and fate. Nature Reviews Neuroscience, 11(2), 139–145.
The BOT-2 is designed to assess motor proficiency in children and adults from ages 4 to 21 years and 11 months. This was intended to cover the age range for children served by the American Individuals with Disabilities Education Act (IDEA). It is individually administered, standardized, and norm referenced. It is used for treatment planning and evaluation in clinical and school settings as well as for research. Physical and occupational therapists especially may find the test useful. The Complete Form version of the BOT-2 includes 53 items based on activities such as cutting out a circle, copying a square, bouncing a ball, and standing on one leg. Items are organized into eight subtests and further categorized into four motor area composites and one comprehensive score. These composites are strength and
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agility (running speed and agility + strength subtests, meant to measure control of the musculature of body involved in movement); manual coordination (manual dexterity and upper limb coordination subtests, meant to measure the ability to manually manipulate objects and the level of coordination in the hands and arms); body coordination (bilateral coordination and balance subtests, to measure large musculature control of posture, balance, as well as the sequential and simultaneous coordination of the lower and upper limbs); and fine manual control (fine motor precision and fine motor integration subtests, to measure the level of control and coordination of the hand and fingers by looking at an individual’s ability to grasp, draw, and cut with scissors). The nature of the measure makes it fairly easy to administer, as children tend to enjoy performing the variety of activities involved in the testing. The BOT-2 revision has made it much more adaptive for younger children, for instance by increasing the number of blocks to string and adding the balance beam to walk on. The entire battery of tests can take an hour to administer, but a 14-item Short Form of the test is available which only requires 20 min. The short form accounts for 96.3% of the variability in children ages 3–5, so it can be used as a substitute for the complete battery when appropriate. The test manual provides many clear pictures of the tasks being completed. However, scoring of the test is time-intensive, taking at least 20 min according to Deitz et al. (2007). Deitz et al. note that scoring for the BOT-2, although improved over the BOTMP, nonetheless is tedious, sometimes confusing, and easy to make errors. Norm lookup tables are also difficult to use.
Historical Background The BOTMP was originally developed in Russia by Oseretsky in 1923 (Oseretsky 1923). When it was translated into English by Doll in 1946 (Doll 1946), it was known as the “Oseretsky Test of
Bruininks-Oseretsky Test of Motor Proficiency
Motor Proficiency.” However, because the test had been based on Oseretsky’s personal observations of children, it had many problems relating to its psychometric properties. Multiple revisions were made in order to increase the reliability and validity of the measure, and the BOTMP represents the culmination of these revisions. The BOT-2 was published in 2005 with updated and revised materials, items, scales, and norms.
Psychometric Data Criticisms of the original BOTMP included concerns about the normative sample being racially homogenous and functioning at normal levels both intellectually and motorically. A child’s ability to understand and respond to instructions may have confounded motor skill development. Factor analyses showed that 14 of the 17 fine motor ability items loaded at significant levels on the general motor ability factor, implying that the BOTMP was not a good stand-alone measure for assessing fine motor abilities and that the grouping of tests into fine and gross motor skills was problematic. The creators of the BOT-2 revision set out to address these and other issues. The normative sample for the BOT-2 included 1520 individuals from ages 4 to 21, with greater age differentiation for normative comparisons in younger children (in 1-year increments) up to a 5-year age increment for the adult sample. It was targeted to US Census Data from 2001 and included about 11% of children with special education status. Separate clinical samples were tested for autism/Asperger’s, developmental coordination disorder, and mild-to-moderate mental retardation. Interrater reliability reported by the developers of the BOT-2 is above .90 for all but the fine motor scale (adjuster r ¼.86). Test-retest reliability is good for the Total Composite and the Short Form totals, but generally less good (with substantial variability) for the other scale composites and item analyses. Deitz et al. (2007) therefore
Bruininks-Oseretsky Test of Motor Proficiency
recommend that the Composite scores be used wherever possible and that reliance on subscale scores is inadvisable. Test developers utilized Confirmatory Factor Analysis to document a good fit for the four-factor model of the BOT-2, better than the two-factor (Fine vs. Gross Motor) structure of the original BOTMP. The three clinical samples all scored significantly lower than the normative sample on both the Complete and Short forms. Convergent validity was strong for the original BOTMP (adjusted r ¼ .80 for composite scores); the Peabody Test of Developmental Motor Skills – Second Edition (adjusted rs ranging from .51 to .75 for subscales); and the Test of Visual Motor Skills – Revised (comparison of relevant fine motor skills adjusted r ¼ .74). Statistical modeling by Wuang et al. (2009) on a sample of 446 children diagnosed with intellectual disability found that the manual coordination and strength + agility composites fit the whole sample better than the fine motor and body coordination composites, which fit the lowerfunctioning end of the sample better than the higher-functioning end. Their analysis suggested elimination and/or restructuring of a number of items and scales to improve both reliability and discriminant validity.
Clinical Uses Deitz et al. (2007) note that the inclusion of 11% special education students in the normative sample makes the BOT-2 less likely than its BOTMP predecessor to score children with motor disabilities as significantly below average. The BOTMP has been used to characterize motor problems in individuals diagnosed with Autism Spectrum Disorders. One study (Ghaziuddin and Butler 1998) compared BOTMP motor coordination between children diagnosed with autism, Asperger’s syndrome (AS), and pervasive developmental disorder not otherwise specified (PDDNOS). Of the three groups, those with autism were the most clumsy, followed by those with AS
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and then those with PDDNOS. However, there was not a significant difference between the autism group and the AS group. These results indicate that caution should be used before including clumsiness as a diagnostic criterion for only one of the disorders. Dewey et al. (2007), using the BOTMP Short Form, found particular impairment in gestural performance in ASD relative to other clinical groups (developmental motor coordination and ADHD). In the context of generally impaired motor performance for all the clinical groups, Dewey et al. suggest that gestural impairments in autism are not solely attributable to motor problems. The test is also frequently used in studies of developmental coordination disorders, with a few studies of ADHD. There are very few published studies that have used the BOT-2 instead of the original BOTMP.
See Also ▶ Bender Visual-Motor Gestalt Test II
References and Reading Beitel, P., & Mead, B. J. (1980). Bruininks-Oseretsky test of motor proficiency: A viable measure for 3- to 5-yr-old children. Perceptual and Motor Skills, 5, 919–923. Bruininks, R. H. (1978). Bruininks-Oseretsky test of motor proficiency – Owner’s manual. Circle Pines: American Guidance Service. Bruininks, R., & Bruininks, B. (2005). BruininksOseretsky test of motor proficiency (2nd ed.). Minneapolis: NCS Pearson. Deitz, J. C., Kartin, D., & Kopp, K. (2007). Review of the Bruininks-Oseretsky test of motor proficiency, second edition (BOT-2). Physical & Occupational Therapy in Pediatrics, 27, 87–102. Dewey, D., Cantell, M., & Crawford, S. G. (2007). Motor and gestural performance in children with autism spectrum disorders, developmental coordination disorder, and/or attention deficit hyperactivity disorder. Journal of International Neuropsychological Society, 13, 246–256. Doll, E. A. (1946). The Oseretsky tests of motor proficiency. Circle Pines: American Guidance Service. Ghaziuddin, M., & Butler, E. (1998). Clumsiness in autism and Asperger syndrome: A further report. Journal of Intellectual Disability Research, 44, 43–48.
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764 Hattie, J., & Edwards, H. (1987). A review of the Bruininks-Oseretsky test of motor proficiency. British Journal of Educational Psychology, 57, 104–113. Oseretsky, N. I. (1923). A metric scale for studying the motor capacity of children. [In Russian]. Wuang, Y.-P., Lin, Y.-H., & Su, C.-Y. (2009). Rasch analysis of the Bruininks-Oseretsky test of motor proficiency-second edition in intellectual disabilities. Research in Developmental Disabilities, 30, 1132–1144.
Bruxism
reversal, and stress management appear to be common interventions.
See Also ▶ Habit Reversal ▶ Tics
References and Reading
Bruxism Arianne Stevens and Raphael Bernier Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA
Synonyms Sleep bruxism
Definition Bruxism is the nonfunctional and involuntary grinding, gnashing, clenching, or tapping of teeth. Bruxism is considered to be common among individuals with developmental delays or disabilities, including those diagnosed with autism spectrum disorders. Bruxism is classified as nocturnal (occurring during sleep) or diurnal (occurring while awake). Bruxism can be audible when teeth are grinding or gnashing or inaudible when teeth are clenching. Many are not aware of their bruxism, but some will develop symptoms such as tooth sensitivity, headaches, or jaw pain. Bruxism is considered to be a psychophysiological and sleep disorder influenced by anatomical and biological (i.e., dental abnormalities), neurological (i.e., mental retardation), and/or psychological (i.e., stress, trauma, anxiety) factors. Studies examining effective treatments for bruxism in individuals with developmental disabilities are limited to date; however, dental-based approaches, biofeedback, behavior therapy, habit
Allen, K. D., & Polaha, J. (2006). Analysis and treatment of oral-motor repetitive behavior disorders. In D. W. Woods & R. G. Miltenberger (Eds.), Tic disorders, trichotillomania, and other repetitive behavior disorders: Behavioral approaches to analysis and treatment (pp. 269–296). New York: Springer. Glaros, A. G., & Epkins, C. C. (1995). Habit disorders: Bruxism, trichotillomania, and tics. In M. C. Roberts (Ed.), Handbook of pediatric psychology (2nd ed., pp. 558–574). New York: The Guilford Press. Glaros, A. G., & Rao, S. M. (1977). Bruxism: A critical review. Psychological Bulletin, 4, 767–781. Mindell, J. A., & Owen, J. A. (2010). Sleep related rhythmic movements: Bruxism. In A clinical guide to pediatric sleep: Diagnosis and management of sleep problems (2nd ed., pp. 90–93). Philadelphia: Lippincott Williams and Wilkins. PubMed Health. (2010, February 22). Bruxism: Teeth grinding and clenching. Retrieved from http://www. ncbi.nlm.nih.gov/pubmedhealth/PMH0002386/ US Department of Health and Human Service. (2000). Oral health in America: A report of the Surgeon General. Rockville: US Department of Health and Human Services, National Institute of Health and Human Services, National Institute of Dental and Craniofacial Research, National Institute of Health.
BSE ▶ Behavior Summarized Evaluation-Revised (BSE-R)
BSE-R ▶ Behavior Summarized Evaluation-Revised (BSE-R)
Bulimia Nervosa
Buckhannon Versus West Virginia Department of Health and Human Resources: Definition of Prevailing Party Regina Gilroy Quinnipiac University School of Law, Hamden, CT, USA
Definition In General The issue in this case was whether the prevailing party in a claim for violating the Americans with Disabilities Act (ADA) should be awarded attorney’s fees under the catalyst theory. The Supreme Court, quoting Black’s Law Dictionary 1145 (7th ed. 1999), defines “prevailing party” as “[a] party in whose favor a judgment is rendered, regardless of the amount of damages awarded (in certain cases, the court will award attorney’s fees to the prevailing party).” The catalyst theory provides fees to a party where the case serves as a “catalyst” for legislative change. The Supreme Court ruled that the “catalyst theory” is not an appropriate basis for attorney’s fees under certain civil rights cases, including under the ADA. The prevailing party is only entitled to attorney’s fees when they are victorious in court and awarded a judgment in court. Implications for ASD Students Buckhannon has implications for those with ASD. First, in the case of discrimination under the ADA, parents may be less willing to bring cases because of the expense of bringing a lawsuit. The parent would not be guaranteed expensive attorney fees if they end up prevailing outside of a courtroom. Only cases heard in court would be awarded attorney’s fees. Second, IDEA (Individuals with Disabilities Education Act) has made fees available to parents who win in either administrative or
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judicial proceedings. If IDEA follows the Buckhannon decision, then this fee payment for parents would become obsolete if a case is heard in an informal proceeding or dismissed because of a change in law. Litigation Strategies Buckhannon specifically dealt with an assisted living facility which failed a fire inspection. Buckhannon was ordered to be shut down, but filed suit under the ADA. After filing suit, the state legislature removed the language that created the problem. But, because of the Supreme Court decision, Buckhannon was not entitled to an award of attorney’s fees. Most special education cases settle either because of the speed necessary to allow the child to continue his/her education or the school district changes its behavior before a decision is even made. Under Buckhannon, parents would not be able to recover attorney fees. It might be more appropriate to try to mediate between the parents and school district, so that in the mediation agreement the parents can be entitled to some reasonable attorney’s fees.
See Also ▶ Americans with Disabilities Act
References and Reading Buckhannon Bd. & Care Home v. W. Va. Dep’t of Health & Human Res., 532 U.S. 598 (U.S. 2001). Weber, M. C. (2004). Litigation under the Individuals with Disabilities Education Act After Buckhannon Board & Care Home Inc. v. West Virginia Department of Health & Human Resources. Ohio State Law Review, 65, 357–411.
Bulimia Nervosa ▶ Eating Disorders
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Bullying Young-Shin Kim1, Soonjo Hwang2 and Bennett Leventhal3 1 Yale Child Study Center, New Haven, CT, USA 2 Massachusetts General Hospital, Boston, MA, USA 3 Nathan Kline Institute for Psychiatric Research (NKI), Orangeburg, NY, USA
Synonyms
Bullying
individually or in a group bully other students (Schwartz 2000). With recent advances in information technology, bullying has added cyberspace to the schoolyard and neighborhood as sites for bullying. In cyberspace, bullying can take place anonymously, without overtly identifying the perpetrators. Further, children or adolescents may not be safe from bullying even in their homes since unkind text messages, hateful e-mails, videos, or provocatively manipulated messages and materials can reach them 24 h per day, 7 days a week (Pridgen 2009).
Peer victimization; School harassment
Epidemiology/Clinical Expression Short Description or Definition Bullying is an aggressive behavior perpetrated by those who hold and/or try to maintain a dominant position over others (Morita 1985). It is intended to cause mental and/or physical harm or suffering to another: it is a repetitive behavior and almost always involves an imbalance of power between victim and perpetrator in which the victim is usually not able to defend himself/herself (Farrington 1993). Bullying is also the most common form of school violence. Bullying can take various forms including: exclusion, verbal abuse, physical abuse, and/or coercion (Kim et al. 2004). Bullying may be “direct” or “indirect.” Direct bullying includes physical and verbal aggression, such as kicking, threatening, name-calling, and insulting. Indirect or covert/relational bullying includes social exclusion/isolation, such as ignoring, cliques, rumor-mongering, insulting, and humiliating with the spread of embarrassing information about an individual (van der Wal et al. 2003). Students are involved in bullying as victims, perpetrators, victim-perpetrators, or bystanders. Victims may experience many forms of bullying with considerable variability in form. Some students may be involved in bullying as both a victim and perpetrator; that is, they are bullied by one student or a group of students and may also
Children and adolescents with Autism Spectrum Disorder (ASD) have essential difficulty in reciprocal social interaction along with impairment in communication skills (American Psychiatric Association [APA] 1994; Caronna et al. 2008; Frith and Hill 2004; Gura et al. 2011; Kelly et al. 2008; van Roekel et al. 2010). These difficulties make those with ASD especially vulnerable for involvement with bullying as victims and/or perpetrators since bullying is a form of dynamic and complex social interactions (Cappadocia et al. 2011; Sharp and Cowie 1994). Children with ASD also often demonstrate stereotyped behavior and a limited range of interests, often in unusual topics or objects that can make them stand out as quite different from their peers: this often puts them in the position of becoming targets for ridicule (Cappadocia et al. 2011; Gray 2004). Other challenges often facing children with ASD that include unusual sensory responses such as hyper/ hyposensitivity to auditory, olfactory, tactile, or visual stimuli; problems in motor coordination; and poor performance in physical education, can also contribute to the risk of becoming a target of the peer victimization (Bejerot et al. 2011; Kelly et al. 2008). Perpetrators of bullying intend to cause mental and/or physical harm or suffering on other children; perpetrators identify what would cause pain to their victims and, then, plan and execute their actions accordingly. However, it is difficult for
Bullying
children and adolescents with ASD to bully others due to their difficulties in understanding and using the rules governing social behaviors and perspectives of other people. Nevertheless, their behaviors may be regarded as bullying for several reasons. First, children and adolescents with ASD may have increased levels of aggressive behaviors (Mandell et al. 2005; van Roekel et al. 2010). Since bullying is a form of aggression, those with ASD who have increased level of aggression may be considered to be bullying other children or adolescents (van Roekel et al.). Second, because adolescents with ASD have limited insight into social processes (Frith and Hill 2004; van Roekel et al. 2010), they may not be aware of the consequences of their own behavior or words; some of these behaviors may be regarded as bullying (van Roekel et al.). For example, children with ASD may say brutally honest things or violate the physical space of others to the extent that they cause discomfort, even though it may not be intended to be bullying (Montes and Halterman 2007; van Roekel et al. 2010). Although the severity of ASD symptoms is negatively correlated with successful social inclusion and peer relationships, even children and adolescents with high-functioning ASD continue to struggle with social competence as they age (Brauminger and Kasari 2000; Cappadocia et al. 2011; Orsmond et al. 2004); as a result, even with improvement in overall functioning, individuals with ASD remain at increased risks for bullying experiences (Cappadocia et al. 2011). Indeed, several previous studies have reported that children or adolescents with ASD showed increased involvement in bullying as victims or perpetrators (Cappadocia et al. 2011; Little 2001, 2002; Twyman et al. 2010; van Roekel et al. 2010). Little used a website survey of 411 parents of children with Asperger’s disorder (AD) (75% of subjects) or nonverbal learning disorder (25%); they reported that up to 75% of the children with AD were bullied within previous year. The younger children, boys, and children with ASD had greater risk for victimization (Little 2001). In another study of 187 adolescents with ASD attending a special secondary education school,
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van Roekel et al. also reported that 7–30% were victimized more than once a month, and 19–46% bullied others, depending on the informants (teacher, peer, or self-report of bullying) (van Roekel et al. 2010). Samson et al. showed individuals with Autistic Disorder recruited from clinics in Germany and Switzerland (40 with autism and 83 control), who reported higher rates of experiencing teasing or being ridiculed, compared to the control group who did not have ASD diagnoses (Samson and Huber 2010). Interestingly, Shtayermann measured the bullying experiences of 10 adolescents or young adults with Asperger’s Disorder using mailed or online self- or parent’s questionnaires, and reported a negative correlation between the severity of AD symptoms and victimization. The authors considered that children and adolescents with milder AD symptoms received lesser support and supervision from teachers and/or parents than those with severe symptom, leading to greater risks for victimization due to “under-surveillance” by adults (Shtayermman 2007). Although there are significant limitations in his study, including the small number of samples and survey accuracy, this finding suggests that children and adolescents with ASD, irrespective of symptom severity, require appropriate support from caregivers and teachers in order to prevent peer victimization. Additionally, Volker et al., using a standardized behavioral rating scale, demonstrated that children and adolescent with high-functioning ASD recruited from those awaiting participating in social intervention study (N ¼ 62) showed increased scores for bullying participation when compared to a control group, even after being controlled for their IQs (Volker et al. 2010). When examining the experience of school bullying in children and adolescents with ASD, the school setting likely plays an important role: there are advantages and shortcomings in different school settings for children and adolescents with ASD (Burack et al. 1997; Laugeson et al. 2009). On one hand, regular classroom has been associated with increases in the complexity of interactions and decreases in nonsocial activity, in comparison to special education settings. On the other hand, these individuals report often feeling
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lonelier and having poorer quality friendships then their typically developing classmates (Capps et al. 1996; Laugeson et al. 2009; Sigman and Ruskin 1999). Another study also implies important feature that in a special educational setting, teachers report higher rates of bullying among students with ASD than those without (van Roekel et al. 2010). In general, bullying is associated with various psychological problems as consequences or antecedents to bullying experiences (Barker et al. 2008; Kim et al. 2005, 2006; Salmon et al. 1998; Srabstein and Piazza 2008); children and adolescents with ASD who are also involved with bullying are not exceptions. In a study of 192 children diagnosed with ASD recruited from the website for parents of children with ASD or the school system, using parental report of psychopathology, Cappadocia et al. reported that ASD children who were bullied once or more per week had higher levels of anxiety; hyperactivity; self-injurious, stereotypic behaviors; and oversensitivity when compared to those not bullied or bullied less than once per week (Cappadocia et al. 2011). Additionally, correlations between peer victimization and suicidal ideation were reported in adolescents with AD (Asperger’s Disorder) (Shtayermman 2007). Kelly et al. reported that peer victimization was not only directly related to severity of ASD symptoms, but also that poor peer relationship was associated with anxiety and depression symptoms measured by parental survey in 322 children with ASD recruited from the clinics. This suggests that not only do ASD symptoms increased risks for peer victimization but also that victimization may worsen associated symptoms in children with ASD (Kelly et al. 2008). Such bidirectional impacts of social problems and peer victimization on each other have been already demonstrated in a general population of adolescents in a longitudinal study (Kim et al. 2006). In addition to ASD severity, cognitive function may play roles in the risks for the involvement in bullying and development of psychopathological consequences from bullying experiences. For example, children with milder forms of ASD or higher cognitive function may be more accurate in
Bullying
recognizing bullying when they are bullied while those with more severe ASD or lower levels of cognitive function might not; this may lead to more serious adverse consequences from bullying experiences in the higher functioning groups (Sofronoff et al. 2011). The experience of bullying in childhood and adolescence can have long-term sequelae, including in adulthood. Samson et al. recruited 40 adults diagnosed with ASD and 83 adults without ASD to compare their recollection of bullying experience in their childhood and/or youth; compared to the control group, the individuals with Asperger’s Disorder report not only higher rates of recollections of being ridiculed or teased in their childhood or youth, but also fear for being ridiculed at present, indicating that the psychological damage of school bullying persists beyond the school years (Samson and Huber 2010).
Evaluation and Differential Diagnosis Given the high prevalence of bullying and its association with psychiatric and psychological morbidities in children and adolescents with ASD, comprehensive and careful attention and assessment is required for prevention, early identification, and intervention with bullying in ASD children and adolescents. Due to their impairments in making and recognizing social interactions, the utility of self-report as a tool for identifying bullying experiences in the ASD population may be limited. Indeed, van Roekel et al. showed that teachers reported higher prevalence of bullying compared to peer- and selfreports which indicated much lower rates of school bullying in this population: teachers reported 27% of adolescents frequently involved in school bullying (more than once a week), whereas adolescents themselves reporting only 12% in 230 adolescents with ASD (van Roekel et al. 2010). This was distinctly different from the findings in children and adolescents without ASD, when on average self-report or peer nomination measurement report 35–48% of involvement in bullying as victims and/or perpetrators, but teacher or parent report only have 10–18%
Bullying
(Cleary 2000; Hunter et al. 2004; Ladd and Kochenderfer-Ladd 2002; Nansel et al. 2001; Rønning et al. 2009). Such a discrepancy may stem from the combination of two factors: First, teachers may have missed opportunities to witness bullying incidences among typically developing children since bullying usually occur in the absence of adults supervision; children and adolescents with ASD receive higher levels of supervision and monitoring from teachers, resulting in more opportunities for teachers to observe peer interactions and bullying in this population. Second, individuals with ASD have difficulties understanding the mental states of other people, and consequently in understanding the intentions of others (Frith and Hill 2004; van Roekel et al. 2010). It may be difficult for children and adolescents with ASD to recognize or identify bullying incidents due to their limited social insights, unlike typically developing children (van Roekel et al.). Therefore, comprehensive assessment with multiple informants including caregivers, teachers, and peers in addition to selfreport is crucial for the identification of bullying experience in children and adolescents with ASD (Ladd and Kochenderfer-Ladd 2002; Mandell et al. 2005).
Treatment Screening for bullying experience and symptoms for ASD in primary care and community setting is an important first step for early identification and intervention since children and adolescents with ASD are at increased risks for the involvement of bullying (Gura et al. 2011; Tantam and Girgis 2009). Careful school placement is crucial for children and adolescents with ASD. Regular education classroom placement has resulted in mixed outcomes for individuals with ASD (Burack et al. 1997; Laugeson et al. 2009). As mentioned earlier, mainstream classroom placement is associated with the increase of the complexity of interactions and decrease in nonsocial activity in comparison to special education. On the other hand, placement in special education classroom can enhance teachers’ capacities for careful
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attention and intervention for those students with ASD and protection from undesirable social stigma and traumatization (Laugeson et al.). Having close friends in a classroom is protective of becoming a target of bullying (Cappadocia et al. 2011; Nansel et al. 2001; Williams and Guerra 2007). Therefore, interventions such as social skill training to help these children have better friendship will decrease their risks for peer victimization (Laugeson et al. 2009). Children and adolescents with ASD have various comorbid psychopathologies including depression, anxiety, and withdrawal, which are reported to be associated with increased risks for the involvement of bullying in general population (Volker et al. 2010). Appropriate assessment and interventions for comorbid conditions in ASD children is warranted (Volker et al.). When a child is being bullied, particularly a child with a disability, adult support is crucial. Through scaffolding, adults can support children to acquire and develop important social skills such as: adaptive emotional and behavioral regulation strategies and coping skills, identifying and engaging with supportive peers, problem solving, and communicating assertively (Cappadocia et al. 2011; Cummings et al. 2006). Recent research supports the effectiveness and importance of parent-assisted learning with respect to developing social skills among children with ASD (Cappadocia et al. 2011; Frankel et al. 2010; Laugeson et al. 2009). This relationship scaffolding, individualized for each child to capitalize on his or her strengths and support weaknesses, can help the child develop coping skills that may reduce the impact of the bullying on the victimized child and in turn reduce the likelihood of bullying. It is important to encourage children to seek help from a trusted adult and continue to seek help until they find an adult who is willing to listen and offer protection and support. Once the adult understands the particulars of the bullying episodes (e.g., when and where), safe places and safe people can be discussed to minimize the risk for bullying to occur (Cappadocia et al. 2011; Cummings et al. 2006). Finally, validated effective antibullying campaigns/interventions to decrease bullying in
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home, schools, and communities will exert preventive effect on bullying not only for children with ASD but for all children receiving such interventions (Olweus 1994; Olweus and Limber 2010; Vreeman and Carroll 2007). Understanding the relationships between ASD and bullying has been limited due to the shortcomings of previous studies, including small sample size, limited sampling methods, and/or inadequate measurement of bullying (Mandell et al. 2005). Future study should focus on incidence/prevalence of bullying, the impact of bullying experiences on the natural course of ASD, associations between bullying experiences and other comorbid psychopathology, and development and assessment of intervention programs in larger population-based samples of children and adolescents with ASD.
Conclusion Bullying is common among all children, but the children with ASD are at even greater risk of this harmful experience. And, just as is the case for typically developing children, reduction of bullying enhances developmental prospects for all children, including those with ASD. While ASD may not be preventable at this time, we can reduce or even prevent bullying experiences for children with ASD, as we can and must for all children.
References and Reading American Psychiatric Association. (1994). DSM-IV. Washington, DC: American Psychiatric Association. Barker, E. D., Arseneault, L., Brendgen, M., Fontaine, N., & Maughan, B. (2008). Joint development of bullying and victimization in adolescence: Relations to delinquency and self-harm. Journal of the American Academy of Child and Adolescent Psychiatry, 47(9), 1030–1038. Bejerot, S., Edgar, J., & Humble, M. B. (2011). Poor performance in physical education – A risk factor for bully victimization. Acta Paediatrica, 100, 413–419. Brauminger, N., & Kasari, C. (2000). Loneliness and friendship in high functioning children with autism. Child Development, 71, 447–456. Burack, J. A., Root, R., & Zigler, E. (1997). Inclusive education for students with autism: Reviewing
Bullying ideological, empirical, and community considerations. New York: Harper Collins. Cappadocia, M. C., Weiss, J. A., & Pepler, D. (2011). Bullying experiences among children and youth with autism spectrum disorders. Journal of Autism and Developmental Disorders. https://doi.org/10.1007/ s10803-011-1241-x. Capps, L., Sigman, M., & Yirmiya, N. (1996). Selfcompetence and emotional understanding in highfunctioning children with autism. Development and Psychopathology, 7, 137–149. Caronna, E. B., Milunsky, J. M., & Tager-Flusberg, H. (2008). Autism spectrum disorders: Clinical and research frontiers. Archives of Disease in Childhood, 93, 518–523. Cleary, S. D. (2000). Adolescent victimization and associated suicidal and violent behaviors. Adolescence, 35(140), 671–682. Cummings, J. G., Pepler, P., Mishna, F., & Craig, W. (2006). Bullying and victimization among students with exceptionalities. Exceptionality Education, 16, 193–222. Farrington, D. P. (1993). Understanding and preventing bullying. In M. Tonry (Ed.), Crime and justice: A review of research. Chicago: University of Chicago Press. Frankel, F., Myatt, R., Whitham, C., Gorospe, C., & Laugeson, E. A. (2010). A controlled study of parentassisted children’s friendship training with children having autism spectrum Disorders. Journal of Autism and Developmental Disorders, 40, 827–842. Frith, U., & Hill, E. (2004). Autism: Mind and brain. New York: Oxford University Press. Gray, C. (2004). Gray’s guide to bullying parts I-III. Jenison Autism Journal, 16(1), 2–19. Gura, G. F., Champagne, M. T., & Blood-Siegfried, J. E. (2011). Autism spectrum disorder screening in primary care. Journal of Developmental and Behavioral Pediatrics, 32, 48–51. Hunter, S. C., Boyle, J. M. E., & Warden, D. (2004). Help seeking amongst child and adolescent victims of peeraggression and bullying: The influence of school-stage, gender, victimization, appraisal, and emotion. British Journal of Educational Psychology, 74, 375–390. Kelly, A. B., Garnett, M. S., Attwood, T., & Peterson, C. (2008). Autism spectrum symtomatology in children: The impact of family and peer relationships. Journal of Abnormal Child Psychology, 36, 1069–1081. Kim, Y. S., Koh, Y., & Leventhal, B. L. (2004). Prevalence of school bullying in Korean middle school students. Archives of Pediatric and Adolescent Medicine, 158, 737–741. Kim, Y. S., Koh, Y., & Leventhal, B. (2005). School bullying and suicidal risk in Korean middle school students. Pediatrics, 115(2), 357–363. Kim, Y. S., Leventhal, B. L., Koh, Y., Hubbard, A., & Boyce, W. T. (2006). School bullying and youth violence: Causes or consequences of psychopathologic
Bullying behavior? Archives of General Psychiatry, 63(9), 1035–1041. Ladd, G. W., & Kochenderfer-Ladd, B. (2002). Identifying victims of peer aggression from early to middle childhood: Analysis of cross-informant data for concordance, estimation of relational adjustment, prevalence of victimization, and characteristics of identified victims. Psychological Assessment, 14(1), 74–96. Laugeson, E. A., Frankel, F., Mogil, C., & Dillon, A. R. (2009). Parent-assisted social skills training to improve friendships in teens with autism spectrum disorders. Journal of Autism and Developmental Disorders, 39, 596–606. Little, L. (2001). Peer victimization of children with Asperger spectrum disorders. Journal of the American Academy of Child and Adolescent Psychiatry, 40, 995–996. Little, L. (2002). Middle-class mother’s perceptions of peer and sibling victimization among children with Asperger’s syndrome and nonverbal learning disorders. Issue in Comprehensive Pediatric Nursing, 25, 43–57. Mandell, D. S., Walrath, C. M., Manteuffel, B., Sgro, G., & Pinto-Martin, J. A. (2005). The prevalence and correlates of abuse among children with autism served in comprehensive community-based mental health settings. Child Abuse & Neglect, 29, 1359–1372. Montes, G., & Halterman, J. S. (2007). Bullying among children with autism and the influence of comorbidity with ADHD: A population-based study. Ambulatory Pediatrics, 7, 253–257. Morita, Y. (1985). Sociological study on the structure of bullying group. Osaka: Department of Sociology, Osaka City University. Nansel, T. R., Overpeck, M., Pilla, R. S., Ruan, W. J., Simons-Morton, B., & Scheidt, P. (2001). Bullying behaviors among us youth: Prevalence and association with psychosocial adjustment. Journal of the American Medical Association, 285(16), 2094–2100. Olweus, D. (1994). Bullying at school: Basic facts and effects of a school based intervention program. Journal of Child Psychology and Psychiatry, 35(7), 1171–1190. Olweus, D., & Limber, S. P. (2010). Bullying in school: Evaluation and dissemination of the olweus bullying prevention program. The American Journal of Orthopsychiatry, 80(1), 124–134. Orsmond, G. I., Krauss, M. W., & Seltzer, M. (2004). Peer relationships and social and recreational activities among adolescents and adults with autism. Journal of Autism and Developmental Disorders, 34, 245–256. Pridgen, B. (2009). Book forum: Cyberbullying: Bullying in the digital age. Journal of the American Academy of Child & Adolescent Psychiatry, 48(3), 344–346. Rønning, J. A., Sourander, A., Kumpulainen, K., Tamminen, T., Niemelä, S., Moilanen, I., et al. (2009). Cross-informant agreement about bullying and victimization among eight-year-olds: Whose information best predicts psychiatric caseness 10–15 years later? Social Psychiatry and Psychiatric Epidemiology, 44(1), 15–22.
771 Salmon, G., James, A., & Smith, D. M. (1998). Bullying in schools: Self reported anxiety, depression, and selfesteem in secondary school children. BMJ, 317(3), 924–925. Samson, A. C., & Huber, O. (2010). Teasing, ridiculing and the relation to the fear of being laughed at in individuals with Asperger’s syndrome. Journal of Autism and Developmental Disorders, 41, 475–483. Schwartz, D. (2000). Subtypes of victims and aggressors in children’s peer groups. Journal of Abnormal Child Psychology, 28(2), 181–192. Sharp, S., & Cowie, H. (1994). School bullying: Insights and perspectives. London: Routeledge. Shtayermman, O. (2007). Peer victimization in adolescents and young adults diagnosed with Asperger’s syndrome: A link to depressive symptomatology, anxiety symptomatology and suicidal ideation. Issue in Comprehensive Pediatric Nursing, 30, 87–107. Sigman, M., & Ruskin, E. (1999). Continuity and change in the social competence of children with autism, Down syndrome, and developmental delays. Monographs of the Society for Research in Child Development, 64, 114. Sofronoff, K., Dark, E., & Stone, V. (2011). Social vulnerability and bullying in children with Asperger syndrome. Autism, 15(3), 355–372. Srabstein, J., & Piazza, T. (2008). Public health, safety and educational risks associated with bullying behaviors in American adolescents. International Journal of Adolescent Medicine and Health, 20(2), 223–233. Tantam, D., & Girgis, S. (2009). Recognition and treatment of Asperger syndrome in the community. British Medical Bulletin, 89, 41–62. Twyman, K. A., Saylor, C. F., Eds, D. S., Macias, M. M., Taylor, L. A., & Spratt, E. (2010). Bullying and ostracism experiences in children with special health care needs. Journal of Developmental and Behavioral Pediatrics, 31, 1–8. van der Wal, M. F., de Wit, C. A. M., & Hirasing, R. A. (2003). Psychosocial health among young victims and offenders of direct and indirect bullying. Pediatrics, 111(6), 1312–1317. van Roekel, E., Scholte, R. H. J., & Didden, R. (2010). Bullying among adolescents with autism spectrum disorders: Prevalence and perception. Journal of Autism and Developmental Disorders, 40, 63–73. Volker, M. A., Lopata, C., Smerbeck, A. M., Knoll, V. A., Thomeer, M. L., Toomey, J. A., et al. (2010). BASC-2 PRS profiles for students with high-functioning autism spectrum disorders. Journal of Autism and Developmental Disorders, 40, 188–199. Vreeman, R. C., & Carroll, A. E. (2007). A systematic review of school-based interventions to prevent bullying. Archives of Pediatrics & Adolescent Medicine, 161, 78–88. Williams, K. R., & Guerra, N. G. (2007). Prevalence and predictors of internet bullying. Journal of Adolescent Health, 41, 14–21.
B
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Bupropion
Bupropion
Buspirone
Lawrence David Scahill Nursing and Child Psychiatry, Yale Child Study Center, Yale University School of Nursing, New Haven, CT, USA Marcus Autism Center, Children’s Healthcare of Atlanta, Atlanta, GA, USA Department of Pediatrics, Emory University, Atlanta, GA, USA
Lawrence David Scahill Nursing and Child Psychiatry, Yale Child Study Center, Yale University School of Nursing, New Haven, CT, USA Marcus Autism Center, Children’s Healthcare of Atlanta, Atlanta, GA, USA Department of Pediatrics, Emory University, Atlanta, GA, USA
Synonyms
Synonyms
Wellbutrin; Zyban
Buspar; Vanspar
Definition
Definition
Bupropion is a novel antidepressant that is believed to block reuptake of dopamine. It is indicated for the treatment of depression (Wellbutrin) and for smoking cessation (Zyban). It has also been evaluated as a treatment for attention deficit/hyperactivity disorder in children and adults. It has not been evaluated systematically in children or adults with autism.
Buspirone is an antianxiety medication that is an agonist for serotonin 1A receptor. Unlike benzodiazepines, buspirone does not directly affect a GABA system and is not habit-forming. There is limited information on the use of buspirone in children and only one trial in adolescents with pervasive developmental disorders. In that study, buspirone appeared to be only modestly beneficial for disruptive and agitated behavior.
See Also ▶ Antidepressants
See Also ▶ Anxiolytics
References and Reading Martin, A., Scahil, L., & Christopher, K. (2010). Pediatric psychopharmacology: Principles and practice (2nd ed.). New York: Oxford.
Buspar ▶ Buspirone
References and Reading Buitelaar, J. K., van der Gaag, R. J., & van der Hoeven, J. (1998). Buspirone in the management of anxiety and irritability in children with pervasive developmental disorders: Results of an open-label study. The Journal of Clinical Psychiatry, 59(2), 56–59. Martin, A., Scahil, L., & Christopher, K. (2010). Pediatric psychopharmacology: Principles and practice (2nd ed.). New York: Oxford University Press.
C
CABS ▶ Clancy Behavior Scale
California Verbal Learning Test, Children’s Version (CVLT-C) Beau Reilly and Raphael Bernier Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA
Synonyms CVLT – Children’s Version; CVLTC; CVLT-C
Description The California Verbal Learning Test-Children’s Version (CVLT-C; Delis et al. 1994) is an examination of auditory and verbal learning for children between the ages of 5 years and 16 years 11 months. The test makes use of familiar visual categories to generate a measure of short- and long-term memory performance. Encoding and recall are examined via the use of single words verbally presented in the context of “a shopping list” over the course of eight total trials spanning
15–20 min with an additional 20-min period to accommodate delayed recall testing. During the first five trials of CVLT-C, a list of 15 items consisting of three semantic categories (fruit, clothing, and toys), labeled “list A” or “the Monday list,” is read aloud to the child and he or she is asked to recall as many items as possible following each presentation. During the sixth trial, a second 15-item list containing new words that belong to one of the categories from the original list (fruits) as well as words from two new categories with semantic similarities (furniture and sweets) from the original list are presented as “list B” or “the Tuesday list” to the child as an interference task. The child is then asked to recall as many words as possible. After completion of the list B trial, the child is then asked to recall words from list A without presentation of the items. In the seventh trial, list categories are used as cues to elicit recall from the original list via prompts from the examiner such as “Tell me all the things to wear in the Monday list.” Following trial 7 is a 20-min break from the task during which time the child can complete nonverbal tasks or participate in other activities that provide moderate distraction. Following this “long-delay” interval, the child is asked to recall as many words as he or she can from list A (longdelay free-recall trial), asked to recall words from list A after being provided with the categorical cues (long-delay cued-recall trial), and finally read a 45-item list aloud and asked to indicate whether
© Springer Nature Switzerland AG 2021 F. R. Volkmar (ed.), Encyclopedia of Autism Spectrum Disorders, https://doi.org/10.1007/978-3-319-91280-6
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or not each word was on list A (yes/no recognition trial). Responses are recorded and documented by the examiner during every trial. A complete administration of the CVLT-C produces data on eight recall measures, eight learning characteristics, four areas of recall errors, four recognition measures, and five contrast measures. This includes information concerning encoding strategies for success over time as well as the characteristics of errors that occur. In addition to generating information on the quantity of items accurately recalled after each of the eight testing trials, the CVLT-C allows for the detailed examination of characteristics related to acquisition methods utilized during the learning process. Characteristics related to the learning process are examined through the use of learning strategy variables and contrast variables. The learning strategy variables aid in outlining the characteristics of acquisition and encoding that progress throughout the course of the examination. They include semantic clustering (i.e., consecutive words from the same category), serial clustering (i.e., words recalled in the same order in which they were presented), primacy recall (i.e., percentage of words recalled from the first five items of the list), middle recall (i.e., percentage of words recalled from the middle five items of the list), recency recall (i.e., percentage of words recalled from the last five items of the list), learning slope (i.e., the average number of new words recalled per learning trial), consistency (i.e., percentage of words recalled once that were also recalled on the following trial), recognition hits (i.e., number of words correctly identified as belonging to list A during the recognition trial), and discriminability (i.e., accuracy of distinguishing target words in list A from distraction words in list B). Characteristics of errors are also calculated with regard to perseveration (i.e., words repeated in a trial), free intrusions (i.e., extra-list intrusions on all free-recall trials), cued intrusions (i.e., extra-list intrusions on the cuedrecall trials), total intrusions (i.e., extra-list intrusions on all trials), false-positives (i.e., words incorrectly identified as list A items during the recognition trials), and response bias (i.e., the tendency to identify words as belonging on the
California Verbal Learning Test, Children’s Version (CVLT-C)
target list during recognition trials). The learning slope variable, in particular, allows for a thorough examination of specific learning characteristics that may be evident across differing presentations of clinical populations. Deficits in areas related to learning (i.e., flat learning slope across trials with low amounts of new words learned), encoding (e.g., poor trial 1 performance followed by a normative learning slope), or sustaining focus (i.e., normative recall on initial trials with poor recall on later trials) can be identified with the learning slope, allowing for the closer inspection of learning characteristics and discrimination of other possible domains of learning that may be affected (Spreen and Strauss 1998). Children with Down syndrome, attention deficit hyperactivity disorder (ADHD), and other disorders have demonstrated distinct and differentiated characteristics of learning slope in clinical populations (Delis et al. 1994). The CVLT-C contrast variables (Donders 1999) aid in the identification of trial discrepancies and learning differences that occur throughout the learning process. These include aspects of encoding related to proactive interference (i.e., the contrast between list B recall and list A trial 1 recall), retroactive interference (i.e., the contrast between list A short-delay free recall and list A trial 5), rapid forgetting (i.e., contrast between list A long-delay free recall and list A short-delay recall), and retrieval problems (i.e., contrast between discrimination trial and list A longdelay free recall).
Historical Background Delis et al. (2000) observed that while there are a variety of verbal learning instruments that measure the amount of material that is recalled, far fewer examine the processes by which the information is learned and retrieved. Construction of CVLT-C in 1994 followed the same processoriented approach of the original California Verbal Learning Test (CVLT) for adults (Delis et al. 1987). For construction of the task, selection of the target words themselves was chosen based on their frequency of occurrence in the English
California Verbal Learning Test, Children’s Version (CVLT-C)
language as well as the frequency of reported words by children in the sample. The three most common words in each semantic category were removed to avoid recall confounds associated with item familiarity (Miller et al. 2003). The context of a shopping list was selected for its consistent familiarity with children across a wide range of cultural and demographic variables and mapped closely with the CVLT with regard to presentation, timing, and scoring.
Psychometric Data The normative sample for the CVLT-C consists of stratified data taken from the 1988 US census findings and is comprised of 920 children in 12 age ranges from 5 years to 16 years 11 months. Standardized scores were derived from accumulative raw score performance per age group, distribution normalization, and elimination of outliers and skewing effects. The remaining learning score components of the CVLT-C were developed via regression analyses (Delis et al. 1987). Investigations of test-retest reliability among 106 school-age children ranged from .17 (cuedrecall intrusions, for 12-year-olds) to.90 (perseverations, for 8-year-olds) (Delis et al. 1987, 1994; Spreen and Strauss 1998). Alternate forms reliability was reported at .84 (Delis et al. 1987, 1994; Spreen and Strauss 1998), indicating appropriate reliability for multiple administrations with children and tracking results and learning characteristics over time. Gender effects were reported by the authors to be minimal in the initial standardization sample, and significant differences were not found for gender in the 4-year-old sample norms provided by Goodman, Delis, and Mattson (Goodman et al. 1999) for normative populations. However, differences in gender have been reported in followup examinations of the standardization sample (Kramer et al. 1997) and have been evidenced in clinical populations of children with ADHD (Cutting et al. 2003) and significant head injury (Warschausky et al. 2005). Gender effects were also evident in examinations of adolescent populations, with girls outperforming boys
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(Beebe et al. 2000). Low correlations with measures of executive functioning and moderate associations with intelligence measures such as the Wechsler block design and vocabulary subtests have been reported (Beebe et al. 2000). Donders (1999) also identified a significant link between parental education levels and test performance in the standardized sample, with children of parents with higher education consisting of 22% of the highest performing children and children of parents with lower rates of education accounting for 30% of the children in the below-average range of performance. Predictably, age effects were observed among the standardization sample, with steeper learning slopes being present in children as age increased and development progressed. Consistency of recalled items and immediate recall scores were also observed to have developmental trends across the sample. The use of semantic clustering strategy as a learning strategy was first emergent among 9–12-year-old participants. Adolescents in the sample exhibited higher degrees of serial clustering strategy use compared to other age groups (Delis et al. 1994). Investigations of executive functioning and CVLT-C process scores further indicate that perseverative errors evidence strong consistency throughout development with minimal improvement, while rates of intrusions and false-positives exhibit considerable improvement as development progresses into adolescence (Beebe et al. 2002; Delis et al. 1994). Donders (1999) provided maximum likelihood confirmatory factor analysis on 13 qualitative and quantitative variables from the original standardized sample to identify the most salient factors of learning and memory tapped by the CVLT-C. A five-factor model consisting of attention span, learning efficiency, free delayed recall, cued delayed recall, and inaccurate recall showed the greatest fit and was proposed to be a valid and clinically useful predictor of performance on the measure. The CVLT-C has been co-normed with the children’s category test (CCT; Boll 1993), allowing examiners to compare a child’s memory and learning performance with other forms of higher order cognitive functioning. Combining the
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results of both tasks to generate the learning profile of a child can be clinically valuable as the CCT provides explicit feedback on a nonverbal task, while the CVLT-C provides non-explicit feedback on a verbal task through repetition. By taking advantage of the co-normed scores, clinicians are able to tap a wider range of learning areas and skills for characterizing the cognitive capabilities of the child. Donders (1999) examined the psychometric comparisons of the two measures including the magnitude of difference necessary for statistical significance in scores. Standardized sample data from both measures were used to evaluate covariances and statistically significant discrepancies between the T scores of those instruments as well as the base rate of specific discrepancies among 920 children ranging in age from 5 to 16 years. Results suggested that the CCT and CVLT-C share a small degree of common variance. Statistically significant score discrepancies between the two measures (T-score difference greater than 18 among 5–8-year-olds and greater than 16 among 9–16-year-olds) were common, indicating that evaluation of the potential clinical significance of a discrepancy between the obtained results should also include consideration of base rate statistics when evaluating individual children. While the standardization sample focused on children ages 5 years through 16 years 11 months, Goodman et al. (1999) provided normative data for 4-year-old participants on the CVLT-C for potential administration with younger populations to aid in early identification and intervention. Each month of the 4-year-old range was represented among the stratified sample of 80 (40 males and 40 females). Performance characteristics of the younger population were considerably similar to that of the normative sample data, apart from a few learning characteristics. The 4-year-old participants had a tendency for higher extra-list intrusions relative to their correct responses on cued recall that were not present on free recall as well as a higher endorsement of distracter items during the recognition trial. Semantic and serial clustering characteristics were also consistent with developmental trends, providing evidence for utility in identifying early
California Verbal Learning Test, Children’s Version (CVLT-C)
memory and learning characteristics with the younger population.
Clinical Uses The CVLT-C has been used to assess memory and learning in a wide variety of clinical childhood populations and has been used to examine verbal learning in children with ASD. Early studies of memory and list learning among children with ASD highlighted specific deficits in recall co mpared to control groups. Boucher and Warrington (1976) used memory tests that employed pictures, lists, and spoken words with 29 children with ASD and compared recall scores against age-matched controls. During trials of forcedchoice recall, children with autism showed significantly lower rates of recall than controls but demonstrated considerable improvement when provided with semantic descriptive cues of list items and pictures. Initial investigations of verbal recall among children with autism spectrum disorder (ASD) utilizing the CVLT also suggested distinct differences in learning and memory profiles when compared to typically developing peers. Minshew and Goldstein (1993) compared the performance of high-functioning children and adults with ASD ranging in age from 12 to 40 years old to agematched normal controls using the CVLT. The comparison group significantly outperformed the ASD group. Specific scores indicated that while individuals with ASD showed comparable recall and recognition scores when presented with list A of CVLT, they showed significantly more intrusion errors on both list A and list B items and considerably lower recall scores on list B. The authors concluded that the overall characteristics of the ASD scores were indicative of a “subtle inefficiency of verbal memory” that was more suggestive of deficits in mechanisms for effectively organizing information than a reflection of comprehensive memory impairment. More recent investigations into learning strategies and encoding profiles of children with ASD lend support for this theory and suggest that the CVLT-C may be effective in highlighting specific
California Verbal Learning Test, Children’s Version (CVLT-C)
characteristics of verbal learning in children with ASD that differ from those of typical developing peers. Phelan, Filliter, and Johnson (Phelan et al. 2010) compared performance and verbal learning characteristics on the CVLT-C between 15 highfunctioning children with ASD and typical developing controls. Although the learning profiles and performance characteristics of both groups were comparable, children with ASD demonstrated considerable improvement in their cued-recall scores compared to their free-recall scores, suggesting the need for external supports and cueing opportunities to facilitate verbal memory performance among ASD youth. Key clinical strengths of the CVLT-C include its relative ease of use and excellent internal consistency. Considerable research and psychometric data have been gathered with CVLT-C, and it has proven useful in predicting a variety of difficulties and deficits that can inform decision making concerning placements in groups such as head trauma patients and other neurodevelopmental disorders (Nagel et al. 2006; Nichols et al. 2004). As previously noted, the test provides a considerable amount of information about the verbal learning process and learning strategies across a relatively short period of time in such a way that recall and cueing effects can be examined efficiently and reliably. Scores on the CVLT-C have been shown to account for a considerable amount of the variance in the prediction of special education services and long-term educational outcome among children with severe head injury that could translate to other clinical populations (Miller and Donders 2003). The CVLT-C’s implementation across a wide range of childhood populations illustrates its breadth in utility and efficiency across several domains of care. The provision of normative data for 4-yearolds additionally provides valuable opportunities for early screening, intervention, and tracking among children early in development. While the internal consistency of the test has been thoroughly investigated and validated, stability coefficients of many of the variables examined in the CVLT-C fall below acceptable standards, cautioning against the use of single variables as valid examination of cognitive factors (Spreen and
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Strauss 1998). Overall, the test has shown to be an efficient and informative instrument of memory and verbal learning among children that serves as a valuable asset to clinicians involved in diagnostic assessment, treatment planning, service enrollment, and needs assessment.
References and Reading Beebe, D. W., Ris, M. D., & Dietrich, K. N. (2000). The relationship between CVLT-C process scores and measures of executive functioning: Lack of support among community-dwelling adolescents. Journal of Clinical and Experimental Neuropsychology, 22(6), 779–792. Boll, T. (1993). Children’s category test. San Antonio: The Psychological Corporation. Boucher, J., & Warrington, E. K. (1976). Memory deficits in early infantile autism: Some similarities to the amnesic syndrome. British Journal of Psychology, 67(1), 73–87. Cutting, L. E., Koth, C. W., Mahone, E. M., & Denckla, M. B. (2003). Evidence for unexpected weaknesses in learning in children with attention-deficit/hyperactivity disorder without reading disabilities. Journal of Learning Disabilities, 36(3), 257–267. Delis, D. C., Kramer, J. H., Kaplan, E., & Ober, B. A. (1987). California verbal learning test manual (CVLT). San Antonio: The Psychological Corporation. Delis, D. C., Kramer, J. H., Kaplan, E., & Ober, B. A. (1994). California verbal learning test-children’s version (CVLT-C). San Antonio: The Psychological Corporation. Delis, D. C., Kramer, J. H., Kaplan, E., & Ober, B. A. (2000). The California verbal learning test manual (2nd ed.). San Antonio: The Psychological Corporation. Donders, J. (1999). Structural equation analysis of the California Verbal Learning Test-Children’s Version in the standardization sample. Developmental Neuropsychology, 15(3), 395–406. Goodman, A. M., Delis, D. C., & Mattson, S. N. (1999). Normative data for 4-year-old children on the California Verbal Learning Test-Children’s Version. The Clinical Neuropsychologist, 13(3), 274–282. Kramer, J. H., Delis, D. C., Kaplan, E., O’Donnell, L., & Prifitera, A. (1997). Developmental sex differences in verbal learning. Neuropsychology, 11(4), 577–584. Miller, J. J., & Donders, J. (2003). Prediction of educational outcome after pediatric traumatic brain injury. Rehabilitation Psychology, 48, 237–241. Miller, M. J., Bigler, E. D., & Adams, W. V. (2003). Comprehensive assessment of child & adolescent memory: The wide range assessment of memory and learning, the test of memory and learning, and the California verbal learning test-children’s version. In C. R. Reynolds & R. W. Kamphaus (Eds.), Handbook of psychological assessment of children: Intelligence, aptitude, and achievement (pp. 275–304). New York: Guilford Press.
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778 Minshew, N. J., & Goldstein, G. (1993). Is autism an amnesic disorder? Evidence from the California verbal learning test. Neuropsychology, 7(2), 209–216. Nagel, B. J., Delis, D. C., Palmer, S. L., Reeves, C., Gajjar, A., & Mulhern, R. K. (2006). Early patterns of verbal memory impairment in children treated for medulloblastoma. Neuropsychology, 20(1), 105–112. Nichols, S., Jones, W., Roman, M. J., Wulfeck, B., Delis, D. C., Reilly, J., et al. (2004). Mechanisms of verbal memory impairment in four neurodevelopmental disorders. Brain and Language, 88(2), 180–189. Phelan, H. L., Filliter, J. H., & Johnson, S. A. (2010). Brief report: Memory performance on the California verbal learning test – children’s version in autism spectrum disorder. Journal of Autism and Developmental Disorders, 41(4), 518–523. Spreen, O., & Strauss, E. (1998). A compendium of neuropsychological tests: Administration, norms, & commentary (2nd ed.). New York: Oxford University Press. Warschausky, S., Kay, J. B., Chi, P., & Donders, J. (2005). Hierarchical linear modeling of California verbal learning test–children’s version learning curve characteristics following childhood traumatic head injury. Neuropsychology, 19(2), 193–198.
Callosotomy (Surgical Severing) ▶ Agenesis of Corpus Callosum
Callosotomy (Surgical Severing)
administered by a trained assistant. Importantly, interpretation of a patient’s condition can be easily understood by a clinician. Below is a complete list of all tests, correct at time of publication. The tests are categorised as assessing the following cognitive domains: 1. 2. 3. 4. 5. 6. 7. 8.
Induction Visual Memory Executive function Attention Verbal/Semantic Memory Decision Making and Response Control Social Cognition Other tests
CANTAB – Induction These very short tests can be used to familiarize participants with the general idea of responding in a task by touching the screen. They can also be regarded as warm-up tasks, getting the participant used to the general testing situation. They consist of: Motor Screening Task and Big/Little Circle. Motor Screening (MOT) See Fig. 1.
Cambridge Neuropsychological Test Automated Battery Aditya Sharma Academic Child and Adolescent Mental Health, Sir James Spence Institute Newcastle University, Newcastle upon Tyne, UK
Synonyms CANTAB
Description The CANTAB ® tests are simple, computerised, non-linguistic, and culturally blind. They can be
Cambridge Neuropsychological Test Automated Battery, Fig. 1 Motor screening task
Cambridge Neuropsychological Test Automated Battery
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Overview
Overview
The Motor Screening test is typically administered at the beginning of a test battery, and serves as a simple introduction to the touch screen for the participant. If a participant is unable to comply with the simple requirements of this test, it is unlikely that they will be able to complete other tests successfully. This test therefore screens for visual, movement, and comprehension difficulties.
The Big/Little Circle test assesses comprehension, learning, and reversal. It is also intended to train participants in the general idea of following and reversing a rule, before proceeding to the Intra-Extra dimensional Shift test (IED), so it should ideally precede the IED task in a battery. Administration Time
Administration Time
Around 2 min.
Around 2 min Task
Participants must touch the flashing cross which is shown in different locations on the screen.
Participants must first touch the smaller of the two circles displayed, then, after 20 trials, touch the larger circle for 20 further trials.
Outcome Measures
Outcome Measures
This test has two outcome measures which measure the participant’s speed of response and the accuracy of the participant’s pointing.
This test has five outcome measures, covering latency (speed of response) and the participant’s ability to touch the correct circle.
Test Modes
Test Modes
Two modes are available – clinical and high visibility. In high visibility the crosses are drawn using thicker lines and are easier to see.
One mode – clinical
Task
Visual Memory Big/Little Circle (BLC) See Fig. 2.
These tests allow investigation of visual and spatial aspects of memory and consist of: Delayed Matching to Sample, Paired Associates Learning, Pattern Recognition Memory and Spatial Recognition Memory. Delayed Matching to Sample See Fig. 3. Overview
Delayed Matching to Sample (DMS) assesses forced choice recognition memory for novel non-verbalisable patterns, and tests both simultaneous and short-term visual memory. This test is primarily sensitive to damage in the medial temporal lobe area, with some input from the frontal lobes. Cambridge Neuropsychological Test Automated Battery, Fig. 2 Big/Little circle (BLC)
Administration Time
Around 10 min
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Cambridge Neuropsychological Test Automated Battery, Fig. 3 Delayed matching to sample
Cambridge Neuropsychological Test Automated Battery, Fig. 4 Paired associates learning (PAL)
Task
Task
The participant is shown a complex visual pattern (the sample) and then, after a brief delay, four similar patterns. The participant must touch the pattern which exactly matches the sample.
Boxes are displayed on the screen and are opened in a randomized order. One or more of them will contain a pattern. The patterns are then displayed in the middle of the screen, one at a time, and the participant must touch the box where the pattern was originally located. If the participant makes an error, the patterns are re-presented to remind the participant of their locations. The difficulty level increases through the test. In the clinical mode, the number of patterns increases from one to eight, which challenges even very able participants.
Outcome Measures
This test has 19 outcome measures, assessing latency (the participant’s speed of response), the number of correct patterns selected, and statistical analysis measuring the probability of an error after a correct or incorrect response. Test Modes
Outcome Measures
Clinical mode (for testing once); five parallel modes (for repeated testing), and child mode (a simplified version for testing children)
This test has 21 outcome measures, covering the errors made by the participant, the number of trials required to locate the pattern(s) correctly, memory scores, and stages completed.
Paired Associates Learning (PAL) See Fig. 4.
Test Modes
Clinical mode (for testing once); five parallel modes (for repeated testing)
Overview
This challenging test assesses visual memory and new learning, and is a useful tool for assessing patients with questionable dementia, Mild Cognitive Impairment, Alzheimer’s disease, and agerelated memory loss. Administration Time
Around 10 min, depending on level of impairment
Pattern Recognition Memory (PRM) See Fig. 5. Overview
This is a test of visual pattern recognition memory in a two-choice forced discrimination paradigm. This test is often used, in conjunction with Spatial Recognition Memory (SRM), before the Paired
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Cambridge Neuropsychological Test Automated Battery, Fig. 5 Pattern recognition memory
Cambridge Neuropsychological Test Automated Battery, Fig. 6 Spatial recognition memory
Associates Learning (PAL) test, as both these tests help to train the participant for PAL. PRM and SRM contain different elements of PAL and the results considered together help to decide on the exact nature of the cognitive deficit being considered.
modes also has separate immediate and delayed versions available. Spatial Recognition Memory (SRM) See Fig. 6. Overview
Administration Time
Around 5 min, depending on level of impairment Task
The participant is presented with a series of 12 visual patterns, 1 at a time, in the center of the screen. These patterns are designed so that they cannot easily be given verbal labels. In the recognition phase, the participant is required to choose between a pattern they have already seen and a novel pattern. In this phase, the test patterns are presented in the reverse order to the original order of presentation. This is then repeated, with 12 new patterns. The second recognition phase can be given either immediately or after a 20 min delay. Outcome Measures
This test has three outcome measures, including the number and percentage of correct trials and latency (speed of participant’s response). Test Modes
Clinical mode (for testing once); four parallel modes (for repeated testing). Each of these
This is a test of visual spatial recognition memory in a two-choice forced discrimination paradigm. This test is often used, in conjunction with Pattern Recognition Memory (PRM), before the Paired Associates Learning (PAL) test, as both these tests help to train the participant for PAL. PRM and SRM contain different elements of PAL and the results considered together help to decide on the exact nature of the cognitive deficit being considered. Administration Time
Around 5 min, depending on level of impairment Task
The participant is presented with a white square, which appears in sequence at five different locations on the screen. In the recognition phase, the participant sees a series of five pairs of squares, one of which is in a place previously seen in the presentation phase. The other square is in a location not seen in the presentation phase. As with the PRM test, locations are tested in the reverse of the presentation order. This subtest is repeated three more times, each time with five new locations.
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Outcome Measures
This test has three outcome measures, including the number and percentage of correct trials and latency (speed of subject’s response). Test Modes
Clinical mode (for testing once); four parallel modes (for repeated testing)
CANTAB – Executive Function, Working Memory, and Planning Tests These tests address executive function, working memory, and planning; all are associated with the frontal area of the brain.
Cambridge Neuropsychological Test Automated Battery, Fig. 7 Intra-extra dimensional set shift
Attention Switching Task (AST)
responses, commission errors, omission errors, switch cost and congruency cost.
Overview
AST is a test of the participant’s ability to switch attention between the direction or location of an arrow on screen. This test is a sensitive measure of frontal lobe and ‘executive’ dysfunction.
Test Modes
AST has one mode: Clinical Intra-Extra Dimensional Set Shift (IED) See Fig. 7.
Administration Time
Around 8 min, depending on level of impairment
Overview
Task
Intra-Extra Dimensional Set Shift is a test of rule acquisition and reversal. It features:
The test begins with an arrow in the centre of the screen which points either to the left or to the right. The participant is introduced to two buttons, one on the left and one on the right, and is asked to press a button corresponding to the direction in which the arrow is pointing. After this initial training, the participant is then told that the arrow might appear on the left or the right side of the screen, and depending on the cue given at the top of the screen, the participant must either press the left or right button to indicate on which side of the screen the arrow is displayed, or else press the left or right button to correspond with the direction in which the arrow is pointing.
• Visual discrimination and attentional set formation • Maintenance, shifting, and flexibility of attention This test is primarily sensitive to changes to the fronto-striatal areas of the brain. This test is a computerized analogue of the Wisconsin Card Sorting test, and is sensitive to cognitive changes associated with schizophrenia, Parkinson’s Disease, and dopaminergicdependent processes. Administration Time
Outcome Measures
Around 7 min, depending on level of impairment
AST has 7 outcome measures, each of which can have various options applied to it. The AST measures cover latency, correct and incorrect
Task
Two artificial dimensions are used in the test:
Cambridge Neuropsychological Test Automated Battery
• Color-filled shapes • White lines Simple stimuli are made up of just one of these dimensions, whereas compound stimuli are made up of both, namely white lines overlying colorfilled shapes. The participant starts by seeing two simple color-filled shapes, and must learn which one is correct by touching it. Feedback teaches the participant which stimulus is correct, and after six correct responses, the stimuli and/or rules are changed. These shifts are initially intra dimensional (e.g., color-filled shapes remain the only relevant dimension), then later extra dimensional (white lines become the only relevant dimension). Participants progress through the test by satisfying a set criterion of learning at each stage (six consecutive correct responses). If at any stage, the participant fails to reach this criterion after 50 trials, the test terminates. Outcome Measures
This test has 18 outcome measures, assessing errors, and number of trials and stages completed. Test Modes
Clinical mode (for testing once); seven parallel modes (for repeated testing) One Touch Stockings of Cambridge (OTS) See Fig. 8.
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Overview
One Touch Stockings of Cambridge is a spatial planning task which gives a measure of frontal lobe function. OTS is a variant of the Stockings of Cambridge test (see below) and places greater demands on working memory as the participant has to visualize the solution. Administration Time
Around 10 min, depending on level of impairment Task
As for SOC (Stockings of Cambridge), the subject is shown two displays containing three colored balls. The displays are presented in such a way that they can easily be perceived as stacks of colored balls held in stockings or socks suspended from a beam. This arrangement makes the 3-D concepts involved apparent to the participant, and fits with the verbal instructions. There is a row of numbered boxes along the bottom of the screen. The test administrator first demonstrates to the participant how to use the balls in the lower display to copy the pattern in the upper display, and completes one demonstration problem, where the solution requires one move. The participant must then complete three further problems, one each of two moves, three moves, and four moves. Next the participant is shown further problems, and must work out in their head how many moves the solutions to these problems require, then touch the appropriate box at the bottom of the screen to indicate their response. Outcome Measures
OTS has four outcome measures – problems solved on first choice, mean choices to correct, mean latency to first choice, and mean latency to correct. Each of these measures may be calculated for all problems, or for problems with a specified number of moves (one move to five or six moves). Test Modes Cambridge Neuropsychological Test Automated Battery, Fig. 8 One touch stockings of cambridge
OTS has four modes, with varying numbers of problems and boxes.
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Cambridge Neuropsychological Test Automated Battery, Fig. 9 Spatial span
Cambridge Neuropsychological Test Automated Battery, Fig. 10 Spatial working memory
Spatial Span (SSP) See Fig. 9.
Overview
Around 5 min, depending on level of impairment
SWM is a test of the participant’s ability to retain spatial information and to manipulate remembered items in working memory. It is a selfordered task, which also assesses heuristic strategy. This test is a sensitive measure of frontal lobe and “executive” dysfunction. It has been shown in recent studies that impaired performance on SWM emerges as a common factor in prepsychosis.
Task
Administration Time
White squares are shown, some of which briefly change color in a variable sequence. The participant must then touch the boxes which changed color in the same order that they were displayed by the computer (for clinical mode) or in the reverse order (for reverse mode). The number of boxes increases from two at the start of the test to nine at the end, and the sequence and color are varied through the test.
Around 8 min, depending on level of impairment
Overview
Spatial Span assesses working memory capacity, and is a visuospatial analogue of the Digit Span test. Administration Time
Outcome Measures
This test has six outcome measures, covering span length (the longest sequence successfully recalled), errors, number of attempts, and latency.
Task
The test begins with a number of colored squares (boxes) being shown on the screen. The aim of this test is that, by touching the boxes and using a process of elimination, the participant should find one blue “token” in each of a number of boxes and use them to fill up an empty column on the right hand side of the screen. The number of boxes is gradually increased, until it is necessary to search a total of eight boxes. The color and position of the boxes used are changed from trial to trial to discourage the use of stereotyped search strategies.
Test Modes
Two modes: clinical mode and reverse mode.
Outcome Measures
Spatial Working Memory (SWM) See Fig. 10.
The 24 outcome measures for SWM include errors (touching boxes that have been found to be empty, and revisiting boxes which have already
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been found to contain a token), a measure of strategy, and latency measures.
required are taken as measures of the participant’s planning ability.
Test Modes
Outcome Measures
Clinical mode
This test has three outcome measures, including the number and percentage of correct trials and latency (speed of participant’s response).
Stockings of Cambridge (SOC) See Fig. 11.
Test Modes Overview
Clinical mode
SOC is a spatial planning test which gives a measure of frontal lobe function
CANTAB Attention Tests Administration Time
Around 10 min, depending on level of impairment. Task
The participant is shown two displays containing three coloured balls. The displays are presented in such a way that they can easily be perceived as stacks of coloured balls held in stockings or socks suspended from a beam. This arrangement makes the 3-D concepts involved apparent to the participant, and fits with the verbal instructions. The participant must use the balls in the lower display to copy the pattern shown in the upper display. The balls may be moved one at a time by touching the required ball, then touching the position to which it should be moved. The time taken to complete the pattern and the number of moves
Cambridge Neuropsychological Test Automated Battery, Fig. 11 Stockings of Cambridge
These tests measure different aspects of attention and reaction time. Choice Reaction Time (CRT), Rapid Visual Information Processing (RVP), and Simple Reaction Time (SRT) use the press pad exclusively as an input device; Match to Sample Visual Search (MTS) and Reaction Time (RTI) use both the press pad and the touch screen. Choice Reaction Time See Fig. 12. Overview
Choice Reaction Time (CRT) is a two-choice Reaction Time test which is similar to the Simple Reaction Time (SRT) test, except that stimulus and response uncertainty are introduced by having two possible stimuli and two possible responses.
Cambridge Neuropsychological Test Automated Battery, Fig. 12 Choice reaction time
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It is useful for testing general alertness and motor speed.
latency dissociated from movement time. Efficient performance on this task requires the ability to search among the targets and ignore the distractor patterns which have elements in common with the target. This test can help to differentiate between Parkinson’s disease and Alzheimer’s disease, and also between Lewy Body dementia and Alzheimer’s disease.
Administration Time
Around 7 min, depending on level of impairment Task
An arrow-shaped stimulus is displayed on either the left or the right side of the screen. The participant must press the left hand button on the press pad if the stimulus is displayed on the left hand side of the screen, and the right hand button on the press pad if the stimulus is displayed on the right hand side of the screen.
Administration Time
Around 9 min, depending on level of impairment Task
Clinical mode
The participant is shown a complex visual pattern (the sample) in the middle of the screen, and then, after a brief delay, a varying number of similar patterns are shown in a circle of boxes around the edge of the screen. Only one of these boxes matches the pattern in the center of the screen, and the participant must indicate which it is by touching it. Reaction time is measured on the basis of the release of the press pad, which allows for its more accurate measurement.
Match to Sample Visual Search (MTS) See Fig. 13.
Outcome Measures
Outcome Measures
This test has 13 outcome measures, assessing correct and incorrect responses, errors of commission and omission (late and early responses), and latency (response speed). Test Modes
Overview
Match to Sample Visual Search (MTS) is a matching test, with a speed/accuracy trade-off. It is a simultaneous visual search task with response
The 12 outcome measures for SOC cover the number of problems solved with minimum moves, the mean number of moves for n-move problems, mean initial thinking time for n-move problems, and mean subsequent thinking time for n-move problems. Test Modes
Clinical mode Rapid Visual Information Processing (RVP) See Fig. 14. Overview
Cambridge Neuropsychological Test Automated Battery, Fig. 13 Match to sample visual search
Rapid Visual Information Processing (RVP) is a test of sustained attention (similar to the Continuous Performance Task) and has proved useful in many studies in which drugs are used to help develop a disease model. It is sensitive to dysfunction in the parietal and frontal lobe areas of the brain and is also a sensitive measure of general performance.
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Cambridge Neuropsychological Test Automated Battery, Fig. 14 Rapid visual information processing
Cambridge Neuropsychological Test Automated Battery, Fig. 15 Reaction time
Administration Time
Administration Time
Around 7 min
Around 5 min, depending on level of impairment
Task
Task
A white box appears in the center of the computer screen, inside which digits, from 2 to 9, appear in a pseudo-random order, at the rate of 100 digits per minute. Participants are requested to detect target sequences of digits (e.g., 2–4–6, 3–5–7, 4–6–8) and to register responses using the press pad.
The task is divided into five stages, which require increasingly complex chains of responses. In each case, the subject must react as soon as a yellow dot appears. In some stages, the dot may appear in one of five locations, and the subject must sometimes respond by using the press pad, sometimes by touching the screen, and sometimes both.
Outcome Measures
The nine RVP outcome measures cover latency, probabilities, and sensitivity (calculated using Signal Detection Theory), and hits, misses, false alarms, and rejections.
Outcome Measures
Test Modes
Test Modes
Clinical mode, plus 123 mode (for children aged 4–8) and 357 mode (for children aged 7–14)
Clinical mode, parallel mode, and child mode
Reaction Time (RTI) See Fig. 15.
The four outcome measures in RTI are divided into Reaction Time (simple and five-choice) and movement time (simple and five-choice)
Simple Reaction Time (SRT) See Fig. 16. Overview
Overview
Reaction Time (RTI) is a latency task with a comparative history (the five choice task) and uses a procedure to separate response latency from movement time. It is more useful than CRT or SRT where it is necessary to control for tremor.
Simple Reaction Time (SRT) is a test which measures simple Reaction Time through delivery of a known stimulus to a known location to elicit a known response. The only uncertainty is with regard to when the stimulus will occur, by having a variable interval between the trial response and the onset of the stimulus for the next trial. Like
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Cambridge Neuropsychological Test Automated Battery, Fig. 16 Simple reaction time
Choice Reaction Time (CRT), it is useful for testing general alertness and motor speed, and is often sensitive to medication effects. Administration Time
Around 6 min, depending on level of impairment Task
As soon as the participant sees the square on the screen, they must press the button on the press pad. Outcome Measures
The 11 outcome measures for SRT cover latency (response speed), correct responses, and errors of commission and omission. Test Modes
Clinical mode
CANTAB – Semantic/Verbal Memory Tests These tests, which address semantic and/or verbal memory, are relatively new additions to the CANTAB battery consisting of: Graded Naming Test (GNT) and Verbal Recognition Memory (VRM). Graded Naming Test (GNT) See Fig. 17.
Cambridge Neuropsychological Test Automated Battery, Fig. 17 Graded naming test
Overview
The Graded Naming Test has been used extensively in cognitive neuropsychology. The Graded Naming Test (GNT) avoids the problem of ceiling effects in previous naming tests by having participants name drawings of objects in ascending difficulty. Reduced efficiency in retrieving the name of an object can be the first and only indication of impaired language functioning. This test assesses object-naming ability, but is in addition graded in difficulty to allow for individual differences. This means that it may be able to detect any word-finding difficulty even in those with an extensive naming vocabulary. Administration Time
Around 10 min, depending on level of impairment Task
Thirty different line drawings are displayed on the screen, 1 at a time. The participant must identify the object depicted in each drawing.
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Outcome Measures
Task
This test has six outcome measures, which include total correct, total errors, and normative z-score and percentile.
In the VRM test, the participant is shown a list of 12 words, 1 at a time, and then asked to:
Notes
Currently available in UK English only (this test is culturally biased and there are no alternative versions at present). A pencil and paper version of this test is also available. Test Modes
Clinical mode Verbal Recognition Memory (VRM) See Fig. 18. Overview
Despite the general desirability of nonverbal tests because of their culture free applicability, researchers and clinical studies sometimes require verbal tests, perhaps because of need to explore questions relating to language or left hemisphere function. Other verbal tests have a long history of use in psychiatric assessment and clinical studies. The Verbal Recognition Memory test, which assesses immediate and delayed memory of verbal information under free recall and forced choice recognition conditions, should provide comparable results.
• Produce as many of the words as possible immediately following the presentation • Recognize the words they have seen before from a list of 24 words containing the original 12 words and 12 distractors • Following a delay of 20 min, recognize the words they have seen before from another list of 24 words containing the original list and 12 new distractors Outcome Measures
The five outcome measures for VRM cover correct and incorrect responses for the recognition and free recall parts of the test. Notes
Currently available in UK English only Test Modes
Clinical mode and four parallel modes for repeated testing. Each mode has immediate and delayed parts.
CANTAB – Decision Making and Response Control Tests These tests add another dimension to cognitive profiling and investigation of frontal lobe function. Most decisions in life have an emotional or risk-related component, and many clinical conditions are associated with inappropriate risk models/strategies. They consist of Affective Go/No-go (AGN), Information Sampling Task (IST), Cambridge Gambling Task (CGT) and Stop Signal Task (SST). Affective Go/No-go (AGN) See Fig. 19. Overview
Cambridge Neuropsychological Test Automated Battery, Fig. 18 Verbal recognition memory
This test assesses information processing biases for positive and negative stimuli.
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Cambridge Neuropsychological Test Automated Battery, Fig. 19 Affective Go/No-Go
Cambridge Neuropsychological Test Automated Battery, Fig. 20 Cambridge gambling task
Affective cognitive functions are thought to be related to the ventral and medial-prefrontal cortex areas of the brain because of the limbic connections with this region. As such, the Affective Go/ No-go test represents a powerful research assessment tool for current studies on the neural substrates of depression, bipolar disorder, PostTraumatic Stress Disorder (PTSD), and many other affective conditions.
Cambridge Gambling Task (CGT) See Fig. 20.
Administration Time
Around 10 min, depending on level of impairment Task
The test consists of several blocks, each of which presents a series of words from two of three different affective categories: Positive (e.g., joyful), Negative (e.g., hopeless), and Neutral (e.g., element). The participant is given a target category, and is asked to press the press pad when they see a word matching this category.
Overview
The Cambridge Gambling Task was developed to assess decision making and risk-taking behavior outside a learning context. Relevant information is presented to the participants “up-front” and there is no need to learn or retrieve information over consecutive trials. Unlike other “Gambling” tasks, CGT dissociates risk taking from impulsivity, because in the ascending bet condition, the participant who wants to make a risky bet has to wait patiently for it to appear. The likely neural substrate for this task is the orbitofrontal prefrontal cortex. Traumatic Brain Injury, Alcoholism, and Drug abuse are all conditions sensitive to this test. Administration Time
Up to 30 min
Outcome Measures
Task
Twelve outcome measures covering latency and errors of commission and omission
On each trial, the participant is presented with a row of ten boxes across the top of the screen, some of which are red and some of which are blue. At the bottom of the screen are rectangles containing the words “Red” and “Blue.” The participant must guess whether a yellow token is hidden in a red box or a blue box. In the gambling stages, participants start with a number of points, displayed on the screen, and can select a proportion of these points, displayed in
Note
Currently available in English only. Test Modes
Six modes, four using positive and negative stimuli only, and two using positive, negative, and neutral stimuli
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either rising or falling order, in a second box on the screen, to gamble on their confidence in this judgment. A stake box on the screen displays the current amount of the bet. The participant must try to accumulate as many points as possible.
below these boxes. The subject is instructed that they are playing a game for points, which they can win by making a correct decision about which color is in the majority under the gray boxes. They must touch the gray boxes one at a time, which open up to reveal one of the two colors shown at the bottom of the screen. Once a box has been touched, it remains open. When the subject has made their decision about which color is in the majority, they must touch the panel of that color at the bottom of the screen to indicate their choice. After the subject has indicated their choice, all the remaining gray boxes on the screen reveal their colors and a message is displayed to inform the subject whether or not they were correct. The colors change from trial to trial. There are two conditions – the fixed win condition, in which the subject is awarded 100 points for a correct decision regardless of the number of boxes opened, and the decreasing win condition, in which the number of points that can be won for a correct decision starts at 250 and decreases by 10 points for every box touched. In either condition, an incorrect decision costs 100 points.
Outcome Measures
The six CGT outcome measures cover risk taking, quality of decision making, deliberation time, risk adjustment, delay aversion, and overall proportion bet. Test Modes
Ascending first (where stakes are displayed in ascending order for two stages, then in descending order for two stages) and Descending first (where stakes are displayed in descending order for two stages, then in ascending order for two stages). Information Sampling Task (IST) See Fig. 21. Overview
The Information Sampling Task (IST) tests impulsivity and decision making.
Outcome Measures Administration Time
Up to 15 min Task
The subject is presented with a 5 5 array of gray boxes on the screen, and two larger colored panels
The eight IST outcome measures cover errors, latency, total correct trials, mean number of boxes opened per trial, and probability of the subject’s decision being correct based on the available evidence at the time of the decision. Test Modes
IST has two modes: • Fixed win-decreasing win (after practice trials, the fixed win stage precedes the decreasing win stage) • Decreasing win-fixed win (after practice trials, the decreasing win stage precedes the fixed win stage) Stop Signal Task (SST) See Fig. 22. Overview Cambridge Neuropsychological Test Automated Battery, Fig. 21 Information sampling task
SST is a classic stop signal response inhibition test, which uses staircase functions to generate an estimate of stop signal reaction time.
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examining how such conditions may bias cognitive processes involved in social interaction. This domain is assessed by: Emotion Recognition Task (ERT). Emotion Recognition Task (ERT) Overview
ERT measures the ability to identify emotions in facial expressions. The participant is shown a series of faces which appear on the screen briefly and asked to identify the emotion (happiness, sadness, anger, disgust, surprise and fear). Cambridge Neuropsychological Test Automated Battery, Fig. 22 Stop signal task
This test gives a measure of an individual’s ability to inhibit a prepotent response. Administration Time
Up to 20 min Task
This test consists of two parts. In the first part, the participant is introduced to the press pad, and told to press the left hand button when they see a left-pointing arrow, and the right hand button when they see a right-pointing arrow. There is 1 block of 16 trials for the participant to practice this. In the second part, the participant is told to continue pressing the buttons on the press pad when they see the arrows, as before, but, if they hear an auditory signal (a beep), they should withhold their response and not press the button.
Administration Time
Around 10 min, depending on level of impairment. Task
One hundred and eighty stimuli, which are computer morphed images derived from the facial features of real individuals each showing a specific emotion, are displayed on the screen, one at a time, in two blocks of ninety. Each face is displayed for a short while (200 ms) and then immediately covered up, and then six buttons are displayed, each describing an emotion which could be portrayed in the photograph. The participant must decide which is the appropriate button to describe the emotion and touch the button. There are fifteen different photographs for each of the six emotions, each showing different levels of intensity. Outcome Measures
SST has five outcome measures, each of which can have various options applied to it. The SST measures cover direction errors, proportion of successful stops, RT on GO trials, SSD (50%), SSRT.
The outcome measures for ERT cover percentages and numbers correct or incorrect, and overall response latencies. Results can be looked at across individual emotions, or across all emotions at once.
Test Modes
Test Modes
SST has one mode: clinical.
ERT is available for clinical trials immediately, and will be available for academic research in CANTABeclipse 5. Please contact Cambridge Cognition for further information. ERT takes around 10 min to administer in healthy individuals.
Outcome Measures
Social Cognition A range of disorders are known to affect social cognition and there is an expanding research field
Cambridge Neuropsychological Test Automated Battery
Other Tests Visual Analogue Scales (VAS) Overview
Visual Analogue Scales are psychometric response scales which can be used as a measurement instrument for subjective states. The CANTAB VAS assess subjective measurements of drug effect, energy levels, sickness, alertness and mood. Administration Time
Around 5 min, depending on level of impairment. Task
The participant must respond to sixteen questions as they appear on the screen by touching the on-screen slider and moving it to the appropriate position on the scale. Outcome Measures
The outcome measures for this test allow you to look at the data on a question-by-question basis. Test Modes
Please contact Cambridge Cognition for information about availability for academic research.
Historical Background Grounded in the neurosciences, the CANTAB ® neuropsychological tests were developed more than 21 years ago at the University of Cambridge by Professors Robbins and Sahakian, to enable detailed translational assessment and evaluation of cognitive function. Lesion, neuroimaging, clinical and psychopharmacological studies have enabled a unique understanding of the structural, clinical and biochemical sensitivities of each of the tests. (CANTAB) The CANTAB battery was developed for the assessment of cognitive deficits in humans with neurodegenerative diseases or brain damage (Fray and Robbins 1999). It consists of a series of interrelated computerized tests of memory, attention, and executive function, administered via a touchsensitive screen. It allows a decomposition of
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complex tasks commonly used in clinical assessment into their cognitive components and enables the extrapolation of findings from the animal literature. Tests include versions of the Wisconsin Card Sorting Test and the Tower of London and also the Delayed Matching to Sample test, widely used in monkeys for visual recognition memory. The tests are constructed in such a way that they may be given to animals (monkeys) with minimal change. The nonverbal nature of the CANTAB tests makes them largely language independent and culture free. CANTAB has been standardized on a large, predominantly elderly, population and validated in neurosurgical patients as well as in patients with basal ganglia disorders, Alzheimer’s disease, depression, and schizophrenia. In addition, CANTAB has been used to evaluate: (a) the therapeutic effects of dopaminergic and cholinergic medication in neurodegenerative disease; (b) cognition in 5–11-year old normal, learning-disabled, and autistic children; (c) deficits in patients with HIV infection; and (d) early, asymptomatic Huntington’s disease. The latter illustrate its usefulness in early identification of progressive disorders. It is suggested that the battery should have particular utility across a wide range of age and intelligence in longitudinal assessment after exposure to toxicants, and allow meaningful comparison with experimental studies of toxic effects in other species. There is emerging evidence to support the involvement of frontal cortex in autism. CANTAB is particularly useful in helping study the cognitive profile of children who have autism and related disorders.
Psychometric Data CANTAB tests are sensitive to cognitive changes caused by a wide range of CNS disorders and medication effects. Where error scores are a key outcome measure, CANTAB tests are graded in difficulty to avoid ceiling effects. Where accurate measurement of latency is important, responses are made via a press pad. Elsewhere, engaging touch-screen technology maximizes compliance.
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The majority of CANTAB tests are independent of language and culture.
Clinical Uses The following cognitive and other disorders have been investigated using CANTAB ®: AD/HD – Attention deficit hyperactivity disorder AIDS dementia complex Alcoholism Amphetamine addiction Amygdalohippocampectomy Anorexia nervosa Anterior parietal damage Antisocial behavior Antisocial personality disorder Anxiety Attention deficithyperkinetic disorder Autism Basal ganglia lesions Bipolar disorder Borderline personality disorder Camptocormia Capgras syndrome Carcinoid syndrome Chronic drug misuse Chronic fatigue syndrome Chronic occupational solvent encephalopathy Critical illness requiring intensive care Dementia Alzheimer-type (DAT) Dementia lewy body type Dementia of frontal type Developmental dyslexia
Lesion in orbitofrontal cortex Liver failure Long-term health effects of diving Machado-Joseph disease Mad Hatter’s disease Manic depression Melancholia Mercury poisoning Mild cognitive impairment (MCI) Motor neuron disease Multiple sclerosis Multiple system atrophy Narcolepsy Neuronal migration disorders Normal pressure hydrocephalus Obsessive compulsive disorder Organophosphate pesticide exposure Panic disorder Paraphrenia Parkinson’s disease Periventricular brain insult Personality disorder Petrol (gasoline) sniffing Phenylketonuria Post-concussion syndrome Premature birth needing intensive care (continued)
Diabetes Dorsolateral frontal cortical compression Down’s syndrome Drug abuse Dysexecutive syndrome Frontal lobe damage Frontal lobe excision Frontal variant frontotemporal dementia Gluten ataxia Hallucinosis Head injury Hearing loss Heart disease Heart failure Heavy social drinking Hepatic encephalopathy Heroin addiction Herpes encephalitis Hippocampal atrophy HIV/AIDS Huntington’s disease Hydrocephalus Hypercortisolemia Hyperostosis frontalis interna Hypertension Insomnia Korsakoff syndrome Late paraphrenia Lead exposure Left ventricular systolic dysfunction
Premenstrual dysphoric disorder Progressive supranuclear palsy Psychopathy Psychosis Questionable Dementia Renal Cancer Roifman syndrome Schizoaffective disorder Schizophrenia Seasonal affective disorder Self harm Semantic dementia Specific language impairment Social withdrawal in Schizophrenia Solvent encephalopathy Spina bifida Steele-RichardsonOlzsewski syndrome Stiff Person syndrome Striatocapsular infarct Subarachnoid hemorrhage Substance abuse Tardive dyskinesia Temporal lobe excision Temporal lobe lesion Tinnitus Tourette’s syndrome Traumatic brain injury Trichotillomania Tuberous sclerosis White matter lesions
Drugs Pharmacological studies (academic research) have been carried out on the following drugs using CANTAB: Alcohol Amisulphiride Amphetamine
Flumazenil Fluoxetine Galantamine
Modafinil Neuroleptic Nicotine (continued)
Camouflaging Autistic Traits Questionnaire (CAT-Q)
Antipsychotic medication Antiretroviral therapy Atomoxetine Branch chain amino acid drink
Bromocryptine Buspirone Caffeine Cannabis Chlorpromazine Clonidine Clozapine Cocaine delta-9 tetrahydrocannabinol Dexamphetamine Diazepam Donepezil Dopaminergic medication Ecstasy Endozepines
Ginkgo biloba
Olanzapine
Glyburide
Opiates
Guanfacine Highly active antiretroviral therapy (HAART) Haloperidol Heroin
Paroxetine Pergolide
Hydrocortisone Idazoxan Idazoxan plus Clonidine Interferon Interleukin-2 Kava Ketamine
Perindopril Petrol/ Gasoline Phenserine Quetiapine Risperidone Ritalin Rivastigmine Rosiglitazone RU-486
L-Dopa Lecithin MDMA Metamphetamine
Scopolamine SGS742 Sulpiride Tacrine
Methylphenidate Mifepristone
Tryptophan Tyrosine
©2011 Cambridge Cognition – All rights reserved
See Also ▶ CANTAB
References and Reading Cambridge Cognition Ltd. Cambridge automated neuropsychological test automated battery. www. cantab.com Fray, P. J., & Robbins, T. W. (1999). CANTAB battery: Proposed utility in neurotoxicology. Neurotoxicology and Teratology, 18, 499–504. Ozonoff, S., Cook, I., Coon, H., Dawson, G., Joseph, R. M., Klin, A., et al. (2004). Performance on Cambridge Neuropsychological Test Automated Battery subtests sensitive to frontal lobe function in people with autistic disorder: Evidence from the Collaborative Programs of Excellence in Autism network. Journal of Autism and Developmental Disorders, 34, 139–150.
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Camouflaging Autistic Traits Questionnaire (CAT-Q) Laura Hull and William Mandy Research Department of Clinical, Educational and Health Psychology, University College London, London, UK
Synonyms CAT-Q
Description The Camouflaging Autistic Traits Questionnaire (CAT-Q) is a standardized self-report measure of camouflaging behaviors in autistic and nonautistic adults. It comprises 25 items and takes around 5 min to complete, on paper or online. The scale consists of three sub-scales: compensation (strategies used to overcome social difficulties associated with autism), masking (strategies used to hide autistic characteristics or present a less autistic persona), and assimilation (strategies used to avoid standing out during social interactions). In addition to sub-scale scores, a total camouflaging score can be calculated as the sum of all scores (ranging from 25 to 175, with higher scores indicating greater camouflaging). The CAT-Q is completed by the individual themselves, reflecting on their own behaviors at the present time.
Historical Background Camouflaging describes the use of strategies, whether deliberate or automatic, to minimize the appearance of autistic characteristics during social interactions and to compensate for social difficulties associated with autism (Hull et al. 2017). Camouflaging has also been proposed as a potential explanation for the underdiagnosis of autism in females (Lai et al. 2015); if girls and
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women use more, or more successful, camouflaging strategies to hide or compensate for their autism, they are less likely to be identified by clinical services. This can lead to lack of support and acceptance, as well as the potential for resulting mental health difficulties (Bargiela et al. 2016; Milner et al., 2019). Recent research has also suggested that camouflaging strategies themselves may be associated with negative mental health outcomes for autistic adults (Cage et al. 2018; Hull et al. 2017) and young people (Tierney et al. 2016). Until recently there has been no way to measure how much someone is camouflaging. Some researchers have quantified camouflaging as the discrepancy between an individual’s internal autistic experience (such as level of autistic traits) and the external behavioral presentation (such as ADOS score; Lai et al. 2017). This approach has generally concluded that females camouflage more than males (Lai et al. 2017, 2018; Parish-Morris et al. 2017; Ratto et al. 2018). However, the discrepancy approach to measuring camouflaging requires multiple, often time-consuming measures to be taken for each individual and only measures the effect camouflaging has on behavior rather than the effort put into camouflaging. An alternative measurement is the CAT-Q, which directly measures the extent of camouflaging strategies selfreported by an individual. This makes measurement of camouflaging quick and easy, and the CAT-Q is freely available to download (Hull et al. 2018).
Psychometric Data There is limited psychometric data for this measure, particularly across cultures, abilities, and age groups. The CAT-Q has been validated in autistic and non-autistic adult males and females in a large sample (N ¼ 832; Hull et al. 2018) and demonstrated good internal consistency (α ¼ 0.94 for total camouflaging scale) and acceptable test-retest reliability in a smaller
Camouflaging Autistic Traits Questionnaire (CAT-Q)
sample (ICC [C,1] ¼ 0.77). Measurement invariance has also been demonstrated between autistic and non-autistic males and females, demonstrating that the CAT-Q can be used with individuals regardless of whether they have received a formal diagnosis of autism. This is particularly important as camouflaging is likely to exist along a continuum, similarly to autistic traits (Constantino, 2011), and individuals who camouflage extensively may consequently not meet current diagnostic criteria for autism (Kreiser and White 2014). There is some evidence to support the convergent validity of the CAT-Q, with higher scores associated with higher levels of autistic-like traits (Hull et al. 2018).
Clinical Uses Camouflaging has been associated with mental health difficulties including depression (Lai et al. 2017), anxiety (Hull et al. 2018), and suicidal thoughts (Cassidy et al. 2018). The CAT-Q can be used to identify autistic adults who may be a greater risk of these co-occurring conditions and help them access support. However, norms have not yet been established for either autistic or nonautistic adults; therefore clinically meaningful cutoffs have not been identified. There is also potential for the CAT-Q to be used as part of the autism diagnostic assessment process in adults and adolescents. Adults, particularly women, who have not yet received an autism diagnosis, may camouflage their characteristics during autism assessments, leading to underrecognition of their level of need. Further clinical research is needed to examine exactly how the CAT-Q can be integrated into gold standard assessment processes.
See Also ▶ Bias in Assessment Instruments for Autism ▶ Social Camouflaging in Adults with ASD ▶ Suicide Rates in Adults with Autism
Can’t Versus Won’t Dilemma
References and Reading Bargiela, S., Steward, R., & Mandy, W. (2016). The experiences of late-diagnosed women with autism Spectrum conditions: An investigation of the female autism phenotype. Journal of Autism and Developmental Disorders, 46(10), 3281–3294. Cage, E., Di Monaco, J., & Newell, V. (2018). Experiences of autism acceptance and mental health in autistic adults. Journal of Autism and Developmental Disorders, 48(2), 473–484. Cassidy, S., Bradley, L., Shaw, R., & Baron-Cohen, S. (2018). Risk markers for suicidality in autistic adults. Molecular Autism, 9(42), 1–14. Constantino, J. N. (2011). The quantitative nature of autistic social impairment. Pediatric Research, 69(5 Pt 2), 55R–62R. https://doi.org/10.1203/PDR.0b013e3182 12ec6e. Hull, L., Petrides, K. V., Allison, C., Smith, P., BaronCohen, S., Lai, M.-C., & Mandy, W. (2017). “Putting on my best normal”: Social camouflaging in adults with Autism Spectrum Conditions. Journal of Autism and Developmental Disorders, 47(8), 2519–2534. Hull, L., Mandy, W., Lai, M.-C., Baron-Cohen, S., Allison, C., Smith, P., & Petrides, K. V. (2018). Development and validation of the camouflaging autistic traits questionnaire (CAT-Q). Journal of Autism and Developmental Disorders, 49(3), 819–833. Hull, L., Lai, M.-C., Baron-Cohen, S., Allison, C., Smith, P., Petrides, K. V., & Mandy, W. (2019). Gender differences in self-reported camouflaging in autistic and nonautistic adults. Autism, 136236131986480. https://doi. org/10.1177/1362361319864804. Kreiser, N. L., & White, S. W. (2014). ASD in females: Are we overstating the gender difference in diagnosis? Clinical Child and Family Psychology Review, 17(1), 67–84. Lai, M.-C., Lombardo, M. V., Auyeung, B., Chakrabarti, B., & Baron-Cohen, S. (2015). Sex/gender differences and autism: Setting the scene for future research. Journal of the American Academy of Child and Adolescent Psychiatry, 54(1), 11–24. Lai, M.-C., Lombardo, M. V., Ruigrok, A. N. V., Chakrabarti, B., Auyeung, B., Szatmari, P., et al. (2017). Quantifying and exploring camouflaging in men and women with autism. Autism, 21(6), 690–702. Lai, M.-C., Lombardo, M. V., Chakrabarti, B., Ruigraok, A. N., Bullmore, E. T., Suckling, J., et al. (2018). Neural self-representation in autistic men and women and association with ‘compensatory camouflaging’. Autism. https://doi.org/10.1177/1362361318807159. Milner, V., McIntosh, H., Colvert, E., & Happé, F. (2019). A qualitative exploration of the female experience of autism Spectrum disorder (ASD). Journal of Autism and Developmental Disorders. https://doi.org/10. 1007/s10803-019-03906-4. Parish-Morris, J., Liberman, M. Y., Cieri, C., Herrington, J. D., Yerys, B. E., Bateman, L., et al. (2017). Linguistic
797 camouflage in girls with autism Spectrum disorder. Molecular Autism, 8(1), 48. Ratto, A. B., Kenworthy, L., Yerys, B. E., Bascom, J., Trubanova, A., White, S. W., et al. (2018). What about the girls? Sex-based differences in autistic traits and adaptive skills. Journal of Autism and Developmental Disorders, 48(5), 1698–1711. Tierney, S., Burns, J., & Kilbey, E. (2016). Looking behind the mask: Social coping strategies of girls on the autistic spectrum. Research in Autism Spectrum Disorders, 23, 73–83.
Can’t Versus Won’t Dilemma Elaine Coonrod Department of Psychiatry, School of Medicine, TEACCH, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Definition One common issue faced by parents, teachers, and caregivers of individuals with autism is understanding when a behavioral difficulty is due to a skill deficit (“can’t”), rather than due to deliberate noncompliance (“won’t”). Caregivers who attribute behavior problems to deliberate noncompliance often see the behavior as rooted in laziness, stubbornness, or defiance. This attribution has multiple negative consequences, including increased frustration and stress for the caregiver, as well as use of ineffective or confrontational behavior management strategies. Even caregivers who have some understanding of autism may believe that the individual with autism is purposely engaging in misbehavior, and consequently become embroiled in an unproductive power struggle. The confusing behavioral picture presented by individuals with autism contributes to this misunderstanding. For example, individuals with autism often have a very typical physical appearance, so the caregivers’ natural inclination is to expect ageappropriate skills and behavior. In addition, many individuals with autism, including those with language impairments, can repeat back
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verbal directions even when they have not fully understood the content of what was said, giving a misimpression about their level of understanding. Furthermore, poor social insight and communication deficits may mean that individuals with autism are unable to recognize and communicate their own lack of skill or need for assistance, or may cause them to question directions from others in a manner that is perceived as argumentative or disrespectful. Perhaps most confusing for caregivers is the unusual scatter of strengths and weaknesses shown by individuals with autism, as well as their difficulty in generalizing the use of skills from one context to another. For example, parents of a bright 14 year old with autism may simply have difficulty understanding how their son can have extensive working knowledge of his computer, yet not be able to successfully operate the microwave. A teacher of a more impaired 7 year old may be confused as to why the student can independently use the toilet at home but repeatedly soils her clothing at school. In general, when faced with a “can’t versus won’t” dilemma, it is more productive to begin by assuming that the individual with autism “can’t” and then conduct a behavioral assessment focused on the symptoms of autism that may be impeding his or her behavioral success. The caregiver should consider the ways in which the individual’s unique profile of strengths and weaknesses in communication, socialization, flexibility and interests, sensory responses, and learning style may be contributing to the behavioral difficulty. That information can then be used to generate positive, proactive strategies to help support desired behaviors in the future.
References and Reading Marcus, L. M., & Palmer, A. (2010). Families of children with autism: What educational professionals should know. In F. A. Karnes & K. R. Stephens (Eds.), The practical strategies series in autism education. Austin: Prufrock. Marcus, L. M., Kunce, L. J., & Schopler, E. (2005). Working with families. In F. R. Volkmar, A. Klin, R. Paul, & D. J. Cohen (Eds.), Handbook of autism and pervasive
Canada and Autism developmental disorders (Vol. II, pp. 1055–1086). Hoboken: Wiley. Notbohm, E. (2005). Ten things every child with autism wishes you knew. Arlington: Future Horizons. Schopler, E. (1995). Parent survival manual: A guide to crisis resolution in autism and related developmental disorders. New York: Plenum.
Canada and Autism Marc Woodbury-Smith1,2 and Frank Tran3 1 Department of Psychiatry and Behavioural Neuroscience, McMaster University, Hamilton, ON, Canada 2 Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK 3 St. Joseph’s Healthcare, Hamilton, ON, Canada
Background In Canada, as in many other countries, a growing public awareness of autism spectrum disorder (ASD) has emerged in the context of the evidence for a rising prevalence and the existence of lifelong vulnerabilities and complex medical and mental health comorbidities (Anagnostou et al. 2014). Parents and carers have been instrumental in raising this awareness, and the government has responded with the commissioning of new services, principally focused on the needs of children (Motiwala et al. 2006; Auditor General of Ontario 2013). As discussed subsequently, despite increased public funding for ASD-focused services, significant inequalities in service provision exist for specific groups, including adults and higher-functioning individuals (Shattuck et al. 2012), newly arrived immigrants (Khanlou et al. 2017), and individuals with complex health and social care needs (Autism Ontario 2008). Moreover, service inequalities exist between different provinces (Eggleton and Keon 2007). It is now widely recognized that there is an urgent need for uniformly accessible services for all individuals with ASD, irrespective of age or any other characteristic. The provision of a uniform service can only truly be achieved through federal
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involvement and ultimately a national policy or legislative framework. Such a strategy has widespread support and in 2007 was a suggestion made by the Senate Select Committee (Eggleton and Keon 2007), although at this stage there is no indication of the adoption of a federal initiative. By way of background, in Canada, each province (of which there are ten, along with three territories) is responsible for providing healthcare and social services for all individuals. This of course includes children and adults with developmental disabilities, such as ASD. Funds for services are raised through taxation, and each province implements its own model of service delivery. Importantly, for healthcare federal policy – by way of the Canada Health Act – still provides some oversight and direction, including the directive that universal access to publically funded “medically necessary” services must be ensured for all. However, the significant power devolved by the government to provincial policymakers does result in interprovincial variation in services. It is difficult to articulate the minutiae of province-by-province differences, and so this present entry will provide a simple overview, drawing examples from individual provinces but not attempting to present a detailed and comprehensive picture of ASD services across Canada.
Overview of Current Treatments and Centers Early Diagnosis and Intervention One significant development across Canada has been the widespread availability of early intervention services for children with ASD (Anagnostou et al. 2014; Volden et al. 2015). Early intervention, based on the principles of Applied Behavior Analysis (ABA), is funded at the provincial level by those Ministries responsible for child, family, and community care. For example, in Ontario such services are commissioned by the Ministry of Child and Youth Services (separate from the Ministry of Health which is responsible for healthcare), in Alberta by the Ministry of Children’s Services, and in British Columbia (BC) by
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the Ministry of Children and Family Development. Early intervention programs (Intensive Behavioural Intervention or IBI) involve one-onone therapy during which the child is engaged in a series of discrete trials involving reward contingencies to facilitate learning and generalization. The trials themselves focus on language and communication, as well as social and adaptive skills. Intensive intervention typically involves 20–40 h of 1:1 therapy per week over a period of 2 years, which has been shown to maximize the chance of improvement (Reitzel et al. 2015 and references therein). Research has consistently shown that early language and cognition are strong predictors of outcome in later childhood and into adulthood (Henninger and Taylor 2013), although outcome in later adult life may be related more closely to early social adjustment (Howlin et al. 2013). The increased availability of early intervention services has therefore been welcomed by all involved in ASD policy. Moreover, research has shown the success of such programs (Warren et al. 2011), although the research is not clear cut (Reichow et al. 2012). There is still much interprovincial variation in service delivery: for example, in Nova Scotia publicly funded IBI services are available to all young children with ASD, and this level of care is echoed in Ontario. As would be expected, with the evidence for a rising prevalence, currently estimated at ~1% (or 67,000 children age between 3 and 20 years) in Canada (Anagnostou et al. 2014), the demand on these services is large, and consequently wait times are often long between receiving a diagnosis and accessing IBI. Some form of triage is often in place to target those children who are more likely to benefit. For example, in Ontario IBI is reserved for younger children who have “severe autism.” Although “severe” is not explicitly defined, clinicians responsible for intake use a variety of screening tools to determine eligibility. However, even targeting services in this way, wait times are still substantial. For example, in 2013, the waitlist for IBI in Southern Ontario, comprising parts of the Greater Toronto Area (GTA) and the surrounding “Golden Triangle,” included 1748 children (Auditor General of Ontario 2013). Slightly
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different service provision is seen in BC and Alberta, where public funding is provided to partly offset the costs of private intervention sourced by the family itself. Early intervention demands that diagnosis is made as early as possible, which is dependent on the availability of expert clinical diagnostic services. Although certain groups, such as the American Academy of Pediatrics, have recommended universal screening for ASD between the ages of 18 and 24 months (Johnson and Myers 2007), the Canadian Pediatric Society instead advices developmental surveillance (Anagnostou et al. 2014). In parts of Canada, this approach has been facilitated through the use of brief, validated, and reliable screening questionnaires (Zwaigenbaum 2009). While family physicians are in a position to provide early screening, however, the diagnosis itself may be delayed through the unavailability of appropriate expertise. While family physicians are the frontline staff involved in developmental surveillance, the responsibility for early diagnosis rests with existing services, such as developmental pediatrics and child psychiatry. The provision of adequate training to primary and secondary healthcare workers is therefore crucial. School-Aged Children Services for school-aged children have also seen progress in recent years. In some provinces, the focus remains on early intervention for preschoolers, whereas other provinces have also developed services for children with ASD up to the age of 18 years. In Ontario, for example, services have developed to meet the varying needs of this population using ABA principles. Among some children with ASD, particularly those who function typically, the focus may be on social skills, often, although not necessarily, delivered in a group setting, whereas for others, it may be behavioral or adaptive needs. The emphasis is on mastering skills one at a time and then learning to apply these in everyday settings. In 2013, the median age of children accessing this service was 8 years, with 90% age 14 or younger indicating that older children and those in
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transition (i.e., age 17–18 years) may not be accessing services. In addition to therapy that targets the core symptoms of ASD, there is also a need for mental health services to address the high level of emotional distress and comorbidity among children with ASD (Drmic and Szatmari 2014). The impact on later outcome for therapy aimed at school-aged children will need to be fully evaluated. Adults Services for adults with ASD in Canada have lagged behind those for children (Stoddart et al. 2013). It is estimated that in the region of 4900, teenagers with ASD in Canada reach their 18th birthday each year (Shattuck et al. 2012). Based on current epidemiological estimates, as many as 70% of these may have IQs in the typical range (Centers for Disease Control and Prevention 2014). Despite this, however, the outcome for many is poor. For example, studies of outcomes consistently find low to modest levels of independence and the persistence of core phenotypic traits and associated developmental and mental health vulnerabilities beyond childhood (Howlin et al. 2013). It is clear, therefore, that for an individual with ASD – irrespective of their IQ – health and community/social care needs will remain significant throughout much of their lives (Stoddart et al. 2013; Autism Ontario 2008). With increases in life expectancy, this potentially represents a public health crisis. Indeed, on an individual basis, the lifetime costs associated with ASD have been estimated at up to $2.44M US dollars in the USA and UK (Buescher et al. 2014). Healthcare for adults with ASD in Canada is met according to the universal healthcare principals of the Canada Health Act. Specialist mental health services for adults with developmental disabilities do exist but are not government mandated. Such services focus on the mental health and behavioral needs of adults with IQs below 70, and as such, many adults with ASD will not meet the access criteria. For those adults who do have IQs 70 or above, it is expected that existing mental health services will meet their needs, although this is often not the case, with some excluded from community mental health services
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as a result of their ASD diagnosis, essentially leaving them “doubly socially excluded.” This is even more concerning when the statistics are considered: in one study examining comorbidity, as many as 70% of young adults with ASD had experienced one or more episode of major depression, with 50% experiencing recurrent depression and 50% describing an anxiety disorder (Lake et al. 2014). In Canada, vocational and social care needs, including the provision of supported accommodation, are met by the Ministry responsible for the commission of community and social services. Among those who have an intellectual disability, i.e., evidence of IQ 0.85). However, as Havdahl et al. (2017) highlight, the different cutoffs for the ADI-R were needed in the presence or absence of parental concern to achieve comparable SE and SP. Current results from the ADI-R and ADOS highlight the importance of good clinical judgment and the importance of taking parental concern seriously in the diagnostic process and when interpreting scores from parent endorsed instruments such as the ADI-R and consequently the DISCO. As presented in Havdahl et al. (2017), combining instruments such as the ADI-R and the ADOS provides the optimal prediction and clinical guidance in diagnostic processes (Havdahl et al. 2017.
Future Directions There is a pressing need to understand how the various instruments, not only the ADOS and the ADI-R, perform across various populations, ethnicities, and cultures. However, it also how the instruments perform in terms of each other. The ADOS and the ADI-R are currently regarded as gold standard instruments, but the gold standard should be considered as relative rather than absolute (Øien et al. 2018). Further research is needed to ascertain how well the parent endorsed instruments are performing in cases without parental concern and for children within the average
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range IQ. It is crucial to think about diagnostic instruments such as 3di, DISCO, ADOS, and ADI-R as a supplement and helpful tool in the diagnostic process rather than a tool providing a conclusive decision regarding diagnosis.
C See Also ▶ ADI-R ▶ ADOS ▶ Diagnostic Instruments in Autistic Spectrum Disorders ▶ Screening Instruments for ASD
References and Reading American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders (DSM-IV-TR) (4th ed.). Washington, DC: American Psychiatric Association. Corsello, C. (2013). Diagnostic instruments in autistic Spectrum disorders. In F. R. Volkmar (Ed.), Encyclopedia of autism spectrum disorders. New York: Springer. Falkmer, T., Anderson, K., Falkmer, M., & Horlin, C. (2013). Diagnostic procedures in autism spectrum disorders: A systematic literature review. European Child & Adolescent Psychiatry, 22(6), 329–340. https://doi.org/10.1007/s00787-013-0375-0. Gotham, K., Risi, S., Pickles, A., & Lord, C. (2007). The autism diagnostic observation schedule: Revised algorithms for improved diagnostic validity. Journal of Autism and Developmental Disorders, 37(4), 613–627. Havdahl, K. A., Bishop, S. L., Surén, P., et al. (2017). The influence of parental concern on the utility of autism diagnostic instruments. Autism Research, 10(10), 1672–1686. https://doi.org/10.1002/aur.1817. Klin, A., Pauls, D., Schultz, R., & Volkmar, F. (2005). Three diagnostic approaches to Asperger syndrome: Implications for research. Journal of Autism and Developmental Disorders, 35(2), 221–234. [PUBMED: 15909408]. Lord, C., Rutter, M., Goode, S., Heemsbergen, J., Jordan, H., Mawhood, L., et al. (1989). Austism diagnostic observation schedule: A standardized observation of communicative and social behavior. Journal of Autism and Developmental Disorders, 19(2), 185–212. Lord, C., Rutter, M., & Le Couteur, A. (1994). Autism diagnostic interview-revised: A revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. Journal of Autism and Developmental Disorders, 24(5), 659–685.
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Comparison of High-Functioning Autism and Asperger Syndrome
Lord, C., Luyster, R. J., Gotham, K., & Guthrie, W. (2012). ADOS-2. Autism diagnostic observation schedule (2nd ed.). Torrance: Western Psychological Services. Øien, R. A., & Nordahl-Hansen, A. (2018). Bias in assessment instruments for autism. Encyclopedia of autism spectrum disorders. New York: Springer. Øien, R. A., Nordahl-Hansen, A., & Schjølberg, S. (2019). Screening instruments for ASD. Springer Science+Business Media BV. https://doi.org/10.1007/978-14614-6435-8_102295-1. ISBN 978-1-4614-6435-8.s. Spitzer, R. L., Gibbon, M. E., Skodol, A. E., Williams, J. B., & First, M. B. (2002). DSM-IV-TR casebook: A learning companion the diagnostic and statistical manual of mental disorders, text rev. American Psychiatric Publishing, Inc. Stenberg, N., Schjølberg, S., Shic, F., et al. (2020). Functional outcomes of children identified early in the developmental period as at risk for ASD utilizing the the Norwegian mother, father and child cohort study (MoBa). Journal of Autism and Developmental Disorders. https://doi.org/10.1007/s10803-020-04539-8. Volkmar, F. R., Bregman, J., Cohen, D. J., & Cicchetti, D. V. (1988). DSM-III and DSM-III-R diagnoses of autism. The American Journal of Psychiatry. Wing, L., Leekam, S. R., Libby, S. J., Gould, J., & Larcombe, M. (2002). The diagnostic interview for social and communication disorders: Background, inter-rater reliability and clinical use. Journal of Child Psychology and Psychiatry, 43(3), 307–325. World Health Organization. (1992). The ICD-10 classification of mental and behavioural disorders: Clinical descriptions and diagnostic guidelines. Geneva: World Health Organization.
Comparison of HighFunctioning Autism and Asperger Syndrome Lucia Margari, Concetta de Giambattista and Patrizia Ventura Child Neuropsychiatry Unit, University of Bari “Aldo Moro”, Bari, Italy
Definition Since Hans Asperger’s first description, through Lorna Wing’s translation and definition, to its introduction in the fourth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV), Asperger syndrome has always aroused huge interest and debate, until vanishing in the DSM fifth edition (DSM-5).
The following entry will describe the history of Asperger syndrome, from the original description to the current diagnostic classifications, focusing on clinical profile, boundaries with highfunctioning autism and nosographic implications.
Historical Background In the same years of Leo Kanner’s description of infantile autism (Kanner 1943), Hans Asperger wrote a case report on autistic psychopathy (Asperger 1944), describing children with socialcommunication impairment, eccentric manners, unusual interests, and cognitive domains of hyperfunctioning. The American Psychiatric Association (APA) did not immediately recognize Autism as a distinct category: it was introduced as infantile autism in the third edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-III) and included within pervasive developmental disorders (PDD) in the DSM-III-R. In 1981, Wing resumed Asperger’s researches, renaming the autistic psychopathy as Asperger syndrome (AS) (Wing 1981). In 1989, the first diagnostic criteria for AS were proposed (Gillberg and Gillberg 1989; Szatmari et al. 1989), and in the 1990s, AS appeared in the fourth edition of DSM (DSM-IV) within PDD. The diagnosis of AS required at least two symptoms of social interaction impairment and one symptom of behavioral and interest restriction, a normal cognitive functioning, and the absence of significant general delay in language. Moreover, diagnostic criteria for autistic disorder should not be met (otherwise, autistic diagnosis should have precedence). This implied a differential diagnosis between AS and autism, especially the type without cognitive delay, also known as highfunctioning autism (HFA). HFA is not a term used in the DSM, but it is commonly used to identify subjects diagnosed with autistic disorder (AD) or PDD-not other specified (PDD-NOS), with average or above average intellectual abilities (intelligent quotient, IQ, higher than 70). HFA differs from low-functioning autism (IQ lower than 70) in terms of clinical presentation, prognosis and need for support and assistance in daily
Comparison of High-Functioning Autism and Asperger Syndrome
life. Since AS and HFA are both characterized by a normal cognitive functioning, there has been considerable debate over whether AS and HFA are distinct conditions, suggesting different etiological and neurobiological mechanism, or share a similar underlying neuropsychological functioning and should therefore be regarded as variants of a single disorder. According to Hans Asperger original description, his patients differ from those described by Kanner (Asperger 1944). Instead, Lorna Wing, translating Asperger’s work, named the syndrome and Kanner’s autism both part of an autistic continuum (Wing 1981). AS definition and its boundaries with HFA have been the topic of a growing body of literature, providing contradictory results: some authors considered the available evidences insufficient to establish the validity of AS as a syndrome distinct from HFA; others found quantitative rather qualitative differences between them. Sharma et al., in a review of 69 research studies, published from 1981 to 2010, defined “confuse and inconsistent” AS diagnosis, since it is based on criteria mostly overlapping with autism, from which though it could stand out for some fine differences in the nature of social interaction and communication (Sharma et al. 2012). The unsolved confusion in defining AS criteria and the clinical overlap between HFA and AS led to its merging into one unifying category, on the assumption that they cannot be reliably differentiated from one another; the DSM-5 incorporated AS into autistic spectrum disorder (ASD), removing the previously discrete diagnostic presentation of PDD. According with members of the DSM-5 Neurodevelopmental Disorders Committee, the application of DSM-5 criteria demonstrated adequate sensitivity across all PDD subcategories, also including subjects denoted as cognitively higher functioning and AS (Huerta et al. 2012).
Current Knowledge Irrespective of the DSM-5 changes, several studies (also published later than 2013) continue to debate about AS and its conceptualization within ASD (Volkmar and McPartland 2014).
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Tsai and Ghaziuddin (2014), finding in 95 of 125 comparative studies quantitative and qualitative differences between AS and HFA, expressed their concern about the risk that with the single category of ASD, DSM-5 “moved forward into the past.” A recent study conducted a comparison between 150 children and adolescents with diagnosis of PDD, according to DSM-IV Text Revision (DSM-IV-TR) criteria, with an intelligent quotient average or above, among which 80 AS and 70 HFA (including patients with a diagnosis of autistic disorder or pervasive developmental disorder-not otherwise specified), in term of core clinical features, cognitive functioning, school learning abilities, and comorbidities (de Giambattista et al. 2019). Core clinical features were investigated using the Australian Scale for Asperger’s Syndrome (ASAS) (Attwood 2006) and the Michigan Autism Spectrum Questionnaire (MASQ) (Ghaziuddin and Welch 2013). Administering ASAS, social abilities resulted impaired in both groups, even if HFA appeared more compromised above all in avoiding social contact. AS can show more motivation to socialize and desire for friendship, even if this does not translate into superior ability to make friends, because they appeared awkward, eccentric, one-sided, and insensitive. Emotional dysregulation was another common feature in AS and HFA, resulting in need for excessive amount of reassurance and problems especially regard to anger and anxiety management. Among communication abilities, both the conditions presented some deficits in reciprocity of conversation, eye-contact, and modulation of voice tone; nevertheless, over-precise and pedantic speech (characterized by overly formal speech, similar to an in-depth monologue about a topic of special interest, verbose and tangential, lack of the normal prosody, enriched by a sophisticated vocabulary) was significantly more frequent in AS and literal interpretation of comments more common in HFA. The first finding (“over-precise or pedantic speech”) is unsurprising and represents one of the most typical clinical feature of AS, firstly described by Hans Asperger’s original work (1944, in which
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Comparison of High-Functioning Autism and Asperger Syndrome
AS were called “little professors” for their way of talking), then included in the following proposed diagnostic criteria sets (Gillberg and Gillberg 1989; Szatmari et al. 1989) and confirmed by subsequent studies (Woodbury-Smith and Volkmar 2009). The second finding (“literal interpretation of comments”) may be related to the HFA worse cognitive and comprehension abilities, which made them less able in recognizing the speaker’s communicative intention and in social understanding, unlike patients with AS, who adopted intellectual strategies of compensation. Delay in language development resulted significantly more common in HFA, even if also in AS, a mild language delay was reported. In the DSM-IVTR, the “absence of clinically significant general delay in language (e.g., single words used by age 2 years, communicative phrases used by age 3 years)” was a relevant criterion for AS diagnosis. In contrast with this theoretical construct, extensive literature data showed that language delay could not be considered as a diagnostic exclusion criterion for AS because subtle language problems are expected in AS and also because the recognition of a language delay is not always enough reliable, since it is often dependent on parents recall. Clumsiness, that has been originally considered a distinctive feature of AS appeared indifferently present in AS like in HFA, as well as sensory abnormalities (such as unusual fear/distress due to light touch on skin or scalp, and to unexpected noises), restrictive pattern of interests and behaviors, and difficulties in adaptation to changes. The clinical profile of AS is less likely to include motor mannerism and preoccupation with parts of objects (defined “low order repetitive restricted behaviors”) while more often presented circumscribed interests that consumes a great deal of their time amassing information and facts (defined “high order repetitive restricted behaviors”). About cognitive functioning, evaluated with Wechsler scales, AS showed Full Scale Intelligence Quotient mean values and sub-values significantly higher than HFA, without differences between verbal and performance subvalues. This overall better performance in AS represented a well-established mark of
differentiation between AS and HFA. Indeed, some authors identified a qualitatively distinct cognitive profile (characterized by a higher verbal IQ and a lower performance IQ and a reversed pattern in HFA), while others reported mixed cognitive patterns. The two conditions could likely be distinguished at a quantitative cognitive level, represented by FSIQ, rather than at a qualitative level, represented by specific cognitive profile. Nevertheless, as for intellectual disability, the presence of very different IQs could denote distinct levels of severity and clinical profile, in the same way a large discrepancy between AS and HFA intellectual scores could have a qualitative effect on general function. Learning disorders resulted significantly more frequent in HFA, with the exception of dysgraphia, that emerged frequently in both groups, according to the original description of Hans Asperger (Asperger 1944), who described in his patients low performances in handwriting, in terms of forming letters and using the paper space. AS may not show any conspicuous learning disturbances, but an inhomogeneous school profile, characterized by talents in some school subjects, such as precocious learning to read, numerical abilities, or memory, while poor application and performances in other school subjects, perceived as less interesting. The most prevalent comorbidity both in AS and HFA was attention deficit hyperactivity disorder (ADHD). An association between ASD and ADHD and other externalizing disorders have been reported and supported by clinical population research and twin studies, suggesting substantial genetic overlap (Taylor et al. 2015). Considering that comorbidity between these disorders was a relevant and frequent occurrence, differently from the previous edition, DSM-5 allowed the diagnosis of ADHD in the context of an ASD. Subjects with AS displayed more anxiety and depressive symptoms compared to HFA. This could be explained with AS higher cognitive and communicative levels, that made them more able in introspection, more “active but odd” (Ghaziuddin 2008) in relations attempts, more conscious of social difficulties and so more vulnerable to affective disorders.
Comparison of High-Functioning Autism and Asperger Syndrome
Overall, among AS and HFA, differences in neurodevelopmental trajectories and subsequent need for health service could be recognized. Clinical signs in AS might be subtle and camouflaged in preschoolers and could become clear at the age of 7 or 8 years, leading to a later diagnosis. Another focus of debate is the effect of DSM-5 criteria application on high-functioning ASD. Recent studies indicated a percentage between 50% and 75% of individuals maintaining diagnosis under DSM-5 criteria (Smith et al. 2015). A so wide concordance range could reflect diagnostic methodologies adopted in different studies: the gold standard diagnostic instruments for the assessment of autistic disorder could have low sensitivity when applied to high-functioning subjects. Clinical judgment of qualified professional experts and the use of specific rating scales, designed on the clinical characteristics of HFA and AS, may contribute to reduce the risk of underdiagnosing. According with this perspective, the study of de Giambattista et al. (2019) provided evidences that using brief and simple screening instruments such as ASAS and MASQ, it is more likely to recognize specific features of HFA functioning: indeed, 97.3% of the subjects, previously diagnosed according to DSM-IV-TR, met DSM-5 criteria for ASD and administrating MASQ different scores in HFA and AS were found. Moreover, when DSM-5 specifiers of severity levels were applied to the sample, HFA were more frequently classified in Level 2 (“requiring substantial support”) than AS, while AS were more frequently classified in Level 1 (“requiring support”).
Future Directions Considering available evidences, it is likely to assume that a complex aged-related different developmental profile between HFA and AS exists, characterized by quantitative and subtle qualitative differences such as language development and communication style, cognitive functioning, academic achievement, and comorbidities profile.
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Consistently with clinical findings, biological patterns of differentiation were found: a recent genome-wide association meta-analysis revealed evidences for a qualitatively different genetic architecture between subtypes of ASD (Grove et al. 2019), differences in neuroimaging field, in particular in gray matter distribution (Bi et al. 2018), and in EEG connectivity patterns (Luckhardt et al. 2014). While acknowledging the merit of the DSM-5 to have solved lots of previous classification discordances and “splitting” approaches (Volkmar and McPartland 2014), it is necessary to consider the risk that defining autism using the umbrella term ASD, could hide its evident heterogeneity (Lai et al. 2013) and particularly that in the severity specifier “Level 1” of the DSM-5, many phenotypic variances could be squeezed making this level very heterogeneous. It is likely that recognition of different “nuances” of the spectrum may contribute to tailor research, clinical, and assistential approaches. For example, since language development and academic abilities play a central role in global functioning during the first years of life, HFA require more support in terms of rehabilitative treatments and school educational needs than AS, while in middle childhood and adolescence, AS require more support in terms of psychotherapy, presenting more emotional and mood problems than HFA. Several expert researchers and specialists in the field have invited to reflect about the consequences of removing AS from the available classification, just when it was better understood by the society: the lack of a “label” in which subjects recognized themselves might cause confusion and damage the identity sense of the yet established “Aspie community” (“Aspie” is an affectionate term often used to describe a person with an AS diagnosis or profile). Future direction in the field of ASD categorization could move forward in the identification of subtypes within the autism spectrum: “subtype specifier” and “partial remission specifier” could promote progress in autism research and improve clinical practice.
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Comparison of High-Functioning Autism and Asperger Syndrome
DSM-5 allows to use specifiers of language and intellectual impairment that resulted enough restricted to distinguish clinical profile of HFA from classical, low functioning, autism. Nevertheless, a “subtype specifier” for AS could be useful to hypothesize putative developmental trajectories and direct specific paths of treatment. Moreover, recent theories of neurodiversity, accompanied by the preference of the more neutral term of autism spectrum condition (ASC) rather than ASD, consider AS as a natural variation rather than a syndrome. In fact, AS subjects could, in particular conditions (“Aspie-friendly” or in preschool age), temporarily lacked significant global impairment. Their phenotypes may be considered subclinical-AS-forms. Nevertheless, in most cases, these subjects need a sort of support (cognitive-behavioral treatment, parent/teacher psychoeducation, personalization of school program), also for preventive purpose only. As in ADHD coding of DSM-5, regarding level 1 ASD patients, it could be suggested the use of “subclinical” specifier, for those patients who express only subthreshold autistic-traits and “partial remission” specifier, for those patients who do not meet currently the full diagnostic criteria. In conclusion, even continuing to use the broad category of ASD, it could be meaningful to introduce, in future revisions of the manual, subtype specifiers that could capture the “nuances” of the spectrum. Future research about the efficacy of these prospective specifiers could be beneficial.
See Also ▶ Academic Skills ▶ Atypical Autism ▶ Depressive Disorder ▶ DSM-5 and Autism Spectrum Disorder ▶ High-Functioning Autism (HFA) ▶ Neurodiversity
References and Reading Asperger, H. (1944). Die autistisehen Psychopathen im Kindesalter. Archiv für Psychiatrie und Nervenkrankheiten, 117, 76–136.
Attwood, T. (2006). The complete guide to Asperger’s syndrome. Philadelphia: Jessica Kingsley Publishers. Bi, X., Wang, Y., Shu, Q., Sun, Q., & Xu, Q. (2018). Classification of autism spectrum disorder using random support vector machine cluster. Frontiers in Genetics, 9, 18. de Giambattista, C., Ventura, P., Trerotoli, P., Margari, M., Palumbi, R., & Margari, L. (2019). Subtyping the autism spectrum disorder: Comparison of children with high functioning autism and Asperger syndrome. Journal of Autism and Developmental Disorders, 49(1), 138–150. Ghaziuddin, M. (2008). Defining the behavioural phenotype of Asperger syndrome. Journal of Autism and Developmental Disorders, 38, 138–142. Ghaziuddin, M., & Welch, K. (2013). The Michigan autism spectrum questionnaire: A rating scale for high functioning autism spectrum disorders. Autism Research and Treatment, 2013, 708273. Gillberg, I., & Gillberg, C. (1989). Asperger syndrome: Some epidemiological considerations-a research note. Journal of Child Psychology and Psychiatry, 30, 631–638. Grove, J., et al. (2019). Identification of common genetic risk variants for autism spectrum disorder. Nature Genetics, 51(3), 431–444. Huerta, M., Bishop, S. L., Duncan, A., Hus, V., & Lord, C. (2012). Application of DSM-5 criteria for autism spectrum disorder to three samples of children with DSM-IV diagnoses of pervasive developmental disorders. The American Journal of Psychiatry, 169(10), 1056–1064. Kanner, L. (1943). Autistic disturbances of affective contact. The Nervous Child, 2, 217–250. Lai, M. C., Lombardo, M. V., Chakrabarti, B., & BaronCohen, S. (2013). Subgrouping the autism “spectrum”: Reflection on DSM-5. PLoS Biology. https://doi.org/ 10.1371/journal.pbio.1001544. Luckhardt, C., Jarczok, T. A., & Bender, S. (2014). Elucidating the neurophysiological underpinnings of autism spectrum disorder: New developments. Journal of Neural Transmission, 121(9), 1129–1144. Sharma, S., Woolfson, L. M., & Hunter, S. C. (2012). Confusion and inconsistency in diagnosis of Asperger syndrome: A review of studies from 1981 to 2010. Autism, 16(5), 465–486. Smith, I. C., Reichow, B., & Volkmar, F. R. (2015). The effects of DSM-5 criteria on numbers of individuals diagnosed with autism spectrum disorder: A systematic review. Journal of Autism and Developmental Disorders, 45(8), 2541–2552. Szatmari, P., Bartolucci, G., & Bremner, R. (1989). Asperger’s syndrome and autism: Comparison of early history and outcome. Developmental Medicine and Child Neurology, 31, 709–720. Taylor, M. J., Charman, T., & Ronald, A. (2015). Where are the strongest associations between autistic traits and traits of ADHD? Evidence from a community-based twin study. European Child and Adolescent Psychiatry, 24(9), 1129–1138.
COMPASS for Hope Training Program Tsai, L. Y., & Ghaziuddin, M. (2014). DSM-5 ASD moves forward into the past. Journal of Autism and Developmental Disorders, 44(2), 321–330. Volkmar, F. R., & McPartland, J. C. (2014). From Kanner to DSM-5: Autism as an evolving diagnostic concept. Annual Review of Clinical Psychology, 10, 193–212. Wing, L. (1981). Asperger’s syndrome: A clinical account. Psychological Medicine, 11, 115–129. Woodbury-Smith, M. R., & Volkmar, F. R. (2009). Asperger syndrome. European Child and Adolescent Psychiatry, 18(1), 2–11.
COMPASS for Hope Training Program Grace Kuravackel1 and Lisa Ruble2 1 Pediatrics, University of Louisville, Louisville, KY, USA 2 Educational School and Counseling Psychology, University of Kentucky, Lexington, KY, USA
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and Sameroff 1989; Ruble and Dalrymple 1996) that considers the complex interplay between the individual, family, school, social, community, and economic resources – the critical proximal (i.e., closely connected to the individual) and distal influences and interactions between individuals with ASD and their environments. We first tested COMPASS for preschool and elementary age students as a consulting intervention to improve IEP outcomes with a large effect size (1.1) compared to IEP outcomes of students receiving services as usual (Ruble et al. 2010). In a second RCT, we replicated our findings and also demonstrated the efficacy of web-based coaching in COMPASS (Ruble et al. 2013). The third RCT, COMPASS for Transition (COMPASS-T), was tested with high school students in their final year of school with a focus on improving IEP and postsecondary outcomes (Ruble et al. 2018). A very large effect size (2.1) was observed for IEP goal attainment outcomes based on an independent observer.
Definition COMPASS for Hope (C-HOPE) is a manualized parent-mediated intervention for children with challenging behavior and autism spectrum disorder (ASD). C-HOPE is based on an existing clinical decision-making framework known as the Collaborative Model for Promoting Competence and Success (COMPASS; Ruble et al. 2012). C-HOPE has effectiveness for telehealth or in-person delivery for decreasing behavior problems in children with autism spectrum disorder (ASD), increasing parent competency, and decreasing parent stress.
Historical Background C-HOPE comes from COMPASS, a validated intervention that has been tested in randomized controlled trials (RCTs) and 20 years of work (Ruble and Dalrymple 1996) in public schools. COMPASS stands for Collaborative Model for Promoting Competence and Success and is an alternative framework for conceptualizing, assessing, and intervening to improve outcomes in autism based on a transactional approach (Fiese
Rationale or Underlying Theory ASD is a neurodevelopmental disorder that presents many challenges for parents as well as teachers, therapists, and other professionals, often in part because of challenging problem behavior (Crone and Mehta 2016). These problem behaviors are secondary to, or exacerbated by, the core symptoms of ASD [Centers for Disease Control and Prevention (CDC) 2012]. The presence of problem behaviors impacts both children and their families. For children, they experience fewer community outings, less positive interaction with peers, and less access to intervention and education (Anderson et al. 1992; Matson and Nebel-Schwalm 2007). For parents, they report a greater sense of helplessness compared to parents of neurotypical children (Pisula and Kossakowska 2010). A meta-analysis conducted by Hayes and Watson (2013) suggests that the overall level of problem behaviors accounts for the most variance in explaining parent stress when compared to other child factors, such as IQ, autism severity, and adaptive skills, a finding confirmed by others (Krakovich et al. 2016).
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Thus, we developed C-HOPE from the existing COMPASS framework to target three outcomes: decreased parent stress, decreased child problem behavior, and increased parent competency. COMPASS is unique in that it focuses on ASD-specific goal areas of social communication skills meaningful for quality of life (QOL). COMPASS is based on the developmental theory that competency is the result of reciprocal and dynamic interactions between individuals and their environments (i.e., transactional) (Ruble and Dalrymple 1996). Thus, if we can carefully examine and identify the contribution that the environment makes toward reducing individual risk factors and enhancing protective factors, we can influence the development of important QOL skills. Competence looks different across the lifespan and is person-specific. Thus C-HOPE behavior plans are sensitive to the specialized learning needs of individuals with ASD and the required instruction across social, communication, and learning skills competencies to continue to be refined and utilized throughout development into adulthood. Further, the extreme heterogeneity in ASD requires a framework, like COMPASS, that can be helpful independent of the language, cognitive, or social abilities of individuals with ASD. Another unique dimension of C-HOPE, that separates it from other parenting programs, is the emphasis placed on a collaborative, strengthsbased approach in working with families. C-HOPE is delivered in an individual and a group format. The group format is intended to be therapeutic as well as educational and draws upon the concept of universality (“I am not the only one struggling”; Yalom 1995) and offers the opportunity for families to also provide emotional support to others and to feel less isolation. C-HOPE requires that the facilitator(s) draw upon basic counselling skills that promote active listening and empathy. Facilitator(s) also have to be aware of group process for the purpose of promoting cohesion among members and to ensure that the goals and needs of the group are met. The importance of a strong therapeutic alliance is foundational to a good helping relationship (Crits-Christoph et al. 2009; Zuroff et al. 2010).
COMPASS for Hope Training Program
Goals and Objectives C-HOPE is designed to promote positive parent and child outcomes, including decreased child problem behavior, decreased parent stress, and increased parent competency using an evidencebased practices in psychology (EBPP) framework such as COMPASS (McGrew et al. 2016). Parents are the primary intervention targets in C-HOPE. COMPASS emphasizes clinical decision-making based on the three elements of an EBPP-informed approach of child preferences and strengths, parent and family resources, and evidence-based practices. Through positive parent-therapist collaboration, utilizing the COMPASS framework, direct training and support are provided in order to facilitate parent-implemented behavior intervention plans.
Treatment Participants C-HOPE was developed for parents of young children with ASD and/or intellectual disability or other comorbid diagnoses. C-HOPE is most suited for parents of children between the ages of 3 and 12 years who also experienced challenging behavior. C-HOPE was developed for in-person or telehealth delivery. Thus, parents who are unable to attend in-person sessions may benefit, and this can be a tremendous benefit for families in areas where local resources are scarce.
Treatment Procedures The C-HOPE curriculum includes activities that support parent-to-parent interaction as well as parent knowledge and skill and include four individual sessions (1 h) and four group sessions (2 h). Prior to the start of the treatment sessions, parents complete a COMPASS profile (see Ruble et al. 2012). The profile guides the discussion for the first individual session and assists with clarifying the problem behavior and possible underlying communicative intents behind the behavior. Once the purposes of behavior are understood, goals are developed that target replacement skills
COMPASS for Hope Training Program
to increase positive behaviors and decrease problem behaviors. Individual sessions primarily focus on developing, implementing, and fine-tuning the individualized behavior plan that describes the identified problem behavior(s) and replacement skills. After developing a common understanding and language (e.g., joint attention, antecedents, consequences) to describe child skills, parenting practices such as encouraging positive child behaviors and how to use supports proactively to decrease challenging behaviors are written into the behavior plan. Emphasis is placed on using positive behavior supports (i.e., environmental manipulations) that are designed to promote prosocial skills effective in reducing disruptive behaviors for children with ASD (Buschbacher and Fox 2003; Iovannone et al. 2003; National Research Council 2001; Turnbull et al. 2002). Behavior plans included understanding the antecedents or causes of behavior, making the behavior ineffective, teaching replacement skills that result in desired outcomes for the child, and rewarding positive skills. The group sessions provide basic information on ASD, including discussion of the learning differences specific to ASD, as well as how these learning differences impact behavior, socialization, and communication of their child. Typically individual sessions follow group sessions so that information learned in group session is discussed and considered during the individual session so that behavior plans are fine-tuned as new information is learned. Psychoeducation also includes information on evidence-based approaches for problem behaviors such as functional behavior assessment including antecedent manipulation, changes in instructional context, differential reinforcement, and self-management strategies (Braithwaite and Richdale 2000; Buggey 2005; Frea et al. 2001; Keeling et al. 2003; Schilling and Schwartz 2004). In addition, group sessions target parent stress and coping skills. A variety of coping strategies for parental stress are presented, and parents are asked to identify what strategies they find helpful and what new strategies they would consider using in the future. Coping strategies include
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general stress reduction techniques, mindfulnessbased interventions, and relaxation strategies that have been shown to have long-term positive effects on stress levels and psychological wellbeing of parents of children with ASD (Cachia et al. 2016). Order of sessions and content of each individual and group session can be retrieved from Kuravackel et al. (2018). The C-HOPE manual outlining all session details can be requested from the authors.
Efficacy Information C-HOPE has been tested in two studies. The first was a randomized wait list design that tested telehealth vs face-to-face delivery of C-HOPE. The second was a pre-posttest design that tested an online-only condition where parents met virtually rather than in person (Kuravackel et al. 2018; Rodgers 2018). Both studies examined outcomes of C-HOPE on variables of child problem behavior, parent competency, parent stress, group alliance, and parent satisfaction. Results from the first study indicated significant between-group differences between the treatment and control. Further, pre-posttreatment gains in child problem behavior scores, parent competency, and parent stress were observed for the C-HOPE group. Telehealth modality was equally effective as face-to-face intervention, and no differences were detected with regard to group alliance or parent satisfaction in either modality. Overall parent satisfaction was high across both telehealth and face-to-face modalities (Kuravackel et al. 2018). For the second study when C-HOPE was tested with the group session meeting virtually and the individual sessions conducted using telephone, significant improvements were noted in parent stress and child behaviors and no changes for parent competency (Rodgers 2018).
Outcome Measurement For primary outcomes of reduced parent stress, increased parent competency, and reduced child
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problem behavior, the following were used, respectively: the Parental Stress Index-Fourth Edition Short Form (PSI-4-SF; Abidin 1995) to measure parent stress, the Eyberg Child Behavior Inventory (ECBI; Eyberg and Pincus 1999) to measure child problem behavior, and the Being a Parent Scale (BPS; Johnston and Mash 1989) to measure parent competency. For the secondary outcomes of parent satisfaction and group alliance, the following measures were used, respectively: Consultation Satisfaction Questionnaire (CSQ; Ruble et al. 2010) and the Group Session Rating Scale (GSRS; Duncan and Miller 2007).
Qualifications of Treatment Providers C-HOPE therapists should have a background in a field that provides mental health intervention services (e.g., clinical/counselling/school psychology, rehabilitation psychology, social work). Therapists who participated in the initial trial of C-HOPE included those with the following backgrounds: doctoral students in school psychology and licensed psychologists. C-HOPE therapists should have prior experience working with parents and clients with ASD, implementing behavioral interventions, and familiarity with evidence-based interventions recommended for ASD. It is recommended that therapists new to C-HOPE receive supervision of their first C-HOPE intervention. A manual has been developed for further reading and intervention fidelity and is available from the authors.
See Also ▶ Behavior Analysis ▶ Developmentally Appropriate Practice (DAP) ▶ Irritability in Autism ▶ Parental Response to Diagnosis ▶ Positive Behavioral Interventions and Supports (PBIS) ▶ Progressive Applied Behavior Analysis
COMPASS for Hope Training Program
References and Reading Abidin, R. (1995). Parenting Stress Index (3rd ed.). Lutz: Psychological Assessment Resources. Anderson, D. J., Lakin, K. C., Hill, B. K., & Chen, T. (1992). Social integration of older persons with mental retardation in residential facilities. American Journal on Mental Retardation, 96(5), 488–501. Multiple imputation with Mplus. Retrieved from http://www. statmodel.com/download/Imputations7.pdf Braithwaite, K., & Richdale, A. (2000). Functional communication training to replace challenging behaviors across two behavioral outcomes. Behavioral Interventions, 15(1), 21–36. https://doi.org/10.1002/ (SICI)1099-078X(200001/03)15:13.0.CO;2-#. Buggey, T. (2005). Video self-modeling applications with students with autism spectrum disorder in a small private school setting. Focus on Autism and Other Developmental Disabilities, 20(1), 52–63. https://doi.org/10.1177/10883576050200010501. Buschbacher, P. W., & Fox, L. (2003). Understanding and intervening with the challenging behavior of young children with autism spectrum disorder. Language, Speech & Hearing Services in Schools, 34(3), 217–227. https:// doi.org/10.1177/10883576050200010501. Cachia, R. L., Anderson, A., & Moore, D. W. (2016). Mindfulness, stress and well-being in parents of children with autism spectrum disorder: A systematic review. Journal of Child and Family Studies, 25(1), 1–14. https://doi.org/10.1007/s10826-015-0193-8. Centers for Disease Control and Prevention. (2012). Autism spectrum disorders(s). Retrieved from http:// www.cdc.gov/ncbddd/autism/signs.html Crits-Christoph, P., Gallop, R., Temes, C. M., Woody, G., Ball, S. A., Martino, S., & Carroll, K. M. (2009). The alliance in motivational enhancement therapy and counseling as usual for substance use problems. Journal of Consulting and Clinical Psychology, 77(6), 1125–1135. Crone, R. M., & Mehta, S. S. (2016). Parent Training on Generalized Use of Behavior Analytic Strategies for Decreasing the Problem Behavior of Children with Autism Spectrum Disorder: A Data-Based Case Study. Education and Treatment of Children, 39(1), 64–94. Retrieved from https://search-ebscohost-com. echo.louisville.edu/login.aspx?direct¼true&db¼eric& AN¼EJ1092632&site¼ehost-live. Duncan, B. L., & Miller, S. D. (2007). The Group Session Rating Scale. Jensen Beach: Author. Eyberg, S. M., & Pincus, D. (1999). Eyberg child behavior inventory and Sutter-Eyberg student behavior inventory-revised: Professional manual. Odessa: Psychological Assessment Resources. Fiese, B. H., & Sameroff, A. J. (1989). Family context in pediatric psychology: A transactional perspective. Journal of Pediatric Psychology, 14(2), 293–314. Frea, W., Arnold, C., & Vittimberga, G. (2001). A demonstration of the effects of augmentative
Competitive Employment communication on the extreme aggressive behavior of a child with autism within an integrated preschool setting. Journal of Positive Behavior Interventions, 3(4), 194–198. Hayes, S. A., & Watson, S. L. (2013). The impact of parenting stress: A meta-analysis of studies comparing the experience of parenting stress in parents of children with and without autism spectrum disorder. Journal of Autism and Developmental Disorders, 43(3), 629–642. https://doi.org/10.1007/s10803-012-1604-y. Iovannone, R., Dunlap, G., Huber, H., & Kinkaid, D. (2003). Effective educational practices for students with autism spectrum disorders. Focus on Autism and other Developmental Disabilities, 18, 150–165. Johnston, C., & Mash, E. J. (1989). A measure of parenting satisfaction and efficacy. Journal of Clinical Child Psychology, 18(2), 167–175. Keeling, K., Smith Myles, B., Gagnon, E., & Simpson, R. (2003). Using the power card strategy to teach sportsmanship skills to a child with autism. Focus on Autism and Other Developmental Disabilities, 18(2), 103–109. Krakovich, T. M., McGrew, J. H., Yu, Y., & Ruble, L. A. (2016). Stress in parents of children with autism spectrum disorder: An exploration of demands and resources. Journal of Autism and Developmental Disorders, 46(6), 2042–2053. https://doi.org/10.1007/ s10803-016-2728-2. Kuravackel, G. M., Ruble, L. A., Reese, R. J., Ables, A. P., Rodgers, A. D., & Toland, M. D. (2018). COMPASS for Hope: Evaluating the effectiveness of a parent training and support program for children with ASD. Journal of Autism & Developmental Disorders, 48(2), 404–416. https://doi.org/10.1007/s10803-017-3333-8. Matson, J. L., & Nebel-Schwalm, M. S. (2007). Comorbid psychopathology with autism spectrum disorder in children: An overview. Research in Developmental Disabilities: A Multidisciplinary Journal, 28(4), 341–352. McGrew, J. H., Ruble, L. A., & Smith, I. M. (2016). Autism spectrum disorder and evidence-based practice in psychology. Clinical Psychology: Science and Practice, 23(3), 239–255. National Research Council (NRC). (2001). Educating students with autism. Washington, DC: National Academy Press. Pisula, E., & Kossakowska, Z. (2010). Sense of coherence and coping with stress among mothers and fathers of children with autism. Journal of Autism and Developmental Disorders, 40(12), 1485–1494. Rodgers, A. D. (2018). Examining an asynchronous group discussion board adaptation of a parent-mediated behavior intervention for children with autism spectrum disorders. Retrieved from https://searchebscohost-com.echo.louisville.edu/login.aspx? direct¼true&db¼ddu&AN¼DB9B3D37FB8BF79D& site¼ehost-live Ruble, L. A., & Dalrymple, N. J. (1996). An alternative view of outcome in autism. Focus on Autism and Other Developmental Disabilities, 11(1), 3–14.
1145 Ruble, L. A., Dalrymple, N. J., & McGrew, J. H. (2010). The effects of consultation on individualized education program outcomes for young children with autism: The collaborative model for promoting competence and success. Journal of Early Intervention, 32(4), 286–301. Ruble, L. A., Dalrymple, N. J., & McGrew, J. H. (2012). Collaborative model for promoting competence and success for students with ASD. New York: Springer Science + Business Media. Ruble, L. A., McGrew, J. H., Toland, M. D., Dalrymple, N. J., & Jung, L. A. (2013). A randomized controlled trial of COMPASS webbased and face-to-face teacher coaching in autism. Journal of Consulting and Clinical Psychology, 81(3), 566–572. https://doi.org/10.1037/a0032003. Ruble, L. A., McGrew, J. H., Toland, M., Dalrymple, N., Adams, M., & Snell-Rood, C. (2018). Randomized control trial of COMPASS for improving transition outcomes of students with autism spectrum disorder. Journal of Autism and Developmental Disorders, 48(10), 3586–3595. Schilling, D., & Schwartz, I. (2004). Alternative seating for young children with autism spectrum disorder: Effects on classroom behavior. Journal of Autism and Developmental Disorders, 34(4), 423–432. Turnbull, H. R., III, Wilcox, B. L., & Stowe, M. J. (2002). A brief overview of special education law with focus on autism. Journal of Autism & Developmental Disorders, 32(5), 479. Yalom, I. D. (1995). The Theory and Practice of Group Psychotherapy (4th ed.). New York: Basic Books. Yalom, I. D., & Leszcz, M. (2005). The theory and practice of group psychotherapy (5th ed.). New York: Basic Books. Zuroff, D. C., Kelly, A. C., Leybman, M. J., Blatt, S. J., & Wampold, B. E. (2010). Between-therapist and withintherapist differences in the quality of the therapeutic relationship: Effects on maladjustment and self-critical perfectionism. Journal of Clinical Psychology, 66(7), 681–697.
Competitive Employment David J. Krainski Vocational Independence Program, New York Institute of Technology, Central Islip, NY, USA
Definition Competitive employment is work that is performed on either a full- or part-time basis in which individuals are compensated for their work.
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The compensation paid must be at or above the set minimum wage, but not less than the wages paid to individuals who are not disabled and performing work that is the same or similar. The individual must be employed in an integrated setting in which the individual has the ability to interact with individuals who are not disabled (NYSED.gov, 2011).
Historical Background Historically, competitive employment was not considered a likely outcome for individuals with autism spectrum disorders (Wehman et al. 2003). Typically, individuals with disabilities were placed in segregated or sheltered vocational settings in which they worked among other individuals with disabilities only to be supervised by non-disabled individuals (Lutfiyya et al. 1988). These sheltered settings were considered to be transitional with the hopes that most individuals placed in these environments would transition into competitive employment. However, Taylor in 2002 noted that only 3.5% of those placed in sheltered work environments move on to competitive employment in any single year. In the opening of the Americans with Disabilities Act (ADA) in 1990, Congress notes that society has had a tendency to segregate individuals in important areas such as employment and therefore states that “no covered entity shall discriminate against a qualified individual on the basis of disability in regard to job application procedures, the hiring, advancement, or discharge of employees, employee compensation, job training, and other terms, conditions, and privileges of employment” (ada.gov, retrieved 2011). The passage of the ADA was to bring about a greater integration of individual with disabilities in the workplace. According to Blanck (2008), discrimination of individuals with disabilities has been reduced and more opportunities have opened.
Competitive Employment
Survey of Americans with Disabilities (2010) reports that 21% of individuals with disabilities aged 18–64 had been employed with 59% of the general population maintaining employment. Data specific to individuals with autism spectrum disorders (ASD) are harder to find, but the information that is available suggests that those with ASD are less likely to be employed compared to other disability groups. In 2009, the National Longitudinal Transition Study 2 had found the following as it relates to individuals with autism spectrum disorder and employment (www.nlts2.org): • 32.5% of young adults with autism spectrum disorders currently worked for pay versus an average of 59% of all respondents. • 47.7% of youth with autism spectrum disorders had worked for pay in the past 2 years versus an average of 78.4% of all participants. • 29% of young adults with autism spectrum disorders were looking for work if currently unemployed compared to 47.7% of all participants. The study does not specify if those surveyed are in a competitive employment situation or not. However, it is important to note that individuals that are in competitive employment work a greater number of hours per week and earn more per week than individuals in noncompetitive employment (Schaller and Yank 2005). Even though there is great benefit to the individual to be working in a competitive employment situation, only about 15% of individuals were doing so (Wehman et al. 2003). The rest were in a number of day habilitation services that were noncompetitive, and oftentimes, these individuals were not integrated with the community. Current research indicates that individuals with ASD can work in a variety of community-based businesses. However, majority of individuals on the spectrum currently remain unemployed (Dew and Alan 2007).
Current Knowledge
See Also
Available statistics on employment by individuals with disabilities vary. However, the National
▶ Sheltered employment ▶ Supported employment
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References and Reading Americans with disabilities act of 1990, as amended. (2008). Retrieved 27 May 2011 from http://www.ada. gov/pubs/adastatute08.htm Blanck, P. (2008). “The right to live in the world”: Disability yesterday, today and tomorrow. Texas Journal on Civil Liberties and Civil Rights, 13, 367–401. Dew, D. W., & Alan, G. M. (2007). Rehabilitation of individuals with autism spectrum disorders (Institute on Rehabilitation Issues Monograph no. 32). Washington, DC: The George Washington University, Center for Rehabilitation Counseling Research and Education. Gottlieb, A., Myhill, W. N., & Blanck, P. (2011). Employment of people with disabilities. In J. H. Stone & M. Blouin (Eds.), International encyclopedia of rehabilitation. Retrieved 27 May 2011 http://cirrie.buffalo. edu/encyclopedia/en/article/123/. Lutfiyya, Z. M., Rogan, P., & Shoultz, B. (1988). Supported employment: a conceptual overview. Syracuse: Center on Human Policy, Syracuse University. Retrieved 27 May 2011 from http://thechp.syr.edu/ workovw.htm. New York State Education Department, ACCES-VR. (2003). Employment outcome policy (010.00P: Employment Outcome Procedure). Albany. Retrieved 27 May 2011 from http://www.acces.nysed.gov/vr/ current_provider_information/vocational_rehabilita tion/policies_procedures/0010_employment_outcome/ policy.htm#Definitions Schaller, J., & Yank, N. K. (2005). Competitive employment for people with autism: correlates of successful closure in competitive and supported employment. Rehabilitation Counseling Bulletin, 49(1), 4, 13. Taylor, S. J. (2002, September 2). Disabled workers deserve real choices, real jobs. Retrieved 27 May 2011 from http://www.accessiblesociety.org/topics/ economics-employment/shelteredwksps.html Wehman, P., Revell, W. G., & Brooke, V. (2003). Competitive employment: has it become the: “first choice” yet? Journal of Disability Policy Studies, 14(3), 163–173.
Complete Agenesis ▶ Agenesis of Corpus Callosum
Complete Exhaustion ▶ Posttraumatic Stress Disorder
Components of Advance Theory of Mind in Autism Spectrum Disorder Tereza-Maria Booules-Katri and Jordi E. Obiols Department of Clinical and Health Psychology, Psychopathology and Neuropsychology Research Unit, Universitat Autonoma de Barcelona, Barcelona, Spain
Short Description or Definition In the study of social cognition, the routes to understand others are subserved by separable, independent brain networks. Theory of mind (ToM) otherwise been referred to as “mentalizing” or “mind reading” constitutes one of the neuropsychological mechanisms, providing the understanding of someone else’s thoughts or intentions. However, within this concept as a whole, have been identified distinct components of ToM accounting for the appreciation of the socio-affective and socio-cognitive information (Shamay-Tsoory et al. 2002; Hynes et al. 2006). Cognitive ToM reflects the understanding of someone’s beliefs, while affective ToM requires the empathic appreciation of someone’s emotional state. Several mechanisms of atypical ToM are associated with autism spectrum disorders (ASD) (Cantio et al. 2016; Happé et al. 2017) converting its measurement into a clinically relevant feature that may explain and mitigate social difficulties in ASD population. While subjects with autism present difficulties in standard ToM understanding, the majority of individuals with HFA/AS, whose intelligence ranges from normal to near-normal level and does not present language delay, are characterized by higher-order mind-reading abilities, often referred to as “advanced theory of mind.” Consequently, the conceptualization of the term “components of advanced theory of mind in autism spectrum disorder” refers to the assessment of subtle mentalizing difficulties within the autistic spectrum generated by distinct subdomains.
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Historical Background Autism Origins as a Distinct Diagnostic Entity Only from 1980 and on, autism appeared for the first time as an independent classification in the DSM-3, published in 1980. Until that moment the concept of autism was encompassed in the core symptoms of schizophrenia. At this point, the perception of the condition changed from the description of a symptom to the description of a behavioral, developmental phenotype encompassing a range of symptoms in the social, communicative domain, as well as symptoms of rigidity and repetitiveness (Rapin 1997). Clinical features, such as the presence of autistic traits in patients with schizophrenia (Kastner et al. 2015) and the development of schizophrenia in a significant proportion of children diagnosed with autism spectrum disorder (Larson et al. 2017), supported the connection between the two spectra at a clinical level in subsequent investigations, leading to numerous comparative studies (e.g., Ozguven et al. 2010; Lugnegard et al. 2013; Tin et al. 2017). However, beyond their similarities in certain features (Tin et al. 2017) to date, there is a clear-cut distinction in respect to neurocognitive profiles and separate diagnosis. Currently, the umbrella term of ASD is used to describe all people diagnosed within the spectrum, including autism, pervasive developmental disorder not otherwise specified (PDD-NOS), and Asperger syndrome (AS) (APA 2013). Since the disturbances of social cognition are among the central characteristics of ASD, it has been considered as a disorder of the social brain, and it is nowadays categorized in the DSM-5/SCD as a communication disorder in the broader domain of neurodevelopmental disorders. ASD and ToM Connection As commonly described, the term theory of mind (ToM) refers to the ability of someone to understand another’s thoughts, beliefs, and other internal states such as desires, emotions, or intentions (Frith and Frith 2006). The first application of the term in ASD research was in an experiment which used false belief paradigms to explore ToM in
children with autism (Baron-Cohen et al. 1985). To investigate this, they developed the Sally-Anne false belief task (Baron-Cohen et al. 1985; Wimmer and Perner 1983), in which Sally places an object in a box and leaves the scene. After this, Anne relocates the ball and Sally returns. Participants with well-developed ToM capacities succeed in predicting Sally’s behavior based on her false belief about the ball’s location. Since then a growing body of evidence on social cognition deficits has provided data consistent with the hypothesis that social difficulties in ASD are explained by a decreased ability to use mental state concepts to interpret and predict one’s own and other people’s behavior (Baron-Cohen et al. 2000; Tager-Flusberg 2007). This theoretical connection with ToM on social abilities remains highly correlated among recent literature. More specifically the two cognitive accounts of ASD that have received the most attention are ToM and executive function. Likewise, the degree of ToM difficulties may vary widely depending on intelligence quotient (IQ) and language development defining particular ASD diagnosis, being more severe in autism than in Asperger syndrome (AS) or in highfunctioning autism (HFA) (Baron-Cohen et al. 2000). Additionally, children diagnosed with ASD generally develop ToM later and continue to fail on these tests until late childhood and even adolescence (Happé 1995); therefore, age might be considered a positive correlate in healthy participants, but not in people diagnosed with ASD.
Current Knowledge Advanced ToM Traditionally, ToM is assessed throughout false belief understanding. These tasks relate to the understanding that different people can have different thoughts about the same situation. “Firstorder” false beliefs indicate what someone thinks about real events, while more advanced “secondorder” false beliefs refer to what someone thinks about other people’s thoughts. Thus, within the broad construct of ToM, advanced measures were created to examine adults’ ability to reason about
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mental states in more complex contexts including higher-order false belief understanding, emotion recognition, and social understanding (Livingston et al. 2018). Impairments in advanced ToM tasks include the selection of subtle nonverbal social cues, interpretation of social stories, construction of social inferences, and comprehension of implied or nonliteral meanings in the language (Baron-Cohen et al. 2001; Mathersul et al. 2013). Overall, conducting traditional or advanced ToM research in ASD individuals depends on the clinical expression in which impairments vary according to their age and the severity of their social and communicative difficulties. In consideration of the diverse structure and performance, ToM may have overdevelopment, and measurements become more task-specific with age. While typically developed children between the ages of 3 and 4 years can solve verbally presented first-order false belief tasks (Wellman et al. 2001; Wimmer and Perner 1983), most children with ASD do not pass these tasks until the age of 11 years (Happé 1995). Then dealing with second-order false belief tasks can be solved by children around the age of 6 and 7 years, whereas advanced ToM abilities are required to process complex social situations in which the wrong behavior has to be represented by a cognitive and affective component, first appearing in typically developed children between the ages of 9 and 11 (Baron-Cohen et al. 1997). Preschoolers, middle childhood, and adults should, therefore, be examined with age-appropriate adaptations. Distinct Theoretical Components of ToM Since ToM origins as a concept of investigation, its study expanded to the identification of different aspects, grounded in a distinct neurobiological substrate, brain regions, and differing interacting functions. Understanding others’ internal states may be a multidimensional process that interacts with other abilities, a process which may not occur in a single conceptual framework (Warnell and Redcay 2019). Tager-Flusberg and Sullivan (2000) had put forward “the componential view of theory of mind” which suggests the study of ToM as two differentiated processes: the social
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cognitive, connected with what has traditionally been called theory of mind and is based on the conceptual understanding of the mind as a representational system, and then the social-perceptual or empathetic component, associated with the affective system and is based on the rapid “online” judgment of another individual’s mental state from her/his facial expressions, gestures, tone of voice, movements, and actions. Considerable research confirm over the years that affective and cognitive aspects of ToM are indeed somewhat separable involving overlapping but dissociable brain systems by highlighting the mediating role of ventromedial (VM) frontal lobes in ToM research (ShamayTsoory and Aharon-Peretz 2007; Hynes et al. 2006; Duval et al. 2011; Schaafsma et al. 2015). Depending on which ToM task is used and which aspect of ToM is focused on, research on ToM derived from autism has reported somewhat different results. The bulk of studies consistently support impairments in the general ToM performance of ASD individuals even in highfunctioning adults with ASD (Cantio et al. 2016; Happé et al. 2017; Wellman et al. 2001; Ponnet et al. 2004). However, not all research argues for a ToM deficit as the key mechanism underlying the social interaction impairments seen in ASD (Stone and Gerrans 2006; Van de Cruys et al. 2014). Recent evidence from behavioral data suggested that cognitive and affective ToM is differentially impaired in individuals with ASD. Some ASD literature supports findings for greater impairment in the socio-cognitive component of ToM (Rogers et al. 2007; Dziobek et al. 2008; Mazza et al. 2014) while others for the socioaffective ToM (Lockwood et al. 2013; ShamayTsoory et al. 2002). Understanding the way that cognitive and affective aspects of ToM relate to each other is complicated also due to the inconsistent and under-specified uses of relevant terminology. Some of these problems can be reconciled if ToM began to conceptualize as a multifactorial construct, comprised of multiple sub-processes. Studying the effects of localized brain lesions constitute a set of trustworthy evidence. Comparative studies of the effects of localized brain
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lesions suggest that there are some commonalities in the neural mechanisms of emotional processing and ToM. (Mitchell and Phillips 2015). In the past, decoding has often been conflated with emotional processing and reasoning with ToM. Nevertheless, this may not always be appropriate, since basic decoding mechanisms can specialize for either emotional information or intentional information, while other tasks require the perception and combination of both emotional and intentional information (e.g., when trying to decode sarcasm or deception; Adams Jr. and Kleck 2005). In this context, various theoretical models support that emotional perception always precedes ToM in the processing chain, while ToM cannot proceed without input from emotional perception (Baron-Cohen 2005; Mitchell 2006; Ochsner 2008). The fact that emotional process occurs at an earlier temporal stage compared to ToM (Corrigan 1997; Mitchell 2006) further supports the assumption that emotional processing and ToM are indeed related. Along with the review conducted by Mitchell and Phillips (2015) collecting data from several lesion studies, it is suggested a possible overlap between emotional processing and ToM located in the medial PFC, probably in the right hemisphere. Moreover, neuroimaging data focus on studies that have compared the perception of affective cues against the inference of thoughts, intentions, and beliefs, indicating overlapping function between the substrates of emotional processing and ToM in dorsal PFC regions and at the temporal pole. In other researches have directly been compared the neuroanatomy of affective vs. cognitive ToM by manipulating experimental stimuli (e.g., “what are the characters thinking” vs. “what are they feeling”). Hynes et al. (2006) found a differential response in ventral prefrontal regions of the brain with affective ToM inducing greater activation of ventromedial PFC compared to non-affective ToM. A greater association between dorsolateral PFC activity and cognitive ToM was further supported by transcranial magnetic stimulation (TMS) data where suppression of right dorsolateral PFC activity has been shown to impair only cognitive ToM (Kalbe et al. 2010). In summary affective ToM (which bears some
resemblance to EP) and cognitive ToM partly share neural correlates but can also be differentiated. Therefore, overall research up to date theorizes that EP and ToM share some common components yet without excluding functional independence between the two processes in specific areas. How Is Cognitive and Affective ToM Being Measured Up to Now? In the last 15 years, several tests have been developed to tap higher-order mind-reading abilities, which are often referred to as “advanced theory of mind.” Numerous instruments have been proposed to asses ToM, or some aspect(s) of ToM, in individuals with ASD, although there is not yet a universally accepted operationalization of ToM. The more widely used instruments of this kind are “The Strange Stories Test” (Happé 1994), which contains written stories/scenarios with persuasion, white lies, double buffs, and deception; the “Faux Pas Test” (Stone et al. 1998), which involves the understanding of social situations where someone says something without considering the listener’s point of view, with negative consequences that the speaker never intended; and the “Reading the Mind in the Eyes Test” (RMET; Baron-Cohen et al. 2001), which involves recognizing a person’s mental state from the expression in their eyes. A large number of studies continue to consider ToM a unitary construct, employing only one or two measures in order to capture ToM. Across a variety of specific tasks and modalities which have been argued to be important components of ToM (e.g., verbal versus nonverbal, deliberate versus automatic, implicit versus explicit), the distinction between affective and cognitive ToM captures the use of different cognitive abilities to solve these tasks. Both aspects of mentalizing considered critical predictors of well-being. Affective ToM is most often assessed by presenting either pictorial stimuli depicting complex affective states or verbal narratives that describe a protagonist’s emotion. The Reading the Mind in the Eyes Test reflects this former approach and is one of the most widely used and well-validated measures of this construct. Moreover, in the clinical assessment, it is also used
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the Awareness of Social-Inference Test-Revised (TASIT; McDonald et al. 2006) a dynamic audiovisual-based assessment of social cognition. TASIT comprised of a series of 15–60 sec video vignettes and requires participants to implicitly encode and integrate contextual information in order to recognize emotions and determine the meaning and intention of speakers’ dialogue, emotional expression, and other paralinguistic information. In respect to cognitive ToM, one of the bestvalidated methods of assessing this construct is via a comparison of true and false belief tasks. As mentioned before the Strange Stories and the Faux Pas Test tasks tap those domains and are well-validated measures that appear to have good sensitivity to social cognitive failures in clinical groups. Recent research would ideally study these specific characteristics combining both cognitive and perceptual processes within the same test. In the Cambridge Mindreading (CAM) Face-Voice Battery (Golan et al. 2006) is required recognition of complex emotional mental states from dynamic faces, based on voice prosody and content of the spoken sentences. Recognition of complex emotional states is assumed to involve higher-level integration and mindreading but also low-level perceptual processes (Mitchell and Phillips 2015). Moreover, this test proves good discriminator validity between clinical (autism, Asperger syndrome) and nonclinical groups (Golan et al. 2006), and it is sensitive to developmental changes in ToM. Novelties Measuring ToM To date, a series of novel and previously overlooked approaches have been proposed in order to improve the measurement of ToM in research and clinical settings. Classic tests such as those described above “Strange Stories Test” and “Faux Pas Test” are considered “explicit” measures since ToM is prompted by direct mental state questions. Several findings suggest that individuals with ASD show relatively minor impairments on classical tasks (evaluated usually by explicit measurements) compared with their difficulties observed in clinical settings and everyday social situations (e.g., Lever and Geurts 2016;
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Scheeren et al. 2013; Wilson et al. 2014). Clements and Perner (1994) were the first to empirically investigate a difference between implicit and explicit ToM; however, this distinction is currently debated (Carruthers 2017). An implicit measure of social cognition is based on fast spontaneous, unconsciously, and inflexibly action, while explicit ToM reasoning is conquered around age 4 and allows for a deliberate consideration like the judgment of others’ mental states. To this end, various studies (e.g., Apperly and Butterfill 2009; Sodian et al. 2014) support the notion of two ToM reasoning systems: an explicit system that verbally explains behavior based on mental states and an implicit system for spontaneous sensitivity to other’s mental states. These differences in the representational repertoire of the two systems should thus manifest themselves in distinctive and differential patterns of performance. Deschrijver et al. (2016) used implicit tasks to measure ToM suggesting that when ToM atypicalities are measured by response time on appropriate cognitive tasks, it may help to understand autistic symptoms in adulthood. Measuring and controlling reaction time (RT) have not been used systematically as a technique within classical ToM, while accuracy is considered as a ToM ability. Nevertheless, measuring accuracy alone can lead to misunderstandings when alternative cognitive strategies might compensate for ToM performance (Scheeren et al. 2013; Livingston et al. 2018). Furthermore, when participants are administered, such tests are given as much time as necessary to process the material, which may explain why individuals may pass the probe test yet still struggle in everyday situations. Thus, combining reaction time and accuracy data could further improve the efficacy of advanced ToM measurements. Evidence from current studies measuring also implicit mentalizing such as spontaneous looking patterns that reflect intuitive tracking of another person’s belief state, documenting clear difficulties in adults with ASD. Besides, recent neuroimaging studies (Kovács et al. 2014; Schneider et al. 2014) showed that tasks developed to measure implicit ToM engage brain areas that are core to
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the explicit processing of other’s beliefs, suggesting that social cognitive processes most likely underlie implicit ToM. Other recently developed tasks have sought to measure ToM by examining accuracy, via open-ended or multiplechoice questions, of mental state attribution after watching video clips of social situations (Brewer et al. 2017; Dziobek et al. 2008; Murray et al. 2017). The advantage of these tasks resides in the prerequisite of more than simple inferences from images or stories and consequently reflecting more realistic social situations. Past measures of social cognition tended to focus exclusively on comprehension of a social situation (Mathersul et al. 2013), while recent research has begun to ask participants to generate possible social responses to typical interactions (e.g., “What would you do in this situation?”; Jameel et al. 2014) and found that autistic traits are associated with fewer pro-social responses (Murray et al. 2017).
of ToM are most impacted in ASD, and whether distinct components of ToM predict symptom severity and social functioning in ASD. Discrepancy also occurs between laboratory and naturalistic settings. Laboratory-based ToM tasks vary in their sensitivity to real-life task demand. For instance, Ponnet et al. (2008) and Chevallier et al. (2015) claim for real-life social contexts when measuring ToM as a parameter to capture the dynamics of social interactions. Deficits linked with ASD in interpreting emotional expressions are observed to be more pronounced when the context was naturalistic and unstructured rather than highly organized or predictable. Finally, the majority of ToM measures present information verbally, while fewer present information visually. Thereupon in many cases, ToM ability is underestimated due to verbal impairments, whereas individuals with good verbal skills pass ToM tasks that are mainly explicit in form presumably due to compensatory strategies.
Future Directions
See Also
A critical examination of limitations into existing ToM findings as well as the lack of convergence among conventional ToM measures will offer insight into cognition and behavior more broadly. Inconsistencies in the literature may account for various methodological differences across studies; however, recent work challenges to fill these gaps by understanding and controlling such limitations and further designing improved evaluation tools and theoretical contexts. Briefly, among the most essential shortcomings, future research needs to disentangle is the variability in the tasks used to measure ToM. The administration of many instruments has not been standardized, and thereby reliable normative data are currently nonexistent. Another dearth emerges from past studies that did not adequately distinguish performance on tasks measuring distinct theoretical components of ToM. Future research needs to clarify theoretically as well as experimentally the extent to which the tasks make emotional and cognitive demands, which components
▶ Autism: Social Communication Disorder ▶ Autism Theory
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Components of Advance Theory of Mind in Autism Spectrum Disorder Baron-Cohen, S., Jolliffe, T., Mortimore, C., & Roberts, M. (1997). Another advanced test of theory of mind: Evidence from very high functioning adults with autism or Asperger syndrome. Journal of Child Psychology and Psychiatry, 38, 813–822. Baron-Cohen, S., Tager-Flusberg, H., & Cohen, D. J. (2000). Understanding other minds: Perspectives from developmental cognitive neuroscience (2nd ed.). New York: Oxford University Press. Baron-Cohen, S., Wheelwright, S., Hill, J., Raste, Y., & Plumb, I. (2001). The “Reading the Mind in the Eyes” test revised version: A study with normal adults, and adults with Asperger syndrome or high-functioning autism. Journal of Child Psychology & Psychiatry, 42, 241–251. Brewer, N., Young. R.L., Barnett. E. (2017). Measuring Theory of Mind in Adults with Autism Spectrum Disorder. Journal of Autism and Developmental Disorders, 47, 1927–194. Cantio, C., Jepsen, J. R., Madsen, G. F., Bilenberg, N., & White, S. J. (2016). Exploring ‘the autisms’ at a cognitive level. Autism Research, 9(12), 1328–1339. Carruthers, P. (2017). Mindreading in adults: Evaluating two-systems views. Synthese, 194, 673–688. Chevallier, C., Parish-Morris, J., McVey, A., Rump, K. M., Sasson, N. J., Herrington, J. D., & Schultz, R. T. (2015). Measuring social attention and motivation in autism spectrum disorder using eyetracking: Stimulus type matters. Autism Research, 8, 620–628. Clements, W. A., & Perner, J. (1994). Implicit understanding of belief. Cognitive Development, 9, 377–395. Corrigan, P. W. (1997). The social perceptual deficits of schizophrenia. Psychiatry, 60, 309–326. Deschrijver, E., Bardi, L., Wiersema, J. R., & Brass, M. (2016). Behavioural measures of implicit theory of mind in adults with high functioning autism. Cognitive Neuroscience, 7(1–4), 192–202. Duval, C., Piolino, P., Bejanin, A., Eustache, F., & Desgranges, B. (2011). Age effects on different components of theory of mind. Consciousness and Cognition, 20, 627–642. Dziobek, I., Rogers, K., Fleck, S., Bahnemann, M., Heekeren, H. R., Wolf, O. T., et al. (2008). Dissociation of cognitive and emotional empathy in adults with Asperger syndrome using the multifaceted empathy test (MET). Journal of Autism and Developmental Disorders, 38, 464–473. Frith, C. D., & Frith, U. (2006). How we predict what other people are going to do. Brain Research, 1079, 36–46. Golan, O., Baron-Cohen, S., & Hill, J. (2006). The Cambridge mindreading (CAM) face-voice battery: Testing complex emotion recognition in adults with and without Asperger syndrome. Journal of Autism and Developmental Disorders, 36, 169–183. Happé, F. (1994). An advanced test of theory of mind: Understanding of story characters thoughts and feelings by able autistic, mentally handicapped and normal
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children and adults. Journal of Autism and Developmental Disorders, 24, 129–154. Happé, F. (1995). The role of age and verbal ability in the theory of mind task: Performance of subjects with autism. Child Development, 66, 843–855. Happé, F., Cook, J. L., & Bird, G. (2017). The structure of social cognition: In(ter) dependence of sociocognitive processes. Annual Review of Psychology, 68, 243–267. Hynes, C. A., Baird, A. A., & Grafton, S. T. (2006). Differential role of the orbital frontal lobe in emotional versus cognitive perspective-taking. Neuropychologia, 44, 374–483. Jameel, L., Vyas, K., Bellesi, G., Roberts, V., & Channon, S. (2014). Going ‘Above and Beyond’: Are those high in autistic traits less pro-social? Journal of Autism and Developmental Disorders, 44, 1846–1858. Kalbe, E., Schlegel, M., Sack, A. T., Nowak, D. A., Dafotakis, M., Bangard, C., Brand, M., ShamayTsoory, S., Onur, O. A., & Kessler, J. (2010). Dissociating cognitive from affective theory of mind: A TMS study. Cortex, 46, 769–780. Kastner, A., Begemann, M., Michel, T. M., Everts, S., Stepniak, B., Bach, C., et al. (2015). Autism beyond diagnostic categories: Characterization of autistic phenotypes in schizophrenia. BMC Psychiatry, 15, 115. Kovács, A. M., Kühn, S., Gergely, G., Csibra, G., & Brass, M. (2014). Are all beliefs equal? Implicit belief attributions recruiting core brain regions of theory of mind. PLoS One, 9(9), e106558. Larson, F. V., Wagner, A. P., Jones, P. B., Tantam, D., Lai, M. C., Baron-Cohen, S., et al. (2017). Psychosis in autism: Comparison of the features of both conditions in a dually affected cohort. British Journal of Psychiatry, 210(4), 269–275. Lever, A. G., & Geurts, H. M. (2016). Age-related differences in cognition across the adult lifespan in autism spectrum disorder. Autism Research, 9(6), 666–676. Livingston, L. A., Bethany, C., & Punit, S. (2018). Recent advances and new directions in measuring theory of mind in autistic adults. Journal of Autism and Developmental Disorders, 49, 1738–1744. Lockwood, P. L., Bird, G., Bridge, M., & Viding, E. (2013). Dissecting empathy: High levels of psychopathic and autistic traits are characterized by difficulties in different social information processing domains. Frontiers in Human Neuroscience, 7, 760. Lugnegard, T., Hallerbδck, U. M., Hjδrthag, F., & Gillberg, C. (2013). Social cognition impairments in Asperger syndrome and schizophrenia. Schizophrenia Research, 143(2–3), 277–284. Mathersul, D., McDonald, S., & Rushby, J. A. (2013). Understanding advanced theory of mind and empathy in high-functioning adults with autism spectrum disorder. Journal of Clinical and Experimental Neuropsychology, 35(6), 655–668. Mazza, M., Pino, M. C., Mariano, M., Tempesta, D., Ferrara, M., De Berardis, D., et al. (2014). Affective and cognitive empathy in adolescents with autism spectrum disorder. Frontiers in Human Neuroscience, 8, 791.
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1154 McDonald, S., Bornhofen, C., Shum, D., Long, E., Saunders, C., & Neulinger, K. (2006). Reliability and validity of the awareness of social inference test (TASIT): A clinical test of social perception. Disability and Rehabilitation, 28(24), 1529–1542. Mitchell, J. P. (2006). Mentalizing and Marr: An information processing approach to the study of social cognition. Brain Reseasrch, 1079, 66–75. Mitchell, R. L. C., & Phillips, L. H. (2015). The overlapping relationship between emotion perception and theory of mind. Neuropsichologia, 70, 1–10. Murray, K., Johnston, K., Cunnane, H., Kerr, C., Spain, D., Gillan, N., et al. (2017). A new test of advanced theory of mind: The “Strange Stories Film Task” captures social processing differences in adults with autism spectrum disorders. Autism Research, 10(6), 1120–1132. Ochsner, K. N. (2008). The social–emotional processing stream: Five core constructs and their translational potential for schizophrenia and beyond. Biological Psychiatry, 64, 48–61. Ozguven, D. H., Oner, O., Baskak, B., Oktem, F., Olmez, S., & Munir, K. (2010). Theory of mind in schizophrenia and Asperger’s syndrome: Relationship with negative symptoms. US National Library of Medicine. National Institutes of Health, 20(1), 5–13. Ponnet, K. S., Roeyers, H., Buysse, A., De Clercq, A., & Van Der Heyden, E. (2004). Advanced mind-reading in adults with Asperger syndrome. Autism, 8, 249–266. Ponnet, K., Buysse, A., Roeyers, H., & Clercq, A. (2008). Mindreading in young adults with ASD: Does structure matter? Journal of Autism and Developmental Disorders, 38, 905–918. Rapin, I. (1997). Autism. New England Journal of Medicine, 337(2), 97–104. Rogers, K., Dziobek, I., Hassenstab, J., Wolf, O. T., & Convit, A. (2007). Who cares? Revisiting empathy in Asperger syndrome. Journal of Autism and Developmental Disorders, 37, 709–715. Schaafsma, S. M., Pfaff, D. W., Spunt, R. P., & Adolphs, R. (2015). Deconstructing and reconstructing theory of mind. Trends in Cognitive Sciences, 19(2), 65–72. Scheeren, A. M., de Rosnay, M., Koot, H. M., & Begeer, S. (2013). Rethinking theory of mind in highfunctioning autism spectrum disorder. Journal of Child Psychology and Psychiatry, 54(6), 628–635. Schneider, D., Slaughter, V. P., & Dux, P. E. (2014). What do we know about implicit false-belief tracking? Psychonomic Bulletin & Review, 22(1), 1–12. Shamay-Tsoory, S. G., & Aharon-Peretz, J. (2007). Dissociable prefrontal networks for cognitive and affective theory of mind: A lesion study. Neuropsychologia, 45(13), 3054–3067. Shamay-Tsoory, S. G., Tomer, R., Yaniv, S., & AharonPeretz, J. (2002). Empathy deficits in Asperger syndrome: A cognitive profile. Neurocase, 8, 245–252. Sodian, B., Schuwerk, T., & Kristen, S. (2014). Implicit and spontaneous theory of mind reasoning in autism spectrum disorders. https://doi.org/10.5772/59393.
Compoz ® Nighttime Sleep Aid [OTC] Stone, V. E., & Gerrans, P. (2006). What’s domain-specific about theory of mind? Social Neuroscience, 1, 309–319. Stone, V. E., Baron-Cohen, S., & Knight, R. T. (1998). Frontal lobe contributions to theory of mind. Journal of Cognitive Neuroscience, 10, 640–656. Tager-Flusberg, H. (2007). Evaluating the theory-of-mind hypothesis of autism. Current Directions in Psychological Science, 16(6), 311–315. Tager-Flusberg, H., & Sullivan, K. (2000). A componential view of theory of mind: Evidence from Williams syndrome. Cognition, 76, 59–90. Tin, L. N. W., Lui, S. S. Y., Ho, K. K. Y., Hung, K. S. Y., Wang, Y., Yeung, H. K. H., et al. (2017). Highfunctioning autism patients share similar but more severe impairments in verbal theory of mind than schizophrenia patients. Psychological Medicine, 48(8), 1264–1273. Van de Cruys, S., Evers, K., Van der Hallen, R., Van Eylen, L., Boets, B., de-Wit, L., & Wagemans, J. (2014). Precise minds in uncertain worlds: Predictive coding in autism. Psychological Review, 121, 649–675. Warnell, K. R., & Redcay, E. (2019). Minimal coherence among varied theory of mind measures in childhood and adulthood. Cognition, 191, 103997. Wellman, H. M., Cross, D., & Watson, J. (2001). Metaanalysis of theory-of-mind development: The truth about false belief. Child Development, 72(3), 655–684. Wilson, E., Happé, F., Wheelwright, S. J., Ecker, C., Lombardo, M. V., Johnston, P., et al. (2014). The neuropsychology of male adults with high functioning autism or asperger syndrome. Autism Research, 7(5), 568–581. Wimmer, H., & Perner, J. (1983). Beliefs about beliefs: Representation and constraining function of wrong beliefs in young children’s understanding of deception. Cognition, 13(1), 103–128.
Compoz ® Nighttime Sleep Aid [OTC] ▶ Diphenhydramine
Comprehension ▶ Listening Comprehension ▶ Receptive Language
Comprehensive Assessment of Spoken Language
Comprehensive Assessment of Spoken Language Kristin Ratliff Research and Development, Western Psychological Services, Torrance, CA, USA
Synonyms CASL
Description The CASL is an oral language assessment battery for individuals aged 3–21 years and is based on a comprehensive theory of language. Consisting of 15 tests, the CASL measures auditory comprehension, oral expression, and word retrieval processes in four linguistic categories: Lexical/Semantic, Syntactic, Supralinguistic, and Pragmatic. The CASL battery provides an in-depth evaluation of oral language processing systems, knowledge and use of grammatical structures, ability to use language requiring higher-level processing, and adaptation of language across contexts. The results of the CASL assessment provide value in measuring linguistic skill among children with language delays and disorders as well as the competence of individuals who are learning English as a second language. Each of the 15 CASL tests is constructed differently and measures different aspects of oral language. First, the Lexical/Semantic tests assess knowledge and use of words and word combinations through five tests. Comprehension of Basic Concepts measures the ability of young children to comprehend words representing basic perceptual and conceptual relations. Antonyms assesses expression of a word that has the opposite meaning of a given word. Synonyms assesses the ability to identify a word with the same meaning as a given word. Sentence Completion measures the ability to retrieve and express one of the few appropriate words that fit the meaning of spoken
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sentence. Idiomatic Language measures the expressive knowledge of a group of words that, when used together, have a conventional meaning different from the literal meaning of the individual words. Next, there are five Syntactic tests that assess knowledge and use of grammar (morphology and syntax). Syntax Construction assesses the ability to generate sentences using a variety of morphosyntactic rules. Paragraph Comprehension measures the comprehension of syntax through a series of narratives spoken by the examiner. Grammatical Morphemes measures the metalinguistic knowledge of the form and meaning of the English language. Sentence Comprehension measures the ability to comprehend meaning of the syntactic-structure organization of sentences. Grammaticality Judgment assesses the ability to recognize and correct grammatical errors within sentences. Then, four Supralinguistic tests measure comprehension of complex language in which the meaning is not directly available from lexical or grammatical information. Nonliteral Language measures the ability to comprehend figurative speech, indirect requests, and sarcasm. Meaning from Context measures inference ability that does not require the examinee to use world knowledge. Inference assesses the ability to integrate appropriate world knowledge that is not explicitly stated with information provided by the speaker. Ambiguous Sentences assesses the ability to comprehend multiple meanings given sentences containing elements that can be interpreted in more than one way. Finally, Pragmatic Judgment measures awareness of the appropriateness of language and ability to modify language in relation to the situation in which it is used. The CASL is individually administered and requires no reading or writing on part of the examinee. Each test can stand alone, and each score can be reported as a standard score (mean of 100, standard deviation of 15, range of 40–160), percentile, normal curve equivalent (NCE), stanine, and test-age equivalent. These scores allow each test to be compared with every other test, both within a category and between
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categories, as well as between tests of comprehension, expression, and retrieval. Additionally, a global measure of oral language can be calculated from a subset of representative tests to derive a CASL Core Composite. Examiners may also administer all of the tests appropriate for the child’s age to use as a comprehensive battery. Depending on the students’ age, Index Scores may be computed as representative measures of language categories (Lexical, Syntactic, Supralinguistic; available for ages 7–21) and processing (Expressive, Receptive; available for ages 7–10). Administration time varies based on the examinee’s age and the number of tests administered. When administering the Core Battery tests only, it takes approximately 30 min to test children aged 3–5 years and 45 min to 1 h for older examinees. The CASL has two record forms: Record Form 1 is appropriate for children 3–6 years, and Record Form 2 is for those 7–21 years. Three self-standing test easels contain all of the picture stimuli and item text, as well as administration and scoring procedures for each test. General testing information is provided in the easels as well, such as basal and ceiling criteria, allowance of item repetition if the examinee appears to not understand the task, and prompting guidelines for specific tests. Test easels are tabbed for each individual test, and starting pages are marked for the appropriate age ranges. The manual also provides procedural information, interpretation of the results specific to each of the 15 CASL tests, and technical properties such as the description of test development, standardization, reliability, and validity data. A separate Norms Book contains all the normative tables for the CASL in order to convert raw scores to standard scores and provides significance values for comparing differences between tests scores.
Historical Background Originally published in 1999, the CASL was developed by author Dr. Elizabeth CarrowWoolfolk as a comprehensive test of oral language based on her Integrative Language Theory, ILT (Carrow-Woolfolk 1988, 1994; Carrow-Woolfolk
Comprehensive Assessment of Spoken Language
and Lynch 1981). ILT combines and relates the linguistic, cognitive, and pragmatic aspects of language, which forms the basis for the classification of the CASL tests. In this view, a comprehensive understanding of language must take into account both the meaning of language and its form. The content of language conveys meaning and is largely dependent on substantive words and word combinations. Words that refer to a relatively specific category of things are considered lexical morphemes (vocabulary). Throughout the CASL, the term “semantics” is used to describe the content of only the lexical morphemes of language. Conversely, language can be more abstract with a general meaning that refers to a wide range of things (pronouns, prepositions). These are considered grammatical morphemes (functors and inflections) which along with syntax (word order and structure of language) represent the form or structure of the language. Language can be ambiguous when word constructions have multiple interpretations. Often an individual must use inference or contextual information to interpret an underlying nonliteral meaning of language. This type of nonliteral language is labeled supralinguistic, which implies that the interpretation of an utterance extends beyond the literal meaning and requires higher-level analysis to comprehend. This category of language emerges later in development and is an integral component in measuring linguistic skill during adolescence. Additionally, the relation of form and meaning is significantly influenced by context including social variables (setting, age, relationship), linguistic variables (type of discourse-conversation, a narrative, a lecture), and personal variables (intention, motivation, knowledge, and style of the sender). These contextual aspects of communication are considered pragmatic and can affect not only the manner in which language is expressed but also how it is interpreted. The categorization of CASL tests follows the structure of the ILT. Lexical/Semantic tests address the meaning of language (Basic Concepts, Antonyms, Synonyms, Sentence Completion, Idioms). The Syntactic tests address meaning
Comprehensive Assessment of Spoken Language
derived from the structure of language (Syntax Construction, Paragraph Comprehension, Grammatical Morphemes, Sentence Comprehension, Grammaticality Judgment). The Supralinguistic tests measure understanding and use of complex meanings that require analysis above that of lexical and syntactic meaning (Nonliteral Language, Meaning from Context, Inference, Ambiguous Sentences). The Pragmatic test measures understanding of language that is dependent on context and social understanding (Pragmatic Judgment). The theoretical distinction of these categories is reflected in the developmental literature as well. Children begin to use language at a lexical level and proceed to using two-word phrases and simple morphemes, such as prepositions. Gradually, utterances lengthen by the addition of complex morphemes and the use of structurally advanced syntax, such as noun and verb agreement. Eventually children can adapt their language to the needs and demands of the environment. Only then do children begin to comprehend and express nonliteral language and idioms. Comprehension and expression of complex language in which the utterance units rely on one another for meaning evolves gradually, once the use of basic elements has matured. The CASL procedures and content reflect the two distinct dimensions of language: knowledge (structure, form, and content of language) and performance (internal systems used to comprehend and express language). As a result, the CASL separates listening comprehension (Receptive Index) from oral expression (Expressive Index). Although these two major processes of language have common elements (e.g., they both draw upon the same basic knowledge of language structure and meaning), there is empirical evidence for their distinction (Carrow-Woolfolk 1985).
Psychometric Data In developing the CASL, Dr. Woolfolk created items that would elicit information not only about an individual’s language knowledge and competency but the individual’s use of language and functionality of communication as well.
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Initial items were piloted in 1992, and normative data were collected between 1996 and 1997. The CASL was normed on a nationwide standardization sample of 1,700 individuals, 3–21 years, which matched the most current US Census data (1994 Current Population Survey) on gender, race/ethnicity, region, and mother’s educational level. Additionally, CASL sampling procedures drew children from public and private schools without respect to special education status. As a result, children receiving various special education services were represented in the normative sample in approximately the same proportions that occur in the US school population. The following five major special education categories were included: Specific Learning Disability (3.4% of CASL sample), Speech or Language Impairments (1.5%), Mental Retardation (0.6%), Emotional Disturbance (0.3%), and Other Impairments (0.4%). The CASL tests, Index Scores, and Core Composites have high internal consistency and testretest reliabilities. Item analyses, including Rasch scaling methods, were utilized to obtain the final test items. For each age band (12 groups), all of the Core Composite reliabilities are in the. 90s, and the indexes range from. 85 to. 96. Test-retest reliability was assessed by administering the CASL twice (median 6 week interval) to 148 randomly selected examinees in three age groups: 5–0 to 6–11 (41 cases), 8–0 to 10–11 (38 cases), and 14–0 to 16–11 (69 cases). Results suggest only a minor practice effect and provide strong evidence of the stability of CASL scores. Five criterion-related validity studies and eight clinical validity studies were conducted during standardization, with over 600 participants. In the first group of studies, CASL scores were compared with four measures of oral language (Test for Auditory Comprehension of LanguageRevised [TACL-R]; Listening Comprehension and Oral Expression Scales of the Oral and Written Language Scales [OWLS]; Peabody Picture Vocabulary Test, Third Edition [PPVT-III]; Expressive Vocabulary Test [EVT]) and a measure of cognitive ability (Kaufman Brief Intelligence Test [K-BIT]). The highest overall correlations are with the OWLS, which assesses
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the same four linguistic categories as the CASL. While the OWLS assessment has a wide-range approach, the CASL allows for an in-depth study of specific skills. For the clinical validity studies, performance of eight different clinical groups was examined: Speech Impairment, Language Delay, Language Impairment, Mental Retardation, Learning Disability (Reading), Learning Disability (Undifferentiated), Emotional Disturbance, and Hearing Impairment. Each clinical case was matched with a case from the standardization sample on the following variables: age, gender, race/ethnicity, and SES as measured by the mother’s education level. Children in the matched control groups were not receiving any special services. The manual includes tables reporting the mean and standard deviation of each clinical group and the matched control groups as well as the significance value of the difference between groups. In general, the clinical group scored about the same as the control group on most of the CASL tests for the Speech Impairment group. In contrast, the Language Delay, Language Impairment, Emotional Disturbance, and both Learning Disability groups scored significantly lower than their matched control groups. Adolescents with mild mental retardation scored approximately two standard deviations below the mean on all CASL tests. For Hearing Impairment, the clinical group scored lower on all CASL tests (e.g., about one standard deviation below the mean); however, interpretation is limited due to a small sample size for this group.
Clinical Uses The CASL can assist speech/language pathologists, psychologists, educational diagnosticians, early childhood specialists, and other professionals in measuring oral language processing skills and knowledge in preschoolers through young adults. The age-based norms are useful in identifying language impairments to meet the requirements of Public Law 94-142 (now incorporated into the Individuals with Disabilities Education Act [IDEA] reauthorized as Public
Comprehensive Assessment of Spoken Language
Law 105-17). Further, CASL standard scores can be directly compared with many other tests when using the available standard scores based on the common metric scale (mean of 100, standard deviation of 15). The CASL assists clinicians in understanding the relation between an individual’s score and any delays or disorders in language. The presence of poor performance in one or more of the language categories may be indicative of a language disorder. Further, a significant discrepancy in performance among the categories may indicate a problem. For example, comparisons can be made between receptive and expressive tasks or between tasks highly dependent on retrieval and those that are not. In-depth qualitative information from the CASL may also be used to identify the possibility of a disorder even though the quantitative level of performance is not atypical. Clinicians are also able to create a profile of an examinee’s oral language strengths and weaknesses across distinct categories of language. The CASL can also provide a record of growth in language skills and knowledge across a broad time span (3–21 years) through using the same instrument. Communication deficits are often associated with autism spectrum disorders (ASD). The CASL can provide clinicians with in-depth information about an individual’s knowledge and use of language, which may help to identify problem areas associated with ASD. For example, the CASL can be used to identify deficits in expressive compared to receptive language as well as problems in the use of language (e.g., pragmatics) as opposed to the content or form of language (lexical/semantic and syntactic). This may be particularly useful for high-functioning ASD individuals who often have advanced lexical/semantic knowledge and syntactic skills, but relatively poor pragmatic skills associated with difficulties in social interaction (Paul and Wilson 2009). Recent evidence suggests that the Pragmatic and Supralinguistic areas of the CASL are useful particularly in documenting the difficulties that individuals with ASD have in communicating flexibly across contexts (Reichow et al. 2008).
Comprehensive Transition Program
References and Reading Carrow-Woolfolk, E. (1985). Test for Auditory Comprehension of Language-Revised (TACL-R). Allen: DLM Teaching Resources. Carrow-Woolfolk, E. (1988). Theory, assessment, and intervention in language disorders: An integrative approach. Philadelphia: Grune & Stratton. Carrow-Woolfolk, E. (1994). Learning to read: An oral language perspective of beginning reading. San Antonio: The Psychological Corporation. Carrow-Woolfolk, E. (1999/2008). Comprehensive assessment of spoken language. Los Angeles: Western Psychological Services. Carrow-Woolfolk, E., & Lynch, J. I. (1981). An integrative approach to language disorders in children. San Antonio: The Psychological Corporation. Paul, R., & Wilson, K. P. (2009). Assessing speech, language, and communication in autism spectrum disorders, Ch. 7. In S. Goldstein, J. A. Naglieri, & S. Ozonoff (Eds.), Assessment of autism spectrum disorders (pp. 171–208). New York: Guilford Press. Reichow, B., Salamack, S., Paul, R., Volkmar, F. R., & Klin, A. (2008). Pragmatic assessment in autism spectrum disorders: A comparison of a standard measure with parent report. Communication Disorders Quarterly, 29, 169–176.
Comprehensive Transition Program Paul K. Cavanagh Vocational Independence Program, New York Institute of Technology, Central Islip, NY, USA
Definition A Comprehensive Transition and Postsecondary Program is a college-based program for students with an intellectual disability defined and created by the enacting of Public Law 110–135: the Higher Education Opportunities Act (HEOA, 2008). As defined in the law, a Comprehensive Transition and Postsecondary Program is “designed to support students with intellectual disabilities who are seeking to continue academic, career and technical, and independent living instruction at an institution of higher education in order to prepare for gainful employment” (HEOA, sec. 760). Such a program must be
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offered by an institution of higher education and have an advising and curriculum structure. The curriculum must provide for the students with an intellectual disability to spend at least one-half of their time on academic components with nondisabled individuals. A Comprehensive Transition and Postsecondary Program is a program based at a college or technical school to assist students with an intellectual disability, transitioning out of secondary education, who are not yet ready to enter the workforce or to enroll full-time in a college or technical school. The programs are designed to provide the necessary instruction and support in social functioning and independent living skills to enable the student to prepare for gainful employment. Institutions of higher education must apply to the United States Department of Education in order to have their program approved as a Comprehensive Transition and Postsecondary Program. In order to be eligible to apply for approval, the institution of higher education must already be approved to administer Federal Financial Aid for its students. A student enrolled in an approved Comprehensive Transition and Postsecondary Program is eligible for Federal financial assistance under the Federal Pell Grant, Federal Supplemental Education Opportunity Grants (FSEOG), and Federal Work Study (FWS) programs. As opposed to the general requirements for a postsecondary student to be eligible for federal financial aid, a student with an intellectual disability in an approved transition program does not have to be enrolled for the purpose of obtaining a degree or certificate and the student is not required to have a high school diploma, a recognized equivalent of a high school diploma, or have passed an ability to benefit test. However, the student must be making satisfactory progress according to the institution’s published standards for students enrolled in its comprehensive transition and postsecondary programs (34 CFR, § 668.233).
See Also ▶ Individualized Transition Plan (ITP) ▶ Individuals with Disabilities Education Act (IDEA)
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▶ Intellectual Disability ▶ Transition Planning ▶ Transitional Living
References and Reading Higher Education Opportunities Act, Pub. L. No. 110–135 § 760, 34 CFR subpart O § 668.230–233.
Compulsiveness
volume of data can then be manipulated via computer algorithm to observe and measure bodily structures (including brain structures) based on their ability to block the X-ray beam. This was one of the first techniques available to neuroscientists to study the structure of the living human brain. As such, it was one of the first techniques to be used to study brain structure in individuals with autism providing some of the earliest insights into the neural systems affected by autism.
Compulsiveness
See Also
▶ Perfectionism
▶ Magnetic Resonance Imaging
References and Reading
Computed Axial Tomography (CAT) ▶ Computed Tomography
Damasio, H., Maurer, R. G., Damasio, A. R., & Chui, H. C. (1980). Computerized tomographic scan findings in patients with autistic behavior. Archives of Neurology, 37(8), 504–510. Harcherik, D. F., Cohen, D. J., Ort, S., Paul, R., Shaywitz, B. A., Volkmar, F. R., et al. (1985). Computed tomographic brain scanning in four neuropsychiatric disorders of childhood. American Journal of Psychiatry, 142(6), 731–734.
Computed Tomography Kevin A. Pelphrey Harris Child Study Center, Yale University School of Medicine, New Haven, CT, USA
Synonyms Computed axial tomography (CAT); CT; X-ray computed tomography
Definition Computed tomography is a medical imaging method employing tomography created by computer processing of X-ray images. A form of digital geometry processing is used to generate a three-dimensional image of the inside of an object from a large series of two-dimensional X-ray images taken around an axis of rotation. This
Computer-Assisted Instruction to Teach Academic Skills Jenny R. Root1, Bradley Stevenson2 and Luann Ley Davis3 1 School of Teacher Education, College of Education, Florida State University, Tallahasee, FL, USA 2 University of North Carolina Charlotte, Charlotte, NC, USA 3 University of Memphis, Memphis, TN, USA
Definition Computer-assisted instruction (CAI) has been defined as a systematic approach to developing students’ knowledge and/or skills that uses a
Computer-Assisted Instruction to Teach Academic Skills
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computer as a central feature to support instruction via activities including, but not limited to, presenting materials, assessing progress, and guiding activities (Anohina 2005). CAI is a subset of technology-aided instruction, and is distinct from online, Internet, or other network learning in that CAI implies the software is local to the computer, not accessed via the Internet.
unnecessary for presentation of programmed materials, being too expensive, and individuals’ doubts about the extent to which the teaching machines could teach (Benjamin 1988). The teaching machines developed by Skinner and others, such as the AutoTutor (Crowder 1962), were based on the principles of programmed instruction (Molenda 2008). Programmed instruction was described by Bijou et al. (1966) as the process of systematically and effectively arranging and rearranging an environment to elicit a change in a targeted behavior. The intent of programmed instruction is to make the teaching-learning process effective and customized to meet individual needs (Molenda 2008). Skinner’s early work in programmed instruction focused on content that was arranged in small chunks of information that built upon each other. In a forward-chaining procedure, the student did not progress forward to a new “chunk” of information until the previous had been mastered. In this form of programmed instruction, learners progressed at an individual pace. The use of reinforcement in the form of knowledge of a correct response increased the likelihood of future correct responding. Programmed instruction was incorporated into textbooks as well, relieving the necessity for hardware. Research using teaching machines to deliver programmed instruction led Skinner to refer to the use the term technology of teaching (Skinner 1968). The evidence-base of CAI to teach academics to students with autism spectrum disorders (ASD) goes back over three decades. Panyan (1984) was one of the first to make the argument for the incorporation of computers into the education of students with disabilities in her review of literature on the use of computers with individuals with ASD. She concluded her review by proposing several distinct benefits computers may offer. These included consistency of instruction, the potential to facilitate generalization, and the inclusion of preferred sensory stimuli into instruction. As technology has rapidly changed in format and capabilities since Panyan’s initial review, the field of special education has followed with empirical evaluations on how this may enhance the lives of individuals with ASD. Because
Historical Background Technology has transformed everyday life for many people in the twenty-first century. From its infancy in the form of teaching machines (Pressey 1926; Skinner 1958) to the wide forms and platforms that are ever evolving (Stephenson and Limbrick 2015), instructors and researchers have evaluated the usefulness of technology as the central feature of an intervention to increase academic skills. The current application of CAI originates from the work of psychologists in the early twentieth century. Sidney Pressey was the first to experiment with teaching machines. He defined the teaching machine as an automatic or self-controlling device that presented a unit of information, provided some means for the learner to respond to the information, and provided feedback about the correctness of the learner’s responses (Pressey 1926). Approximately 30 years later, B. F. Skinner (1958) published descriptions of his own teaching machines, which were capable of requiring constructed responses. Skinner attributed the lack of professional enthusiasm and adoption of Pressey’s machines to cultural inertia, in that there was not a need for instruction to be automated when there was an abundance of teachers. However, there was an increased interest in teaching machines in the 1950s and 1960s when the baby boom led to a shortage of teachers. The promise of teaching machines was that they provided a solution to the problem of insufficient teacher attention to address all students varying needs. It was presumed that the teaching machine could replace some functions the teacher was currently providing. There were, however, criticisms of teaching machines in the late 1960s, including being
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educators of students with ASD are responsible for providing access to and ensuring progress in the general curriculum (Browder et al. 2007), a specific focus has been on how to effectively use technology to teach academics to students with ASD. Empirical investigations and periodic reviews of research are critical to fulfilling this responsibility because they identify which uses of technology are actually effective, and when educators use practices that are supported by scientific evidence, student outcomes improve (Cook et al. 2008, 2012).
ASD, (c) used a recognized experimental or quasiexperimental design, (d) had a dependent variable that measured an academic skill, and (e) CAI served as a key component of the independent variable, excluding video modeling. Authors coded each study for QIs using guidelines of the National Technical Assistance Center on Transition (NTACT 2016). These guidelines provided operational definitions for the QIs outlined for single-case research by Horner et al. (2005) and group experimental research by Gersten et al. (2005). After coding each study for QIs, authors evaluated the evidence to determine if CAI to teach academics to students with ASD was an EBP. Of the 29 studies that met inclusion criteria, 24 used single-case methods. Of those 24, two met criteria for high quality studies and an additional eight met criteria for acceptable quality, therefore satisfying criteria for classification as an EBP. Of the five group experimental studies, two met criteria for high quality, further meeting the NTACT requirements for classification as an EBP. After determining that CAI was an EBP for teaching academics to students with ASD, Root et al. (2017) further analyzed high and adequate quality studies included in their review for instructional variables to provide a more descriptive picture of the evidence base in terms of content, context, and instructional materials and methods. Nine out of 12 high and acceptable quality studies targeted literacy skills, meaning the evidence for teaching nonliteracy skills (e.g., math, science, and social studies) is not high. The context of CAI was primarily the special education classroom, with other settings included a clinic, conference room, computer lab, and a participant’s home. A total of 94 participants with ASD were included in high or adequate quality studies, the majority of whom were male (78%). Unfortunately, only five studies reported ethnicity, making inferences into the generalization of findings according to ethnicity impossible. The instructional materials used in the high and adequate quality studies included desktops, tablets, and laptops. Interventions were delivered through software or applications in the majority of the studies. Every study used behavioral
Current Knowledge In the first literature review on the use of CAI to teach academics to students with ASD, Pennington (2010) found 15 articles that (a) were published in a peer-reviewed journal between the years of 1998 and 2008, (b) were based on experimental research, (c) manipulated an independent variable involving the use of CAI, and (d) collected data related to an academic skill. All of the studies that met inclusion criteria addressed literacy skills. Pennington found a total of 15 articles that met inclusion criteria, and concluded that CAI was effective for a limited number of skills, namely literacy skills, but that many of the designs lacked experimental control. Knight et al. (2013) had similar findings with 25 out of 25 included studies teaching literacy skills. Inclusion criteria for Knight et al. (2013) consisted of the following: (a) published in a peer reviewed journal between 1973 and 2012, (b) teaching an academic skill, and (c) inclusion of technology as the intervention or a part of the intervention package, including video modeling. Knight et al. (2013) found a total of 25 articles that met these criteria, the majority of which targeted literacy skills. However, the quality was limited as most of the studies met only a few quality indicators (QIs) for single-subject or group design as outlined by Horner et al. (2005). An updated review by Root et al. (2017) included 29 studies that (a) were published in a peer reviewed journal between 1995 and May 2015, (b) included at least one participant with
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instructional methods, aligning with the origin of CAI in Skinner’s teaching machines and what is known about the effectiveness of systematic instruction to teach students with ASD. With the establishment of CAI to teach academics to students with ASD as an EBP, practitioners should consider ways CAI can be incorporated into instruction to overcome barriers to skill acquisition or give students access to academic content. This implication is especially relevant for literacy, as the majority of the evidence to support CAI targets literacy skills. In addition to efficacy, incorporating CAI into instruction can have several other benefits. For instance, the electronic format of many CAI interventions easy facilitates creation, presentation, and transfer of learning materials more feasible for practitioners. This may enable sharing among educators and families, allowing for continuity of interventions across classrooms, school years, and between home and school. In addition, CAI allows for modification to learning materials based on individual student needs (Knight et al. 2013). For example, although one PowerPoint or SMARTboard lesson may be created to address a common learning objective, it can be individualized to address students’ interest topics, prerequisite skills, or to adjust for low- or high-ability learners. CAI is not intended to replace teacher instruction; rather, it may supplement or enhance teacher-directed instruction. It may increase the efficiency of instruction by reducing the amount of intensity of teaching monitoring required (Knight et al. 2013). This would allow for teachers to target multiple students’ objectives within one lesson.
technology interventions for students with ASD (Wong et al. 2015). Additionally, assistive technology tools, devices, software, and services are increasingly interwoven into the fabric of all settings (e.g., general education, resource, separate), changing the dynamics of today’s classrooms. As such, additional research is needed to keep pace with the rapid advances in technology. First, research into content areas beyond literacy remains a need for students with ASD, as nonliteracy areas can have meaningful impact on students’ postsecondary success (Wei et al. 2017). Students with disabilities, including students with ASD, are increasingly being included within the general education classroom for all subject areas. As a result, it is important for future experimental studies to target core content academic skills such as inquiry-based science, and mathematical problem-solving. Current technology can be used to modify content for accessibility, to align content to state standards, and to address the individual needs of students with ASD. Second, the majority of the research has taken place within separate settings. More and more students with ASD are being educated within less restrictive settings, and CAI can serve as the catalyst to provide access to general education for students with ASD. Therefore, it is important for the context of research to expand beyond the special education classroom and investigate the effects of CAI to teach academics to students with ASD within general education settings as well. Future research should include treatment packages that look to academics for students with ASD through the use of combining CAI and behavior analytic approaches to instruction. Third, the results of the analysis of instructional materials and methods of high and adequate quality studies may be of interest in future research using CAI. Instructional materials are no longer limited to consumable worksheets; they now include devices, software, assistive technology tools and services to deliver CAI to students with ASD. Looking to future trends in CAI, some of the technology listed should be considered for future research:
Future Directions CAI practices are constantly evolving, and as a result educators may find it challenging to keep pace. Classrooms no longer maintain previous staple items such as chalkboards and overhead projectors, which have given way to items such as SMARTboards and portable tablets. This new technology and the practices associated with them reflect an expansion of the definition of
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1. Websites: Although there are many websites available for teachers to use in academic instruction, there is limited experimental support (Bouck et al. 2014). As a result, expanded research using materials available on the Internet is warranted. 2. Mobile devices (cell phones): What was once “smart” is now “cognitive.” Smart devices such as meters, lighting, or machines evolve with artificial intelligence enhancements that provide more sensory, context-aware, and complex capabilities. Cognitive technology is modeled on the human brain and has potential to have self-learning abilities. As our mobile devices evolve, research must keep pace with the effects of these devices on student academic progress and how they can best be used within educational environments. 3. Projection apps: First introduced for mobile devices, projection apps now work specifically with portable tablets to increase engagement and collaboration. The projection app will allow teachers and students to wirelessly display content from portable tablets on attached projectors. Using the app’s Moderator function, educators can create a collaborative classroom by displaying up to four different portable devices simultaneously, with a maximum of 50 devices connected. Using the app, students and educators also can connect to mobile devices anywhere with wireless connection, making it useful for a one-to-one classroom context. Research is warranted on how to do this effectively. 4. Virtual reality (VR): VR is beginning to be used in business communications, retail, entertainment, sports, health, and other industries. Makers of VR have claimed that VR has the potential to completely change how we work, shop, play, and consume media. Future research should include using VR to teach academics to students with and without ASD. 5. Voice control to sentient: Voice control software has moved from simply voice-to-text to now include such skills as Internet searches, making phone calls, turning lights on/off, and interfacing with devices that are embedded into our environment (e.g., mobile devices).
Sentient tools are aware of their context, environment, and social interactions. As technologies such as cloud-based artificial intelligence, collaborative robots, and evermore sophisticated machine-learning algorithms begin to converge, we will start to see the emergence of these capabilities in smart cars, homes, manufacturing, and education. Thus, research is needed for how to use this technology to improve education for students with ASD. 6. Artificial intelligence personal assistants, chatbots, and conversational interfaces: Advances in machine learning and natural language processing have already made conversational mediators possible. As they grow in sophistication, they look to move from the fringe and become a primary interface for Internet of Things devices and networks. Research is warranted on how the use of AI can enhance or impede educational and functional skills for students with and without ASD. These are only a few examples of the multitude of ways CAI instructional materials for students with ASD have made their way into classrooms. Looking to future trends, it is important for educators to continue to investigate all the various assistive technology tools, devices, and services to determine their effectiveness, efficiency, and evidence-base. Finally, future experimental research should adhere to established design QIs. For future studies to be able to contribute to the evidence base, they need to attend to QIs such as those described by Gersten et al. (2005) and Horner et al. (2005).
References and Reading Anohina, A. (2005). Analysis of the terminology used in the field of virtual learning. Journal of Educational Technology & Society, 8(3), 91–102. Benjamin, L. T. (1988). A history of teaching machines. American Psychologist, 43, 703–712. Bijou, S. W., Birnbrauer, J. S., Kidder, J. D., & Tague, C. (1966). Programmed instruction as an approach to teaching of reading, writing, and arithmetic to retarded children. The Psychological Record, 16, 505–524.
Computer-Based Intervention Assistive Technology Bouck, E. C., Satsangi, R., Doughty, T. T., & Courtney, W. T. (2014). Virtual and concrete manipulatives: A comparison of approaches for solving mathematics problems for students with autism spectrum disorder. Journal of Autism and Developmental Disorders, 44, 180–193. Browder, D. M., Wakeman, S. Y., Flowers, C., Rickelman, R. J., Pugalee, D., & Karvonen, M. (2007). Creating access to the general curriculum with links to gradelevel content for students with significant cognitive disabilities: An explication of the concept. The Journal of Special Education, 41, 2–16. Cook, B. G., Tankersley, M., & Harjusola-Webb, S. (2008). Evidence-based special education and professional wisdom: Putting it all together. Intervention in School and Clinic, 44, 105–111. https://doi.org/10.1177/ 1053451208321566. Cook, B. G., Smith, G. J., & Tankersley, M. (2012). Evidence-based practices in education. In K. R. Harris, S. Graham, & T. Urdan (Eds.), APA Educational Psychology Handbook (Vol. 1, pp. 495–528). Washington, DC: American Psychological Association. Crowder, N. A. (1962). Intrinsic and extrinsic programming. In J. E. Coulson (Ed.), Programmed learning and computer-based instruction (pp. 58–66). New York: Wiley & Sons. Gersten, R., Fuchs, L. S., Compton, D., Coyne, M., Greenwood, C., & Innocenti, M. S. (2005). Quality indicators for group experimental and quasi-experimental research in special education. Exceptional Children, 71, 149–164. Horner, R. H., Carr, E. G., Halle, J., McGee, G., Odom, S., & Wolery, M. (2005). The use of single-subject research to identify evidence-based practice in special education. Exceptional Children, 71, 165–179. Knight, V., McKissick, B., & Saunders, A. (2013). A review of technology-based interventions to teach academic skills to students with autism spectrum disorder. Journal of Autism and Developmental Disorders, 43, 2628–2648. Molenda, M. (2008). The programmed instruction era: When effectiveness mattered. Techtrends: Linking Research and Practice to Improve Learning, 52(2), 52–58. National Technical Assistance Center on Transition. (2016). Criteria for levels of evidence. Retrieved from: http://transitionta.org/system/files/effective practices/EP_Criteria_2017.pdf. Panyan, M. V. (1984). Computer technology for autistic students. Journal of Autism and Developmental Disorders, 14, 375–382. Pennington, R. C. (2010). Computer-assisted instruction for teaching academic skills to students with autism spectrum disorders: A review of literature. Focus on Autism and Other Developmental Disabilities, 25, 239–248. Pressey, S. L. (1926). A simple apparatus which gives tests and scores-and teaches. School and Society, 23, 373–376.
1165 Root, J. R., Stevenson, B. S., Ley Davis, L., Geddes-Hall, J., & Test, D. W. (2017). Establishing computerassisted instruction to teach academics to students with autism as an evidence-based practice. Journal of Autism and Developmental Disorders, 47, 275–284. Skinner, B. F. (1958). Teaching machines. Science, 128(3330), 969–977. Skinner, B. F. (1968). The technology of teaching. New York: Appleton-Century-Crofts. Stephenson, J., & Limbrick, L. (2015). A review of the use of touch-screen mobile devices by people with developmental disabilities. Journal of Autism and Developmental Disorders, 45, 3777–3791. Wei, X., Yu, J. W., Shattuck, P., & Blackorby, J. (2017). High school math and science preparation and postsecondary STEM participation for students with an autism spectrum disorder. Focus on Autism and Other Developmental Disabilities, 32, 83–92. Wong, C., Odom, S. L., Hume, K. A., Cox, A. W., Fettig, A., Kucharczyk, S., et al. (2015). Evidence-based practices for children, youth, and young adults with autism spectrum disorder: A comprehensive review. Journal of Autism and Developmental Disorders, 45, 1951–1966.
Computer-Based Intervention Assistive Technology Vannesa T. Mueller Speech-Language Pathology Program, University of Texas at El Paso College of Health Science, El Paso, TX, USA
Definition Computer-based interventions are those that use technology in some form to provide an interactive, multisensory learning experience. Also called computer-assisted instruction (CAI), this form of intervention is used to present information, allow a user to practice certain skills repeatedly, or to test knowledge or comprehension.
Historical Background Computers have been used with individuals with autism since the 1970s. Colby (1973) used a computer and keyboard to increase the spontaneous verbalizations of children with autism. It was the
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intent of the researcher to allow the children to engage in free play with the computer system that was designed to say the names of the letters the children typed or provide some kind of visual reinforcer when a letter was typed. Thirteen out of the 17 participants in the study showed increased language after the therapy. Throughout the rest of the 1970s and early 1980s, most of the evidence that was published regarding computerbased technology was anecdotal and not systematic. These interventions focused on learning a computer language (Goldenberg 1979), playing a computer game (Frost 1981), responsiveness (Geoffrion and Goldenberg 1981), attention (Pleinis and Romanczyk 1983), and social skills (Panyan et al. 1984). By the mid 1990s, approximately 45 commercially available software programs were marketed for children and young adults with autism. These programs were created by a small number of developers to target cause and effect and language and cognitive development. Higgins and Boone (1996) described best practices for the creation of software for individuals with autism. The authors noted that the symptoms of autism manifest differently in each individual, so they encouraged educators and others working with students with autism to create their own computer-based interventions to implement specific strategies that are known to work with the students. Eighteen software guidelines were provided for those who wished to create software for children with autism. The guidelines cover many aspects of software design such as age appropriateness and principles of repeated practice. Additionally, Higgins and Boone (1996) provided tips and strategies related to creating software for children with autism. Recently, the field of computer-based interventions has seen a steady growth in the quantity and creativity applied to this population. Virtual reality technology has been utilized to address social difficulties and pragmatic impairments typically seen in individuals with autism (Self et al. 2007), an electronic tutor has been developed to aid in vocabulary development (Bosseler and Massaro 2003), and robots have been created to target social interactions and joint attention (Robins
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et al. 2005; Robins et al. 2004). Additionally, with the fervor over the Apple IPod touch, IPad, and approximately 350,000 apps that are available, much attention and speculation has been focused on the use of this new technology. GeekSLP.com is a blog designed for speechlanguage pathologists focused on technology use. There is also a GeekSLP app available for the IPad which helps identify apps that are appropriate for use by speech-language pathologists in the clinical setting.
Rationale or Underlying Theory What human beings lack in patience, predictability, and consistency computers make up for. Microcomputers were introduced in the interventions of children with autism due to the finding that children with autism spent a large amount of time playing with machines compared to playing with human beings (Colby 1973). Computerbased interventions have the ability to control where and how stimuli are presented to help children with autism compensate for attentional difficulties and problems with filtering tangential or unnecessary information. Difficulty with unpredictability is a common symptom of autism. Computer-based instruction can vary a routine or program in very small increments, helping an individual with autism to accept these changes and build tolerance for change. See Panyan (1984) for a discussion on the early rationale for computer-based instruction for children with autism. More recently, Blischak and Schlosser (2003) have argued that computer-based interventions can contribute in unique ways to the services provided to individuals with autism due to the cognitive styles and learning preferences of the population.
Goals and Objectives The goals of computer-based intervention are as varied as the purposes for them. Recently, a computer-based intervention can be found for nearly every area of difficulty afflicting
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individuals with autism from vocabulary to attention to pragmatic abilities. The ultimate goal for these programs could be generalization of the skills learned to the individuals’ natural environment. However, some programs aim at allowing the individual user practice at a certain skill. In this case, the software is a tool to be used as part of a rehabilitation package. It is the responsibility of the intervener to help the individual with autism to generalize the skills learned and practiced.
Treatment Participants Due to their versatility, ability to be individualized, and recent availability (related to smaller size and lower price), computers and technology are being used to a much greater extent today than ever before. Technology is a mainstay of the general education curriculum as well as the special education curriculum. Because of this, computerbased interventions could be appropriate for any individual.
Treatment Procedures The specific procedures vary by the computerbased intervention. Due to the variety of programs available and the myriad of difficulties they target, generalizations regarding treatment procedures cannot be made.
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favors computer-based intervention (BernardOpitz et al. 1990). Compared with traditional instruction, children with autism have been shown to attempt more responses, answer more questions correctly, and had improved behavior and developed better literacy skills with a treatment that consisted of computer-based instruction (Chen and Bernard-Opitz 1993; Heimann et al. 1995). An underlying question in the above studies pertained to generalization. Can an individual with autism transfer the skills learned from a computer-based intervention to real life? A computer-based intervention used by Hertzroni and Tannous (2004) was shown to generalize to the children’s natural classroom environment. After children with autism were exposed to the computer-based intervention that focused on the use of relevant utterances and reduction of the amount of echolalic utterances, the children produced more relevant speech and reduced the number of immediate echolalic utterances (Hertzroni and Tannous 2004). A review of computer-based intervention was recently published by Pennington (2010). In that review, the results of 15 studies showed that targeted academic skills were acquired by the participants with autism spectrum disorders. Pennington (2010) concludes that computer-based instruction is a promising area of intervention for this population.
Outcome Measurement Efficacy Information Computers have been used successfully in interventions with individuals with autism (Chen and Bernard-Opitz 1993; Colby 1973; Higgins and Boone 1996). Computers are predictable, cueing can be systematically removed to promote independence in a skill, and an individual can practice skills in an environment which is nonjudgmental. These factors have increased the likelihood of the success of an intervention which utilizes some kind of computer-based intervention. The outcome of research that compares computer-based intervention with teacher-based intervention
Generalization of the target skills acquired from computer-based intervention to a naturalistic setting is what should be measured as the outcomes of this type of intervention. Although it is important for individuals to be able to acquire and practice skills in the relative safety of a computer program, it is more important for them to be able to use the skills in their everyday lives.
Qualifications of Treatment Providers Many of the commercially available computerbased intervention software packages are easy to
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install and, their use is fairly intuitive. However, for the computer-based intervention to truly have an impact on the language or cognitive skills of an individual with autism, a trained interventionist should prescribe their use and oversee the effectiveness of the tool. Speech-language pathologists, occupational therapists, special educators, and autism specialists are all trained in the use of these types of tools and can help with knowing which computer-based interventions would be most appropriate. These interventionists can also help with monitoring the outcomes of the intervention and will know how quickly cues and prompts can be faded to result in the most efficient and effective treatment.
See Also ▶ Academic Supports ▶ Education
References and Reading Bernard-Opitz, V., Ross, K., & Tuttas, M. L. (1990). Computer assisted instruction for autistic children. Annals of the Academy of Medicine, 19, 611–616. Blischak, D. M., & Schlosser, R. W. (2003). Use of technology to support independent spelling by students with autism. Topics in Language Disorders, 23, 293–304. Bosseler, A., & Massaro, D. (2003). Development and evaluation of a computer-animated tutor for vocabulary and language learning in children with autism. Journal of Autism and Developmental Disorders, 33, 653–672. Chen, S. H., & Bernard-Opitz, V. (1993). Comparison of personal and computer-assisted instruction for children with autism. Mental Retardation, 31, 368–376. Colby, K. (1973). The rationale for computer-based treatment of language difficulties in non-speaking autistic children. Journal of Autism and Childhood Schizophrenia, 3, 254–260. Frost, R. E. (1981). An interactive computer environment for autistic children. In Proceedings of the Johns Hopkins first national search for applications of personal computing to aid the handicapped. Los Angeles: IEEE Computer Society. Geoffrion, L. D., & Goldenberg, E. P. (1981). Computerbased learning systems for communicationhandicapped children. Journal of Special Education, 15, 325–332. Goldenberg, E. P. (1979). Special technology for special children. Baltimore: University Park Press.
COMT Heimann, M., Nelson, K. E., Tjus, T., & Gillberg, C. (1995). Increasing reading and communication skills in children with autism through an interactive multimedia computer program. Journal of Autism and Developmental Disorders, 25, 459–480. Hertzroni, O. E., & Tannous, J. (2004). Effects of a computer-based intervention program on the communicative functions of children with autism. Journal of Autism and Developmental Disorders, 34, 95–113. Higgins, K., & Boone, R. (1996). Creating individualized computer assisted instruction for students with autism using multimedia authoring software. Focus on Autism and Other Developmental Disabilities, 11, 69–78. Panyan, M. V. (1984). Computer technology for autistic students. Journal of Autism and Developmental Disorders, 14, 375–382. Panyan, M., McGregor, G., Bennett, A., Rysticken, N., & Spurr, A. (1984). The effects of microcomputer based instruction on the academic and social progress of autistic students. Paper presented at the CEC technology in special education conference, Reno. Pennington, R. C. (2010). Computer-assisted instruction for teaching academic skills to students with autism spectrum disorders: A review of literature. Focus on Autism and Other Developmental Disabilities, 25, 239–248. Pleinis, A., & Romanczyk, R. G. (1983). Computer assisted instruction for atypical children: Attention, performance, and collateral behavior. Paper presented at the applied behavior analysis conference, Milwaukee. Robins, B., Dickerson, P., Stribling, P., & Dautenhahn, K. (2004). Robot-mediated joint attention in children with autism. Interaction Studies, 5, 161–198. Robins, B., Dautenhahn, R., Boekhorst, R. T., & Billard, A. (2005). Robotic assistants in therapy and education of children with autism: Can a small humanoid robot help encourage social interaction skills? Universal Access in the Information Society, 4, 105–120. Self, T., Scudder, R. R., Weheba, G., & Crumrine, D. (2007). A virtual approach to teaching safety skills to children with autism spectrum disorders. Topics in Language Disorders, 27, 242–253.
COMT ▶ Catechol-O-Methyltransferase
Conceptually Accurate Signed English (CASE) ▶ Manual Sign ▶ Sign Language
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Categorization
Conduct Disorder Ricardo Canal-Bedia Clinical Psychology Department, Department of Personality, Assessment, and Psychological Treatment, Centro de Atención Integral al Autismo (INFOAUTISMO), University Institute of Community Integration (INICO), University of Salamanca, Salamanca, Spain
Synonyms Behavioral disorder; Challenging behavior; Disruptive behavior; Dissocial behavior; Problem behavior
Short Description or Definition Conduct disorder (CD) is a behavioral disturbance occurring in childhood and adolescence characterized by a persistent and repetitive pattern of behavior that violates the basic rights of others or major age-appropriate societal rules. CD involves a number of problematic behaviors, including oppositional and defiant behaviors, and antisocial activities (e.g., lying, stealing, running away from home, truancy, physical aggression, and coercive behaviors). People with autism spectrum disorder (ASD) may have several problematic behaviors that are also found in people with CD, such as hyperactivity, impulsivity, aggression, and difficulty with following directions or being cooperative, and both conditions may share other features such as deficit in attributing mental states, poor verbal abilities for describing affective states, or reduced pragmatic skills. It is, however, rare that both conditions occur together (i.e., as comorbid conditions) (Simonoff et al. 2008). While presenting problematic behavior in individuals with CD is considered to arise from abnormal learning, problematic behavior of individuals with ASD can be explained by their difficulties in developing social and communicative skills.
CD is classified in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) (American Psychiatric Association [APA] 2013) within the Disruptive, Impulse-Control, and Conduct Disorders group, which also includes Oppositional Defiant Disorder (ODD), Intermittent Explosive Disorder (IED), Antisocial Personality Disorder (APD), Pyromania, Kleptomania, and Other specified or unspecified disruptive, impulse control, and conduct disorders. Because all they share problems with emotional and behavioral self-control, this new group brings together disorders previously described in the chapter Disorders usually first diagnosed in infancy, childhood, or adolescents and in the chapter Impulse control disorders not otherwise specified (see Fig. 1). However, the International Statistical Classification of Diseases (ICD-10) (World Health Organization [WHO] 1992) categorizes the ODD as a particular form of CD, usually occurring in younger children, primarily characterized by markedly defiant, disobedient, disruptive behavior that does not include delinquent acts, or the more extreme forms of problem behavior. On the other hand, dimensional classification systems, based on studies of factor analysis to cluster specific behavioral symptoms, place both disorders within the group of externalizing disorders (Achenbach and Rescorla 2001). There are several reasons for classifying CD and ODD as two different categories of disorders. First, age of onset for ODD (usually before age 8) is always at an earlier age than the onset of CD, which can have a childhood onset (if at least one symptom is present before 10 years old) or an adolescent onset (if no behavioral problems occur before the age 10). The second reason is that only a small group of children with ODD will develop CD at later ages. Finally, the third reason is that, although both disorders share behavioral characteristics of aggressiveness, aggressive behaviors are qualitatively different for each type of disorder. This distinction has been demonstrated by Frick et al. (1993) who showed that CD and ODD can be differentiated from a dimensional point of view. They conducted a meta-analysis with 60 factors
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Conduct Disorder
DSM-5 Disruptive Impulse-Control and Conduct Disorders
Oppositional Defiant Disorder (ODD)
Conduct Disorder (CD)
Intermittent Explosive Disorder (IED)
Antisocial Personality Disorder (APD)
Childhood onset: One symptom before 10 years old
Pyromania
Other specified or unspecified disruptive, impulse control, and conduct disorders
Kleptomania
Other conduct disorders
Conduct disorder, unspecified
Adolescent onset: No behavioral problems before the age 10
ICD-10 Conduct Disorder (CD)
Oppositional Defiant Disorder (ODD)
Conduct disorder confined to the family context
Unsocialized conduct disorder
Socialized conduct disorder
Conduct Disorder, Fig. 1 Classification of conduct disorder according to DSM-5 (APA 2013) and ICD-10 (WHO 1992)
identifying two bipolar dimensions: “destructivenondestructive” behaviors and “overt-covert” behaviors. They concluded that most conduct problems could be classified within these two orthogonal dimensions. The overt-nondestructive cluster reflected the criteria for ODD, whereas the other three clusters, with features more indicative of property and status violations, represented CD symptoms (see Fig. 2). For diagnosis of CD, the individual must have presented in the past 12 months “a repetitive and persistent pattern of behavior in which the basic rights of others or major age-appropriate societal norms or rules are violated” (APA 2013). Behaviors that must be present fall into the following four categories of aggressive behavior:
1. 2. 3. 4.
Aggressive acts toward people or animals Destruction of property Deceit or theft Violation of rules
At least one of the above criteria must have been presented during the last 6 months. Moreover, the disturbance in behavior must cause clinically significant impairment in social, academic, or occupational functioning. The age of onset determines the type of CD (see Fig. 1), and the severity depends on the damage caused to others. The behaviors that characterize a mild severity include lying, truancy, and running away at night without permission. Moderate severity may include acts of vandalism and
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Conduct Disorder, Fig. 2 Classification of disruptive behaviors (Adapted from Frick et al. 1993)
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theft without confrontation. Severe cases include forced sex, physical cruelty, and use of a weapon and breaking and entering. Recent studies indicate that a significant proportion of children with ASD may also be characterized with ODD symptoms. For example, two recent studies found that the percentage of children with ASD who meet DSM-IV criteria for ODD is 13% in the age group of 3–5 years and 27% in the age group of 6–12 years according to parental ratings, and when rates from teachers were considered, the numbers were 21% and 25%, respectively (Gadow et al. 2004, 2005). Problem behaviors that are most often identified in individuals with ASD are physical aggression, self-injury, destruction of property, arguing nature, temper tantrums, and disruption. But other behaviors, such as explosive behavior, running away, stubbornness, violation of rules, defiant, threatening, or not to accept being guilty, have also been identified as moderate or severe conduct problems in people with ASD (Lecavalier 2006). Individuals with ASD present more problem behaviors than typically developing children, and overall levels of problem behavior are positively correlated with severity of ASD (Matson
et al. 2009). About one third of individuals with intellectual disability (ID) who exhibit problem behavior have comorbid diagnosis of ASD (Myrbakk and von Tetzchner 2008), and the more severe the ID, the greater the risk of problem behavior (Holden and Gitlesen 2006). A common way of categorizing problem behaviors in people with ASD is based on the function of these behaviors in their natural context, but there are no systematic reviews about the possible functions that may play a role. In addition, a behavior problem may have more than one function. The most common functions that can be found in the literature are attention-seeking, avoiding, tangible benefit, or being alone (i.e., nonsocial, self-stimulatory, or automatic reinforcement). Avoiding pain or discomfort has also been described as a possible function of problem behavior.
Epidemiology Rates of prevalence estimates of CD vary widely depending on the methodology used in each study and on the ascertainment procedures. The disorder
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is considered to be a common mental health problem in children and adolescents and appears to have increased in the recent years. CD may be higher in urban than in rural areas and appears more often in boys than girls. Prevalence rates for the disorder in childhood and adolescence range from 2% to 10% in nonreferred samples (APA 2013). Prevalence rates increase from infancy to adolescence and are higher in males than in females. Recent studies show prevalence rates of 9.5% (12% for males and 7.1% for females) (Nock et al. 2006). It seems that malefemale ratios might be stronger in childhood than in adolescent-onset groups. Although individuals with ASD often exhibit behavior problems that could have a negative impact in everyday activities, and several studies suggest a high prevalence of aggressive behavior in people with ASD, few studies have examined the prevalence of maladaptive behaviors that warrant a clinical diagnosis of CD in individuals with ASD. Prevalence of problem behaviors within the ASD population is relatively high. Most studies indicate that at least half of the people with ASD exhibit behavior problems, with an estimated prevalence ranging between 35.8% and 94.3% (Kozlowski and Matson 2012). Lecavalier (2006) presented a study on prevalence of problem behaviors of children and adolescents with ASD. In a sample of 303 children and adolescents with ASD, he found that the proportion of children and adolescents who, according to parents and teachers, showed “conduct problems” was 13.9% (parents) and 8.4% (teachers). Behavior problems rated by parents and teachers as more frequent were stubbornness, temper tantrums, defiant behavior, arguing nature, not to accept being guilty, and explosive behavior. Stubborn behavior was rated as a moderate or severe problem for 50.7% (parents) and 44.4% (teachers). Also, temper tantrums, defiant behavior, not to accept being guilty, and explosive behavior were classified with a high frequency. Finally, aggressive acts, such as attacking others, were observed by teachers in 14.3% and in 9.9% by parents. Both informants (parents and teachers) indicate physical fights as moderate-to-serious problem for 5.3% of the sample. Property
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destruction was a moderate or severe problem for 11–12% (according to information from parents and teachers, respectively), and threatening people was rated as a moderate-to-severe problem for 4.5% (parents) and 7.6% (teachers) of the sample. The study also found that lower adaptive skills were associated with greater problem behaviors among the sample. Regarding high-functioning individuals with ASD, research and clinical observations suggest that a relatively large number of these individuals have behavioral problems at some point in their development. These symptoms may indicate the presence of ODD comorbid with ASD. They may also have symptoms of CD that are more severe in school-age time than in preschool (Gadow et al. 2005).
Natural History, Prognostic Factors, and Outcomes The onset of CD can occur very early, even at preschool age, although the most obvious symptoms usually occur between middle childhood and middle adolescence. Onset is rare after age 16. ODD is a common precursor to the childhoodonset type. Other different factors affect the onset of symptoms of CD. The scientific literature highlights three main factors: 1. Personal characteristics such as a difficult temperament in early childhood, a callousunemotional personality style (lack of empathy, remorselessness, and shallow affected), propensity for risk-taking, low to threatening and emotional reactions stimuli, reduced sensitivity to cues of punishment, and low levels of conscience and moral development 2. Bad parenting practices such as harsh, punitive, abusive, and/or inconsistent discipline 3. Repeated peer rejection and socializing with a deviant peer group (Hughes et al. 2008. In addition, there is evidence suggesting that childhood-onset CD could be more related to personal and familial factors, whereas adolescentonset could be more related to exposure to deviant
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peers and environmental disadvantages associated with ethnic minority status (McCabe et al. 2001). Finally, CD in childhood is associated with other problems, including the likelihood of repeating a grade in school, being suspended or expelled from school, an earlier age of onset of alcohol dependence, and having to attend a greater number of treatments for drug abuse (Hughes et al. 2008). There is not a single risk factor that determines the onset of the CD. Experts emphasize that the multiple risk factors mentioned above interact to facilitate and perpetuate the disorder. Problem behaviors play a critical role in ASD. However, the heterogeneity of symptoms present in persons with ASD (differences in cognitive functioning, or in adaptive behavior, the nature and severity of autistic behaviors) and changes in the development difficult to understand how these individual differences affect the occurrence and presentation of behavior problems beyond the core symptoms that define the ASD population. Despite the differences, consequences of problem behaviors are similar in most cases. These behaviors prevent the development of social relationships (Matson et al. 2010; Myrbakk and von Tetzchner 2008), place the individual and their family members in very difficult situations, and interfere with effective education (Carr et al. 1991). Also, it has been shown that the fact of problem behaviors (specially aggressiveness) causing more distress to caregivers than the core autistic symptoms (Lecavalier et al. 2006) is one of the most important impediments to placement in less-restrictive environments (Shoham-Vardi et al. 1996), can also interfere with intervention efforts, and, if present during early childhood, is of particular concern given that these are critical years for intervention. Thereby problem behaviors impact the long-term prognosis. Research on risk factors has provided some important data. Tonge and Einfeld (2003) studied a group of 118 children with autism in a period of 8 years. Results indicated that 73.5% of children with autism had behavioral alterations in a clinically significant range, with scores fairly stable over time. These researchers reported that children and adolescents with ASD are at high risk of severe and persistent behavioral disturbances
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beyond those that define the disorder. Matson et al. (2009) have studied the potential causal factors of problem behaviors in children with ASD, showing that overall levels of problem behavior were positively correlated with severity of ASD (Matson et al. 2009). Lecavalier (2006) in his epidemiological study found that lower adaptive skills were associated with greater behavioral problems, but age and gender do not seem to influence behavior problems. In a recent study, Hartley et al. (2008) examined a large sample of children with ASD, classifying 27% of sample in the clinically significant range on the CBCL Externalizing Problems’ subscale, and 22% fell within the clinically significant range on the aggression subscale. Results indicated that externalizing problems were significantly correlated with poorer adaptive skills, lower nonverbal cognitive functioning, and poorer expressive language. Also Dominick et al. (2007) found that individuals with ASD with low cognitive functioning and adaptive behavior and with lowexpressive language skills exhibit more problem behavior than high-functioning individuals (Dominick et al. 2007).
Clinical Expression and Pathophysiology CD may manifest itself in various symptoms that are classified into four categories: aggression toward people or animals, destruction of property without aggression, deception or theft, and serious violation of the social rules. These symptoms are behaviors that usually occur in early childhood. Many children commit acts of aggression, break property of others, commit petty thefts, say some lies, and violate some social rules. But in the case of children who have a CD, all these behaviors are very frequent and persistent, and some appear in an age too early such as running away from home at night (with no objective reasons to escape such as being abused) or truancy before age 13. The clinical expression of problem behaviors in ASD will depend on the subject’ age and on whether it is associated with ID individuals with greatest deficits engaging in more severe problem behaviors. Severity of ASD symptomatology
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affects the severity of problem behaviors. Also, symptoms of other disorders such as attentiondeficit hyperactivity disorder or obsessive compulsive disorder will affect the clinical expression of conduct problems in people with ASD. But it remains unknown whether the comorbidity of ASD with these disorders leads to different clinical expressions of behavior problems. This is important to better understand the pathophysiology of CD (and also of ASD). The neurobiological disorder of ASD results in difficulties in social cognition with its own characteristics, such as the difficulty to infer mental states and recognize (e.g., intentions, beliefs, desires, etc.) in self and others, which can interact with environmental factors leading to atypical behavioral patterns and behavior problems. Some CD symptoms (such as physical aggression, lying, and stealing) are relatively common in early childhood, and to distinguish them from normal childhood behavior, the clinician must take into account the frequency and persistence of problem behavior beyond the age of four. In childhood, most of the manifestations are limited to family and school contexts, but they affect the overall functioning of the child. In adolescence, behavior problems tend to have more serious consequences encompassing the whole adolescent’s social setting and including behavior problems that are much more serious.
Evaluation and Differential Diagnosis CD is a complex problem affecting multiple domains of functioning and often showing a high rate of comorbidity with other disorders. Assessment requires a comprehensive approach encompassing the child, family, school, peers, and community factors. Well-trained professionals should conduct assessments, to ensure the proper treatment. Otherwise, the behavior problems will continue or even worsen. Language disorders and intellectual disability usually found in ASD complicate the assessment and diagnosis. Even mild deficiencies in language can make challenging the study and identification of subtle differences in emotions, health status
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(presence of pain or physical discomfort), or feelings of annoyance at being unable to control own decisions. Assessment requires combining qualitative and quantitative methods to obtain information, and collecting data from several sources such as parents, teachers, and peers is mandatory. Information from different sources should be compared to detect and prevent possible biases caused by the partial view of each reporter and for a better understanding of the disorder being evaluated. Behavior rating scales can be completed by parents, teachers, and children to obtain comparable information. Clinicians and researchers consider behavior rating scales a time-efficient method of collecting reliable information and providing an assessment of several domains of behavior, usually both on the healthy functioning and the maladaptive. Behavioral observation is a useful tool that is normally used in assessing problem behaviors, both to describe the function (i.e., functional assessment) of problem behavior and to monitor treatment progress. Functional assessment is the process of identifying the variables that predict and maintain problem behavior. The literature review on the treatment of problem behaviors suggests that interventions based on functional assessment are more likely to produce the reduction of problem behavior (Horner et al. 2002). The process of conducting a functional assessment typically involves the following: (1) identifying the problem behavior, (2) building hypotheses about the events that occasion and maintain problem behavior, (3) testing/confirming the functional hypothesis, and (4) designing an intervention based on the confirmed information (Horner et al. 2002).
Treatment A variety of treatment procedures have been developed for children and adolescents with CD, but only some have been shown to reduce CD behaviors. Intervention procedures seem to be more effective in children under 8 years, when behavior problems have recently begun and when it includes a multimodal and multicomponent
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strategy, specifically adapted to the individual needs (Hughes et al. 2008). The evidence-based recommendations emphasize the need for a multicomponent intervention aimed at prevention and early intervention. The treatment mainly consists of psychological, educational, and social interventions focusing on children, parents, and teachers. Psychotropic medication and applied behavior analysis are the most frequently used treatments. Child-directed interventions aim to improve skills to manage anger and control of aggressive impulses, and improve empathy with others, strengthening relationships with peers. With the family it is necessary first to ensure commitment and motivation and then to begin training within the family context and subsequently generalized to other places of the community (INSERM 2005). Pharmacological treatment is appropriate when used in the context of a comprehensive psychoeducational evaluation and provided as part of a global treatment strategy (Connor 2002). The best practice for psychoeducational treatment of behavior problems should be based on principles and methods of positive behavior support (Rogers and Vismara 2008). That is, it should be a treatment that uses functional assessment to determine the function (or functions) of problem behavior. After identifying the function, a positive behavior support plan must be designed and implemented, aimed at teaching new functional behaviors that serve to replace the problem behavior. Horner et al. (2002) suggest that a support plan should take into account several elements, and a few of those are as follows: 1. Prevent behavior problems by organizing the environment in order to experience less negative events and greater accessibility of rewarding activities. 2. If there are behavioral problems, conduct a functional assessment. 3. Build a behavioral intervention to make the problem behavior irrelevant, by teaching socially appropriate behaviors that make the person much more competent in the context and produce the same effect in the context of the problem behavior.
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4. Organize the consequences for appropriate behavior to compete with problem behavior, avoiding also the reinforcement of problem behavior. 5. Ensure that the procedures are within the skills, resources, and values of those who must implement them. Parent training based on principles of applied behavior analysis has great empirical support. Successful programs for individuals with ASD and problem behavior include a parent training component usually based on principles of applied behavior analysis. Common parent training elements include teaching behavioral principles and management techniques, role-playing, homework assignments, teaching play and social skills, use of visual schedules, and home visits or telephone consultation, among others. Pharmacotherapy is common among individuals with ASD with behavior difficulties. The most used agents include selective serotonin reuptake inhibitors, antipsychotics, alpha 2 adrenergic agonists, psychostimulants, and anticonvulsants. But empirical support for use of these agents in ASD varies widely. A classic study on the use of Risperidone (McCracken et al. 2002) concluded that this psychoactive drug is effective and well tolerated for the treatment of tantrums, aggression, or self-injurious behavior in children with ASD, although the short period of the trial in this study limits inferences about adverse effects. Risperidone, like other atypical antipsychotics, is associated with adverse events, such as weight gain, and the subsequent risk of metabolic syndrome. A recent trial by Aman et al. (2009) tested whether combined treatment with risperidone and parent training in behavior management is superior to medication alone in improving severe behavioral problems. Results indicated that parent training plus medication produced greater reduction of problem behavior than medication alone in children with ASD.
See Also ▶ Behavior Analysis ▶ Behavior Modification
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▶ Behavior Plan ▶ Behavioral Assessment ▶ Challenging Behavior ▶ Maladaptive Behavior ▶ Positive Behavioral Support
References and Reading Achenbach, T. M., & Rescorla, L. A. (2001). Manual for ASEBA school-age forms & profiles. Burlington: University of Vermont, Research Center for Children, Youth, & Families. Aman, M. G., McDougle, C. J., Scahill, L., Handen, B., Arnold, L. E., et al. (2009). Medication and parent training in children with pervasive developmental disorders and serious behavior problems: Results from a randomized clinical trial. Journal of the American Academy of Child and Adolescent Psychiatry, 48(12), 1143–1154. American Psychiatric Association (2013). Diagnostic and statistical manual of mental disorders, fifth edition, DSM-5. Washington, DC: American Psychiatric Publishing. Carr, E. G., Taylor, J. C., & Robinson, S. (1991). The effects of severe behavior problems in children on the teaching behavior of adults. Journal of Applied Behavior Analysis, 24(3), 523–535. Connor, D. F. (2002). Aggression and antisocial behavior in children and adolescents: Research and treatment. New York: Guilford Press. Dominick, K. C., Davis, N. O., Lainhart, J., TagerFlusberg, H., & Folstein, S. (2007). Atypical behaviors in children with autism and children with a history of language impairment. Research in Developmental Disabilities, 28, 145–162. Frick, P. J., Van Horn, Y., Lahey, B. B., Christ, M. A. G., Loeber, R., Hart, E. A., et al. (1993). Oppositional defiant disorder and conduct disorder: A meta-analytic review of factor analyses and cross-validation in a clinic sample. Clinical Psychology Review, 13, 319–340. Gadow, K. D., DeVincent, C. J., Pomeroy, J., & Azizian, A. (2004). Psychiatric symptoms in preschool children with PDD and clinic and comparison samples. Journal of Autism and Developmental Disorders, 34(4), 379–393. Gadow, K. D., Devincent, C. J., Pomeroy, J., & Azizian, A. (2005). Comparison of DSM-IV symptoms in elementary school-age children with PDD versus clinic and community samples. Autism, 9(4), 392–415. Hartley, S. L., Sikora, D. M., & McCoy, R. (2008). Prevalence and risk factors of maladaptive behaviour in young children with autistic disorder. Journal of Intellectual Disability Research, 52(10), 819–829. Holden, B., & Gitlesen, J. P. (2006). A total population study of challenging behaviour in the county of Hedmark, Norway: Prevalence, and risk markers. Research in Developmental Disabilities, 27(4), 456–465.
Conduct Disorder Horner, R. H., Carr, E. G., Strain, P. S., Todd, A. W., & Reed, H. K. (2002). Problem behavior interventions for young children with autism: A research synthesis. Journal of Autism and Developmental Disorders, 32(5), 423–446. Hughes, T., Crothers, L., & Jimerson, S. (2008). Identifying, assessing, and treating conduct disorder at school. New York: Springer. INSERM. (2005). Conduct disorder in children and adolescents. Collective expert report. France: Institut national de la santé et de la recherche médicale. Available from http://www.ncbi.nlm.nih.gov/books/NBK7133/ Kim-Cohen, J., Arseneault, L., Caspi, A., Tomás, M. P., Taylor, A., & Moffitt, T. E. (2005). Validity of DSM-IV conduct disorder in 4½-5-year-old children: A longitudinal epidemiological study. American Journal of Psychiatry, 162, 1108–1117. Kozlowski, A. M., & Matson, J. L. (2012). An examination of challenging behaviors in autistic disorder pervasive developmental disorder not otherwise specified: Significant differences and gender effects. Research in Autism Spectrum Disorders, 6, 319–325. Lahey, B. B., Loeber, R., Burke, J. D., & Applegate, B. (2005). Predicting future antisocial personality disorder in males from a clinical assessment in childhood. Journal of Consulting and Clinical Psychology, 73, 389–399. Lecavalier, L. (2006). Behavioral and emotional problems in young people with pervasive developmental disorders: Relative prevalence, effects of subject characteristics, and empirical classification. Journal of Autism and Developmental Disorders, 36, 1101–1114. Lecavalier, L., Leone, S., & Wiltz, J. (2006). The impact of behaviour problems on caregiver stress in young people with autism spectrum disorders. Journal of Intellectual Disability Research, 50(Pt 3), 172–183. Matson, J. L., Wilkins, J., & Macken, J. (2009). The relationship of challenging behaviors to severity and symptoms of autism spectrum disorders. Journal of Mental Health Research in Intellectual Disabilities, 2, 29–44. Matson, J. L., Neal, D., Fodstad, J. C., & Hess, J. A. (2010). The relation of social behaviours and challenging behaviours in infants and toddlers with autism spectrum disorders. Developmental Neurorehabilitation, 13(3), 164–169. Maughan, B., Rowe, R., Messer, J., Goodman, R., & Meltzer, H. (2004). Conduct disorder and oppositional defiant disorder in a national sample: Developmental epidemiology. Journal of Child Psychology and Psychiatry, 45, 609–621. McCabe, K. M., Hough, R., Wood, P. A., & Yeh, M. (2001). Childhood and adolescent onset conduct disorder: A test of the developmental taxonomy. Journal of Abnormal Child Psychology, 29, 305–316. McCracken, J. T., McGough, J., Shah, B., Cronin, P., Hong, D., Aman, M. G., et al. (2002). Risperidone in children with autism and serious behavioral problems. New England Journal of Medicine, 347(5), 314–321.
Confidentiality Myrbakk, E., & von Tetzchner, S. (2008). Psychiatric disorders and behavior problems in people with intellectual disability. Research in Developmental Disabilities, 29(4), 316–332. Nock, M. K., Kazdin, A. E., Hiripi, E., & Kessler, R. C. (2006). Prevalence, subtypes, and correlates of DSMIV conduct disorder in the National Comorbidity Survey Replication. Psychological Medicine, 36, 699–710. Rogers, S. J., & Vismara, L. A. N. (2008). Evidence-based comprehensive treatments for early autism. Journal of Clinical Child and Adolescent Psychology, 37, 8–38. Shoham-Vardi, I., Davidson, P. W., Cain, N. N., SloaneReeves, J. E., Giesow, V. E., Quijano, L. E., et al. (1996). Factors predicting re-referral following crisis intervention for community-based persons with developmental disabilities and behavioral and psychiatric disorders. American Journal of Mental Retardation, 101(2), 109–117. Simonoff, E., Pickles, A., Charman, T., Chandler, S., Loucas, T., & Baird, G. (2008). Psychiatric disorders in children with autism spectrum disorders: Prevalence, comorbidity, and associated factors in a populationderived sample. Journal of the American Academy of Child and Adolescent Psychiatry, 47, 921–929. Tonge, B. J., & Einfeld, S. L. (2003). Psychopathology and intellectual disability: The Australian child to adult longitudinal study. In L. M. Glidden (Ed.), International review of research in mental retardation (Vol. 26, pp. 61–91). San Diego: Academic. World Health Organization. (1992). International classification of diseases: Diagnostic criteria for research (10th ed.). Geneva: Author.
Web-Sites American Academy of Child and Adolescent Psychiatry (AACAP). http://www.aacap.org ConductDisorders. http://www.conductdisorders.com Internet Mental Health. http://www.mentalhealth.com/
Confidentiality Celia Tam1 and James C. McPartland2 1 Yale Child Study Center, New Haven, CT, USA 2 School of Medicine, Child Study Center, Yale University, New Haven, CT, USA
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researchers to maintain the privacy of a patient’s personal health information and records unless consent to release the information is obtained from the patient. In the case of minors, this consent may be obtained from the patient’s parent (s) or legal guardian(s), and rules around confidentiality related to minors may vary by state.
Historical Background Confidentiality is important in order to establish trust between providers and patients and to protect patient privacy. Confidentiality can be bound by both ethical and legal standards. For example, the American Psychological Association’s Ethical Principles of Psychologists and Code of Conduct provides ethical guidelines regarding a psychologist’s obligation to take reasonable precautions to protect confidential information related to the patient (Standard 4; APA 1992). In 1996, The U.S. Department of Health and Human Services (HHS) established the Health Insurance Portability and Accountability Act (HIPAA), Public Law 104–191, and issued a Privacy Rule to implement the legal requirement for providers to protect the privacy of a patient’s medical records or personal health information, including information related to mental health. In the case of minors, according to HIPAA, parents have the legal right to access their child’s records and personal information. In certain specific situations, providers may share information without the patient’s consent. Common exceptions may include cases that involve a court order to release protected information, cases in which there is suspicion of domestic violence, abuse or neglect of children, the elderly, or people with disabilities, or in order to protect the patient or the public from serious harm (Tarasoff 1974, 1976).
Current Knowledge Definition Confidentiality refers to a general standard of professional conduct, as well as, in some cases, the legal requirement for providers and/or
Since it was created, the HIPAA has undergone several revisions and updates in order to further protect the confidentiality and privacy of health information, including electronic protected health
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information (PHI). In 2002, the HHS modified the Privacy Rule associated with HIPAA to set national standards for the protection of individually identifiable health information by health plans, health care clearinghouses, and health care providers. In 2003, HHS published a final Security Rule to set national standards for protecting the confidentiality of electronic PHI.
Confirmatory Factor Analysis Koocher, G. P., & Keith-Spiegel, P. C. (1990). Children, ethics, and the law: Professional issues and cases. Lincoln: University of Nebraska Press. Tarasoff v. Regents of the University of California, 118 Cal. Rptr.129, 529 P.2d.533 (Cal. 1974). Tarasoff v. Regents of the University of California, 113 Cal. Rptr. 14, 551 P.2d.334 (Cal. 1976). U.S. Department of Health and Human Services. Summary of the HIPAA Privacy Rule. Retrieved 1 Dec 2016 from http://www.hhs.gov/hipaa
Future Directions
Confirmatory Factor Analysis
Confidentiality remains a sensitive and, at times, controversial topic, particularly as it relates to determining exceptions or limitations to confidentiality, such as in the treatment of minors or when the risks may outweigh the benefits (e.g., Tarasoff 1974, 1976). Some have proposed guidelines and recommendations related to dealing with issues of confidentiality when working with various populations (APA 1992; Gustafson and McNamara 1987; Koocher and Keith-Spiegel 1990). Future investigations may focus on further delineating the ethical and legal boundaries and limits around cases of confidentiality. Furthermore, research should explore the important considerations to make with regards to assessing competence and respecting confidentiality in individuals with autism spectrum disorders and other developmental disabilities.
▶ Latent Variable Modeling
See Also ▶ Consent ▶ Ethics
References and Reading American Psychological Association. (1992). Ethical principles of psychologists and code of conduct. American Psychologist, 47, 1597–1611. Gustafson, K. E., & McNamara, J. R. (1987). Confidentiality with minor clients: Issues and guidelines for therapists. Professional Psychology: Research and Practice, 18(5), 503–508. Health Insurance Portability and Accountability Act of 1996 (HIPAA), Pub. L. No. 104–191, 110 Stat. 1936 (1996).
Congenital Aphasia ▶ Childhood Aphasia
Congenital Disorders Claudia Califano Yale-New Haven Hospital, New Haven, CT, USA
Definition Congenital disorders are those disorders that are present at the time of birth and involve an abnormality of structure and/or function that has arisen during development. Congenital disorders are not necessarily genetic though do include genetic disorders. All genetic disorders are congenital as they are present at birth even if they are not yet detected at birth. Congenital disorders may arise as a result of the intrauterine environment, errors in embryonic development, and infections. The outcome of such disorders varies widely and is dependent upon the disorder itself and the availability of possible postnatal treatments. Examples of congenital disorders include diseases such as cystic fibrosis, physical anomalies such as having a sixth finger on the hand, metabolic diseases such as congenital adrenal hyperplasia, and trisomy 21 which is also known as Down syndrome.
Conners’ Continuous Performance Test
See Also ▶ Chromosomal Abnormalities ▶ Genetics ▶ Inborn Errors of Metabolism
References and Reading Kliegman, R. (Ed.). (2007). Nelson textbook of pediatrics (18th ed.). Philadelphia: Saunders Elsevier. Stocker, J., & Dehner, L. (Eds.). (2001). Pediatric pathology. Philadelphia: Lippincott Williams & Wilkins.
Congenital Metabolic Diseases ▶ Inborn Errors of Metabolism
Conners’ Continuous Performance Test Renee Folsom1 and Philip Levin2 1 Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles (UCLA) The Help Group/UCLA Neuropsychology Program, Los Angeles, CA, USA 2 The Help Group – UCLA Neuropsychology Program, Los Angeles, CA, USA
Synonyms CCPT; CPT; CPT-II
Description The Conners’ Continuous Performance Test is an attention test for research and clinical settings (Conners 1995). It is used for measuring processes related to vigilance, response inhibition, signal detection, and other aspects of performance
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(Conners et al. 2003). The test is presented in a game-like format where 360 letters (approximately 1 in. in size and bold faced) appear on the computer screen, one at a time, for approximately 250 ms. Respondents are required to press the space bar or click the mouse button when any letter except the letter “X” appears on the screen (Conners and MHS Staff 2000). The CPT-II standard paradigm consists of six blocks, with each block divided into three 20-trial subblocks. Each sub-block has a separate interstimulus interval (i.e., the time in between the letter presentations). The inter-stimulus intervals (ISIs) are 1, 2, or 4 s. The order of the three different ISI conditions varies from block to block (Conners et al. 2003). The CPTII can be completed in 14 min. The test can be administered to participants 6 years of age and above. After the test session, the program generates a report that includes response times, omission errors (i.e., when a response is not given after a non-X appears on screen), commission errors (i.e., when a response is given after an X appears on screen), change in reaction time speed and consistency as the test progresses, and change in reaction time speed and consistency for different inter-stimulus intervals. Examination of the results by blocks and varying ISIs allows for the assessment of vigilance and the ability to adjust to changing tempo and task demands (Conners and MHS Staff 2000).
Historical Background The continuous performance test was first introduced by Rosvold and colleagues in 1956 (Spreen and Strauss 1998) to detect lapses of attention in patients with petit mal epilepsy. In this early version, the participants were required to press a key in response to a rare target, such as the letter “X.” Subsequent CPTs have made changes to this original paradigm including having the participants press a key when the target letter is preceded by another letter (e.g., “X” preceded by “A”) or upon the second successive presentation of a letter (e.g., S-S). There have also been variations with regard to modality (i.e., visual or auditory), the type of
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stimuli (e.g., letters, numbers, colors, or geometric figures), and the type of data that are evaluated (e.g., omissions, commissions, inter-stimulus interval, measures of sensitivity; Spreen and Strauss 1998). Conners’ introduction of his version of the CPT in 1995 represented a departure from the more traditional CPT paradigm. In the earlier versions, participants typically sit passively while observing the presentation of nontarget stimuli and must respond to the occasional target stimulus (usually an “X”). In Conners’ version, which is also sometimes called the “not-X” CPT, participants are asked to press a button on each trial (usually letters), except for the letter X. Barkley (2006) notes that this task requires a different form of response inhibition. Conners et al. explain that Conners’ “not-X” CPT places a greater demand on response inhibition due to the frequent responding interrupted by the occasional nontargets (the less probable “X”) as opposed to the more passive responding of the conventional “X” task. Conners has since come out with an updated version of his CPT, the Conners’ Continuous Performance Test (2nd ed.; Conners’ CPT-II; Conners and MHS Staff 2000). The updated version differs from the previous version in that it is based on new and expanded norms that include a large subsample of neurologically impaired individuals. This allows for comparison of responses to general population norms, ADHD norms, and neurologically impaired norms. The program itself includes validity checks to flag certain conditions that may adversely affect CPT II administration and a Confidence Index that enables the practitioner to gauge the certainty of the assessment/classification.
Psychometric Data The CPT-II normative data included 2,521 participants. Of this, 1,920 were healthy individuals from the general population, 378 were diagnosed with Attention-Deficit/Hyperactivity Disorder (ADHD), and 223 were adult individuals identified with some type of neurological impairment
Conners’ Continuous Performance Test
(e.g., head injuries, dementias). Normative data were collected from 30 sites in 16 states and three Canadian provinces. The multi-site, nonclinical data came from schools, organizations, science centers, and controlled research settings. The norms were divided into eight age groups. For children of ages 4 through 17, norms were provided in 2-year increments. For adults aged 18 and older, they were divided into three age groups (18–34, 35–54, and 55 +). The applicability of CPT-II norms to Asian and African American groups was also assessed. Scores for the Asian group were consistent with those obtained in the general population. However, the African American group made slightly fewer commission errors than the general population, and showed slightly better discriminatory power as measured by the statistic d prime. Overall, the general population norms were reportedly applicable to these minority groups. In fact, there were no significant differences on the overall profile indexes (Conners and MHS Staff 2000). Three types of reliability information were provided on the CPT-II manual: Split-half reliability, test-retest reliability, and standard error of measurement (Conners and MHS Staff 2000). The split-half reliability information from the original CPT was cited. These appeared adequate and ranged from 0.66 to 0.95. Test-retest reliability was obtained using 23 participants in the standardization of the CPT-II. The average interval between administrations was 3 months. The test-retest reliability estimates ranged from 0.05 to 0.92 with most of the variables showing good consistency across administrations. However, the Block change and ISI change statistics have low test-retest correlations, suggesting that these variables do not produce good consistency across administrations. When measures are combined into indices for ADHD and neurological assessment, the test-retest reliabilities were excellent, 0.89 and 0.92 respectively. Using the same test-retest data, it was also demonstrated that the CPT-II had no significant practice effect. In addition, information on standard error of measurement and standard error of prediction for the various CPT-II measures across gender and age was presented.
Conners’ Continuous Performance Test
Conners and the MHS Staff (2000) cited research to support the clinical utility of the CPT. In a study based on the original standardization sample, significant differences were seen between the ADHD group and other diagnoses across most of the CPT variables. The ADHD group responded more slowly, had greater variability of reaction times, made more omission and commission errors, and was more affected by changes in ISI. In similar analyses using the updated CPT-II data, no significant difference was observed between ADHD and nonclinical groups; for all other analyses, there was a large and significant difference between ADHD and nonclinical groups with the ADHD groups performing worse on all of the measures. For the adults aged 18 years and older, planned comparisons were done to see if the nonclinical group differed from the clinical groups, and if the two clinical groups differed from each other. As predicted, the clinical groups performed significantly worse than the nonclinical group. Compared to the ADHD group, the Neurological group made significantly more omission errors, had significantly slower reaction times, and was significantly less consistent across the interstimulus intervals.
Clinical Uses The CPT paradigm has traditionally been included in evaluations for ADHD. Barkley (2006) states that, “A wide-ranging literature has shown it to be the most reliable of psychological tests for discriminating groups of children with ADHD from nondisabled children” (p. 377). Spreen, Risser, and Edgell (1995) report that on a continuous performance task hyperactive children make more errors of omission and of commission, show more rapid deterioration in performance than controls, and are less able to inhibit premature or repetitive responding, indicating poor impulse control. Lezak, Howieson, and Loring (2004) state that on the CPT, adults with ADHD have a high reaction time variability and higher rate of commission errors than control subjects, which suggests that they have trouble inhibiting responses. According to Spreen
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and Strauss, the CPTs have also been shown to distinguish between normal controls and certain patient groups including adults with head injuries and children with conduct disorder, learning disabilities, and those at high risk for schizophrenia. In addition, Barkley and Spreen and Strauss report that CPTs are sensitive to stimulant drug effects among children and adolescents with ADHD. Barkley has raised some concern about the diagnostic utility of the Conners’ CPT, in particular, in ADHD assessments. Citing one study that investigated associations between Conners’ CPT scores and several other measures, including parent and teacher ratings as well as neuropsychological and achievement tests, Barkley reported that the Conners CPT’s overall index failed to relate to parent and teacher ratings. In addition, only half of those participants who met criteria for ADHD “failed” the CPT. Barkley also reported poor discriminant validity, in that children with a reading disability actually performed more poorly than children with ADHD. In another study on the ecological validity of the CPT-II in a schoolbased sample, Barkley cited findings showing nonsignificant relationships between CPT performance and three other kinds of measures (parent ratings, teacher ratings, and classroom observations). He also reported negative correlation between IQ and omission errors on the CPT-II, suggesting that the CPT-II may measure letter recognition skills or phonological awareness rather than impulsivity or inattention per se. Despite these concerns, Barkley still holds that the CPT is the only psychological measure that seems to directly assess the core symptoms of ADHD, namely, impulsivity and attention. However, he warns that if a child performs well on this measure, it does not indicate that the child is nondisabled or without ADHD because of the high rate of false negatives (i.e., children who are rated by parents and teachers as having ADHD, but who obtain average scores on the test) associated with CPTs. He joins Conners (2000) in reminding the clinician that the test provides one source of information to be integrated with other sources (e.g., self-report data, observerbased data, historical information, interview data,
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and results from other tests) in reaching a final diagnostic decision. The use of continuous performance tests in assessing children with autism spectrum disorders is less common. Three studies were found but none of them used the Conners’ CPT-II. In the first study, 23 children with autism were compared with two control groups (one matched based on verbal mental age and another based on performance mental age) on several attention measures including three versions of the traditional CPT paradigm (Pascualvaca et al. 1998). The results showed that none of the CPT versions differentiated between the groups. In the second study, Schatz, Weimer, and Trauner (2002) explored the use of the Test of Variables of Attention (TOVA) in assessing attention deficit symptoms in a group of eight children and young adults with Asperger’s Syndrome (AS). The TOVA is a continuous performance test that is similar to the Conners’ CPT but with an additional auditory component. Five of eight subjects with AS received scores that suggested the presence of an attention deficit, whereas only two of eight subjects received scores suggestive of an attention deficit. The authors looked at this as a pattern that could be explored using a bigger sample. In the third study, Corbett and Constantine (2006) compared children with autism spectrum disorder (ASD) with those that have been diagnosed with ADHD and typically developing children using the Integrated Visual and Auditory Continuous Performance Test, another version of a CPT. They found that children with ASD show significant deficits in visual and auditory attention and greater deficits in impulsivity than children with ADHD or typically developing children. The authors note that the findings suggest that many of the children with ASD demonstrate significant ADHD-like symptoms. They point out that this study adds to the growing literature that calls into question the current exclusionary practice of offering a diagnosis of ADHD in pervasive developmental disorders. Two other variables that might be relevant in the performance of individuals with autism spectrum disorders on the CPT-II are anxiety and intelligence. Conners and the MHS Staff presented data showing that anxiety may affect a participant’s CPT-II response style and lead to
Conners’ Continuous Performance Test
response inhibition for physiological anxiety, and decrease in response inhibition for cognitive anxiety. In terms of intelligence, the manual included two studies that showed nonsignificant correlations between IQ as measured by the WISC and CPT performance. Still it was noted in the manual that some individuals with severe cognitive impairment, agitation, or severe psychotic symptoms cannot be administered the CPT-II.
See Also ▶ Attention ▶ Attention Deficit/Hyperactivity Disorder
References and Reading Barkley, R. A. (2006). Attention-deficit hyperactivity disorder: A handbook of diagnosis and treatment (3rd ed.). New York: The Guilford Press. Conners, C. K. (1995). Conners’ continuous performance test computer program: User’s manual. Toronto: Multi-Health Systems. Conners, C. K., & MHS Staff. (2000). Conners’ continuous performance test (CPT II): Technical guide and software manual. Toronto: Multi-Health Systems. Conners, C. K., Epstein, J. N., Angold, A., & Klaric, J. (2003). Continuous performance test performance in a normative epidemiological sample. Journal of Abnormal Child Psychology, 31, 555–562. Corbett, B. A., & Constantine, L. J. (2006). Autism and attention deficit hyperactivity disorder: Assessing attention and response control with the integrated visual and auditory continuous performance test. Child Neuropsychology, 12, 335–348. Lezak, M. D., Howieson, D. B., & Loring, D. W. (2004). Neuropsychological assessment (4th ed.). New York: Oxford University Press. Pascualvaca, D. M., Fantie, B. D., Papageorgiou, M., & Mirsky, A. (1998). Attentional capacities in children with autism: Is there a general deficit in shifting focus? Journal of Autism and Developmental Disorders, 28, 467–478. Schatz, A. M., Weimer, A. K., & Trauner, D. A. (2002). Brief report: Attention differences in asperger syndrome. Journal of Autism and Developmental Disorders, 32, 333–336. Spreen, O., & Strauss, E. (1998). A compendium of neuropsychological tests (2nd ed.). New York: Oxford University Press. Spreen, O., Risser, A. H., & Edgell, D. (1995). Developmental neuropsychology. New York: Oxford University Press.
Conners’ Parent Rating Scale
Conners’ Parent Rating Scale Hillary Hurst Department of Psychology, University of Massachusetts Boston, Boston, MA, USA
Description The Conners’ Parent Rating Scale (CPRS) is a parent-report measure that assesses children’s problem behaviors, particularly symptoms of attention deficit hyperactivity disorder (ADHD) and related disorders (including oppositional defiant disorder and conduct disorder). At the time of publication, the Conners 3-P (2008) is the current version of the CPRS. Parents of children with autism spectrum disorders may be asked to complete the Conners 3-P because of the shared symptoms between ADHD and autism spectrum disorders. The Conners 3-P was developed by C. Keith Conners, Ph.D., who also designed two related measures: the Conners’ Teacher Rating Scales (CTRS), a teacher-report measure, and the Conners’ Self-Report Scales (CSRS), a selfreport measure for children and adolescents. Because these measures are meant to be used in conjunction, the family of Conners’ tests is considered to be a “multi-informant” mode of assessment. This is valuable because it can yield information about children’s behaviors in multiple settings. For example, asking a parent to complete the Conners 3-P and asking a teacher to complete the Conners 3-T (for “teacher”) can shed light on how a child’s behavior may differ between home and school.
Historical Background Gianarris, Golden and Greene (2001) provide a detailed overview of the multiple versions of the Conners’ Parent Rating Scales. The roots of the Conners 3-P date back to the 1960s, when C. Keith Conners, Ph.D., created behavior rating scales based on his multiple observations of children and adolescents with behaviors consistent
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with what is now known as ADHD. He performed factor analysis to determine how these behaviors fit together and first published his findings in 1970. One of the earliest aims of Conners’ work was to track changes in children’s behavior following medication use. In 1973, Conners released a 93-item checklist of behaviors that came to be known as the original Conners’ Parent Rating Scale. It was quickly adopted as a diagnostic tool even though it did not have a normative sample of the kind structured for empirical support that is required to establish a new assessment tool today. It was not until 1989 that Conners’ sample was formalized and expanded, and that the CPRS was published and shared widely. In 1998, the CPRS-R (for “revised”) was released, and in 2008, the third and most recent edition, the Conners 3-P, was released. The Conners 3-P is currently available in English and Spanish. The similarities and differences between these versions are explored below in the section “Psychometric Data.”
Psychometric Data The format and response style of the measure have remained quite consistent throughout the revisions of the Conners 3-P. In all three versions, the respondent (the parent completing the measure) is asked to reflect on his or her child’s actions over the past month and to respond to a series of items describing mainly problem behaviors (e.g., “gets distracted when given instructions to do something”). For each item, the respondent is asked to mark 0 (“not true at all/never/seldom”), 1 (“just a little true/occasionally”), 2 (“pretty much true/often/quite a bit”), or 3 (“very much true/very often/very frequent”) to describe the extent to which their child engages in or demonstrates the given behavior. The Conners 3-P has a long-form version (110 questions), a short-form version (45 of the 110 total questions), an ADHD Index (10 of the 110 total questions), and a Global Index (10 of the 110 questions). Previous versions also offered longer and shorter forms. There are circumstances in which one form would be preferable over
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another; this largely has to do with the reason for testing. For example, using the long-form version of the Conners 3-P might be preferable to using one of the shorter forms when conducting an initial assessment. Revisions were made (e.g., transitioning from the CPRS to the CPRS-R, and again from the CPRS-R to the Conners 3-P) in order to strengthen the psychometric properties of the instrument. Most available literature focuses on the psychometric properties of the CPRS-R and the Conners 3-P. The CPRS-R contains the following subscales: Oppositional (long and short forms), Social Problems (long form only), Cognitive Problems/Inattention (long and short forms), Psychosomatic (long form only), Hyperactivity (long and short forms), DSM-IV Symptom Subscales (long form only), Anxious-Shy (long form only), ADHD Index (long and short forms), Perfectionism (long form only), and Conners’ Global Index (long form only). The same subscales appear on the CTRS-R with the exception of the Psychosomatic subscale, which is not included. Both raw scores and T scores are generally reported for each subscale; T scores are standardized scores with a mean of 50 and a standard deviation of 10. For example, a child with a T score of 50 on the Oppositional subscale would have about the same level of oppositional behaviors as the average child his or her age in the normative sample. Higher T scores are associated with higher levels of problem behaviors. Internal consistency coefficients of the CPRS-R (in other words, measures of how well the individual items of the measure “hang” together and form the subscales listed above) for the total sample range from .77 to .96. The testretest reliability coefficients – a measure of how similarly a child will be rated shortly following an initial assessment with the CPRS-R – range from .47 to .86. As previously discussed, the CPRS-R is designed to be sensitive to changes in behavior, particularly following medication use, and this might explain partially the lower test-retest reliability coefficients. Some significant changes were made to the CPRS-R to create the Conners 3-P. The publishers of the Conners 3-P point to the large normative sample (n ¼ 1200) that reflects the 2000 US
Conners’ Parent Rating Scale
Census information on race and ethnicity, gender, and parental education level as particular strength of the measure. While the test creators were careful to include a diverse normative sample in their development of the Conners 3-P, it is important to point out that the validity of the Conners’ tests in diverse cultures has not yet been established and represents an active area to study. The Conners 3-P includes the use of 1-year, instead of 3-year, age bands to compare children’s scores to the normative sample (e.g., 4-year-olds are now compared only to other 4-year-olds, instead of to 4-, 5-, and 6-year-olds). Also, the Conners 3-P includes optional combined gender norms for boys and girls – the norms had been strictly separated by gender in the previous versions – and the combined norms can be helpful for understanding behavior within the context of settings, such as the classroom, that are very frequently coed. The Conners 3-P also has a greater focus on differential diagnosis (or, in other words, teasing apart symptoms of ADHD from symptoms of related disorders) and this is reflected in its normative sample. Additionally, the age range of the Conners 3-P, at 6–18 years old, is slightly narrower than the age range, 3–17 years, of the previous CPRS versions. The age range was extended to 18 years, 11 months to capture adjustment through the end of high school; it was limited to 6 years, 0 months so that early experiences can be assessed more thoroughly with a separate measure, the Conners Early Childhood (EC). The Conners 3-P contains the following content scales: inattention, hyperactivity/impulsivity, learning problems, executive functioning, aggression, and peer/family relations. It also contains four symptoms scales – ADHD Inattentive, ADHD Hyperactive-Impulsive, Conduct Disorder, and Oppositional Defiant Disorder – that map onto the diagnostic criteria put forth in the Diagnostic and Statistical Manual of Mental Disorders, fourth edition, text revision (DSM-IVTR). New to the Conners 3-P include validity scales (which indicate how a parent is responding to the items and whether or not the information he or she provides is interpretable), screening items for childhood anxiety and depression, critical items (which require immediate follow-up by the researcher or clinician administering the
Conners’ Parent Rating Scale
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measure), and impairment items (which indicate decreased functioning in certain life areas, like social relationships). The Conners 3-P is also notable for the way in which it maps onto the Individuals with Disabilities Education Act (IDEA); in other words, how a child is rated by his or her parent on the Conners 3-P might carry implications for the services he or she is eligible to receive in school. Both test-retest reliability and internal consistency have been found to be very good for the Conners 3-P, and for the overall family of Conners 3 assessments. According to data put forth by the publisher, internal consistency coefficients (in other words, measures of how well the individual items of the measure “hang” together and form the subscales listed above) for the overall content scales is .91 and for the DSM-IV-TR scales, .90. A breakdown of internal consistency coefficients by Conners 3-P subscale are the following: inattention ¼ .93, hyperactivity/impulsivity ¼ .94, learning problems ¼ .90, executive functioning ¼ .92, aggression ¼ .91, peer relations ¼ .85, ADHD Inattentive ¼ .93, ADHD Hyperactive-Impulsive ¼ .92, Conduct Disorder ¼ .83, and Oppositional Defiant Disorder ¼ .83. The 2–4-week test-retest reliability coefficients – a measure of how similarly a child will be rated 2 weeks and again 4 weeks following an initial assessment with the Conners 3-P – was also very good in the overall Conners’ sample (Cronbach’s alpha ¼ .71 to .98, with all correlations significant at the p 20 years, with further breakdown for those over 60). Jang et al. (2014) found that 94% of all studies (n ¼ 2688) which they collected and analyzed examined infants, toddlers, children, and adolescents (between 0 and 19 years of age). In contrast, 3% of studies (n ¼ 75) examined adults on the autism spectrum aged 60 years or older. Jang et al. (2014) concluded that most of our knowledge about the autism spectrum has been obtained from infants, children, and adolescents. Furthermore, based on this finding, Jang et al. (2014) proposed that more research should be conducted on the adults since most of a person’s life occurs in adulthood.
Current Knowledge There are publications which have reviewed the types of research published about elderly people on the autism spectrum (Bennett 2016; Happé and Charlton 2012; Patra 2016; van Heijst and Geurts 2015; van Niekerk et al. 2011; Wright et al. 2013). Bennett (2016) cited five studies about elderly people on the autism spectrum which examined executive functions and memory, diagnostic instruments used to identify Asperger syndrome in seniors, and diagnosing depression in elderly people with Asperger syndrome. Happé and Charlton (2012) published a literature review focused on articles which explored elderly people on the autism spectrum. The studies which they collected were assigned to one of three categories: (1) case studies of elderly people on the autism spectrum, (2) discussion papers which outlined the challenges of diagnosing autism in seniors, and (3) articles explaining empirical findings from surveys of studies about elderly people with autism. Patra (2016) published a systematic literature review of studies
Elderly with Autism Spectrum Disorders
about seniors on the autism spectrum stored on the PubMed electronic citation database. They identified 12 English language studies which met their inclusion criteria. The oldest study which they retrieved was published in 2013, and the most recent study was published in June 2016. The studies which they examined explored the physical health, psychological well-being, and cognitive or social functioning of elderly people with autism. Finally, van Niekerk et al. (2011) published a systematic review of studies about the instruments used to diagnose the autism spectrum in elderly people. They searched for relevant articles on PubMed, PsychLIT, and EMBASE which were published from 1996 to December 2009. They identified three studies which met their inclusion criteria. These studies examined all showed the importance of identifying the autism spectrum in the elderly and explained the long-term consequences of not identifying this condition within the elderly. In summary, there is a paucity of literature about elderly on the autism spectrum. Aside from systematic literature reviews, studies have also examined the executive functions and memory abilities of elderly people with autism (Geurts and Vissers 2012). Geurts and Vissers’s (2012) study compared the neuropsychological profile of elderly adults on and not on the autism spectrum. They sampled 23 elderly adults on the autism spectrum (18 males, 5 females; mean age ¼ 63.6 years, SD ¼ 7.5 years) and 23 elderly adults not on the autism spectrum (18 males, 5 females; mean age ¼ 63.7 years, SD ¼ 8.1 years). In addition, out of 23 elderly adults on the autism spectrum, 16 had Asperger syndrome, 2 had autism, 1 had a diagnosis of pervasive developmental disorder-not otherwise specified, 1 had an autism spectrum disorder diagnosis, and the remaining 3 had autistic tendencies. In comparison to those not on the autism spectrum, Geurts and Vissers (2012) found that elderly people on the autism spectrum had deficits with their working memory and had different cognitive profiles and trajectories of cognitive development. Jang et al. (2014) posited that most studies about the autism spectrum focused on children
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rather than adults or elderly. It is possible that a barrier to research around elderly people is less likely to have been diagnosed with an autism spectrum disorder. According to Bennett (2016), current assessment processes and instruments used to diagnose autism have not been tailored to identify autism in the elderly. Often an autism diagnosis is based on a mixture of clinical tests, clinical observations of the patient, and the patient’s family providing the clinician with a description of their relative’s behaviors. For patients who are elderly, however, such an approach might not be effective in diagnosing autism. During old age, elderly people often live alone or in nursing homes where regular contact with immediate or extended family members is limited (Bennett 2016). Additionally, James et al. (2006) explain that the questions which clinicians use in autism diagnostic assessments for children might not be appropriate for seniors. For example, while a clinician might be able to diagnose a child with autism by asking him/her questions about his/her friendships, such questions might not be applicable for seniors because their social network is smaller and they might have less social contacts. Furthermore, Geurts and Vissers (2012) explain that the clinical tests used to diagnose children on the autism spectrum are difficult to translate and apply to elderly people on the autism spectrum. Finally, Mukaetova-Ladinska et al. (2012) explain that, aside for developing tests to diagnose the autism spectrum in the elderly, there is also a need to develop cognitive and behavioral assessment tools for elderly people who are on the autism spectrum.
Future Directions Despite the small amount of literature about elderly people on the autism spectrum systematically increasing each year, there are still many areas of research to be explored (Bennett 2016; Happé and Charlton 2012; Hategan et al. 2017; Totsika et al. 2010). Bennett (2016) proposed that the impact of nursing home care programs on elderly people on the autism spectrum is an area yet to be researched. In addition, Happé and
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Charlton (2012) explained that social isolation often has a detrimental impact on elderly people not on the autism spectrum. However, it is unknown if seniors on the autism spectrum who engage in solitary hobbies and interests, which they find enjoyable and motivating, can somehow shield themselves from the negative impacts of social isolation. Michael, a person on the autism spectrum, has also voiced views about the importance of researching elderly people on the autism spectrum (Michael 2016). According to Michael (2016), very little is known about how elderly people on the autism spectrum manage medical conditions which occur during middle or older age, such as arthritis, menopause, type 2 diabetes, and some types of cancer. Similarly, Kats et al. (2013) published a study about clinical problems which older adults with autism and an intellectual disability exhibited. They found that there was an urgent need for research about the nature and treatment of behavioral problems in elderly on the autism spectrum. Finally, Perkins and Berkman’s (2012) article showed that people on the autism spectrum have a lower life expectancy than those not on the autism spectrum. However, it remains unknown why there is a life expectancy discrepancy. While increasing our knowledge and understanding about elderly people on the autism spectrum is a sensible goal, it cannot, however, be perused without support. Researchers may also struggle to work with elderly adults on the autism spectrum to gain an insight into their lived experience which may impact on their willingness to research in this area. Communication and emotional difficulties can be exacerbated in old age, and there has been little impetus to look at the well-being or economic costs of this growing population so far. Piven and Rabins (2011) explained that a prolonged, integrated, and multipronged approach is required to study seniors on the autism spectrum from a psychological, sociological, and economic perspective. Piven and Rabins (2011) also propose that clinical and research training programs should develop ways to attract and retain early career researchers, from a diverse range of disciplines, to study elderly people on the autism
Elderly with Autism Spectrum Disorders
spectrum. Similarly, Bennett (2016) suggested that governments should increase funding for projects which can evaluate healthcare systems and services for seniors on the autism spectrum. Support services which are effective will be of benefit to both elderly on the autism spectrum and the wider community in terms of improving social outcomes and increasing economic efficiency. Although there have been many recent developments in the study of elderly on the autism spectrum (Wise et al. 2017), there remains many areas where additional research can be conducted (Amanullah and Rajeh 2019). For example, despite literature showing that the elderly are often financially (Wajnberg et al. 2020) and physically abused (Yon et al. 2020), there is a dearth of literature about these topics for elderly on the autism spectrum. In the future, researchers should explore if senior citizens on the autism spectrum encounter greater rates of elder abuse than those who are not on the autism spectrum. They might possibly experience greater rates of elder abuse since their social difficulties might leave them with greater vulnerabilities (Flynn and Healy 2012). As Bennett (2016) has explained, most of the children currently living with an autism spectrum diagnosis, at current the focus of research, will inevitably become elderly. If the amount of research about elderly people on the autism spectrum does not increase, then today’s children on the autism spectrum may be disadvantaged in their old age. Furthermore, if there is a lack of research about elderly on the autism spectrum in the future, then the needs of elderly people with this condition will become the subject of speculation. To avoid this speculation altogether, and to help future generations of elderly people on the autism spectrum, it is crucial that research about the autism spectrum in old age continues to increase.
References and Reading Amanullah, S., & Rajeh, A. (2019). An overview of autism in the elderly. Asian Journal of Psychiatry, 101, 897. https://doi.org/10.1016/j.ajp.2019.101897.
Electroconvulsive Therapy (ECT) Bennett, M. (2016). “What is life like in the twilight years?” A letter about the scant amount of literature on the elderly with autism spectrum disorders. Journal of Autism and Developmental Disorders, 46(5), 1883–1884. Flynn, L., & Healy, O. (2012). A review of treatments for deficits in social skills and self-help skills in autism spectrum disorder. Research in Autism Spectrum Disorders, 6(1), 431–441. https://doi.org/ 10.1016/j.rasd.2011.06.016. Geurts, H. M., & Vissers, M. E. (2012). Elderly with autism: Executive functions and memory. Journal of Autism and Developmental Disorders, 42(5), 665–675. Happé, F., & Charlton, R. A. (2012). Aging in autism spectrum disorders: A mini-review. Gerontology, 58(1), 70–78. Hategan, A., Bourgeois, J. A., & Goldberg, J. (2017). Aging with autism spectrum disorder: An emerging public health problem. International Psychogeriatrics, 29(4), 695–697. James, I. A., Mukaetova-Ladinska, E., Reichelt, F. K., Briel, R., & Scully, A. (2006). Diagnosing Aspergers syndrome in the elderly: A series of case presentations. International Journal of Geriatric Psychiatry, 21(10), 951–960. Jang, J., Matson, J. L., Adams, H. L., Konst, M. J., Cervantes, P. E., & Goldin, R. L. (2014). What are the ages of persons studied in autism research: A 20-year review. Research in Autism Spectrum Disorders, 8(12), 1756–1760. Kanner, L. (1943). Autistic disturbances of affective contact. The Nervous Child, 2(3), 217–250. Kats, D., Payne, L., Parlier, M., & Piven, J. (2013). Prevalence of selected clinical problems in older adults with autism and intellectual disability. Journal of Neurodevelopmental Disorders, 5(1), 27. Michael, C. (2016). Why we need research about autism and ageing. Autism, 20(5), 515–516. Mukaetova-Ladinska, E. B., Perry, E., Baron, M., & Povey, C. (2012). Ageing in people with autistic spectrum disorder. International Journal of Geriatric Psychiatry, 27(2), 109–118. Patra, S. (2016). Autism spectrum disorder in the elderly: A review of healthcare issues and challenges. Journal of Geriatric Care and Research, 3(1), 3–6. Perkins, E. A., & Berkman, K. A. (2012). Into the unknown: Aging with autism spectrum disorders. American Journal on Intellectual and Developmental Disabilities, 117(6), 478–496. Piven, J., & Rabins, P. (2011). Autism spectrum disorders in older adults: Toward defining a research agenda. Journal of the American Geriatrics Society, 59(11), 2151–2155. Totsika, V., Felce, D., Kerr, M., & Hastings, R. P. (2010). Behavior problems, psychiatric symptoms, and quality of life for older adults with intellectual disability with and without autism. Journal of Autism and Developmental Disorders, 40(10), 1171–1178.
1663 van Heijst, B. F., & Geurts, H. M. (2015). Quality of life in autism across the lifespan: A meta-analysis. Autism, 19(2), 158–167. van Niekerk, M. E., Groen, W., Vissers, C. T. W., van Driel-de Jong, D., Kan, C. C., & Voshaar, R. C. O. (2011). Diagnosing autism spectrum disorders in elderly people. International Psychogeriatrics, 23(5), 700–710. Wajnberg, A., Koppel, S., & Kahan, F. (2020). Elder abuse and neglect. In Home-based medical care for older adults (pp. 41–47). Cham: Springer. https://doi.org/10. 1007/978-3-030-23483-6_7. Wise, E. A., Smith, M. D., & Rabins, P. V. (2017). Aging and autism Spectrum disorder: A naturalistic, longitudinal study of the comorbidities and behavioral and neuropsychiatric symptoms in adults with ASD. Journal of Autism and Developmental Disorders, 47(6), 1708–1715. Wright, S. D., Brooks, D. E., & Grandin, T. (2013). The challenge and promise of autism spectrum disorders in adulthood and aging: A systematic review of the literature (1990–2013). Autism Insights, 5, 21. Yon, Y., Lam, J., Passmore, J., Huber, M., & Sethi, D. (2020). The public health approach to elder abuse prevention in europe: progress and challenges. In Advances in elder abuse research (pp. 223–237). Cham: Springer. https://doi.org/10.1007/978-3-03025093-5_15.
Elective Mutism ▶ Selective Mutism
Electroconvulsive Therapy (ECT) Nathalie Szilagyi Yale Child Study Center, New Haven, CT, USA
Synonyms ECT
Definition Electroconvulsive Therapy (ECT) is a medical procedure in which a seizure (convulsion) is
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induced via the external application of brief, focused electrical current in order to effect alterations in the electrophysiology of the brain with the goal of mitigating severe psychiatric symptoms. Although the exact mechanism of action remains unclear, evidence suggests that ECT may produce changes across multiple domains, including neuroendocrine systems, neurottransmitter tone and activity, and neuroplasticity. For example, studies have provided evidence that ECT potentiates the effects of the neurotransmitters serotonin, norepinephrine, and dopamine and increases cortical Gamma-Aminobutyric Acid (GABA) concentration. ECT has also been shown to be a strong inducer of dendritic arborization and new cell formation in the hippocampus, as well as reducer of dendritic arborization and excitatory synapses in the amygdala. These findings are consistent with the clinical effects observed with ECT: improvements in depressive and manic symptoms and suicidality, mitigation of catatonia, and decreased severity of some Parkinsonian symptoms. More recent studies have also reported decreased frequency of self-injurious behaviors after ECT. Per guidelines from the American Psychiatric Association and the American Academy of Child and Adolescent Psychiatry, ECT may be indicated for the treatment of adolescents and adults with persistent, significantly disabling or lifethreatening mood disorders such as depression or mania, psychosis, or catatonia refractory to other treatment modalities. ECT can also be used to treat neuroleptic malignant syndrome, a rare but potentially fatal condition that can occur in patients taking neuroleptic medications. Inadequate evidence exists regarding the use of ECT in preadolescents. However, multiple studies across decades of research in adolescents and adults have shown ECT to have efficacy superior to other treatment modalities for indicated conditions, with response rates for severe, intractable mood disorders, and catatonia as high as 80%. More recently, researchers have reported success in using ECT to decrease severe, repetitive
Electroconvulsive Therapy (ECT)
self-injurious behavior in both typically developing patients with psychiatric comorbidities and patients with Autism Spectrum Disorder (ASD). As noted in other sections, repetitive, restricted, and stereotyped behavior is a primary feature of ASD. In up to 25% of individuals with ASD, such behavior is manifested as self-injurious behavior (SIB), which can range from occasional, mild episodes to frequent, unrelenting, and severe behaviors, leading to potentially serious injuries. Sterotyped movements also form part of the diagnostic criteria for catatonia, and some researchers have posited that catatonia has been historically underdiagnosed and inadequately treated in individuals with ASD. In fact, several large studies have estimated that 12–18% of autistic adolescents and young adults exhibit some symptoms of catatonia. While first-line treatments for SIB and catatonia include medication and (for SIB) behavioral interventions, some individuals present with severe and disabling symptoms refractory to these treatments. Although more research is needed, ECT may be considered as an option in patients with such refractory symptoms in order to decrease patient distress and avoid potentially catastrophic outcomes. There are no absolute medical or neurological contraindications to the use of ECT, though certain CNS tumors, active chest infections or cardiac conditions are relative contraindications. Likewise, there are no psychiatric comorbidities or developmental disabilities which would be contraindications to the procedure when it is otherwise indicated. ECT has been shown to be a relatively safe procedure in modern use, welltolerated by most patients receiving treatment. Modern ECT is performed under brief general anesthesia, and the reported mortality rate of up to four deaths per 100,000 treatments is believed to be primarily due to the risks associated with the general anesthesia. Of note, the mortality rate of ECT remains significantly lower than mortality rates for the intractable depression, mania, and catatonia for which ECT is indicated. Common side effects after ECT sessions include transient headache, nausea, fatigue, confusion, difficulties
Electroencephalogram (EEG)
in learning, and memory loss. Of these, memory loss has often been reported to be the most troubling to patients, and although it is most often transient or self-limited, there have been credible reports of persistent or even permanent memory loss, though usually limited to the time period around treatment. A course of ECT for acute psychiatric symptoms usually includes from 6 to 20 treatments, with the number of treatments generally determined by each patient’s response to treatment and tolerance of the procedure. Some patients also appear to benefit from maintenance ECT to provide ongoing relief and prevent recurrence of symptoms.
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Electroencephalogram (EEG) Benjamin Aaronson Psychiatry and Behavioral Sciences, UW Autism Center, University of Washington, Seattle, WA, USA
Synonyms Electroencephalography
Definition References and Reading American Academy of Child and Adolescent Psychiatry, Ghaziuddin, N., et al. (2004). Practice parameter for use of electroconvulsive therapy with adolescents. Journal of the American Academy of Child and Adolescent Psychiatry, 43(12), 1521–1539. American Psychiatric Association; Committee on Electroconvulsive Therapy, Weiner, R. D. (chairperson), et al. (2001). The practice of electroconvulsive therapy: recommendations for treatment, training and privileging (2nd ed.). Washington, DC: American Psychiatric Publishing. D’Agati, D., Chang, A. D., Wachtel, L. E., & Reti, I. M. (2017). Treatment of severe self-injurious behavior in autism Spectrum disorder by neuromodulation. Journal of ECT, 33(1), 7–11. Mazzone, L., Postorino, V., Valeri, G., & Vicari, S. (2014). Catatonia in patients with autism: Prevalence and management. CNS Drugs, 28, 205–215. McCall, W. V., Andrade, C., & Sienart, P. (2014). Searching for the mechanism(s) of ECT’s therapeutic effect. Journal of ECT, 30(2), 87–89. Reti, I. M. (2015). How does electroconvulsive therapy work? In I. M. Reti (Ed.), Brain stimulation: Methodologies and interventions (1st ed., pp. 107–122). Hoboken: Wiley Blackwell. Sadock, B. J., & Sadock, V. A. (2007). Brain stimulation methods. In B. J. Sadock & V. A. Sadock (Eds.), Kaplan and Sadock’s synopsis of psychiatry: Behavioral sciences/clinical psychiatry (10th ed., pp. 1117–1124). Philadelphia: Wolters Kluwer. Wachtel, L. E., Dhossche, D. M., & Kellner, C. H. (2011). When is electroconvulsive therapy appropriate for children and adolescents? Medical Hypotheses, 76, 395–399.
An electroencephalogram is a graphical representation of the brain’s electrical activity. In human assessment, electrodes are placed at various locations on the scalp. As neurons in the brain fire, these electrodes capture the resulting voltage, which is then amplified and recorded. This electrical activity oscillates quite rapidly. The number of oscillations over a phase of time can be measured and are termed “frequency.” Variations in frequency have been historically quantified in terms of oscillations per 1-s period as hertz (Hz). Common categorizations of frequencies include alpha (8–13 Hz), beta (13–30 Hz), gamma (30–600 Hz), delta (4 Hz), theta (4–8 Hz), and mu (8–13 Hz). A subtype of electroencephalography examines changes in brain activity time-locked to particular events, termed event-related potentials (ERPs). ERPs are generally assessed in small windows of time, in the tens and hundreds of milliseconds.
See Also ▶ Electroencephalography ▶ Event-Related Potential ▶ Evoked Potentials ▶ Mu Rhythm
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References and Reading Andreassi, J. L. (2007a). Psychophysiology: Human behavior & physiological response (5th ed.). Mahwah: Lawrence Erlbaum Associates. Andreassi, J. L. (2007b). The nervous system and measurement of its activity. In J. T. Cacioppo, L. G. Tassinary, & G. G. Berntson (Eds.), Handbook of psychophysiology (3rd ed., pp. 85–119). Cambridge, UK: Cambridge University Press. Herrmann, C. S., Grigutsch, M., & Busch, N. (2005). EEG oscillations and wavelet analysis. In T. C. Handy (Ed.), Event-related potentials: A methods handbook (pp. 229–259). Cambridge, MA: MIT Press.
Electroencephalography
windows of time, in the tens and hundreds of milliseconds.
See Also ▶ Electroencephalogram (EEG) ▶ Event-Related Potential ▶ Evoked Potentials ▶ Mu Rhythm
References and Reading
Electroencephalography Benjamin Aaronson Psychiatry and Behavioral Sciences, UW Autism Center, University of Washington, Seattle, WA, USA
Synonyms
Andreassi, J. L. (2007a). Psychophysiology: Human behavior & physiological response (5th ed.). Mahwah: Lawrence Erlbaum Associates. Andreassi, J. L. (2007b). The nervous system and measurement of its activity. In J. T. Cacioppo, L. G. Tassinary, & G. G. Berntson (Eds.), Handbook of psychophysiology (3rd ed., pp. 85–119). Cambridge, UK: Cambridge University Press. Herrmann, C. S., Grigutsch, M., & Busch, N. (2005). EEG oscillations and wavelet analysis. In T. C. Handy (Ed.), Event-related potentials: A methods handbook (pp. 229–259). Cambridge, MA: MIT Press.
Electroencephalogram (EEG)
Definition
Electrophysiology
Electroencephalography (EEG) is the recording of brain’s electrical activity. In human assessment, electrodes are placed at various locations on the scalp. As neurons in the brain fire, these electrodes capture the resulting voltage, which is then amplified and recorded. This electrical activity oscillates quite rapidly. The number of oscillations over a phase of time can be measured and are termed “frequency.” Variations in frequency have been historically quantified in terms of oscillations per 1-s period as hertz (Hz). Common categorizations of frequencies include alpha (8–13 Hz), beta (13–30 Hz), gamma (30–600 Hz), delta (4 Hz), theta (4–8 Hz), and mu (8–13 Hz). A subtype of electroencephalography examines changes in brain activity time-locked to particular events, termed event-related potentials (ERPs). ERPs are generally assessed in small
▶ Neurophysiology
Elementary and Secondary Education Act (No Child Left Behind) Kristin Ruedel Department of Special Education, University of Maryland Washington State University, Richland, WA, USA
Definition The No Child Left Behind (NCLB) Act of 2001 is the most recent reauthorization of the
Elementary and Secondary Education Act (No Child Left Behind)
Elementary and Secondary Education Act (ESEA). It was signed into law on January 8, 2002, and received an overwhelming bipartisan majority in both the House and the Senate. NCLB represents the most significant expansion of the federal government into education in our history as it has dramatically increased federal mandates and requirements on states, school districts, and public schools. The Obama administration released its blueprint for revising the Elementary and Secondary Education Act (ESEA) on March 13, 2010. The primary goals of NCLB are to increase achievement of all students in American public schools; reduce the achievement gap based on race, ethnicity, language, socioeconomic status, and disability; and ensure that students in every public school achieve important learning goals while being educated in safe classrooms by wellprepared teachers. NCLB is based on the following key principles: 1. Accountability for results: By the 2005–2006 school year, each state must test students annually in grades 3 through 8 in reading and math and students in grades 10–12 at least once in reading and math. Every student will be assessed according to each state’s standard for proficiency in reading/language arts, math, and science by the end of the 2013–2014 school year. Student test scores must be made accessible to parents and communities. Test score data must be disaggregated by identified subgroups such as race/ethnicity, socioeconomic status, and disability. Schools that do not demonstrate “adequate yearly progress” (AYP) in student proficiency levels at large and within each of the subgroups must provide students with supplemental services such as free tutoring or after-school assistance. 2. Parental choice: Schools must provide parents with a report about the overall achievement of students in the school their child
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attends, the qualifications of their child’s teacher, and school safety. Parents of children in schools that are not making AYP for at least two consecutive years have the option of transferring their child to an alternate school within the school district. Parents also have the right to transfer their student if the school their child is attending is considered persistently dangerous or if their child has been a victim of a violent crime. 3. Teacher quality: Each state must develop a plan to ensure that all teachers of core academic subjects will be highly qualified by the end of the 2005–2006 school year. Highly qualified means that each teacher has full certification and a bachelor’s degree and has passed a state-administered test on core academic subject knowledge. 4. Scientifically based methods of teaching: There is an emphasis on the use of scientifically based methods of teaching under NCLB. Schools that fail to meet adequate student achievement goals are required to use scientifically based instructional methods in order to remain open. 5. Local flexibility: NCLB grants states and school districts with unprecedented flexibility in how they use federal education funds under NCLB in order to permit school districts to respond to specific local problems.
References and Reading ESEA reauthorization: A blueprint for reform. U.S. Department of Education. Retrieved 6 June 2011, from http://www2.ed.gov/policy/elsec/leg/blueprint/ index.html. Overview and introduction to the No Child Left Behind Act. U.S. Department of Education. Retrieved 7 June 2001, from http://www2.ed.gov/nclb/landing.jhtml. The No Child Left Behind Act of 2001. U.S. Department of Education. Retrieved 6 June 2011, from http:// www2ed.gov/policy/elsec/leg/esea02/index.html. Yell, M. L., & Drasgow, E. (2005). No child left behind act: A guide for professionals. Upper Saddle River: Merrill/ Prentice Hall.
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Elevated, Expansive, or Irritable Mood ▶ Mania
Elevated, Expansive, or Irritable Mood
approaches to autism used worldwide today – was inspired, at least in part, by Sybil Elgar’s pioneering work in the United Kingdom. Dr. Eric Schopler, who founded TEACCH in North Carolina in the 1970s, visited the Sybil Elgar School in the United Kingdom in the previous decade and was very impressed by what he saw.
Elgar, Sybil Adam Feinstein Autism Cymru and Looking Up, London, UK
Major Appointments (Institution, Location, Dates) Sybil Elgar was a pioneer in the education and care of both children and adults with autism both in the United Kingdom and internationally. As Lorna Wing has written, Elgar was “the UK’s first autism-specific teacher and an inspiration to those who knew her, at a time when autism was still illdefined and widely misunderstood.” In 1964, Sybil Elgar and the newly formed Society for Autistic Children (now the National Autistic Society) managed to purchase premises to open the world’s first residential school for children with autism, in Ealing, West London. Elgar was a pioneer twice over, as she also founded Somerset Court, the first residential community for adults with autism, in Brent Knoll, Somerset, in 1974.
Major Honors and Awards Sybil Elgar was a founding member of the British National Autistic Society. She was awarded member of the Order of the British Empire (MBE) in 1975.
Short Biography Sybil Lilian Elgar was born in Willesden, North West London, on June 10, 1914. After leaving school, she became a town clerk’s assistant before getting a job as a school secretary. She had no money to go to college, and staying at home to be with her then widowed and unwell mother, she took a Montessori teaching diploma as a correspondence course. Her interest in helping children was first sparked during her Montessori training when a visit to a hospital in London for “severely emotionally disturbed children” in 1958 left her deeply shocked. Elgar revisited the hospital in 1960 and, seeing that nothing had changed, decided to set up her own school, initially in the basement of her home. She developed a structured approach to teaching, giving her pupils clear and simple instructions and visual aids to ensure they understood what was required of them. Her methods ran counter to mainstream educational thought at the time. However, all the children in her class made substantial progress. Demand for her teaching rapidly grew, and in 1964, Elgar bought the premises to open the world’s first residential school for children with autism, in Ealing, West London. She also founded Somerset Court, the first residential community for adults with autism, in Brent Knoll, Somerset, in 1974. Elgar died on January 8, 2007, at the age of 92.
Landmark Clinical, Scientific, and Professional Contributions There is some evidence to suggest that the structured approach which forms the basis of TEACCH – one of the main educational
References and Reading Feinstein, A. (2010). A history of autism: Conversations with the pioneers. Oxford, England: Wiley-Blackwell.
Eligibility (for Services Under IDEA/ADA, etc.)
Eligibility (for Services Under IDEA/ADA, etc.) John W. Thomas Independent Educational Consultant, Durham, NC, USA Quinnipiac University School of Law, Hamden, CT, USA
Definition “Eligibility” in the context of this encyclopedia refers to qualification to receive reimbursement for or benefits from a variety of private and public programs.
Historical Background Eligibility issues regarding ASD patients may present in private health care insurance, public health care insurance, education, disability benefits, and antidiscrimination contexts.
Current Knowledge Private Health Care Insurance Treatment for ASD can be costly, with annual expenses exceeding $50,000 (NCSL) and mean lifetime care expenses totaling $3.2 million (Ganz 2006). As a result, eligibility for private health insurance coverage for ASD-related treatment can be important to people with ASD and their families. Insurance coverage is especially difficult to obtain for behavioral therapies such as Applied Behavioral Analysis therapy (ABA). Kaiser Permanente, for example, classifies ABA therapy as educational rather than clinical treatment and, thus, denies insurance reimbursement for its cost (Reinke 2008). In response to insurance company reluctance to cover treatment for ASD, state legislatures have begun to address the issue. At this writing, the legislatures of 35 states and the District of
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Columbia have enacted laws mandating some types of insurance coverage for the treatment of ASD. Statutes in 23 of those states – Arizona, Colorado, Connecticut, Florida, Illinois, Indiana, Iowa, Kansas, Kentucky, Louisiana, Maine, Massachusetts, Missouri, Montana, Nevada, New Hampshire, New Jersey, New Mexico, Pennsylvania, South Carolina, Texas, Vermont, and Wisconsin – make specific reference to treatment for ASD or autism. The remaining dozen mandate coverage for mental health conditions that may include ASD. The types of coverage mandated under these laws vary from diagnostic testing to a variety of therapies and treatment options. Some laws prohibit insurers from setting premium or coinsurance rates based solely on an ASD diagnosis. State laws, however, are not applicable to all insurance policies. The federal Employee Retirement Income Security Act (ERISA) exempts selffunded, employer-provided health insurance plans from state coverage mandates (ERISA). As a result, in 2009, the United States Congress considered enacting the Autism Treatment Acceleration Act. But, the bill failed to pass either in the house of representatives or the senate. As of this writing, there is no federal mandate that insurers cover treatments or therapies for ASD. Public Health Insurance: Medicaid Conventional Medicaid Eligibility
Medicaid is a national health care program funded jointly by the federal and state governments. Adopted in 49 states and the District of Columbia and represented in the fiftieth state, Arizona, by a similar program, Medicaid provides services to the poor. Eligibility is largely limited to those with incomes no higher than the federal poverty level, with slightly higher incomes allowed for women, children, and other groups. Those who meet the income and other resource prerequisites qualify for a wide range of health care benefits, including treatment for ASD. Services available to those with ASD vary by state. Medicaid insurers in some states cover a wide range of services, including Applied Behavioral Analysis (ABA). In other states, insurers are
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more reluctant to cover ABA and other treatment modalities for ASD. In 2003, the Centers for Medicare and Medicaid Services issued a directive that characterized ABA as habilitative rather than rehabilitative and, thus, not mandatory coverage. In response, many state Medicaid agencies have ceased providing reimbursement for ABA (Mauch et al. 2011). Medicaid “Waiver” Eligibility
All 49 states, the District of Columbia, and Arizona also participate in the Medicaid waiver program. Under these programs, parental financial resource limitations are “waived” for certain services for people under the age of 21 with developmental disabilities or long-term illnesses. These waiver programs cover home- and communitybased services (HCBS). A person qualifies for HCBS services if he or she otherwise “would require the level of care provided in a hospital or a nursing facility or intermediate care facility for the mentally retarded” (42 U.S.C. § 1396n(c)). Thirty states have ASD-specific waiver programs that delineate the specific services (Autism NOW). Connecticut’s waiver, for example, covers “residential habilitation, personal supports, respite, clinical behavioral supports, supported employment, job coaching, community transition services, life skills coaching, community transition services or short term crisis stabilization to remain in their own home, family home or other community home.” Patient Protection and Accountable Care Act (PPACA) By 2014, the PPACA, which congress enacted in 2010, will expand the Medicaid program to cover nearly all people with incomes up to 133% of the federal poverty level. The PPACA will also preclude Medicaid from considering in an eligibility determination resources other than income. Disability Benefits The Federal Social Security Program offers two types of disability benefits for which children with ASD may qualify.
Eligibility (for Services Under IDEA/ADA, etc.)
Supplemental Security Income (SSI) The SSI program provides monthly payments to children from birth to age 18. Children whose parents’ income and other financial resources are within the statutory limits are eligible for benefits if they are not “engaged in substantial gainful activity” and are diagnosed with an impairment that meets SSI’s disability definition for children. Listed impairments include “mental disorders,” which encompasses ASD. At SSI’s enactment in 1973, the Social Security Administration (SSA) required for eligibility for benefits that a child’s impairment be “comparable in severity” to an impairment that would qualify an adult for benefits. In 1996, congress modified the standard by enacting the Personal Responsibility and Work Opportunity Reconciliation Act of 1996 (PRA). This Act replaced the “comparable severity” standard with a test to determine whether a child suffers a “marked and severe functional limitation.” Eligibility for benefits requires two findings. First, the child’s impairment must be “medically determinable.” Second, the impairment must “meet, medically equal, or functionally equal” a listed impairment (20 CFR §416.92 and §416.926a). SSA regulations establish six “domains” to determine functional equivalence to a listed impairment: “acquiring and using information,” “attending and completing tasks,” “interacting and relating with others,” “moving about and manipulating objects,” “caring for yourself,” and “health and physical well-being.” A child must exhibit a “marked” limitation in at least two of these categories or an “extreme” limitation in one. Factors relevant to these determinations include “the child’s age; the effects of treatment and/or medication; the effect of structured settings; the need for assistive devices or adaptations; school functioning, including attendance; time spent in therapy; the effects of chronic illness; and the combined effects of multiple impairments.” An “extreme” limitation exists when the impairment or impairments “very seriously” interfere with the ability to initiate, sustain, or complete activities independently. This status is consistent with a
Eligibility (for Services Under IDEA/ADA, etc.)
score three standard deviations below the mean on an applicable standardized test. A “marked” limitation exists when the impairment or impairments “seriously” interfere with those same skills. This status is consistent with a score of at least two but fewer than three standard deviations below the mean on an applicable standardized test. The first step for determining eligibility for SSI benefits based on ASD, the “medically determinable” inquiry, requires a finding of deficiencies in “reciprocal social interaction and in verbal and nonverbal communication and imaginative activity, and a markedly restricted repertoire of activities and interests” (Ruskell). The second step typically finds “functional equivalence” to the “mental disorders” listing and resolves not on a finding of “extreme” limitation in a single domain but on a “marked” limitation in two domains, most commonly in “attending and completing tasks,” “interacting and relating with others,” “caring for yourself,” and “health and physical well-being.” Finally, the ASD limitation must have existed for at least 12 months. Social Security Disability Insurance (SSDI) SSDI provides benefits to adults who have a disability that began before they reached the age of 22 years. Because they are based on the Social Security earning record of the beneficiary’s parents, the social Security Administration considers SSDI to be a “child’s” benefit. A disabled adult is entitled to SSDI benefits if one of his or her parents is currently receiving Social Security retirement or disability benefits or died after working long enough to qualify for some form of Social Security benefit. In addition, a “child” is eligible for SSDI benefits if he or she received dependent benefits on a parent’s Social Security earnings record prior to reaching the age of 18. SSDI eligibility differs from SSI eligibility in that the recipient must meet the impairment definition for adults. The adult disability impairments include “autistic disorder and other pervasive developmental disorders.” Finally the “child” must not be engaged any substantial work.
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Education: The Individuals with Disabilities Education Act (IDEA) IDEA is a federal law that mandates the availability of a Free Appropriate Public Education (FAPE) for all eligible children with disabilities. IDEA defines “disability” as a person “(1) with mental retardation, hearing impairments . . . speech or language impairments, visual impairments . . . serious emotional disturbance . . . orthopedic impairments, autism, traumatic brain injury, other health impairments, or specific learning disabilities . . . (2) who needs special education and related services because of his or her disability or disabilities” (IDEA § 802, emphasis supplied). Thus, children with ASD are eligible for IDEArelated services. IDEA’s regulations define autism as “a developmental disability significantly affecting verbal and nonverbal communication and social interaction, generally evident before age three that adversely affects a child’s educational performance. Other characteristics often associated with autism are engagement in repetitive activities and stereotyped movements, resistance to environmental change or change in daily routines, and unusual responses to sensory experiences.” A child with ASD is not eligible for IDEArelated services if the “child’s educational performance is adversely affected primarily because the child has an emotional disturbance.” An “emotional disturbance” is “a condition exhibiting one or more of the following characteristics over a long period of time and to a marked degree that adversely affects a child’s educational performance”:
• “An inability to learn that cannot be explained by intellectual, sensory, or health factors” • “An inability to build or maintain satisfactory interpersonal relationships with peers and teachers” • “Inappropriate types of behavior or feelings under normal circumstances” • “A general pervasive mood of unhappiness or depression”
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• “A tendency to develop physical symptoms or fears associated with personal or school problems” IDEA’s regulations also classify schizophrenia as an emotional disturbance. For those children diagnosed with ASD and not an emotional disturbance, Part C of the IDEA, Infants and Toddlers with Disabilities, provides for developmental services for children from birth to 3 years of age. Part B, Assistance for Education of All Children with Disabilities, mandates educational services for children from ages 3 to 21. The educational requirements of Part B include an individualized education program (IEP) for each child and demand the placement of each child in the least restrictive environment (LRE) possible. An “appropriate” education must address a child’s specific educational needs. Determining what is appropriate entails several steps. The responsible state actor must conduct an individualized assessment to ascertain a student’s strengths and weaknesses. Next, an IEP team, comprising representative of the school district, a teacher, the child’s parents, and, if appropriate, the child, must identify appropriate goals and objectives for the student and construct an IEP designed to aid the student in meeting the goals and objectives. Finally, the IEP team is charged with identifying the aids and services necessary for the child to succeed in the IEP. These services consist of “transportation and such developmental, corrective, and other supportive services as are required to assist a child with a disability to benefit from special education.” Specifically, they may include:
• Audiology • Counseling services • Early identification and assessment of disabilities in children • Medical services for diagnosis or evaluation • Occupational therapy • Parent counseling and training • Physical therapy
Eligibility (for Services Under IDEA/ADA, etc.)
• • • • • • •
Psychological services Recreation Rehabilitation counseling School health services Social work services Speech pathology Transportation An IEP must include:
• “A statement of the child’s present levels of academic achievement and functional” • “A statement of measurable annual goals including academic and functional” • “A description of how the child’s progress toward meeting the annual goals will be measured” • “A statement of the special education and related services and supplementary aids and services . . . to be provided” • “An explanation of the extent, if any, to which the child will not participate with nondisabled children in the regular class and in extracurricular and other nonacademic activities” • “A statement of any individual appropriate accommodations that are necessary to measure the academic achievement and functional performance of the child” • “The projected date for the beginning of the services and program modifications and the anticipated frequency, location, and duration of those services and modifications” • “Beginning not later than the first IEP to be in effect when the child is 16 and updated annually thereafter: appropriate measurable postsecondary goals” IDEA conditions state receipt of federal funding on meeting its minimum requirements. States may not provide fewer services but may provide greater services. The needs of children vary, and, as a result, children with ASD will be eligible for varying IEPs and services. Their needs and the services to which they are entitled will also likely change over time.
Eligibility (for Services Under IDEA/ADA, etc.)
Antidiscrimination: The Americans with Disabilities Act (ADA) The ADA prohibits discrimination on the basis of disability in employment, state and local governmental services, public accommodations, transportation, and telecommunications. A person is “disabled” if he or she has a physical or mental impairment that substantially limits one or more major life activities, has a history or record of impairment, or is perceived by others as having an impairment. The ADA does not enumerate the impairments that are “substantially limiting,” leaving that question to the regulators and courts. In the decade and a half following the ADA’s enactment in 1990, the federal courts, including the United States Supreme Court, issued a number of decisions construing the statutory phrase very narrowly. This resulted in the denial of many ADA claims, including claims of those with ASD. In response, congress enacted the Americans with Disabilities Act Amendments Act of 2008 (ADAAA). The ADAAA’s central feature was a command that “the definition of disability in this Act shall be construed in favor of broad coverage of individuals under this Act, to the maximum extent permitted under the terms of the Act.” Specifically, the ADAAA expanded the definition of “major life activities” by articulating a non-exhaustive list of the activities: “caring for oneself, performing manual tasks, seeing, hearing, eating, sleeping, walking, standing, lifting, bending, speaking, breathing, learning, reading, concentrating, thinking, communicating, and working.” In addition, the Act included “major bodily functions” within “major life activities” and presented a non-exhaustive list of these functions: “functions of the immune system, normal cell growth, digestive, bowel, bladder, neurological, brain, respiratory, circulatory, endocrine, and reproductive functions.” The ADAAA also mandated that, with the exception of eyeglasses and contact lenses, the determination that impairment substantially limits a major life activity “be made without regard to the ameliorative effects of mitigating measures” such as medication and other “assistive
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technology.” Most importantly for people with ASD, the determination must be made without regard to “learned behavioral or adaptive neurological modifications.” A temporary impairment, or one that is “episodic or in remission” qualifies as a disability even when inactive “if it would substantially limit a major life activity when active.” Examples would include posttraumatic stress disorder, epilepsy, or cancer. On May 24, 2011, new regulations designed to implement the ADAAA and issued by the Equal Employment Opportunity Commission (EEOC), the agency charged with enforcing the ADA, went into effect. Echoing the ADAAA, the regulations provide that “[t]he definition of disability . . . shall be construed broadly, to the maximum extent permitted by the terms of the ADA.” More importantly, the regulations announce that the ADAAA shifts the focus of an ADA claim from whether a disability exists to “whether discrimination occurred.” Furthermore, the regulations provide that the question whether an individual is “substantially limited” in a major life activity “should not demand extensive analysis” and “usually will not require scientific, medical, or statistical analysis.” Most importantly for those with ASD, the regulations provide the first explicit recognition that autism constitutes an ADA-recognized impairment. Indeed, Section 1630.2(j) (3) (iii) provides that “in virtually all cases,” a number of conditions, including autism, meet the definition of disability. According to the EEOC, “autism substantially limits brain function” and thus “will, at a minimum, substantially limit . . . major life activities.” As a result of the ADAAA and its accompanying regulations, those diagnosed with ASD are assured eligibility for remedies should they experience discrimination in employment, governmental services, public transportation, public accommodations, or in telecommunications. In the employment context, the ADA prohibits discrimination in recruitment, hiring, promotions, training, pay, social activities, and other employment-related privileges. Governmental services include public education, employment,
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transportation, recreation, health care, social services, courts, voting, and town meetings. Public transportation includes city busses, subways, trains, and the like. Public accommodations include businesses and nonprofit service providers that are open to the public. These enterprises include privately operated educational facilities, transportation services, and commercial facilities such as restaurants, retail stores, hotels, movie theaters, private schools, convention centers, doctors’ offices, homeless shelters, transportation depots, zoos, funeral homes, day care centers, and recreation facilities including sports stadiums and fitness clubs. Accommodations accorded those with ASD vary according to a person’s impairments and the context. In the educational context (also covered by the Individuals with Disabilities Education Act (IDEA)), accommodations include special education teachers, teacher aides, more frequent feedback from teachers, and help with learning strategies or study skills. Other accommodations include the use of technological aids such as calculators, computer software, and audiobooks. In the employment context, ASD-related accommodations include the articulation of clear job expectations, mandates that superiors and colleagues communicate in a direct manner, minimization of multitask assignments, and the use of instructional visuals.
Future Directions Eligibility for private and public health insurance will be subject in the future to the fortunes of the Patient Protection and Affordable Care Act (PPACA). Eligibility for social security, educational, and antidiscrimination benefits will be subject to the priorities and directions of federal and state legislatures.
See Also ▶ Individuals with Disabilities Education Act (IDEA) ▶ PL94-142
Eligibility (for Services Under IDEA/ADA, etc.)
References and Reading 20C.F.R. (2012). Ch. III, Pt. 404, Sbpt. P., Appen. 1, §12.10. 29 Code of Federal Regulations (2012). §1630.2. Administration, S. S. (2009). SSR 09-1p: Title XVI: Determining childhood disability under the functional equivalence rule – The “whole child” approach. Federal Register, 74(30), 7527. http://www.ssa.gov/OP_ Home/rulings/ssi/02/SSR2009-01-ssi-02.html. Americans with Disabilities Act (2008). 42. U.S.C. §§12101 et seq. Autism Now. http://autismnow.org/funding-and-publicpolicy/funding-and-public-policy-introduction/ Autism Treatment Acceleration Act of 2009, S. 819, H.R. 2413 (2008). Barner, A. (2009). Unlocking access to insurance coverage for autism treatment. Journal of Law, Economics & Policy, 6, 107–135. Bates, M. W. (1994). Free appropriate public education under the individuals with disabilities education act: Requirements, issues, and suggestions. Brigham Young University Education and Law Journal, 215–222. Breslin, M. A. (2009). No child left behind and the inherent conflict with the individuals with disabilities education act: Leaving special education students further behind. Albany Government Law Review, 2, 653–676. Caruso, D. (2010). Autism in the U.S.: Social movement and legal change. American Journal of Law & Medicine, 36, 483–539. Department of Social Services, Department of Developmental Services, Notice of Intent to Seek Three Medicaid Waivers for Individuals with Autism Spectrum Disorders Who Do Not Also Have a Diagnosis of Mental Retardation. (2011). Retrieved July 5, 2012, from http://www.ct.gov/dds/cwp/view.asp?a¼2730& Q¼476378 Employee Retirement Income Security Act (ERISA) (2012). 29 USC §1001, et. Seq. Ganz, M. (2006). The costs of autism. In S. Moldin & J. Rubenstein (Eds.), Understanding autism: From basic neuroscience to treatment. New York: CRC Press. Hinson, C. (2009). A supreme paradox: Autism spectrum disorder and Rowley misapplication of judicial relic to an unprecedented social epidemic. Florida A & M University Law Review, 5, 87–105. Holland, C. D. (2010). Autism, insurance, and the IDEA: Providing a comprehensive legal framework. Cornell Law Review, 95, 1253–1282. IDEA Regulations (2010). § 300.8 Child with a disability. Individuals with Disabilities Education Act (IDEA) (2006). 20 U.S.C. §§ 1400, et seq. Mauch, D., Pfefferle, S., Booker, C., Pustell, M., & Levin, J. (2011). Report on state services to individuals with autism spectrum disorders (ASD), Centers for Medicare & Medicaid Services (CMS) ASD Services Project 2011. Cambridge, MA: ABT Associates.
Elopement National Conference of State Legislators. (2010). Insurance coverage for autism. Washington, DC: NCSL Press. Patient Protection and Affordable Care Act (2012). Pub. L. No. 111–148, § 2001(a)(1), 124 Stat. 272–75. Regulations to Implement the Equal Employment Provisions of the Americans With Disabilities Act, as Amended. (2011). Retrieved July 5, 2012, from http:// www.federalregister.gov/articles/2011/03/25/20116056/regulations-to-implement-the-equalemployment-provisions-of-the-americans-withdisabilities-act-as#p-326 Reinke, T. (2008). States increasingly mandate special autism services. Managed Care, 17(8), 35–36. Ruskell, R. C. (2010). Social Security Disability Claims Handbook. §2:33. Social Security Act of 1965 (2012). 42 U.S.C. §§ 13961396W-5. Social Security Administration. (2008). Disability Evaluation Under Social Security, §12.00 Mental Disorders – Adult. Retrieved July 5, 2012, from http://www.ssa. gov/disability/professionals/bluebook/12.00MentalDisorders-Adult.htm
ELM Scale-2 ▶ Early Language Milestone Scale
Elopement Allan M. Andersen1, Paul H. Lipkin2,3 and J. Kiely Law2,3 1 Department of Psychiatry, University of Iowa, Iowa City, IA, USA 2 Department of Medical Informatics, Kennedy Krieger Institute, Baltimore, MD, USA 3 Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD, USA
Definition Elopement behavior, sometimes termed “wandering,” is defined as the leaving of a supervised, safe space by a dependent person, such as a person with Autism Spectrum Disorder (ASD). It refers to incidents lasting longer than a brief period of
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running away, which is developmentally normal in toddlers but is rare in the typically developing population, particularly in those over 5 years of age (Anderson et al. 2012).
Historical Background Historically, elopement was grouped with other “challenging behaviors” commonly encountered among individuals with ASD and/or Intellectual Disability (ID), such as aggressive and disruptive behavior, and received little specific attention (Dekker et al. 2002; Doehring et al. 2014; Matson and Nebel-Schwalm 2007; Matson and Rivet 2008; Murphy et al. 2005). The majority of research specifically focused on elopement behavior has been conducted on populations of individuals with dementia rather than ASD, potentially limiting applicability of findings to the ASD population (Algase et al. 2009; Cipriani et al. 2014; Lai and Arthur 2003). Within ASD-related literature, the small body of published studies on elopement consist largely of case reports or small case series reporting on efforts to manage the problem using behavior analytic approaches, particularly Applied Behavioral Analysis (ABA), and the use of related interventions such as functional communication training, differential reinforcement of other behaviors, and noncontingent reinforcement (Call et al. 2011; Falcomata et al. 2010; Lang et al. 2010; Perrin et al. 2008; Piazza et al. 1997; Stevenson et al. 2016; Tarbox et al. 2003).
Current Knowledge Interest in the problem of elopement has increased in the last decade as a result of increasing awareness of its potentially deadly consequences for individuals with ASD. High profile incidents including drownings and traffic accidents prompted calls for more research focused on understanding and preventing elopement (The Autism Wandering Awareness Alerts Response and Education (AWAARE) Collaboration; McIlwain and Fournier 2012). In parallel, research on the causes
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of elevated mortality among individuals with ASD (Gillberg et al. 2010; Guan and Li 2017; Mouridsen 2013; Schendel et al. 2016), including one study documenting a 40-fold increased risk of drowning for these individuals, (Guan and Li 2017), pointed toward elopement as a potentially important modifiable risk factor for individuals with ASD. Increased awareness of the risks of elopement and advocacy by parents and ASD organizations (McIlwain and Fournier 2010) led to the first large-scale study of elopement among individuals with ASD. Surveying over 1200 caregivers of children with ASD recruited via the Interactive Autism Network (IAN), an online research database and autism registry, Anderson and colleagues (2012) reported that 49% of children with ASD over the age of 4 had reported attempts of elopement at least once after the age of 4. Rates in children with ASD were dramatically higher than in their unaffected siblings, particularly among older children. Among survey respondents, 26% reported their children had been missing long enough to cause concern, with 24% of them reported in danger of drowning and 65% in danger of a traffic injury. Elopement risk was correlated with greater ASD severity, with an increased risk of 9% on average for each 10-point increase in Social Responsiveness Scale (SRS) T score. Families reported high levels of stress and lost opportunities to participate in activities outside the home due to elopement, yet only 50% of families reported receiving any advice or guidance on addressing the behavior. Unfortunately, as noted by the authors, the study was not able to provide an estimate on the number of fatalities specifically due to elopement due to inclusion of only living children with ASD. Subsequent to the study of Anderson and colleagues, two other large-scale epidemiological studies published in 2016 provided populationbased estimates of elopement prevalence and correlates of the behavior. Utilizing the Centers for Disease Control (CDC) “Pathways” Survey data, consisting of telephone survey responses from over 4000 parents of children with a developmental disability, including ASD and Intellectual Disability (ID) or Developmental Delay (DD), Rice
Elopement
and colleagues (2016) reported on rates of elopement behavior occurring within the year prior to parental survey participation, with the highest rate (37.7%) seen among children with ASD and ID, followed by children with ASD but not ID (32.7%) and those with only ID (23.7%). Rice and colleagues also reported an elevated risk of elopement in male children with ASD versus female children with ASD (OR 2.79, CI 1.03–7.57; P < 0.05). Children with ASD and ID had the highest use of prevention measures for elopement (40.8%), compared with approximately 1 in 4 children with ASD without ID, or ID without ASD. However, Rice and colleagues noted that despite the high rate of use of prevention measures, details on the use of specific measures and their effectiveness were lacking, as was information on the circumstances and motivations for elopement in different individuals with ASD. Analyzing the same CDC “Pathways” dataset, Kiely and colleagues (2016) reported that 26.7% of children with ASD, ID/Developmental Disability (DD), or both had eloped within the previous year, with the highest rates among children with ASD and ID (34.6%), followed by ASD only (32.7%) and ID/DD only (22.7%), consistent with the analysis of Rice and colleagues. Kiely and colleagues similarly found that greater ASD severity, as indicated by the Children’s Social Behavior Questionnaire (CSBQ), was correlated with elopement behavior, consistent with the findings of Anderson and colleagues (2012). Despite increasing awareness of the prevalence of EB and associated risks, little evidence has been available to guide families and caregivers in managing the behavior (McIlwain and Fournier 2010). Responding to this knowledge gap, advocacy organizations have provided resources for caregivers such as the National Autism Association’s “Big Red Safety Toolkit” (National Autism Association 2014), which includes recommendations for preventative interventions such as door alarms, shoe ID tags, and visual prompts, as well as a recommendation for swimming lessons in order to reduce drowning risk. At the same time, the use of tracking technologies such as GPS-enabled bracelets has continued to receive interest as a means of alerting
Elopement
caregivers when individuals with ASD elope (Hayward et al. 2016). In particular, following the tragic 2013 death of Avonte Oquendo during an elopement incident, a 14-year-old boy from Queens, New York, with ASD, New York Senator Charles Schumer pledged federal funding for tracking devices, reflecting hope that widespread use of these devices could be an effective means of locating missing children. Despite this interest, adoption of tracking devices has been limited thus far, as reflected by Rice and colleagues who found only about 3% of parents using them (Rice et al. 2016), potentially due to associated costs as well as limited evidence on their effectiveness (Hayward et al. 2016). Research into behavioral treatments for elopement also continues to be an area of active investigation, (Boyle et al. 2019; Lehardy et al. 2013) with one recent study reporting a substantial reduction in elopement occurrences (Cohen’s d ¼ 1.18) in a sample of 11 subjects treated at an intensive day treatment clinic for an average of 37 sessions (Call et al. 2017). Overall, however, as reported in a 2017 review of functional analysis and function-based treatments for elopement by Boyle and colleagues (2017), the body of evidence supporting behavioral therapies for elopement is limited, with Boyle and colleagues finding only 12 studies totaling 20 subjects that met criteria for review and noting that “conclusions regarding effectiveness of treatments should be drawn tenuously.” Boyle and colleagues noted inherent challenges in the study of elopement from a functional behavioral perspective, in particular that the consequences of the behavior are confounded by the requirement that the clinician retrieves the child before harm occurs and that the behavior itself may be infrequent and therefore unlikely to occur during the period of observation. To address the knowledge gap in real-world effectiveness of interventions for elopement behavior such as those noted above, Andersen and colleagues (2019) reported on caregiver responses to elopement and elopement patterns, using a sample of over 500 caregivers of children with ASD recruited via IAN. Most respondents reported using multiple types of interventions to prevent elopement, finding this strategy in general
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to be effective but burdensome. Tracking devices were used infrequently and not rated as highly effective, whereas some environmental interventions such as fencing and window locks were rated as highly effective. The use of behavioral specialists to address the behavior was also common and rated as effective. Given high rates of off-label psychotropic medication administration reported among individuals with ASD with challenging behaviors (Matson and Neal 2009), the frequency and effectiveness of medications used for elopement was also surveyed. Although offlabel use of psychotropic medications to reduce elopement was reported by 16% of respondents, most commonly antipsychotics (29%), no medications were rated as effective in preventing the behavior, and most were associated with high rates of side effects. In reporting on elopement patterns in this study, the most common locations for the behavior were the home (71%) and parks or outdoor spaces (49%). The most commonly associated motivations included anxious situations (43%) or running or exploring for enjoyment (41%). Similar to prior reports, the frequency of elopement behavior was associated with greater levels of ASD severity, as indicated by higher SRS scores, comorbid ID, Language Disorder (LD), and other challenging behaviors including aggression, selfinjury, and disruptive behavior. Interestingly, children with ASD and comorbid ADHD were found to be significantly more likely to elope due to having “too much energy” (P < 0.001), whereas those with comorbid Anxiety Disorder, NOS were significantly more likely to elope from “stressful” environments (P < 0.001), supporting the hypothesis that motivations underlying elopement behavior vary and that different approaches to preventing the behavior may be required depending on the individual.
Future Directions High priorities for preventing elopement and related injury or death include improving understanding of its causes and the identification of effective measures to prevent the behavior.
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Significant advances have been made in the last decade in our understanding elopement’s prevalence and correlates, while evidence on the relative effectiveness of different interventions for elopement remains significantly limited. Available research suggests that caregivers have responded to this lack of evidence by employing whatever measures are available to them to prevent their loved ones with ASD from suffering elopement-related death or injury, but that these efforts come at a substantial cost in terms of daily burden and stress. Going forward, more effective tools to identify high risk individuals and prevent or respond to elopement among individuals with ASD are greatly needed. In addition, efforts to overcome barriers to implementing interventions that have some evidence of effectiveness such as fencing and window locks, may reduce the risks of injury and death among this population whose families already shoulder significant economic burdens associated with the disorder (Lavelle et al. 2014). Thankfully, with the addition of an ICD billing code for “wandering in diseases classified elsewhere” (Z91.83), reimbursement for elopement behavior in individuals with ASD, ID, and dementia may be possible, offering hope that families will be able to meet some of the expense associated with preventing and treating the behavior. Improved awareness of the prevalence and risks of elopement on the part of clinicians will also be essential in recognizing the behavior and offering families guidance and treatment strategies. At the public health level, efforts to mobilize existing resources and develop new strategies to protect children who elope will continue to be high priorities. Community agencies such as local police departments and school districts should continue to work with families to identify individuals at high risk for elopement, develop preventative strategies, and respond appropriately when elopement occurs. Finally, continued efforts by organizations such as the National Autism Association (2014) and the National Center for Missing and Exploited Children (2019) to both provide families with resources on managing elopement and advocate for more research on
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this important clinical problem will be essential to creating a future where these vulnerable individuals are safe from harm.
References and Reading Algase, D. L., Antonakos, C., Beattie, E. R., Beel-Bates, C. A., & Yao, L. (2009). Empirical derivation and validation of a wandering typology. Journal of the American Geriatrics Society, 57(11), 2037–2045. Andersen, A. M., Law, J. K., Marvin, A. R., & Lipkin, P. H. (2019). Elopement patterns and caregiver strategies. Journal of Autism and Developmental Disorders. https://doi.org/10.1007/s10803-019-03961-x. Anderson, C., Law, J. K., Daniels, A., Rice, C., Mandell, D. S., Hagopian, L., & Law, P. A. (2012). Occurrence and family impact of elopement in children with autism spectrum disorders. Pediatrics, 130(5), 870–877. https://doi.org/10.1542/peds.2012-0762. Boyle, M. A., & Adamson, R. M. (2017). Systematic review of functional analysis and treatment of elopement (2000–2015). Behavior Analysis in Practice, 10(4), 375–385. Boyle, M. A., Keenan, G., Forck, K. L., & Curtis, K. S. (2019). Treatment of elopement without blocking with a child with autism. Behavior Modification, 43(1), 132–145. Call, N. A., Pabico, R. S., Findley, A. J., & Valentino, A. L. (2011). Differential reinforcement with and without blocking as treatment for elopement. Journal of Applied Behavior Analysis, 44(4), 903–907. Call, N. A., Alvarez, J. P., Simmons, C. A., Lomas Mevers, J. E., & Scheithauer, M. C. (2017). Clinical outcomes of behavioral treatments for elopement in individuals with autism spectrum disorder and other developmental disabilities. Autism, 21(3), 375–379. https://doi.org/10. 1177/1362361316644732. Cipriani, G., Lucetti, C., Nuti, A., & Danti, S. (2014). Wandering and dementia. Psychogeriatrics, 14(2), 135–142. Dekker, M. C., Koot, H. M., Ende, J. v. d., & Verhulst, F. C. (2002). Emotional and behavioral problems in children and adolescents with and without intellectual disability. Journal of Child Psychology and Psychiatry, 43(8), 1087–1098. Doehring, P., Reichow, B., Palka, T., Phillips, C., & Hagopian, L. (2014). Behavioral approaches to managing severe problem behaviors in children with autism spectrum and related developmental disorders: A descriptive analysis. Child and Adolescent Psychiatric Clinics of North America, 23(1), 25–40. https://doi. org/10.1016/j.chc.2013.08.001. Falcomata, T. S., Roane, H. S., Feeney, B. J., & Stephenson, K. M. (2010). Assessment and treatment of elopement maintained by access to stereotypy. Journal of Applied Behavior Analysis, 43(3), 513–517.
Elopement Gillberg, C., Billstedt, E., Sundh, V., & Gillberg, I. C. (2010). Mortality in autism: A prospective longitudinal community-based study. Journal of Autism and Developmental Disorders, 40(3), 352–357. https://doi.org/ 10.1007/s10803-009-0883-4. Guan, J., & Li, G. (2017). Injury mortality in individuals with autism. American Journal of Public Health, 107(5), 791–793. https://doi.org/10.2105/AJPH.2017.303696. Hayward, B., Ransley, F., & Memery, R. (2016). GPS devices for elopement of people with autism and other developmental disabilities: A review of the published literature. Journal of Policy and Practice in Intellectual Disabilities, 13(1), 69–74. https://doi.org/10.1111/jppi. 12148. Kiely, B., Migdal, T. R., Vettam, S., & Adesman, A. (2016). Prevalence and correlates of elopement in a nationally representative sample of children with developmental disabilities in the United States. PLoS One, 11(2), e0148337. https://doi.org/10.1371/journal. pone.0148337. Lai, C. K., & Arthur, D. G. (2003). Wandering behaviour in people with dementia. Journal of Advanced Nursing, 44(2), 173–182. Lang, R., Davis, T., O’Reilly, M., Machalicek, W., Rispoli, M., Sigafoos, J., . . . Regester, A. (2010). Functional analysis and treatment of elopement across two school settings. Journal of Applied Behavior Analysis, 43(1), 113–118. Lavelle, T. A., Weinstein, M. C., Newhouse, J. P., Munir, K., Kuhlthau, K. A., & Prosser, L. A. (2014). Economic burden of childhood autism spectrum disorders. Pediatrics, 133(3), e520–e529. https://doi.org/10.1542/ peds.2013-0763. Lehardy, R. K., Lerman, D. C., Evans, L. M., O’Connor, A., & Lesage, D. L. (2013). A simplified methodology for identifying the function of elopement. Journal of Applied Behavior Analysis, 46(1), 256–270. https://doi. org/10.1002/jaba.22. Matson, J. L., & Neal, D. (2009). Psychotropic medication use for challenging behaviors in persons with intellectual disabilities: An overview. Research in Developmental Disabilities, 30(3), 572–586. https://doi.org/ 10.1016/j.ridd.2008.08.007. Matson, J. L., & Nebel-Schwalm, M. (2007). Assessing challenging behaviors in children with autism spectrum disorders: A review. Research in Developmental Disabilities, 28(6), 567–579. Matson, J. L., & Rivet, T. T. (2008). Characteristics of challenging behaviours in adults with autistic disorder, PDD-NOS, and intellectual disability. Journal of Intellectual & Developmental Disability, 33(4), 323–329. https://doi.org/10.1080/13668250802492600. McIlwain, L., & Fournier, W. (2010). Wandering and autism: The need for data and resources. National Autism Association. Retrieved from https://iacc.hhs. gov/meetings/iacc-meetings/2010/full-committee-mee ting/october22/slides_fournier_mcilwain_102210.pdf McIlwain, L., & Fournier, W. (2012). Lethal outcomes in autism spectrum disorders (ASD) wandering/
1679 elopement. National Autism Association, January. Retrieved from http://nationalautismassociation.org/ wp-content/uploads/2012/01/Lethal-Outcomes-InAutism-Spectrum-Disorders_2012.pdf Mouridsen, S.-E. (2013). Mortality and factors associated with death in autism spectrum disorders:-a review. American Journal of Autism, 1, 17–25. Murphy, G. H., Beadle-Brown, J., Wing, L., Gould, J., Shah, A., & Holmes, N. (2005). Chronicity of challenging behaviours in people with severe intellectual disabilities and/or autism: A total population sample. Journal of Autism and Developmental Disorders, 35(4), 405–418. National Autism Association. (2014). Big red safety toolkit. Retrieved from http://nationalautismas sociation.org/docs/BigRedSafetyToolkit.pdf National Center for Missing and Exploited Children. (2019). Autism wandering tips. Retrieved from http:// www.missingkids.com/theissues/autism Perrin, C. J., Perrin, S. H., Hill, E. A., & DiNovi, K. (2008). Brief functional anlaysis and treatment of elopement in preschoolers with autism. Behavioral Interventions: Theory & Practice in Residential & CommunityBased Clinical Programs, 23(2), 87–95. Piazza, C. C., Hanley, G. P., Bowman, L. G., Ruyter, J. M., Lindauer, S. E., & Saiontz, D. M. (1997). Functional analysis and treatment of elopement. Journal of Applied Behavior Analysis, 30(4), 653–672. https:// doi.org/10.1901/jaba.1997.30-653. Rice, C. E., Zablotsky, B., Avila, R. M., Colpe, L. J., Schieve, L. A., Pringle, B., & Blumberg, S. J. (2016). Reported wandering behavior among children with autism spectrum disorder and/or intellectual disability. The Journal of Pediatrics, 174, 232–239. Schendel, D. E., Overgaard, M., Christensen, J., Hjort, L., Jorgensen, M., Vestergaard, M., & Parner, E. T. (2016). Association of psychiatric and neurologic comorbidity with mortality among persons with autism spectrum disorder in a Danish population. JAMA Pediatrics, 170(3), 243–250. https://doi.org/10.1001/jamapediatr ics.2015.3935. Stevenson, M. T., Ghezzi, P. M., & Valenton, K. G. (2016). FCT and delay fading for elopement with a child with autism. Behavior Analysis in Practice, 9(2), 169–173. https://doi.org/10.1007/s40617-015-0089-5. Tarbox, R. S., Wallace, M. D., & Williams, L. (2003). Assessment and treatment of elopement: A replication and extension. Journal of Applied Behavior Analysis, 36(2), 239–244. https://doi.org/10.1901/jaba.2003.36239. The Autism Wandering Awareness Alerts Response and Education (AWAARE) Collaboration. Retrieved from https://awaare.nationalautismassociation.org/
Weblinks Autism Society Safe and Sound Initiative. https://www. autism-society.org/living-with-autism/how-the-autismsociety-can-help/safe-and-sound/
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ELS ▶ Test of Early Language Skill
ELS Checklist
ELS
or surrounding gestalt. The test requires the participant to spot a simple form within a more complex figure; the color and form of the latter create a gestalt within which the part is hidden (see Fig. 1). In the Children’s EFT, the complex figure is also meaningful (e.g., a pram, within which the triangle to be found is hidden in the hood). Groupadministered and short versions are also available. People with ASD are often extremely good at the EFT, as first demonstrated by Shah and Frith (1983). Fast and accurate performance on this test is thought to reflect the ability to see parts within wholes, to ignore the distracting effect of the gestalt, and to focus on details. Indeed, superior performance on the EFT (compared to age- and IQ-matched comparison group) was one of the first demonstrations of so-called weak central coherence in ASD and remains one of the best replicated findings (see Happé and Frith 2006 for review) (Fig. 2).
▶ Test of Early Language Skill
Emancipatory Autism Studies ▶ Critical Autism Studies
Embedded Figures Test (EFT) Francesca Happé MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
Description The Embedded Figures Test (EFT) was designed by Witkin in 1971 to assess his concept of “field dependence – independence” (e.g., Witkin and Goodenough 1981). Good performance on the EFT was taken as a marker of field independence, the ability to disembed information from context
Embedded Figures Test (EFT), Fig. 1 The participant’s task is to find the simple shape within the complex and camoflaging gestalt
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References and Reading Happé, F., & Frith, U. (2006). The weak coherence account: Detail-focused cognitive style in autism spectrum disorders. Journal of Autism and Developmental Disorders, 36, 5–25. Shah, A., & Frith, U. (1983). An islet of ability in autistic children: A research note. Journal of Child Psychology and Psychiatry, 24, 613–620. Witkin, H. A., & Goodenough, D. R. (1981). Cognitive styles: Essence and origins. New York: International University Press. Witkin, H. A., Oltman, P. K., Raskin, E., & Karp, S. (1971). A manual for the embedded figures test. Palo Alto: Consulting Psychologists Press.
Embedded Figures Test (EFT), Fig. 2 In the children’s version of this task, the complex figure within which the target is hidden, is identifiable, adding meaning to the gestalt-to-be-ignored
Historical Background Witkin (1916–1979) was a founder of the notion of cognitive and learning styles. He proposed the idea that personality could be measured in part by how people perceived their environment. In particular, he attempted to create objective tests (in contrast to questionnaire methods), such as the Rod-and-Frame test, to measure individual differences in reliance on external versus internal frames of reference. The Embedded Figures Test was created by Witkin as a more portable and convenient test designed to measure these same facets of field dependence or independence. Shah and Frith (1983) were the first to use the Embedded Figures Test with individuals with autism, showing superiority compared to matched comparison groups. This was one of the key findings that led Uta Frith to propose her theory of “weak central coherence” in autism.
See Also ▶ Executive Function (EF) ▶ Global Versus Local Processing ▶ Theory of Mind
Emergency Department Utilization and Autism Guodong Liu Division of Health Services and Behavioral Research, Department of Public Health Sciences, The Pennsylvania State University, College of Medicine, Hershey, PA, USA
Definition People with autism spectrum disorder (ASD) (American Psychiatric Association 2000) are susceptive to big emotional swings, and sometimes violent actions toward themselves and those around them, leading to frequent crises. Those with mental breakdowns and/or with acute, severe injuries often were often rushed to emergency departments (ED) for immediate treatments. Researchers have found a steady increase in ED visits by children and adolescents with ASD in the 2000s for both non-psychiatric and psychiatric referrals (McCraig and Burt 2005; Mahajan et al. 2009). This trend has also been documented more recently (Kalb et al. 2018; Liu et al. 2017, 2019) for adolescents and young adults with ASD who had higher documented ED visits compared to their same-age peers without ASD. Obsession with strict routines, stereotypy, self-injurious behavior (SIB) (Sterling et al. 2005), and plus deficits in social communication
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make ASD patients prone to emotional instability ever since very early childhood. Physical maturation and the accompanying hormonal surge, among other triggers of various nature, further amplify the adverse consequence from those outbursts among young ASD patients. In addition, other mental and physical health comorbidities coupled with core ASD deficits which create barriers to accessing health care services for individuals with ASD often make those with ASD prone to acute adverse health events requiring treatment in the emergency department (ED) (Liu et al. 2019). These mental and physical comorbidities also lead to high rates of healthcare utilization and contribute to a growing cost for healthcare needs for children and adolescents with ASD. In fact, overall cost of medical care is greater for children with ASD compared to children without ASD (Croen et al. 2006; Leslie and Martin 2007; Peacock et al. 2012).
Historical Background Non-psychiatric and psychiatric referrals have been increasing at a higher rate when compared to individuals without ASD (McCraig and Burt 2005; Gurney et al. 2006; Mahajan et al. 2009; Liu et al. 2017; Kalb et al. 2018). Deavenport-Saman et al. (2016) found that children and adolescents with ASD had 0.26 more documented visits to the ED when compared to individuals without ASD and that older children with ASD (i.e., school-age compared to preschool-age) were utilizing the ED more. A recent examination of ED utilization in adults with ASD found a similar trend in high utilization of the ED (Nicoliadis et al. 2013). Middle to late adolescence and residing in rural areas appeared to reflect higher rates of ED utilization in comparison to early adolescence and residing in urban areas. In addition, higher ED utilization was observed in females as compared to males with ASD, as well as a significant increase in behavioral health visits in the ED over time. Adolescents with ASD do not appear to be adequately supported through the transition to adulthood as they experience more social and communication difficulties often while dealing
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with new-onset mental health conditions. This may be particularly true for older youth, those residing in rural settings, and for adolescent females with ASD.
Current Knowledge Population Vulnerability ASD patients typically have deficits in social communication, sensory sensitivities, stereotypical and self-injurious behaviors (SIB), as well as other well-known ASD signature behaviors. These ASD hallmark characteristics contribute to emotional dysregulation for ASD patients starting in early childhood (Samson et al. 2014) and consequently subject them more susceptible to emotional breakdown and physical injuries that often need emergency medical attentions, such as visits to emergency departments. Stereotypy refers to repetitive but typically not goal-directed behaviors (Sterling et al. 2005). Some stereotypical behaviors even not appear to be violent, are often associated with SIB. For example, reparative licking often cause skin damages. Young ASD children seldom cause server bodily-damage to themselves. In contrast, it was often acute flaring of lingering medical problems that leads them to ED. There have been reports that those with have lingering comorbid physical health concerns, such as epilepsy (Tuchman and Cuccaro 2011; Berg and Plioplys 2012) and gastrointestinal (GI) illnesses (Chaidez et al. 2014; Dinan and Cryan 2017) which often lead to increased ED utilization. In addition, medical conditions such as respiratory problems (Kohane et al. 2012; Ming et al. 2016) and other miscellaneous injuries (Jain et al. 2014; Summers et al. 2017) have high prevalence of ED utilization among people diagnosed with ASD. A majority of children and adolescents with ASD are also diagnosed with multiple comorbid diagnoses, including anxiety, depression, and disruptive behavior disorders (Gotham et al. 2015; Gordon-Lipkin et al. 2018). For some children with ASD, physical maturation and the accompanying hormonal changes in adolescence, among other stressors associated with
Emergency Department Utilization and Autism
adolescence, amplify the adverse consequence from depleted coping abilities among young ASD patients as they grow to face up the rules, disciplines, as well as social pressures from peers as well as teachers and school administrators at schools, even parents at home. The transition to adolescence is a particularly stressful time for individuals with ASD and their caregivers as social and societal demands increase, physical and sexual maturity advances, and parental emotional stress often peaks. Another vulnerability faced by those with ASD is polypharmacy, generally defined as medications prescribed to individuals which are simultaneously filled across two or more classes of medications (Spencer et al. 2013). Given that the majority of children and adolescents with ASD present with comorbid mental and physical health issues, it is not uncommon for physicians to prescribe multiple medications both within and across drug classes, even though the majority of this practice is “off-label.” In particular, prescription of psychotropic medications, especially multiple medications often put those with ASD in great danger of having acute adverse events, often leading to visits to the emergency departments. Many of the medical conditions among those with ASD may not require care in the ED if managed properly with adequate and regular access to health care services. As with psychiatric care, there are likely several barriers to routine healthcare, management of chronic health concerns, and accessing urgent care for adolescents with ASD. These barriers may include lack of access to health care services, socioeconomic barriers, deficits in communication and social interaction skills, distress or fear responses particularly in novel settings, and sensory sensitivities related to the healthcare setting (Gurney et al. 2006). Additionally, families who have children with ASD are also more likely to experience unmet health care needs, more likely to delay or forgo health care, and more likely to report a high dissatisfaction with care (Kogan et al. 2008; Chiri and Warfield 2012). Taken together, these barriers may contribute to a higher utilization of the ED to identify and manage acute healthcare needs in adolescents with autism.
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Non-psychiatric and Psychiatric ED Visits There has been a steady increase in utilization of the ED for both non-psychiatric and psychiatric referrals by children and adolescents over time (McCraig and Burt 2005; Mahajan et al. 2009). This trend has also been documented in children and adolescents with ASD and has been found to be increasing at a higher rate when compared to individuals without ASD (Gurney et al. 2006). The most common non-psychiatric problems presenting at ED visits include epilepsy seizures and/or neurological symptoms (9–15%); gastrointestinal disturbances (15%) such as nausea, vomiting, diarrhea, abdominal pain, and constipation (Tuchman et al. 2010; Cohen-Silver et al. 2014; Deavenport-Saman et al. 2016); upper respiratory infections (7%) and viral infections (7%); otitis media (5%); and head or dental injuries (7%) (Deavenport-Saman et al. 2016). Epilepsy has been long found to be highly comorbid with ASD. Studies reported a pooled ASD prevalence of 6.3% among those with epilepsy and prevalence of epilepsy among those with ASD range from 2.7% to 44.4% (Bolton et al. 2011), while the prevalence of epilepsy in the general population is only approximately 0.5–2% (Nguyen and Tellez Zenteno 2009). GI problems has been widely studied and reported in association with ASD, despite the fact that a causal link between these two remains to be established (Chaidez et al. 2014; Dinan and Cryan 2017). Nevertheless, the prevalence of various GI issues has been reported ranging anywhere from 23% to 70% (Chaidez et al. 2014). As ASD children grow up, those who visited ED more often involve mental health emergencies. While psychiatric-related ED visits have been found to be increasing at a swifter rate than non-psychiatric reasons for referral to the ED (Liu et al. 2017). It is reported that psychiatric-related ED visits among ASD patients have out-paced those of non-ASD population; most recently during year 2013 with about 20% of all ED visits among ages 12–21 years, ASD patients involve psychiatric/behavior health service (Liu et al. 2017). Kalb et al. (2012) found that ED visits for children and adolescents with ASD were more likely to be related to a psychiatric concern then
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non-psychiatric concern. In regard to common psychiatric reasons for referral to the ED, Iannuzzi et al. (2014) found that as children with ASD entered elementary, school behavioral issues became a common reason for referral to the ED. However, around the time of entry into middle school, in addition to behavioral issues, mood symptoms, self-injurious behavior, and more significant aggression entered into the top reasons for referral to the ED and remained stable into young adulthood. Wharff et al. (2011) found that children with developmental disabilities, including ASD, were 2.5 times more likely to utilize the ED while waiting for an opening on a psychiatric inpatient facility. Approximately one-fourth of children and adolescents with ASD have been found to make repeated visits to the ED, and of this 25%, half of the individuals had been to the ED in the 2 weeks prior to the current visit (Cohen-Silver et al. 2014). Of the children and adolescents with ASD who present to the ED, almost 20% have been subsequently admitted to the hospital compared to a rate of 10% in children and adolescents without ASD (Cohen-Silver et al. 2014). Deavenport-Saman et al. (2016) found that children and adolescents with ASD were less likely to be admitted to the hospital from the ED if they arrived at the ED during weekday daytime or evening hours, were female, were Englishspeaking, and had no insurance. In the same sample, being 6 years old or older, being non-Hispanic, and traveling a greater distance to the ED led to high admittance rates.
Risk Factor and Predictors of ED Visits A variety of factors, acting independently, or interacting with each other, contribute to the mental and/or physical crises among young ASD patients, leading to higher ED utilization. It is known that a wide spectrum of ASD patients may have different comorbid mental illnesses. Kalb et al. (2012) reported that externalizing mental health illnesses were associated with ED visits and subsequent hospitalizations. On the other hand, Liu et al. (2017, 2019) found that female
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adolescents and young adults with more internalizing comorbid illnesses, such as anxiety disorders, mood disorders, and depression, were more likely to visit ED compared to those with comorbid externalizing mental health diagnoses (e.g., ADHD, ODD, psychosis). Females with ASD are more often to have comorbid internalizing disorders but are less likely to be referred to the specialists for assessment than males ((Rucklidge 2010). However, if left undiagnosed, or undertreated, symptoms of these illnesses may exacerbate to the level of level of crises and medical emergencies and ED visits. Entering high school (age 12–14) and exiting high school (age 18–21), times of transition often put tremendous pressure on female adolescents and young adults with ASD because often during which when new social relationships are to be established and new skills mastered. They struggle with establishing and maintaining appropriate peer relationships (Holtmann et al. 2007). This new layer of difficulty is particularly distressing for female adolescents with ASD as they may have had some success with peers when younger due to shared interests which cannot be sustained secondary to growing social communication deficits (Van WijngaardenCremers et al. 2014). Many researchers have proposed that increased ED utilization for children and adolescents with ASD may be related to deficits in first-line, community-based, outpatient care (Green et al. 2001; Leichtman et al. 2001). Caregivers of typically developing children and adolescents who reported that their PCP stresses family centeredness, timeliness, and coordinated care have been found to report decreased numbers of visits to the ED (Brousseau et al. 2009). Similarly, caregivers of children and adolescents with ASD report they are more likely to access the ED for healthcare when they perceived their healthcare providers do not listen to their concerns, display cultural insensitivity, do not supply needed information, and do not involve caregivers in decisionmaking. Therefore, it is less likely that caregivers of youth with ASD access their PCPs for routine visits that are for minor issues as a result of having less assurance for help with acute, emergent,
Emergency Department Utilization and Autism
and/or complex behavioral or health issues (Kogan et al. 2008). Finally, those living in rural areas have been found to have less access to regular and specialty medical and mental healthcare and therefore may be more likely to present at the ED for mental healthcare. Approximately half of families of children with ASD living in a rural community reported that they experienced problems with the services they received, most often as a result of lack of availability of well-trained providers. Caregivers of individuals with ASD living outside of metropolitan centers in Australia noted a lack of ASD expertise in healthcare providers in rural and remote areas of the country. Thomas and colleagues found that caregivers of children with ASD who lived in rural communities in the United States reported significantly less access to special summer camps and respite care. Another indicator of a paucity of services in rural communities is the documented older mean age of diagnosis of ASD for individuals living in a rural community when compared to those in suburban or urban settings (Mandell et al. 2005). Recent research has begun to examine the efficacy of telehealth remote technology, group-based parent interventions, and training models for PCPs to meet the needs of individuals with ASD and their families in rural settings.
Future Directions Not only are ASD youth utilizing the ED at disproportionate rates, they are accessing the ED for behavioral health concerns at increasing rates outpacing non-ASD youth. By 2013, over 20% of ASD youth were accessing ED services for a readily identified behavioral health concern (Liu et al. 2017, 2019; Kalb et al. 2018). This appears to be a significant factor as the changes in behavioral health presenting concerns mirror the overall increased ED utilization. Adolescents with ASD who present for emergent psychiatric care appear to have difficulty accessing community-based mental health services. Children and adolescents with ASD have been documented as having significant difficulty accessing specialty medical and
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mental health care when compared with other youth with disability. Better access to behavioral health services designed for adolescents with ASD, such as social skills training and specialized care coordination, appears to be a critical need. Greater understanding of service utilization before and after ED visits for youth with ASD is needed to craft more impactful interventions for individuals at risk.
E References and Reading American Psychiatric Association. (2000). Diagnostic and statistical manual (4th ed., Text Rev.). Washington, DC: APA Press. Berg, A. T., & Plioplys, S. (2012). Epilepsy and autism: Is there a special relationship? Epilepsy & Behavior, 23(3), 193–198. https://doi.org/10.1016/j.yebeh.2012. 01.015. Bolton, P. F., Carcani-Rathwell, I., Hutton, J., Goode, S., Howlin, P., & Rutter, M. (2011). Epilepsy in autism: Features and correlates. The British Journal of Psychiatry, 198(4), 289–294. Brousseau, D. C., Gorelick, M. H., Hoffmann, R. G., Flores, G., & Nattinger, A. B. (2009). Primary care quality and subsequent emergency department utilization for children in Wisconsin Medicaid. Academic Pediatrics, 9, 33–39. Chaidez, V., Hansen, R. L., & Hertz-Picciotto, I. (2014). Gastrointestinal problems in children with autism, developmental delays or typical development. Journal of Autism and Developmental Disorders, 44(5), 1117–1127. Chiri, G., & Warfield, M. E. (2012). Unmet need and problems accessing core health care services for children with autism spectrum disorder. Maternal and Child Health Journal, 16(5), 1081–1091. Cohen-Silver, J. H., Muskat, B., & Ratnapalan, S. (2014). Autism in the emergency department. Clinical Pediatrics, 53(12), 1134–1138. Deavenport-Saman, A., Lu, Y., Smith, K., & Yin, L. (2016). Do children with autism overutilize the emergency department? Examining visit urgency and subsequent hospital admissions. Maternal and Child Health Journal, 20, 306–314. Dinan, T. G., & Cryan, J. F. (2017). Gut-brain axis in 2016: Brain-gut-microbiota axis – Mood, metabolism and behaviour. Nature Reviews. Gastroenterology & Hepatology, 14(2), 69–70. https://doi.org/10.1038/ nrgastro.2016.200. Gordon-Lipkin, E., Marvin, A. R., Law, J. K., & Lipkin, P. H. (2018). Anxiety and mood disorder in children with autism spectrum disorder and ADHD. Pediatrics, 141(4). https://doi.org/10.1542/peds.20171377.
1686 Gotham, K., Brunwasser, S. M., & Lord, C. (2015). Depressive and anxiety symptom trajectories from school age through young adulthood in samples with autism spectrum disorder and developmental delay. Journal of the American Academy of Child and Adolescent Psychiatry, 54(5), 369–376. e363. Green, J., Kroll, L., Imrie, D., Frances, F. M., Begum, K., Harrison, L., & Anson, R. (2001). Health gain and outcome predictors during inpatient and related day treatment in child and adolescent psychiatry. Journal of the American Academy of Child and Adolescent Psychiatry, 40(3), 325–332. Gurney, J. G., McPheeters, M. L., & Davis, M. M. (2006). Parental report of health conditions and health care use among children with and without autism. Archives of Pediatrics and Adolescent Medicine, 160, 825–830. Holtmann, M., Bolte, S., & Poustka, F. (2007). Autism spectrum disorders: Sex differences in autistic behaviour domains and coexisting psychopathology. Developmental Medicine and Child Neurology, 49(5), 361–366. Iannuzzi, D. A., Cheng, E. R., Broder-Fingert, S., & Bauman, M. L. (2014). Brief report: Emergency department utilization by individuals with autism. Journal of Autism and Developmental Disorders, 45, 1096–1102. Jain, A., Spencer, D., Yang, W., Kelly, J. P., Newschaffer, C. J., Johnson, J., . . . Dennen, T. (2014). Injuries among children with autism spectrum disorder. Academic Pediatrics, 14(4), 390–397. https://doi.org/10.1016/j.acap.2014.03.012. Kalb, L. G., Stuart, E. A., Freedman, B., Zablotsky, B., & Vasa, R. (2012). Psychiatric-related emergency department visits among children with an autism spectrum disorder. Pediatric Emergency Care, 28, 1269–1276. Kalb, L. G., Stuart, E. A., & Vasa, R. A. (2018). Characteristics of psychiatric emergency department use among privately insured adolescents with autism spectrum disorder. Autism, 1362361317749951. Kogan, M. D., Strickland, B. B., Blumberg, S. J., Singh, G. K., Perrin, J. M., & van Dyck, P. C. (2008). A national profile of the health care experiences and family impact of autism spectrum disorder among children in the United States, 2005–2006. Pediatrics, 122(6), e1149–e1158. Kohane, I. S., McMurry, A., Weber, G., MacFadden, D., Rappaport, L., Kunkel, L., . . . Churchill, S. (2012). The co-morbidity burden of children and young adults with autism spectrum disorders. PLoS One, 7(4), e33224. https://doi.org/10.1371/journal. pone.0033224. Leichtman, M., Leichtman, M. L., Barber, C. C., & Neese, D. T. (2001). Effectiveness of intensive shortterm residential treatment with severely disturbed adolescents. The American Journal of Orthopsyhciatry, 72(2), 227–235. Liu, G., Pearl, A. M., Kong, L., Brown, S. L., Ba, D., Leslie, D. L., & Murray, M. J. (2017). A profile on emergency department utilization in adolescents and young adults with autism spectrum disorders. Journal
Emergency Department Utilization and Autism of Autism and Developmental Disorders, 47(2), 347–358. PMID: 31414259. Liu, G., Pearl, A. M., Kong, L., Leslie, D. L., & Murray, M. J. (2019). Risk factors for emergency department utilization among adolescents with autism spectrum disorder. Journal of Autism and Developmental Disorders, 49(11), 4455–4467. PMID: 27844247. Mahajan, P., Alpern, E. R., Grupp-Phelan, J., Chamberlain, J., Dong, L., Holubkov, R., et al. (2009). Epidemiology of psychiatric-related visits to emergency departments in a multicenter collaborative research pediatric network. Pediatric Emergency Care, 25(11), 715–720. Mandell, D. S., Novak, M. M., & Zubritsky, C. D. (2005). Factors associated with age of diagnosis among children with autism spectrum disorders. Pediatrics, 116(6), 1480–1486. Marcus, L., Kunce, L. J., & Schloper, E. (1997). Working with families. In D. J. Cohen & F. R. Volkmar (Eds.), Handbook of autism and developmental disorders (2nd ed., pp. 631–649). New York: Wiley. McCraig, L. F., & Burt, C. W. (2005). National hospital ambulatory medical care survey: 2003 emergency department summary (Vol. 358). Hyattsville: US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics. Ming, X., Patel, R., Kang, V., Chokroverty, S., & Julu, P. O. (2016). Respiratory and autonomic dysfunction in children with autism spectrum disorders. Brain Dev, 38(2), 225–232. https://doi.org/10.1016/j. braindev.2015.07.003. Nicoliadis, C., Raymaker, D., McDonald, K., Dern, S., Boisclair, W. C., Ashkenazy, E., & Baggs, A. (2013). Comparison of healthcare experiences in autistic and non-autistic adults: A cross-sectional online survey facilitated by an academic-community partnership. Journal of General Internal Medicine, 28(6), 761–769. Rucklidge, J. J. (2010). Gender differences in attentiondeficit/hyperactivity disorder. The Psychiatric Clinics of North America, 33(2), 357–373. Samson, A. C., Phillips, J. M., Parker, K. J., Shah, S., Gross, J. J., & Hardan, A. Y. (2014). Emotion dysregulation and the core features of autism spectrum disorder. Journal of Autism and Developmental Disorders, 44(7), 1766–1772. Spencer, D., Marshall, J., Post, B., Kulakodlu, M., Newschaffer, C., Dennen, T., Azocar, F., & Jain, A. (2013). Psychotropic medication use and polypharmacy in children with autism spectrum disorders. Pediatrics, 132(5), 833–840. Sterling, L., McLaughlin, A., & King, B. H. (2005). Stereotypy and self-injury. In D. G. Amaral, G. Dawson, & D. H. Geschwind (Eds.), Autism spectrum disorders (Vol. I, pp. 640–649). New York: Oxford University Press. Summers, J., Shahrami, A., Cali, S., D’Mello, C., Kako, M., Palikucin-Reljin, A., . . . Lunsky, Y. (2017). Self-injury in autism spectrum disorder and intellectual disability: Exploring the role of reactivity to pain and sensory input. Brain Sciences, 7(11) 140.
Emergent Literacy Tuchman, R., & Cuccaro, M. (2011). Epilepsy and autism: Neurodevelopmental perspective. Current Neurology and Neuroscience Reports, 11(4), 428–434. Van Wijngaarden-Cremers, P. J., van Eeten, E., Groen, W. B., Van Deurzen, P. A., Oosterling, I. J., & Van der Gaag, R. J. (2014). Gender and age differences in the core triad of impairments in autism spectrum disorders: A systematic review and meta-analysis. Journal of Autism and Developmental Disorders, 44(3), 627–635. Wharff, E. A., Ginnis, K. B., Ross, A. M., & Blood, E. A. (2011). Predictors of psychiatric boarding in the pediatric emergency department. Pediatric Emergency Care, 27, 483–489.
Emergent Literacy Dawn Vogler-Elias Communication Sciences and Disorders, Nazareth College, Rochester, NY, USA
Synonyms Early literacy; Preliteracy
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children, emergent literacy instruction is most effective when it is targeted authentically instead of during contrived instructional “skill and drill” activities (Price and Ruscher 2006). Emergent literacy is associated with later literacy achievement and the development of other important skills. For example, children who experience more high-quality reading experiences early in childhood have larger vocabularies than children with fewer experiences (see Temple and Snow 2003 for a review). Emergent literacy activities provide a rich context for learning and practicing joint attention skills, which are particularly vulnerable in children with autism spectrum disorders. Historically, emergent literacy activities were withheld from some children who did not demonstrate signs of readiness, including oral language abilities and behavioral precursors. However, there is no evidence to suggest that any child should be excluded from emergent literacy activities. Because children with autism spectrum disorders are at risk for reduced opportunity for literacy achievement, providing rich and diverse emergent literacy activities is one way to support the literacy access for this population.
Definition
See Also
Emergent literacy refers to the many literacy-rich activities children participate in prior to formal reading and writing instruction from birth to about 5 years of age. Specifically, emergent literacy has been defined as “the reading and writing behaviors of young children before they become readers and writers in the conventional sense” (Justice 2006, p. 3). Examples of emergent literacy activities include engaging in shared storybook reading, pretending to write or draw, incorporating literacy themes into play, and engaging in oral wordplay such as rhyming. Shared storybook reading is arguably the most common emergent literacy activity for many children. Parents read to children who are very young before they can verbally participate. Parents often engage in scaffolding or supportive behaviors during emergent literacy activities. Through scaffolding, parents adapt the experience to match the child’s growing abilities, thus supporting the child’s participation. For typically developing
▶ Literacy ▶ Reading ▶ Shared Storybook Reading
References and Reading Justice, L. M. (Ed.). (2006). Clinical approaches to emergent literacy intervention. San Diego: Plural Publishing. Price, L. H., & Ruscher, K. Y. (2006). Fostering phonological awareness using shared book reading and an embedded explicit approach. In A. van Kleeck (Ed.), Sharing books and stories to promote language and literacy (pp. 15–76). San Diego: Plural Publishing. Temple, J. D., & Snow, C. E. (2003). Learning words from books. In A. van Kleeck, S. Stahl, & E. Bauer (Eds.), On Reading books to children: Parents and teachers. Mahwah: Lawrence Erlbaum. van Kleeck, A. (Ed.). (2006). Sharing books and stories to promote language and literacy. San Diego: Plural Publishing. van Kleeck, A., Stahl, S. A., & Bauer, E. B. (Eds.). (2003). On reading books to children: Parents and teachers. Mahwah: Lawrence Erlbaum Associates.
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Emergent Literacy Skills of Preschoolers with Autism
References and Reading
Emergent Literacy Skills of Preschoolers with Autism Jaclyn M. Dynia Crane Center for Early Childhood Research and Policy, The Ohio State University, Columbus, OH, USA
Synonyms Early literacy; Reading
Definition Emergent literacy is the stage of reading development that precedes conventional reading. Children are in the emergent literacy stage from birth to about 6 years old. During this stage, children are being exposed to experiences that will increase their knowledge and abilities in emergent literacy skills. Emergent literacy skills include alphabet knowledge, print-concept knowledge, phonological awareness, and narrative skills. For children who are typically developing, researchers have found that emergent literacy skills are strong predictors of later success with reading. There is an emerging body of research on emergent literacy and its role in the acquisition of conventional literacy skills for children with autism. Several common findings have emerged across the literature. First, as a group, children with autism tend to have a discrepant profile of strengths in code-related skills (i.e., alphabet knowledge) but meaningrelated skills that seem to lag behind their peers. However, there is significant variance in emergent literacy skills for children with autism. The heterogeneity in emergent literacy has been found to be related to autism severity, oral language, and nonverbal cognition.
Dynia, J. M., Brock, M. E., Logan, J. A. R., Justice, L. M., & Kaderavek, J. N. (2016). Comparing children with ASD and their peers’ growth in print knowledge. Journal of Autism and Developmental Disorders, 46(7), 2490–2500. https://doi.org/10.1007/s10803-016-2790-9. Dynia, J. M., Brock, M. E., Justice, L. M., & Kaderavek, J. N. (2017). Predictors of decoding for children with autism spectrum disorder in comparison to their peers. Research in Autism Spectrum Disorders, 37, 41–48. https://doi.org/10.1016/j.rasd.2017.02.003. National Early Literacy Panel. (2008). Developing early literacy. Washington, D.C.: National Institute for Literacy. Westerveld, M. F., Trembath, D., Shellshear, L., & Paynter, J. (2016). A systematic review of the literature on emergent literacy skills of preschool children with autism spectrum disorder. The Journal of Special Education, 50(1), 37–48. https://doi.org/10. 1177/0022466915613593. Whitehurst, G. J., & Lonigan, C. J. (1998). Child development and emergent literacy. Child Development, 69(3), 848–872. https://doi.org/10.1111/ j.1467-8624.1998.tb06247.x.
Emotion and Decision-Making ▶ Frontal Lobe Findings in Autism
Emotion Awareness and Skills Enhancement (EASE) Program Carla A. Mazefsky1, Susan W. White2,3, Kelly B. Beck4 and Caitlin M. Conner1 1 Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA 2 Department of Psychology, University of Alabama, Tuscaloosa, AL, USA 3 Psychology Department, Virginia Tech, Blacksburg, VA, USA 4 Department of Rehabilitation Science and Technology, University of Pittsburgh, Pittsburgh, PA, USA
See Also
Definition
▶ Academic Skills ▶ Literacy
The Emotion Awareness and Skills Enhancement (EASE) Program is an individual therapy
Emotion Awareness and Skills Enhancement (EASE) Program
intervention that was designed to improve emotion regulation skills and overall well-being in adolescents and adults with autism spectrum disorder (ASD).
Historical Background Many people with ASD have interfering problems with emotional and behavioral control that lead to the use of psychiatric services including therapy, medications, and even inpatient hospitalization. Intense and sudden negative reactions, anxiety and worry, aggression and hostility, and persistently high negative affect are some examples of these emotional and behavioral problems, which are unfortunately very common in ASD, substantially impede capacity to learn and function across all life settings, and affect quality of life for the affected individual and family members. Despite the prevalence of emotional and behavioral concerns in ASD and widespread use of psychiatric services to address these concerns, there are few treatments that impact these behavioral and emotional concerns in ASD with proven effectiveness. Psychotherapeutic intervention approaches in ASD have predominantly focused on the use of cognitive-behavioral therapy (CBT). Although applied primarily to anxiety in ASD, CBT has been proven effective for a range of concerns in non-ASD populations (Epp and Dobson 2010). CBT is considered an effective treatment for co-occurring anxiety in individuals with ASD who do not have intellectual disability (ShakerNaeeni et al. 2014). However, CBT protocols for ASD have been developed primarily to treat specific anxiety disorders and, as such, generally do not address many common problems such as explosive behavior, irritability, and depression. In addition, not all individuals respond to CBT, and the improvements tend to be less substantial than observed in non-ASD populations (Kreslins et al. 2015). Given the multiple co-occurring problems seen in this population and concerns about applicability of CBT, a more efficient, and perhaps effective, approach is to target the core processes underlying the clinical phenomena.
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Treatments that target a process that is believed to be shared or common across multiple disorders or problems are often referred to as transdiagnostic treatments. Transdiagnostic treatment models appear to be at least as effective as well-established, disorder-specific treatments in reducing symptomatic impairment (in non-ASD samples) (e.g., Barlow et al. 2010). They also have the advantage of not requiring therapists to be familiar with different treatment manuals for every specific disorder and are easier to apply to youth with complex presentations (e.g., youth who present with multiple types of concerns). The possibility that transdiagnostic approaches may have wider application than disorder-specific treatments suggests that these approaches could promote broader dissemination and adoption. Emotion regulation is a strong candidate to target as a transdiagnostic treatment in ASD. Emotion regulation involves the modulation of arousal and emotional state to promote goaldirected behavior (Thompson 1991). Emotion regulation impairment may underlie many of the social, behavioral, and emotional problems presented by individuals with ASD (Mazefsky et al. 2012, 2013). Individuals with ASD may fail to employ adaptive emotion regulation strategies and instead react impulsively to emotional stimuli, with aggression, extreme social withdrawal, or self-injury (Sofronoff et al. 2007). Such behaviors are often interpreted as deliberate or defiant but may be more accurately understood as reduced capacity to manage emotional states. Furthermore, emotion regulation impairment is thought to underlie internalizing problems in non-ASD populations (Aldao et al. 2010), and it is quite likely that emotion regulation impairment plays a role in the commonly observed anxiety (White et al. 2009), suicidal behavior (Maddox et al. 2017), irritability, and depression (Magnuson and Constantino 2011) in ASD as well. This would suggest that successful treatment of emotion regulation impairment may produce improvement across many different manifestations of emotion regulation impairment in individuals with ASD. The best approach to address emotion regulation in ASD may include mindfulness.
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Emotion Awareness and Skills Enhancement (EASE) Program
Mindfulness, taken from Buddhist tradition and defined in a multitude of ways, generally focuses on meditative and present-moment and nonjudgmental awareness of thoughts, emotions, and actions (Baer 2003). These techniques have been integrated into existing therapies, such as CBT, and into the development of new therapies in which mindfulness principles are the primary focus, such as mindfulness-based cognitive therapy (MBCT; Baer 2003). Additionally, mindfulness-based interventions have been tailored toward clinical, community, health, and nonclinical populations for the improvement of symptoms such as anxiety, depression, anger, overall well-being and quality of life, and stress, among other outcomes (see Gotink et al. 2015, for review). Therefore, the development of EASE was based on existing theories and approaches related to both mindfulness-based and CBT interventions. Dr. Carla Mazefsky, Dr. Susan White, Dr. Kelly Beck, and Dr. Caitlin Conner jointly drafted a new intervention manual, called Emotion Awareness and Skills Enhancement (EASE), in order to apply a blend of these strategies in a manner that would be most effective for ASD. EASE was refined through an iterative process that involved input from key stakeholders, including individuals with ASD, parents of individuals with ASD, and clinicians.
Rationale or Underlying Theory EASE is based on the premise that many of the behavioral and emotional problems experienced by individuals with ASD result from impaired emotion regulation. Although informed by effective interventions for emotion regulation in non-ASD populations, EASE was developed to address ASD-specific obstacles to effective emotion regulation (see Mazefsky et al. 2013; Mazefsky and White 2014). One of the primary considerations is that individuals with ASD often “react to emotional stimuli intensely without clear goaldirectedness. . .and this may be compounded by poor emotional insight and self-monitoring” (Mazefsky et al. 2013, pp. 683–684). Individuals
with ASD without co-occurring Intellectual Disability also report greater use of less adaptive emotion regulation strategies such as suppression and resignation, which function to avoid emotion and generally tend to be ineffective at reducing distress (e.g., Jahromi et al. 2012; Weiss 2014). Therefore, EASE attempts to address these obstacles with a mindfulness-based approach in which awareness of emotion is cultivated, emotions (whether negative or positive) are acknowledged, and behavior is regulated in the face of intense emotion with mindfulness and/or cognitive strategies. EASE uses both “bottom-up” [mindful awareness] strategies (e.g., emotion cuing, present awareness), which primarily address affective under-control, cognitive rigidity, and poor insight, and “top-down” strategies drawn from CBT (e.g., cognitive reframing, skills practice). Given that emotion regulation impairment often manifests in the context of social situations in individuals with ASD, EASE’s treatment targets are addressed therapeutically in social situations. In other words, the social milieu is embedded in the treatment. For example, in learning about how to build tolerance for distress, the youth might practice this first in session with just the therapist and then gradually use the skills in increasingly social settings (e.g., with parents, then at school). Additionally, EASE frequently requires evocation of the emotion regulation impairment in the context of treatment. Social interaction may be used, therefore, to evoke difficulty as well as practice skills.
Goals and Objectives The overall goal of EASE is to develop skills which allow the individual to manage distress and act in ways that are more adaptive (i.e., goal-consistent) rather than avoiding or reducing the experience of negative emotion. EASE has three primary treatment targets: (1) Mindfulness and emotion awareness – This includes noticing emotions and body sensations and building the ability to identify changes in emotional intensity by learning the physical cues of intensifying emotions.
Emotion Awareness and Skills Enhancement (EASE) Program
(2) Recognition of when emotion becomes dysregulated – This includes understanding the relationship between thoughts, feelings, sensations, and behaviors, as well as identifying the influence on and effect of social challenges. (3) Providing “tools to calm the chaos” – This includes building tolerance for distress and practicing different strategies for emotion management (which is referred to as “ABCD”: awareness, breathe, change thoughts, temporary distraction).
Treatment Participants EASE was developed for adolescents and adults who are cognitively able and verbal (i.e., no presence of intellectual disability and verbal IQ of at least 80), because of the cognitive and verbal demands of the therapy. An adaptation of EASE for individuals with ASD and co-occurring Intellectual Disability (EASE-ID) is currently in development. EASE is for individuals who have emotion regulation impairment of any type. This could include problems with depression, anxiety, anger, frustration tolerance, general stress management, or frequent outbursts/meltdowns. It was developed specifically to address the challenges and learning style of individuals with ASD, and thus far EASE has only been applied to individuals with ASD. However, the approaches and content of EASE likely also apply to other populations characterized by emotion regulation impairment. For example, while EASE was developed for individual outpatient therapy settings, a group-based school version of EASE which will include any student with emotion regulation impairment (not solely students with ASD) is also in development.
Treatment Procedures The EASE program includes 16 50–60-minute sessions over the course of approximately 16 weeks. The treatment is delivered individually by a therapist and supported by parent involvement and online content. Each session begins with a mindful
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check-in, review of the weekly practice, agenda setting, skill teaching, skill practice, a mindfulness exercise, and planning for post-session practice. Parents are brought into each session before the mindfulness practice and homework assignment. There is an online platform to support EASE called emotionCoach or “eCoach.” eCoach is available to both the youth and parent. The hope is that having materials from session, as well as additional information to support the concepts introduced in session, available online at any time will allow individuals to learn and process information at their own pace. The parent is encouraged to use eCoach to learn strategies alongside the child. eCoach includes written summaries of the content for each session, all handouts used during EASE, written mindfulness exercises from EASE, and an audio recording of each mindfulness exercise. There are also some additional tips and frequently asked questions on eCoach. Families are encouraged to preview material on eCoach ahead of sessions if they wish. Ideally, parents are asked to review the eCoach content while their child is in session, to aid synchronous learning. A key treatment component of EASE involves community sessions that were designed to provide an opportunity to practice the skills being learned in a more natural environment. Community sessions are structured so as to evoke sufficient emotion/distress in the client and support from the therapist for use of regulatory skills while distressed. Inclusion of the community sessions reinforces learning of new skills and improves generalization outside of the clinic. Often the community sessions literally involve leaving the office and going somewhere where social and environmental challenges will be more likely to naturally occur. Based on feedback from families, we believe these sessions are most impactful when they can take place in situations that are highly challenging and relevant to the client’s daily life.
Efficacy Information Primary data on EASE is from an open trial, indicating that they are feasible, acceptable, and
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can be administered to fidelity across sites (Conner et al. 2019). Over 70% of the participants were treatment-responders via cliniciannaïve report. Parent report of emotion regulation impairment, irritability/aggression, anxiety, and depression symptoms decreased during treatment with medium-large effect sizes (Conner et al. 2019). An ongoing randomized controlled trial, comparing participants who receive EASE to those who receive an individual supportive therapy, will provide more information on efficacy (ClinicalTrials.gov: NCT03432832).
Outcome Measurement EASE was developed to improve overall emotional reactivity and regulation as measured by the Emotion Dysregulation Inventory (Mazefsky et al. 2018a, b). Secondary outcomes include changes in specific problematic symptoms such as depression, anxiety, or aggression.
Qualifications of Treatment Providers EASE therapists should have a background in a field that provides mental health intervention services (clinical psychology, advanced education degrees, rehabilitating sciences, social work, etc.). Therapists who participated in the initial trial of EASE included those with the following backgrounds: clinical psychology doctoral students, licensed psychologists (PsyD and PhD), licensed professional counselor, and master of education. EASE therapists ideally should have some prior experience working with clients with ASD and some familiarity with mindfulness-based interventions and cognitive-behavioral therapy. It is recommended that new EASE therapists receive supervision of their first EASE case and ongoing group supervision or consultation thereafter. EASE includes a variety of recommended readings on emotion regulation in ASD and mindfulness approaches.
See Also ▶ Cognitive Behavioral Therapy (CBT) ▶ Emotion Dysregulation Inventory ▶ Emotional Regulation ▶ Mindfulness Therapy for Individuals with Autism Spectrum Disorder
References and Reading Aldao, A., Nolen-Hoeksema, S., & Schweizer, S. (2010). Emotion-regulation strategies across psychopathology: A meta-analytic review. Clinical Psychology Review, 30 (2), 217–237. https://doi.org/10.1016/j.cpr.2009.11.004. Baer, R. A. (2003). Mindfulness training as a clinical intervention: A conceptual and empirical review. Clinical Psychology: Science and Practice, 10(1998), 125–143. https://doi.org/10.1093/clipsy/bpg015. Barlow, D. H., Farchione, T. J., Fairholme, C. P., Ellard, K. K., Boisseau, C. L., Allen, L. B., & May, J. T. E. (2010). Unified protocol for transdiagnostic treatment of emotional disorders: Therapist guide. New York: Oxford University Press. Conner, C. M., White, S. W., Beck, K. B., Golt, J., Smith, I. C., & Mazefsky, C. A. (2019). Improving emotion regulation ability in autism: The emotional awareness and skills enhancement (EASE) program. Autism, 23(5), 1273–1287. https://doi.org/10.1177/1362361318810709. Epp, A., & Dobson, K. (2010). The evidence base for cognitive-behavioral therapy. In K. Dobson (Ed.), Handbook of cognitive-behavioral therapies (3rd ed., pp. 39–73). New York: Guilford Press. Gotink, R. A., Chu, P., Busschbach, J. J. V., Benson, H., Fricchione, G. L., & Hunink, M. G. M. (2015). Standardised mindfulness-based interventions in healthcare: An overview of systematic reviews and meta-analyses of RCTs. PLoS One, 10(4), 1–17. https://doi.org/10.1371/journal.pone.0124344. Jahromi, L. B., Meek, S. E., & Ober-Reynolds, S. (2012). Emotion regulation in the context of frustration in children with high functioning autism and their typical peers. Journal of Child Psychology and Psychiatry, 53(12), 1250–1258. Konstantareas, M. M., & Stewart, K. (2006). Affect regulation and temperament in children with autism spectrum disorder. Journal of Autism and Developmental Disorders, 36(2), 143–154. https://doi.org/10.1007/ s10803-005-0051-4. Kreslins, A., Robertson, A. E., & Melville, C. (2015). The effectiveness of psychosocial interventions for anxiety in children and adolescents with autism spectrum disorder: A systematic review and meta-analysis. Child and Adolescent Psychiatry and Mental Health, 9(1), 22. https://doi.org/10.1186/s13034-015-0054-7. Maddox, B. B., Trubanova, A., & White, S. W. (2017). Untended wounds: Non-suicidal self-injury in adults
Emotion Coregulation Processes Between Parents and Their Children with ASD with autism spectrum disorder. Autism, 21(4), 412–422. Magnuson, K. M., & Constantino, J. N. (2011). Characterization of depression in children with autism spectrum disorders. Journal of Developmental and Behavioral Pediatrics, 32(4), 332–340. https://doi.org/10.1097/ DBP.0b013e318213f56c. Mazefsky, C. A., & White, S. W. (2014). Emotion regulation: Concepts & practice in autism spectrum disorder. Child and Adolescent Psychiatric Clinics of North America, 23(1), 15–24. Mazefsky, C. A., Pelphrey, K. A., & Dahl, R. E. (2012). The need for a broader approach to emotion regulation research in autism. Child Development Perspectives, 6 (1), 92–97. https://doi.org/10.1111/j.1750-8606.2011. 00229.x. Mazefsky, C. A., Herrington, J., Siegel, M., Scarpa, A., Maddox, B. B., Scahill, L., & White, S. W. (2013). The role of emotion regulation in autism spectrum disorder. Journal of the American Academy of Child and Adolescent Psychiatry, 52(7), 679–688. https://doi.org/10. 1016/j.jaac.2013.05.006. Mazefsky, C. A., Day, T. N., Siegel, M., White, S. W., Yu, L., Pilkonis, P. A., & for the ADDIRC. (2018a). Development of the emotion dysregulation inventory: A PROMIS ® ing method for creating sensitive and unbiased questionnaires for autism spectrum disorder. Journal of Autism and Developmental Disorders, 48(11), 3736–3746. https://doi.org/10.1007/s10803016-2907-1. Mazefsky, C. A., Yu, L., White, S. W., Siegel, M., & Pilkonis, P. A. (2018b). The emotion dysregulation inventory: Psychometric properties and item response theory calibration in an autism spectrum disorder sample. Autism Research, 11(6), 928–941. https://doi.org/ 10.1002/aur.1947. Shaker-Naeeni, H., Govender, T., & Chowdhury, U. (2014). Cognitive behavioral therapy for anxiety in children and adolescents with autism spectrum disorder. British Journal of Medical Practitioners, 7, a723– a731. Sofronoff, K., Attwood, T., Hinton, S., & Levin, I. (2007). A randomized controlled trial of a cognitive behavioural intervention for anger management in children diagnosed with Asperger syndrome. Journal of Autism and Developmental Disorders, 37(7), 1203–1214. https:// doi.org/10.1007/s10803-006-0262-3. Thompson, R. A. (1991). Emotional regulation and emotional development. Educational Psychology Review, 3(4), 269–307. https://doi.org/10.1007/BF01319934. Weiss, J. A. (2014). Transdiagnostic case conceptualization of emotional problems in youth with ASD: An emotion regulation approach. Clinical Psychology: Science and Practice, 21(4), 331–350. White, S. W., Oswald, D., Ollendick, T., & Scahill, L. (2009). Anxiety in children and adolescents with autism spectrum disorders. Clinical Psychology Review, 29(3), 216–229. https://doi.org/10.1016/j.cpr. 2009.01.003.
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White, S. W., Mazefsky, C. A., Dichter, G. S., Chiu, P. H., Richey, J. a., & Ollendick, T. H. (2014). Socialcognitive, physiological, and neural mechanisms underlying emotion regulation impairments: Understanding anxiety in autism spectrum disorder. International Journal of Developmental Neuroscience, 39, 22–36. https:// doi.org/10.1016/j.ijdevneu.2014.05.012.
Emotion Coregulation Processes Between Parents and Their Children with ASD Valentina Valentovich1, Wendy A. Goldberg1, Dana Rose Garfin2 and Yuqing Guo2 1 Department of Psychological Science, University of California, Irvine, Irvine, CA, USA 2 Sue & Bill Gross School of Nursing, University of California, Irvine, Irvine, CA, USA
Definition Children’s emotion regulation capabilities are strategies that are used to manage (inhibit, enhance, or maintain) emotional experiences (Thompson 1994) and develop within the context of social interactions. Parents facilitate the advancement of their children’s emotion regulation through emotion coregulation processes. Conceptually, emotion coregulation involves transactional exchanges between parent-child dyads; however, there is a lack of consensus regarding the optimal definition of emotion coregulation. Numerous related terms and definitions have been used to describe dyadic emotion processes between parents and their children. For example, synchrony refers to parents and children matching their behaviors and affect states to regulate emotional arousal (Feldman 2003); parent-driven coregulation involves behaviors that parents engage in to motivate or scaffold children’s emotion regulation strategies (Gulsrud et al. 2010; Ting and Weiss 2017), and shared affect describes matched emotion expression between parents and children. In our program of research (Guo et al. 2017a; Valentovich et al.
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2018), emotion coregulation is defined as a dynamic process of mutual construction of affective states and regulation of arousal between parents and children (see also Butler and Randall 2013; Tronick 1989). Emotion coregulation with parents provides children the foundation for the development of their own emotion regulation capabilities. The ability to self-regulate emotional states and behavioral actions begins to emerge in infancy and continues to develop throughout childhood through interactions with important others (Kopp 1982, 1989), with parent-child interactions playing a critical role. During the early years, children’s growing self-regulation capabilities require additional support to manage emotional arousal. Parents demonstrate emotion regulation strategies and provide external regulation for young children (Kopp 1989). Children initially depend on their parents to assist in modulating their emotional experiences (e.g., parent physically soothes child by holding her) given young children’s limited self-regulation skills. During social interactions, parents and children jointly regulate their emotional experiences through emotion coregulation (Cole et al. 2004; Feldman 2003). Repeated dyadic interactions allow for children to practice effective emotion regulation strategies and for parents to facilitate (e.g., scaffold) children’s strategies. As children become older, they gain a wider range of emotion regulation strategies and learn to increasingly rely on their own self-regulation strategies (Calkins and Hill 2007; Kopp 1989). An important aspect to consider when studying emotion coregulation is the emotional content of parent-child interactions. The emotional content of dyadic interactions, which may include mutual positivity, mutual negativity, or mismatched emotion engagement (e.g., child in negative state and mother in positive state), has been the focus of many studies on neurotypical (NT) children and children with behavioral problems. For example, one study examined parentNT child shared emotion expression during a semi-structured play activity and children’s behavioral outcomes (Lindsey et al. 2013). Results showed that mutual positivity between
these young children and their mothers predicted fewer aggressive behaviors with peers, whereas mutual negativity predicted more aggressive behaviors with peers 8 months later. For parents and their children with behavioral problems, the emotional content of emotion coregulation differs from those of NT dyads. In one study, mother-child dyads of preschool-aged children with behavior problems engaged in more mutual negativity and mismatched emotion engagements relative to dyads of NT children (Cole et al. 2003). Increased mutual positivity and decreased mutual negativity in dyads over time were also found to be related to improvements in behavioral problems. These findings suggest that emotional content is an important aspect of emotion coregulation and children’s adaptive functioning. It may be particularly important to include a focus on emotional content when examining emotion coregulation processes in children with developmental challenges. Emotion coregulation processes between parents and their children with developmental disorders, such as Autism Spectrum Disorder (ASD), require unique consideration. Children with ASD have deficits in social interactions that impact parent-child social exchanges, such as a tendency to focus on objects and impaired joint attention on a shared task (American Psychiatric Association 2015; Dawson et al. 2004). Moreover, children with ASD tend to have impaired emotion regulation capabilities. The role that parents of children with ASD have with respect to teaching key emotion regulation strategies may be particularly challenging, as these children tend to lack adaptive emotion regulation strategies and may instead react with intense emotional responses and poor emotional control (Mazefsky et al. 2013). Children with ASD may not respond to parents’ attempts at effective coregulation strategies in a manner analogous to NT children. Thus it is important to gain insight into early emotion coregulation processes between parents and their children with ASD as children are developing their self-regulation abilities. Such knowledge may in turn inform parent-child interventions targeting children’s emotion regulation and adaptive functioning.
Emotion Coregulation Processes Between Parents and Their Children with ASD
Historical Background ASD is a lifelong, neurodevelopmental disorder that is characterized by challenges in social communication and restricted, repetitive behaviors (American Psychiatric Association 2015). In addition to core symptoms, children with ASD also commonly exhibit impairments in behavioral and emotional functioning. Prevalence rates for at least one co-occurring behavioral problem, such as internalizing symptoms (e.g., anxiety and depression) or externalizing symptoms (e.g., aggression and impulsivity), range from approximately 70% to 86% for children with ASD (Chandler et al. 2016; Ooi et al. 2011; Simonoff et al. 2008). Children with ASD also tend to have difficulties with aspects of emotional functioning, such as emotion expression, recognition, and regulation (Konstantareas and Stewart 2006; Uljarevic and Hamilton 2013). Importantly, impairments in children’s ability to regulate their reactions to emotionally arousing situations may represent one key factor in the mechanisms that underlie the behavioral problems that children with ASD display (Mazefsky and White 2014). The ability to regulate emotional experiences across contexts is a significant goal in early child development and is associated with concurrent and overall adaptive functioning (Denham et al. 2003; Eisenberg et al. 1993). Effective emotion regulation strategies (e.g., problem-solving and cognitive reappraisal) are linked to adaptive functioning, whereas ineffective strategies (e.g., physical aggression and avoidance) are associated with a host of emotional, behavioral, and social problems throughout the life span (Aldao et al. 2010; Southam-Gerow and Kendall 2002). Prior research on the emotional challenges that children with ASD face has primarily focused on children’s emotion expression and recognition (e.g., Rump et al. 2009; Sasson 2006; Yirmiya et al. 1989); findings show difficulties in both domains. Over the past two decades, emotion regulation in children with ASD has begun to receive more attention (Cai et al. 2018; Mazefsky et al. 2013). Although the body of work investigating emotion regulation capabilities of children with ASD is relatively small, such work
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represents important advances in our understanding of emotional functioning in children with ASD. Research on emotion regulation has examined diagnostic group differences between children with ASD and NT children as well as the relationship between emotion regulation and socioemotional and behavioral outcomes in children with ASD. Overall, children with ASD have greater impairments in emotion processes relative to their NT peers. Specifically, in terms of emotion regulation skills, studies using various methodologies, including behavioral observations, child reports, and parent reports, have demonstrated that children and adolescents with ASD use more ineffective strategies and fewer effective strategies than NT peers across a variety of contexts (e.g., positive and negative situations) (Jahromi et al. 2012; Konstantareas and Stewart 2006; Samson et al. 2015). For example, observations of children during frustrating tasks demonstrated that preschool- and school-aged children with ASD used fewer efficient strategies (e.g., distraction) and more maladaptive strategies (e.g., physical objection) compared to NT peers (Jahromi et al. 2012; Konstantareas and Stewart 2006). Similarly, another study using parent report found that relative to NT peers, schoolaged children and adolescents with ASD engaged in increased maladaptive strategies (e.g., repetitive behaviors) and decreased adaptive emotion regulation strategies (e.g., cognitive reappraisal) across positive and negative emotion states (Samson et al. 2015). Moreover, these children were less effective at utilizing strategies such as problem-solving, social support, and distraction. In addition to the increased challenges that children with ASD encounter in regulating their emotional experiences relative to NT peers, emotional difficulties are also associated with socioemotional and behavioral problems. For example, in one study, parent ratings of emotion regulation skills of their preschool-aged children with ASD were related to children’s socioemotional functioning, such that ineffective emotion regulation was associated with decreases in social skills and increases in internalizing and externalizing behaviors 10 months later
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(Berkovits et al. 2017). Similarly, in another study, 9- to 12-year-old children with ASD who self-reported using maladaptive emotion regulation strategies, such as self-blame, also had higher levels of depressive symptoms and anxiety (Rieffe et al. 2011). For children with ASD, early parent-child relationships may be particularly important for the development of efficient emotion self-regulation and for improving maladaptive behaviors. However, parent-child relationships within the context of emotion coregulation have only recently been explored in a handful of empirical studies (e.g., Gulsrud et al. 2010; HirschlerGuttenberg et al. 2015a; Ting and Weiss 2017). Additionally, fathers are less often included in such research relative to mothers, and few studies have investigated emotion coregulation processes between fathers and their children with ASD (e.g., Hirschler-Guttenberg et al. 2015b). Prior studies on NT dyads suggest that fathers play an important role in children’s socioemotional development (e.g., Cabrera et al. 2007; Lindsey et al. 2013). The inclusion of fathers in research on emotion coregulation in dyads of children with ASD may be important in order to expand our understanding of their unique contribution to the development of children’s emotion regulation capabilities and adaptive functioning. Prior work has demonstrated the importance of emotion coregulation, yet our understanding of these dyadic emotion processes for children with ASD and how they relate to their functioning is limited. Thus, the objectives of our program of research were to (a) compare patterns of emotion coregulation in mother-child dyads of children with ASD and NT children (Guo et al. 2017a), (b) examine associations between aspects of emotion coregulation and children’s behavioral functioning in mother-child dyads of children with and without ASD (Valentovich et al. 2018), and (c) investigate emotion coregulation processes and children’s behavioral functioning in father-child dyads of children with ASD (Guo et al. 2017b). Grounded in dynamic systems theory, our studies used a novel application of the State Space Grid (SSG) method (Lewis et al. 1999), a dyadic micro-level approach, to analyze
parent-child emotion engagements during a semistructured play activity at home. Advantages of using a dyadic micro-level method are that parent and child behaviors can be analyzed simultaneously, rather than individually, and that momentary shifts between parent-child behaviors and affect states, rather than global behaviors, can be captured.
Current Knowledge Past and current research indicates that parents shape children’s emotion regulation capabilities during dyadic social exchanges by modeling appropriate behaviors, providing emotional coaching (e.g., labeling and validating emotions), teaching specific regulation strategies (e.g., redirecting attention and complex cognitive strategies), and monitoring and responding to children’s cues, among others (Morris et al. 2007; Thompson and Meyer 2007). Parents of children with and without ASD engage in similar types of emotion coregulation behaviors; however, they differ in the frequency of behaviors. Parents of children with ASD, for example, use more direct, active strategies (e.g., redirecting and prompting) and engage in more physical behaviors (e.g., physical soothing) relative to parents of NT children (Doussard-Roosevelt et al. 2003; Gulsrud et al. 2010; Hirschler-Guttenberg et al. 2015b). Parents of children with ASD are sensitive to children’s cues, and the differences in the frequency of specific strategies used may suggest an awareness of the child’s developmental needs. Additionally, parent emotion coregulation behaviors are associated with behavioral outcomes for children with ASD. For example, greater parental scaffolding (e.g., sensitive responses to child) during discussions of negative past events between parents and their school-aged children with ASD was found to be associated with fewer child externalizing behaviors (Ting and Weiss 2017). Recently, the SSG approach, based on dynamic systems theory, has provided a unique method for examining emotion coregulation in parent-child dyads. The SSG has been used to analyze finegrained parent and child emotion processes
Emotion Coregulation Processes Between Parents and Their Children with ASD
simultaneously to capture the transactional nature of dyadic interactions, rather than a global examination (Hollenstein 2007; Hollenstein and Lewis 2006). Moreover, the SSG allows for operationalizing emotion coregulation in terms of emotional content and structure of dyadic interactions. Emotional content refers to the ability of dyads to initiate and maintain emotion engagement (i.e., mutual positive, mutual negative, or mismatched interactions). Structure refers to the emotional variability of interactions (i.e., the range of emotional interactions, the shifts of emotions, and perseveration in an emotion state) (Granic et al. 2007; Hollenstein et al. 2004). Research utilizing the SSG has demonstrated that both the emotional content and structure of emotion coregulation are associated with children’s behavioral outcomes. For example, Hollenstein et al. (2004) analyzed interactions between parents and their high-risk children during a series of activities, including discussions of problems that cause conflict. Results indicated that decreased dyadic emotional variability (structure) when children were in kindergarten predicted higher levels of children’s externalizing behaviors in first grade. Lunkenheimer et al. (2011) assessed parent-child emotion coregulation in a challenging block design task at home when children were 3 years old. The results showed that the interaction between dyadic parent-child emotional variability (structure) and positive affect predicted lower externalizing problems in children at age 5.5 years. This method previously has been used to examine dyads of NT children and children with behavioral problems. Our lab is the first to use the SSG to examine emotion coregulation processes in families raising children with ASD (Guo et al. 2017a; Valentovich et al. 2018). In our recent studies, the emotional content and structure of emotion coregulation were examined in dyads of children with ASD and dyads of NT children utilizing the SSG approach. Mothers and their preschool-aged children completed the Three Boxes procedure (Tamis-LeMonda et al. 2004; Vandell 1979), a 10-min semi-structured play activity, in their home. During the activity, mothers and their children were given three
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numbered boxes that contained toys, and mothers were asked to open the boxes in sequential order. This procedure reflects activities that mothers and their children typically engage in and reliably elicits maternal and child behaviors. Play sessions were videotaped for later coding of emotion engagement states of mothers and children. Children’s internalizing and externalizing behaviors were also assessed using mother-reported Vineland Adaptive Behavior Scales (Sparrow et al. 2005) scores. A novel coding scheme was developed to capture emotion engagements, which were predicated on behaviors, body postures, attention, facial expressions, and vocalizations, in both families of NT children and children with ASD. Mothers and children were coded separately for low, medium, and high levels of positive engagement, negative engagement, and disengagement states; children were also coded for object engagement (for detailed information on the coding schemes, see Guo et al. 2017a). Mangold International’s INTERACT 9.47 (Mangold 2007) software program was used to code engagement states in 5second intervals. The separate codes were merged for each dyad to create dyadic engagement states: mutual positive engagement (mother and child in any positive engagement state), mutual negative engagement (mother and child in any negative engagement or disengagement state), mismatched engagement (mother in any negative engagement or disengagement state and child in any positive engagement state; mother in any positive engagement state and child in any negative engagement or disengagement state), and child-object (mother in any engagement state and child in object engagement). The dyadic engagement codes were exported to the SSG software (State Space Grid GridWare 1.1.; Lamey et al. 2004) to obtain the emotional content (i.e., initiating and sustaining dyadic engagement states) and the structure (i.e., range of emotional interactions, the shifts of emotions, and perseveration in an emotion state) of the interactions. The first goal of our research was to compare the emotional structure and content of interactions between mother-child dyads of children with ASD and dyads of NT children (Guo et al.
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2017a). Results indicated that in terms of the structure, dyads of children with ASD engaged in greater emotional variability (i.e., a wider range of emotional interactions, greater shifts of emotions, and less time spent in an emotion state) relative to dyads of NT children. In terms of emotional content, dyads of children with ASD initiated mutual positive, mismatched, and childobject engagements more frequently and spent more time in mismatched and child-object engagements relative to NT dyads, whereas dyads of NT children spent more time in mutual positive engagements relative to dyads of children with ASD. Results suggest that high emotional variability in a low-stress context may not imply adaptive interactions for children with ASD but rather suggests difficulty maintaining positive interactions with their mothers. The second goal of our research was to investigate whether interactions between emotional variability (structure) and mutual positive/ negative engagements (content) of emotion coregulation were associated with maladaptive behaviors in children with ASD (Valentovich et al. 2018). Results indicated that for dyads of children with ASD, greater emotional variability (structure) and more frequent initiation of mutual positive engagements (but not negative engagements) were associated with fewer maladaptive behaviors (i.e., internalizing and externalizing behaviors) for children with ASD. These findings suggest that both flexible interactions and shared positive interactions may play an important role in preventing behavioral problems in children with ASD. New research by Guo, Goldberg, Valentovich, and Garfin includes fathers and their children with and without ASD in research designed to parallel the mother-child studies. Semi-structured play activities were videotaped and later coded for father and child emotion engagement states using the coding schemes described above. Fathers also completed the Vineland Adaptive Behavior Scales (Sparrow et al. 2005) to assess their children’s adaptive and maladaptive behaviors. This is the first study to examine the association between emotion coregulation in fatherchild dyads of children with ASD and children’s
adaptive functioning utilizing the SSG method (Guo et al. 2017b). In general, we found that more time spent in positive engagements (content) and less time spent in mutual negative engagement (content) were associated with higher levels of adaptive behaviors in children with ASD. These results point to the importance of father-child positive interactions in social and emotional development in children with ASD.
Future Directions Studies conducted to date on aspects of emotion coregulation between parents and their children with ASD demonstrate its importance for children’s adaptive functioning; however, this area of inquiry is underresearched. Several key areas should be considered in future studies. Emotion Coregulation Across Contexts In our research, parents and their children were observed during a low-stress play task. The type of play (e.g., toy play, physically orientated play) that parents and children engage in as well as different emotion-eliciting contexts (e.g., low stress vs. high stress) may impact the process of emotion coregulation (Hirschler-Guttenberg et al. 2015b; Jahromi et al. 2012; Warreyn et al. 2005). For example, in past research, mothers’ emotional availability toward their children with ASD varied across free play, structured play, and social play contexts (Dolev et al. 2009). In a mildly frustrating context, children with ASD used a greater range of emotion regulation strategies but were less effective compared to NT children (Konstantareas and Stewart 2006). HirschlerGuttenberg and colleagues (2015b) showed that children with ASD displayed more negative emotions and behaviors with fathers in a fearful context, but not in a joyful context compared to NT peers. Although children with ASD are more vulnerable to negative emotional states, more studies are needed to clarify how both parents and children respond to different social contexts (Mazefsky et al. 2012). Furthermore, additional research should examine how emotion coregulation processes will unfold in terms of both
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emotional structure and content when parent-child behaviors are observed in contexts other than play, such as during high-stress tasks.
vs. challenging laboratory task) as well as explore how dyadic repair processes are related to children’s adaptive functioning.
Longitudinal Design Much of the research on emotion coregulation to date has been cross-sectional. Age effects of emotion regulation on developmental outcomes would be removed in a longitudinal design. Future work with a longitudinal study design would help clarify relationships between emotion coregulation and developmental outcomes in children with ASD. In this way, a mechanism of emotion coregulation for psychiatric comorbidity and social competencies in children with ASD would be better understood (Mazefsky et al. 2013). Specifically, future research could expand emotion coregulation to include not only micro-realtime measurement but also context-to-context emotional variability and developmental changes as described in Hollenstein’s Flex3 model (Hollenstein 2015).
Clinical Implications Past research has emphasized the negative aspects of emotion regulation among children with ASD. Our studies expand prior findings by demonstrating that dyads of parents and their children with ASD can initiate positive interactions, but they have a diminished capacity for sustaining positive interactions relative to NT dyads in a low-stress, home environment. These results could guide future family-systems-based interventions that focus on improving the ability of parent-child dyads to maintain longer positive interactions, which may be particularly important for families raising children with ASD. In particular, children may benefit from interventions that focus on helping parents assist their children in engaging in dyadic positive interactions to reduce children’s behavioral problems and improve adaptive functioning. Recent studies have generated evidence that a variety of techniques could plausibly be used to extend positive interactions in dyads with children with ASD; these include mentalization-based interventions (Slade 2005), relational savoring interventions (Burkhart et al. 2015), and mindfulness-based interventions (Cachia et al. 2016). From our research, we recommend that future studies incorporate microanalytic indicators of different aspects of emotion coregulation when evaluating the effects of interventions.
Dyadic Repair Disruptions that affect positive parent-child interactions occur routinely but can be managed by a process of dyadic repair (Beeghly and Tronick 2011). The ability of parents and children to repair ruptures, or to recover from temporarily mismatched or negative interactions by returning to positive states, could be crucial for children’s adaptive functioning (Beeghly and Tronick 2011; DiCorcia and Tronick 2011). For example, NT preschool-aged children who engaged in higher rates of repair processes with their mothers during a challenging puzzle task also demonstrated the ability to use appropriate regulation strategies and had fewer externalizing behaviors 4 months later (Kemp et al. 2016). Ruptures in positive interactions and attempts to repair such interactions may provide an opportunity for children to internalize efficient strategies for managing challenges and learn that they do not need to remain in negative situations (Tronick and Beeghly 2011). Future work should examine repair processes in dyads of children with ASD across low- and high-stress contexts (e.g., free play in the home environment
See Also ▶ Emotion Regulation ▶ Emotion Regulation Strategies in Preschoolers with Autism ▶ Internalization of Emotion Co-regulatory Support in Children with ASD ▶ Maladaptive Behavior ▶ Mutual Regulation ▶ Social Behaviors and Social Impairment ▶ Vineland Adaptive Behavior Scales (VABS)
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Emotion Coregulation Processes Between Parents and Their Children with ASD
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Emotion Dysregulation Inventory Tronick, E., & Beeghly, M. (2011). Infants’ meaningmaking and the development of mental health problems. American Psychologist, 66, 107–119. Uljarevic, M., & Hamilton, A. (2013). Recognition of emotions in autism: A formal meta-analysis. Journal of Autism and Developmental Disorders, 43, 1517–1526. https://doi.org/10.1007/s10803-012-1695-5. Valentovich, V., Goldberg, W. A., Garfin, D. R., & Guo, Y. (2018). Emotion coregulation processes between mothers and their children with and without autism spectrum disorder: Associations with children’s maladaptive behaviors. Journal of Autism and Developmental Disorders, 48, 1235–1248. https://doi. org/10.1007/s10803-017-3375-y. Vandell, D. L. (1979). Effects of a playgroup experience on mother-son and father-son interaction. Developmental Psychology, 15, 379–385. https://doi.org/10.1037/0012 -1649.15.4.379. Warreyn, P., Roeyers, H., & De Groote, I. (2005). Early social communicative behaviours of preschoolers with autism spectrum disorder during interaction with their mothers. Autism, 9, 342–361. https://doi.org/10.1177/ 1362361305056076. Yirmiya, N., Kasari, C., Sigman, M., & Mundy, P. (1989). Facial expressions of affect in autistic, mentally retarded and normal children. Journal of Child Psychology and Psychiatry, 30, 725–735. https://doi. org/10.1111/j.1469-7610.1989.tb00785.x.
Emotion Dysregulation Inventory Carla A. Mazefsky Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
Synonyms EDI
Description The Emotion Dysregulation Inventory (EDI) is an informant questionnaire that assesses poor emotion regulation and is validated for general community and clinical populations as well as youth with autism spectrum disorder (ASD). It was created using methods developed by the PatientReported Outcomes Measurement Information
Emotion Dysregulation Inventory
System (PROMIS ®). The EDI items were developed based on an iterative process that included caregiver input. The EDI response options ask respondents to rate each item on a 5-point Likert scale from “not at all” to “very severe” for observed functioning over the past 7 days. Caregivers of 1755 youth with ASD completed 66 candidate EDI items, and the final 30 items were selected based on classical test theory and item response theory (IRT) analyses. The analyses identified two factors: (1) Reactivity, characterized by intense, rapidly escalating, sustained, and poorly modulated negative emotional reactions, and (2) Dysphoria, characterized by anhedonia, sadness, and nervousness. The final items did not show differential item functioning (DIF) based on gender, age, intellectual ability, or verbal ability. Because the final items were calibrated using IRT, even a small number of items offers high precision, minimizing respondent burden. IRT co-calibration of the EDI with other commonly used measures demonstrated its superiority in assessing the severity of emotion dysregulation with as few as seven items. Validity of the EDI was supported by expert review, its association with related constructs (e.g., anxiety and depression symptoms, aggression), and higher scores in psychiatric inpatients with ASD compared to a community ASD sample. Analyses in a general sample of 1000 youth matched to the US Census on age, gender, race/ethnicity, years of education, and region supported the EDI’s original factor structure and psychometrics, including its sensitivity, reliability, and validity. The general youth sample data was also used to generate normative cutoffs to determine clinical significance. In sum, the EDI provides an efficient and sensitive method to measure emotion dysregulation for clinical assessment, monitoring, and research in individuals of any level of cognitive or verbal ability across populations. The EDI is available for use (requests can be made at: www.reaact.pitt.edu by completing the “EDI Inquiry Form”). The EDI is available in English, Spanish, French, Dutch, German, Danish, traditional Chinese, and Turkish. Requests for other translations can be made through the inquiry form. Theta score and t-score conversions are
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available based on either the autism norms or general population norms. Examples of Reactivity items include: “Has explosive outbursts,” “Has trouble calming him/herself down,” “Emotions go from 0 to 100 instantly,” and “Cries or stays angry for 5 minutes or longer.” Examples of Dysphoria items include: “Very little makes him/her happy,” “Seems sad or unhappy,” “Not responsive to praise or good things happening,” and “Refuses to leave the house or go to school unless forced.”
Historical Background Emotion dysregulation, or difficulty modulating emotion in the service of one’s goals, is common in individuals with ASD and has significant implications for treatment needs and outcome (Mazefsky 2015; Weiss et al. 2017). However, the lack of sensitive measures suitable for use across the range of functioning in autism spectrum disorder (ASD) is a barrier to treatment development and monitoring. Most emotion regulation measures were developed for the general population and excluded youth with ASD when assessing their psychometric properties. In addition, emotion regulation measures generally rely on self-report or use wording such as “complains about” or “worries about,” which limits their utility for youth with minimal verbal ability. Having a dimensional measure of emotion dysregulation that could be applied across the autism spectrum would enable a more complete understanding of the role of emotion dysregulation in ASD and facilitate treatment planning and monitoring. The EDI was developed to meet this need. As such, it was designed to capture the full range of manifestations of poor emotion regulation in ASD. Another primary goal was for it to be applicable across the autism spectrum, with items that could be rated regardless of the individual’s verbal ability. Therefore, the emphasis is on observable signs of poor emotion regulation, and the format is informant questionnaire. Informant report is an ideal form of assessment in situations when the individual is unable to report due to cognitive or other limitations, making it an appropriate method for assessing minimally verbal youth with ASD.
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In addition, informant perspectives on emotion dysregulation are important because limited emotional insight and awareness is common in ASD (Griffin et al. 2016). The EDI was created using guidelines from an NIH Roadmap Initiative focused on developing sensitive outcome measures, PROMIS ® (www. nihpromis.org). The multiple-site PROMIS Research Network (2012) produced scientific standards for developing measures as well as a comprehensive battery of effective and sensitive outcome measures. The EDI was the first measure for ASD developed based on PROMIS principles. The EDI item development process was described in detail in Mazefsky et al. (2018a). First, a comprehensive review of emotion regulation and existing measures of related constructs was completed to inform development of a conceptual model. The conceptual model was structured with a primary domain (emotion dysregulation), subdomains (affective experience and affective control), factors (negative mood, anxiety, reactivity, and emotional inflexibility), and facets (increased negative affect, decreased positive affect, disturbed behavior, decreased vitality, nervousness and fear, hyperarousal, rapid escalation, intensity, lability, sustained reactions, and poor modulation of emotion). The actual dimensional structure was tested empirically at a later step, but the conceptual model structure was utilized to develop an item hierarchy, which involved assignment of draft items to each facet to ensure adequate coverage of key constructs. The draft of items was reviewed by experts in ASD and measure development. Once the initial item pool was developed, interviews were completed with 19 parents of children and adults with ASD who varied in verbal and intellectual ability and age to assess their understanding of the items and their decision-making processes when selecting their responses. Information generated from these interviews, along with input from a panel of experts in measure development and emotion dysregulation in ASD, was utilized to revise the items, directions, and response options and arrive at the final 66 candidate items that were used for psychometric analyses and calibration.
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The complete EDI item bank, directions, and response options had a Flesch Reading Ease of 66.3 (scale of 0–100 with higher scores indicating easier to read) and a Flesch-Kincaid Grade Level of 7.6, which are both considered adequate. Without the instructions, which include some longer passages with guidance on how to interpret behavior, the items and response options had a Flesch Reading Ease of 100 and a Flesch-Kincaid Grade Level of 2.1.
Psychometric Data The complete psychometrics of the EDI in an ASD sample are described in Mazefsky, Yu, White, Siegel, and Pilkonis (2018b). The EDI psychometrics were established utilizing a sample that was representative of the full spectrum of severity of ASD (Interactive Autism Network; IAN) while also being enriched with the most extreme forms of emotion dysregulation in ASD (Autism Inpatient Collection; AIC). IAN uses the Social Communication Questionnaire Lifetime for confirmation of parental report of professional diagnosis of ASD. Parental report of professional diagnosis of ASD has been verified by medical records. A prior study documented the validity of the ASD diagnoses in IAN through standardized clinical assessments and independent clinician verification (see Lee et al. 2010 for full details). The AIC is a six-site study of children, adolescents, and young adults admitted to specialized inpatient psychiatric units for youth with ASD and other developmental disorders. The full methods of the AIC have been published previously (Siegel et al. 2015). Participants in the AIC had ASD confirmed by the Autism Diagnostic Observation Schedule-2 and expert opinion. A total of 1755 caregivers of children with ASD from IAN (n ¼ 1323) and AIC (n ¼ 432) combined completed the EDI for psychometric analysis. The mean age in both samples was 12 with a standard deviation of 3, though the range for IAN’s sample was 6–17.9, whereas the range for the AIC was 4–20. In the combined sample, 27.8% had intellectual disability, 55.3% were fluently verbal, and 20.9% were female. The
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AIC had a higher proportion of youth with intellectual disability and limited verbal ability, and the gender composition was equal in both sources. The proportion of the combined sample with co-occurring intellectual disability and percent female was consistent with national autism surveillance statistics (Baio et al. 2018). First, the dimensionality of the EDI item bank was explored. In order to do so, the sample was split in half for exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). Given differences in IQ and verbal ability for the IAN and AIC samples, EFA was performed separately, as well as for the combined sample. The eigenvalues and factor structures were similar across the three samples; therefore, the combined sample was used for all additional analysis. The 1- and 2-factor solutions emerged as the most meaningful, given the scree plots, the magnitude of eigenvalues, and clinical interpretation. In the combined sample, the eigenvalues were 29.48 and 4.71 for the first two factors. Factor 1 was characterized by items capturing rapidly escalating, intense, and labile negative affect as well as difficulty downregulating that affect (sustained reactions and trouble calming down). Factor 2 included items that reflect common definitions of general negative affect (sadness, unease, and anxiety) as well as low motivation or anhedonia. The second round of EFA on the combined sample retained 53 items with the same general content, there were no items that loaded >0.45 loading on both factors, and the correlation between the two factors was 0.51. Next, single-factor CFAs were completed on the reduced item pool, using the second half of the sample (n ¼ 885). For F1, all factor loadings were greater than 0.50, and several fit indices were uniformly strong: RMSEA ¼ 0.09, CFI ¼ 0.96, TLI ¼ 0.96. For F2, all factor loadings were larger than 0.45, and fit indices revealed less homogeneity in CFA terms (reflected primarily in a larger RMSEA: RMSEA ¼ 0.12, CFI ¼ 0.86, TLI ¼ 0.84). Based on the EFA and CFA analyses, the original 66 items were trimmed to a pool of 53 items distributed across two factors (Reactivity and Dysphoria). Next, the two corresponding item banks were calibrated separately using two-
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parameter graded item response theory response model (GRM) for polytomous items (i.e., items with three or more ordinal response categories). The items were individually evaluated based on their slope parameter which measures item discrimination (i.e., how well the item differentiates higher versus lower levels of severity or Ɵ in IRT terms). Useful items have larger slope parameters. In addition, threshold parameters which measure item difficulty (i.e., the ease versus difficulty of endorsing different response options for an item) were explored. Local dependency (i.e., residual correlations) in the IRT models was also assessed using the LD chi-square. Finally, item analyses included investigation of differential item functioning (DIF) by gender, age, verbal ability, and IQ flagged no items by both DIF methods, and no further items were eliminated for this reason. Item information curves were also examined to eliminate items with limited information, i.e., less than 0.50. Thus, the final calibrated item bank after completing all of these steps included 30 items: 24 items for factor 1 (Reactivity) and 6 items for factor 2 (Dysphoria). Overall, Reactivity was more robust than Dysphoria, with more items, better IRT parameters, and smaller standard errors. For example, 20 items had item information values over 2.0 for Reactivity, with the following items all having total item information values over 3.0: has explosive outbursts, hard to calm him/her down when mad or upset, has extreme or intense emotional reactions, has trouble calming him/herself down, has emotions go from 0 to 100 instantly, and cries or stays angry for 5 min or longer. For Dysphoria, only three items had item information values over 2.0, and only “very little makes him/her happy” had an item information value over 3.0. The correlation between the two EDI factors was 0.63 in the combined sample, 0.50 for AIC, and 0.59 for IAN, all significant at p female ✓ ✓ ✓ ✓
Female > male > autism
✓ ✓ ✓ ✓
The Role of Prenatal Hormones in the Development of Sex Differences Though genetic sex is determined at conception, it is the gonadal hormones (i.e., androgens, estrogens, and progestins) that are responsible for differentiation of the male and female phenotypes in the developing human fetus. It is thought that behaviors showing large sex differences are the best candidates for studying effects of hormones on later development (Hines 2004). The direct sampling of fetal serum or manipulation of fetal hormone levels would be highly dangerous. As a result, researchers have employed indirect methods of measuring prenatal hormone exposure to study effects on later development. One such indirect measure is the ratio between the length of the 2nd and 4th digit (2D, 4D) of the hand. This ratio has been found to be sexually dimorphic, being lower in males than in females. 2D:4D ratio is thought to be fixed by week 14 of fetal life and has been found to reflect fetal exposure to prenatal sex hormones in early gestation (Lutchmaya et al. 2004; Manning 2002). Results from studies of 2D:4D ratios as proxies for fetal testosterone (fT) levels show that children with ASC have more masculinized digit ratios compared to typically developing boys. These patterns have also been observed in the siblings and parents of children with ASC, indicating the possibility of a link between genetically based elevated fT levels and the development of ASC (Manning et al. 2001).
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The medical condition of Congenital Adrenal Hyperplasia (CAH) leads to abnormally high prenatal and neonatal androgen levels and has provided researchers with an indirect method of examining the effects of elevated androgen exposure. Girls with CAH have more autistic traits (measured using the adult AQ) compared to their unaffected sisters (Knickmeyer et al. 2006a). Given that this condition is usually treated following birth, this suggests their higher AQ scores reflect elevated prenatal androgen levels. These findings should be interpreted with caution, however, since CAH carries a number of related problems (as well as extensive treatment) which may affect the atypical cognitive profiles found in this population. Some studies have also compared measurements of testosterone in umbilical cord blood with postnatal development. A recent study using umbilical cord blood testosterone measures examined pragmatic language ability in girls followed-up at 10 years of age. Results showed that the higher a girl’s free testosterone level at birth, the higher the scores on a pragmatic language difficulties questionnaire (Whitehouse et al. 2010). However, levels of fT are typically at very low levels from about week 24 of gestation, umbilical cord samples can contain blood from the mother as well as the fetus, and hormone levels may vary due to labor itself, so umbilical cord blood testosterone does not allow one to test if outcomes reflect fT per se. Currently the best method to examine the effect of fT is to sample the amniotic fluid surrounding the fetus via amniocentesis. An advantage of amniotic fluid samples is that amniocentesis is often performed for routine clinical purposes within a relatively narrow time period which coincides with the hypothesized critical period for human sexual differentiation between weeks 8 and 24 of gestation (Hines 2004). This is also more direct than the 2D:4D method as the hormones themselves can be assayed, rather than relying on a proxy for these. A number of studies have linked elevated levels of fT in the amniotic fluid with the masculinization of certain behaviors, beginning shortly after birth. Elevated fT has been linked to reduced
Extreme Male Brain (EMB) Theory
eye contact in infants (Lutchmaya et al. 2002a), smaller vocabulary in toddlers (Lutchmaya et al. 2002b), narrower interests and poorer quality of social relationships at 4 years of age (Knickmeyer et al. 2005), less empathy at 4 and 8 years (Chapman et al. 2006; Knickmeyer et al. 2006b), more male-typical play behavior (Auyeung et al. 2009a), better performance on the Children’s Embedded Figures Test (Auyeung et al., in press), and increased systemizing at 8 years (Auyeung et al. 2006). In addition, fT levels have been found to be positively correlated with number of autistic traits (measured using the Quantitative Checklist for Autism in Toddlers (Allison et al. 2008)) in toddlers between 18 and 24 months of age (Auyeung et al. 2010), as well as in older children (ages 6–10 years old), using two independent dimensional measures of autistic traits (the child version of the AQ and the Childhood Autism Spectrum Test (Auyeung et al. 2009b)). Evidence from typically developing children for effects of fT is summarized in Table 3. The use of amniotic fluid to measure prenatal hormonal exposure has several limitations. Ideally, it would be best to make direct measurements of testosterone at regular intervals throughout Extreme Male Brain (EMB) Theory, Table 3 Evidence from typically developing children for effects of fT Evidence from typical children Eye contact is inversely related to fT Social skills are inversely related to fT Vocabulary size is inversely related to fT Empathy is inversely related to fT Autistic traits are positively associated with fT Restricted interests are fT is positively associated Systemizing is positively associated with fT Rightward asymmetry in the isthmus of the corpus callosum is positively associated with fT
Key references Lutchmaya et al. (2002b) Knickmeyer et al. (2005) Lutchmaya et al. (2002b) Chapman et al. (2006), Knickmeyer et al. (2005) Auyeung et al. (2009b, 2010) Knickmeyer et al. (2005) Auyeung et al. (2006) Chura et al. (2010)
Extreme Male Brain (EMB) Theory
gestation and into postnatal life. However, it would be extremely hazardous to attempt direct measurements from the fetus itself for purely research purposes. It is not possible to obtain repeated samples of fT because amniocentesis itself carries a risk of causing miscarriage (of about 1%). As a result, obtaining amniotic fT measures are opportunistic, when the procedure is being carried out for clinical reasons, with never more than a single measurement of fT at one timepoint although it is known that hormones fluctuate during the day and between days, even in fetuses. The representativeness of a single sample of fT thus remains unclear, but would be difficult to explore in an ethical manner. In addition, given the reported time course of testosterone secretion, the most promising time to measure fT is probably at prenatal weeks 8–24 (Smail et al. 1981), but this is still a relatively wide range. In addition, research is nonhuman primates has shown that androgens masculinize different behaviors at different times during gestation, suggesting different behaviors may also have different sensitive periods for development. For all these reasons, the inferences that can be drawn about a single measurement of fT are therefore limited. However, where a significant correlation between amniotic fT and a behavior is observed, this should represent a very conservative estimate of the correlation between overall fT levels and that behavior.
1915 Extreme Male Brain (EMB) Theory, Table 4 Evidence for testosterone effects in people with ASC Evidence from people with ASC 10 sex steroid genes associated with AS or AQ or empathy HSD11B1, LHCGR, CYP17A1, CYP19A1, SCP2, CYP11B1*, ESR1, ESR2, HSD17B4, HSD17B2* Timing of puberty Boys with ASC enter puberty earlier, girls with ASC enter puberty later Testosterone related medical conditions in women with ASC and their mothers (e.g., PCOS, breast and ovarian cancers, acne) Testosterone related characteristics in women with ASC and their mothers Lower 2D:4D ratio in ASC, and parents SRD5A1, and AR genes associated with ASC
Key references Chakrabarti et al. (2009)
E Ingudomnukul et al. (2007), Knickmeyer et al. (2006c), Tordjman et al. (1997) Ingudomnukul et al. (2007)
Ingudomnukul et al. (2007), Knickmeyer et al. (2008) Manning et al. (2001), Milne et al. (2006), Noipayak (2009) Henningsson et al. (2009), Hu et al. (2009)
Other lines of evidence implicating testosterone in the etiology of ASC are summarized in Table 4:
Evidence Implicating Testosterone in the Etiology of Autism
Conclusions
Genetic influences are undoubtedly involved with other factors (such as prenatal hormone levels) which lead to the development of ASC. Evidence of a genetic link to ASC is provided by a recent study which shows that genes regulating sex steroids are associated with autistic traits, as measured by scores on the Autism Spectrum Quotient (AQ), in a typical adult sample (Chakrabarti et al. 2009). A parallel study also showed that genes regulating sex steroids are associated with a diagnosis of Asperger syndrome in a case-control sample (Chakrabarti et al. 2009).
There is a significant body of evidence connecting the characteristic behaviors of ASC to extremes of certain male-typical behaviors. Evidence includes superior performance on a range of tasks where males typically outperform females but impairment compared to typical males on tasks showing a female advantage. This observation has led to the development of the “extreme male brain” theory of autism. Support for this theory can also be found very early in life, and also in some primate studies suggesting the development of sex-typical behaviors is at least partly biological.
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Research using direct measures of potential biological factors such as prenatal hormones as well as multiple measures of empathizing and systemizing, including both observational and behavioral measures are needed to explore the link between these factors in greater detail. Although the findings presented in this chapter lend support to the “extreme male brain” theory of ASC and its link to fT, a thorough evaluation of this theory will require testing not just for associations between fT and autistic traits, but between fT and clinically diagnosed ASC. This remains an active area of research.
Extreme Male Brain (EMB) Theory
See Also ▶ Broader Autism Phenotype ▶ Cognitive Skills ▶ Empathy ▶ Face Recognition ▶ Friendships ▶ Gender Differences ▶ Social Behaviors and Social Impairment ▶ Social Cognition ▶ Systemizing ▶ Theory of Mind
References and Reading Future Directions Future studies of empathizing and systemizing in children could examine whether the relationships between fT levels (and other biological factors) remain consistent using multiple measures of empathy and systemizing (e.g., observational, experimental). In addition, the replication of the findings related to fT levels in larger sample sizes would assist in identifying any factors that are linked with levels in the extreme ranges. Future studies could assess whether relationships between fT levels and the development of autistic traits are consistent for individuals with a clinical diagnosis of ASC since the current samples only included typically developing children. Considering the current support for a role for fT in the development of autistic traits, it would also be beneficial for future studies to examine the relationships between fT levels, genetic variation, and the development of autistic traits. The EMB theory of autism has been developed from studies in typically developing and highfunctioning individuals with ASC. It would be interesting to extend the scope of this theory with an examination of individuals with more severe forms of ASC. It is also important to assess the validity of “empathizing” and “systemizing” measures by correlating these with performance and everyday measures of functioning, and future studies could further explore how these domains develop and also how they correlate with neural structure and function.
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Eye Contact with autism. Journal of Child Psychology and Psychiatry, 42(4), 539–549. Servin, A., Bohlin, G., & Berlin, D. (1999). Sex differences in 1-, 3-, and 5-year-olds’ toy-choice in a structured play session. Scandinavian Journal of Psychology, 40, 43–48. Shah, A., & Frith, U. (1983). An islet of ability in autistic children: A research note. Journal of Child Psychology and Psychiatry, 24(4), 613–620. Shah, A., & Frith, C. (1993). Why do autistic individuals show superior performance on the block design task? Journal of Child Psychology and Psychiatry, 34, 1351–1364. Smail, P. J., Reyes, F. I., Winter, J. S. D., & Faiman, C. (1981). The fetal hormonal environment and its effect on the morphogenesis of the genital system. In S. J. Kogan & E. S. E. Hafez (Eds.), Pediatric andrology (pp. 9–19). Boston: Martinus Nijhoff. Stern, M., & Karraker, K. H. (1989). Sex stereotyping of infants: A review of gender labeling studies. Sex Roles, 20, 501–522. Tordjman, S., Ferrari, P., Sulmont, V., Duyme, M., & Roubertoux, P. (1997). Androgenic activity in autism. The American Journal of Psychiatry, 154(11), 1626–1627. Voyer, D., Voyer, S., & Bryden, M. P. (1995). Magnitude of sex differences in spatial abilities: A meta-analysis and consideration of critical variables. Psychological Bulletin, 117(2), 250–270. Wheelwright, S., Baron-Cohen, S., Goldenfeld, N., Delaney, J., Fine, D., Smith, R., et al. (2006). Predicting autism spectrum quotient (AQ) from the systemizing quotient-revised (SQ-R) and empathy quotient (EQ). Brain Research, 1079(1), 47–56. Whitehouse, A. J., Maybery, M. T., Hart, R., Mattes, E., Newnham, J. P., Sloboda, D. M., et al. (2010). Fetal androgen exposure and pragmatic language ability of girls in middle adulthood: Implications for the extreme male-brain theory of autism. Psychoneuroendocrinology, 35(8), 1259–1264. Witkin, H. A., Dyk, R. B., Fattuson, H. F., Goodenough, D. R., & Karp, S. A. (1962). Psychological differentiation: Studies of development (p. 418). Oxford, UK: Wiley. Yirmiya, N., Sigman, M. D., Kasari, C., & Mundy, P. (1992). Empathy and cognition in high-functioning children with autism. Child Development, 63(1), 150–160.
Eye Contact ▶ Mutual Gaze
Eye Gaze
Eye Gaze Atsushi Senju Centre for Brain and Cognitive Development, Birkbeck, University of London, London, UK
Definition The term “eye gaze” may refer to either of the two distinctive, but related, topics. Firstly, it refers to the perception of other persons’ eye gaze, which can be inferred from the relative position of the iris within the eyelid, in conjunction with the orientation of the head and the body. Secondly, it also refers to the production of eye gaze by a patient or a participant, such as the control of saccades and fixations. This entry mainly covers the former topic. Perception of others’ eye gaze is crucial for social interaction and communication and its impairment in ASD because eye gaze signals the direction of others’ attention and intention.
Current Knowledge Fixations on Another Person’s Eyes Atypical pattern of mutual gaze behavior, or eye contact, is among the most distinguishable manifestation of the qualitative impairment in social interaction in ASD. Since Kanner’s first report (Kanner 1943), such atypical pattern of eye contact has been reported and discussed in many clinical and experimental settings, including recent studies using eye-tracking methods (Senju and Johnson 2009a). Based on this clinical significance, eye contact is currently included in standardized diagnostic criteria such as DSM and ICD. In DSM-IV-TR, it is defined as a “marked impairment in the use of multiple nonverbal behaviors (e.g., eye-to-eye gaze) to regulate social interaction and communication” (American Psychiatric Association [APA] 2000, p. 70). Retrospective home video analyses of infants who were
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later diagnosed with ASD have revealed that atypical patterns of eye contact can be observed within the first year of life, well before the age of diagnosis (Maestro et al. 2005). Some researchers and clinicians once proposed that such atypical eye contact behavior results from “gaze avoidance,” active avoidance of others’ eye gaze due to negatively valenced overarousal (e.g., Hutt and Ounsted 1966). However, follow-up studies reported mixed results, suggesting that the gaze avoidance may be present in some individuals with ASD, but it may not be universal or predominant in this population (Buitelaar 1995). Some of eye-tracking studies have revealed that individuals with ASD fixate others’ eyes less than typically developing individuals do, but other studies failed to replicate or reported mixed results (Senju and Johnson 2009a). Such inconsistencies may result from the differences in task demands and/or the characteristics of stimuli used. In general, reduced fixations on the eyes are most prominent with complex and cognitively demanding face stimuli and/or by using dynamic videotape stimuli, including conversations. Moreover, individual differences in the fixations on the eyes within ASD correlate with the volume and the face sensitivity of amygdala (e.g., Dalton et al. 2005), as well as the degree of self-reported social anxiety (Corden et al. 2008). However, it does not correlate with autistic symptoms measured with ADOS or AQ (Corden et al. 2008). Perception of Direct Gaze Direct gaze signals that the person is looking at the perceiver. It also leads to the establishment of mutual gaze or eye contact, which signals the initiation of communication. In typically developed population, perceived direct gaze, or eye contact, modulates concurrent and/or immediately following cognitive processing and/or behavioral responses, a phenomenon called the eye contact effect (Senju and Johnson 2009b). For example, perceived eye contact facilitates the performance of face-related tasks such as gender discrimination, recognition of face identity, and detection of gaze direction. Results from neuroimaging studies
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also indicate that perceived eye contact modulates the activation of social brain network (defined as the cortical and subcortical structures specialized for the processing of social information, such as fusiform gyrus, superior temporal sulcus, medial prefrontal and orbitofrontal cortex, and amygdala). Sensitivity to the direction of another person’s eye gaze appears very early in typical development (Johnson et al. 2009). For example, newborns preferentially look longer at the faces with direct gaze than those with averted gaze. Perceived eye contact also facilitates the processing of face identity and communicative facial expression during the first half year of life. A series of studies have demonstrated atypical processing of direct gaze in ASD (Senju and Johnson 2009a). For example, Senju, Yaguchi, Tojo, and Hasegawa (2003) used an oddball procedure in which children were asked to detect a rare stimulus presented occasionally within the context of a frequently reoccurring stimulus. The rare stimuli were either faces with direct gaze or those with laterally averted eye gaze (i.e., faces with eyes looking leftward or rightward). The frequent stimuli were faces with eyes looking downward. Thirteen children with autism (mean age: 12 years) were compared with typically developing children of the same range of the age and nonverbal intelligence. Typically developing children showed the eye contact effect, that they were better at detecting direct gaze than averted gaze. By contrast, although children with autism are equally good at detecting averted gaze as typically developing children, they were less skilled at detecting faces with direct gaze, and they failed to show the eye contact effect or the facilitation of performance caused by the perceived direct gaze. Similarly, Pellicano and Macrae (2009) assessed whether direct gaze facilitates the person categorization in children with autism (mean age: 10 years) and age- and IQ-matched typically developing children. Results revealed that typically developing children were faster to categorize faces by sex when they were with direct gaze than with averted gaze. By contrast, although children with autism were as fast as typically developing children to categorize faces, their performance were unaffected by the gaze direction of the faces. These studies
Eye Gaze
demonstrate that unlike typically developing individuals, individuals with ASD do not show the eye contact effect or the facilitation of face and gaze processing caused by the perceived direct gaze. Several studies have recorded field potentials on the scalp with either EEG or MEG to assess the cortical response to direct and averted gazes in individuals with ASD, but results are mixed. Two studies reported that 3- to 7-year-old children (mean age: 5 years, Grice et al. 2005) as well as 7- to 12-year-old children (mean age: 10 years old, Kylliäinen et al. 2006) with ASD showed larger event-related potential (ERP) or event-related field (ERF) response to direct gaze than averted gaze, but control children did not. By contrast, Senju, Tojo, Yaguchi, and Hasegawa (2005) reported that typically developing children (9- to 14-year-olds, mean age: 12 years) showed a larger ERP amplitude for direct than for averted gaze, but ERPs of children with ASD were not modulated by the presence of eye contact. Moreover, Elsabbagh et al. (2009) recorded EEG from highrisk infants (see also ▶ “Early Diagnosis”) as well as from low-risk control infants while they watched faces with either direct or averted eye gaze and conducted ERP and time-frequency analysis (TFA). Results showed that a late ERP component (P400), which is known to relate to face processing, has a longer latency in response to direct gaze in high-risk infants. Secondly, TFA analysis revealed clearly distinguished and temporally sustained high-frequency oscillatory activity in the gamma-band frequency for direct gaze compared to averted gaze in control infants. In contrast, high-frequency oscillatory activity in gamma band for direct gaze compared to averted gaze in high-risk infants was delayed and less persistent. These results suggest that atypical eye contact processing in high-risk infants relates to the top-down modulation (as indicated by the slower P400 latency) and task-relevant synchronization of brain activations (as indicated by the lack of differential gamma-band activation in response to eye contact). Two studies have assessed skin conductance response (SCR), an index of physiological arousal, while individuals with ASD observe faces with either direct or averted eye gaze.
Eye Gaze
However, the results are again mixed – one study found larger SCR in response to direct gaze in 10-year-old children with ASD (Kylliäinen and Hietanen 2006), but the other study did not replicate this findings in 12-year-old children (Joseph et al. 2008). Such inconsistency between physiological studies may result from the different age ranges of the participants and/or the different experimental tasks used for EEG/MEG/SCR recordings. To summarize, behavioral studies have consistently found that direct gaze facilitates cognitive processing of faces in typically developing children, but the face and gaze processing of individuals with ASD are unaffected by the presence of direct gaze (Senju and Johnson 2009a). Inconsistencies in the physiological studies make it difficult to understand the neural basis of such atypical processing of direct gaze in individuals with ASD. Perception of Averted Gaze Averted gaze, by contrast, signals that the person is looking at something in the environment. Thus, following another person’s gaze helps enable the perceiver to infer his or her attention, goal, and/or intention (see also ▶ “Gaze”). In typical development, perceived averted gaze reflexively shifts the attention of the perceiver to the corresponding direction, which is often called gaze cueing effect (Frischen et al. 2007). The gaze cueing effect can be observed from 3 months of age, which corresponds to the age range when a rudimentary form of gaze following starts to appear. Individuals with ASD do not have an overall impairment in detecting the direction of others’ eye gaze. For example, Leekam, Baron-Cohen, Perrett, Milders, and Brown (1997) demonstrated that children with autism are as good as typically developing children at discerning the subtle differences in gaze direction. Moreover, majority of studies demonstrated that individuals with autism across the wide age range, from 2-year-old toddlers to adults, show apparently typical gaze cueing effect (Nation and Penny 2008). In these studies, participants are presented with a picture of a face with gaze averted to the left or right followed by a target stimuli presented either to the left or right of the face. Individuals with ASD, as well as typically developing individuals,
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are faster to detect the target when the face was looking at the location than when the face was looking at the opposite direction. These studies contrast with the impairment in gaze following behavior in individuals with ASD (see also ▶ “Gaze”). However, several studies also suggest the qualitative differences in the gaze cueing effect between individuals with ASD and typically developing individuals. For example, Senju, Tojo, Dairoku, and Hasegawa (2004) compared the cueing effect of eye gaze and an arrowhead and found that children with autism do not show preferential sensitivity to eye gaze unlike typically developing children. Other studies (e.g., Ristic et al. 2005) used a schematic face, instead of a photographic image of a face, and found that only typically developing individuals show the gaze cueing effect in response to the schematic eyes. By contrast, individuals with ASD do not show gaze cueing effect in response to these stimuli. These studies suggest that the gaze cueing effect in individuals with ASD is not as robust as in typically developing individuals and perhaps is not processed in a functionally specialized cognitive mechanism. Individuals with ASD also show difficulties in inferring mentalistic and communicative significance of the eyes. For example, Baron-Cohen, Baldwin, and Crowson (1997) presented two novel objects to 7- to 12-year-old children with ASD as well as typically developing children matched by the mental age and verbally labelled one of these objects. Typically developing children correctly associated the label with the object being looked at by the experimenter, even when the child was looking at the other object when the experimenter uttered the label. By contrast, children with autism tended to apply the label to objects in their own focus rather than the one the experimenter was looking at. An adult neuroimaging study also demonstrated that individuals with ASD, unlike typically developing individuals, do not show differential cortical activation in response to the referential gaze (Pelphrey et al. 2005). To summarize, individuals with ASD have relatively good capacity to encode the direction of others’ eye gaze and reflexively shift their
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attention to the corresponding direction. However, the gaze processing in individuals with ASD may not be as robust or specialized as those of typically developing individuals, which may be related to the difficulty in using others’ eye gaze in the context of social learning.
Future Directions Further studies will be required to clarify the neurodevelopmental basis of eye gaze processing in ASD and its relationship with other clinical symptoms. Firstly, it is not clear why there are such inconsistencies between different neurophysiological studies contrasting direct and averted gaze processing in ASD, as well as the different eye-tracking studies investigating the pattern of face scanning in ASD (Senju and Johnson 2009a). Thus, further studies will be required to systematically control the background of participants (e.g., age, gender, and/or comorbidity) as well as the properties of the stimuli and the tasks used (e.g., dynamic vs. static, active task vs. passive viewing, size and/or facial expression) to reveal the neurophysiological basis of direct gaze processing in ASD. Secondly, it will be important to investigate the relationship between atypical cortical response to eye gaze in early infancy (e.g., Elsabbagh et al. 2009) and the later manifestation of clinical symptoms.
References and Reading American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders (4th ed., Text Rev.), DSM-IV-TR. Washington, DC: Author. Baron-Cohen, S., Baldwin, D. A., & Crowson, M. (1997). Do children with autism use the speaker’s direction of gaze strategy to crack the code of language? Child Development, 68(1), 48–57. Buitelaar, J. K. (1995). Attachment and social withdrawal in autism – Hypotheses and findings. Behaviour, 132, 319–350. Corden, B., Chilvers, R., & Skuse, D. (2008). Avoidance of emotionally arousing stimuli predicts social-perceptual impairment in Asperger’s syndrome. Neuropsychologia, 46(1), 137–147. Dalton, K. M., Nacewicz, B. M., Johnstone, T., Schaefer, H. S., Gernsbacher, M. A., Goldsmith, H. H., et al. (2005). Gaze fixation and the neural circuitry of face
Eye Gaze processing in autism. Nature Neuroscience, 8(4), 519–526. Elsabbagh, M., Volein, A., Csibra, G., Holmboe, K., Garwood, H., Tucker, L., et al. (2009). Neural correlates of eye gaze processing in the infant broader autism phenotype. Biological Psychiatry, 65(1), 31–38. Frischen, A., Bayliss, A. P., & Tipper, S. P. (2007). Gaze cueing of attention: Visual attention, social cognition, and individual differences. Psychological Bulletin, 133(4), 694–724. Grice, S. J., Halit, H., Farroni, T., Baron-Cohen, S., Bolton, P., & Johnson, M. H. (2005). Neural correlates of eyegaze detection in young children with autism. Cortex, 41(3), 342–353. Hutt, C., & Ounsted, C. (1966). The biological significance of gaze aversion with particular reference to the syndrome of infantile autism. Behavioral Science, 11(5), 346–356. Johnson, M. H., Grossmann, T., & Kadosh, K. C. (2009). Mapping functional brain development: Building a social brain through interactive specialization. Developmental Psychology, 45(1), 151–159. Joseph, R. M., Ehrman, K., McNally, R., & Keehn, B. (2008). Affective response to eye contact and face recognition ability in children with ASD. Journal of International Neuropsychological Society, 14(6), 947–955. Kanner, L. (1943). Autistic disturbances of affective contact. The Nervous Child, 2, 217–250. Kylliäinen, A., & Hietanen, J. K. (2006). Skin conductance responses to another person’s gaze in children with autism. Journal of Autism and Developmental Disorders, 36(4), 517–525. Kylliäinen, A., Braeutigam, S., Hietanen, J. K., Swithenby, S. J., & Bailey, A. J. (2006). Face and gaze processing in normally developing children: A magnetoencephalographic study. European Journal of Neuroscience, 23(3), 801–810. Leekam, S., Baron-Cohen, S., Perrett, D., Milders, M., & Brown, S. D. (1997). Eye-direction detection: A dissociation between geometric and joint attention skills in autism. British Journal of Developmental Psychology, 15(1), 77–95. Maestro, S., Muratori, F., Cavallaro, M. C., Pecini, C., Cesari, A., Paziente, A., et al. (2005). How young children treat objects and people: An empirical study of the first year of life in autism. Child Psychiatry and Human Development, 35, 383–396. Nation, K., & Penny, S. (2008). Sensitivity to eye gaze in autism: Is it normal? Is it automatic? Is it social? Development and Psychopathology, 20(01), 79–97. Pellicano, E., & Macrae, C. N. (2009). Mutual eye gaze facilitates person categorization for typically developing children, but not for children with autism. Psychonomic Bulletin and Review, 16(6), 1094–1099. Pelphrey, K. A., Morris, J. P., & McCarthy, G. (2005). Neural basis of eye gaze processing deficits in autism. Brain, 128(Pt 5), 1038–1048. Ristic, J., Mottron, L., Friesen, C. K., Iarocci, G., Burack, J. A., & Kingstone, A. (2005). Eyes are special but not
Eye Movement Desensitization and Reprocessing (EMDR) Therapy in Children and Adults with Autism for everyone: The case of autism. Cognitive Brain Research, 24(3), 715–718. Senju, A., & Johnson, M. H. (2009a). Atypical eye contact in autism: Models, mechanisms and development. Neuroscience and Biobehavioral Reviews, 33(8), 1204–1214. Senju, A., & Johnson, M. H. (2009b). The eye contact effect: Mechanisms and development. Trends in Cognitive Sciences, 13(3), 127–134. Senju, A., Yaguchi, K., Tojo, Y., & Hasegawa, T. (2003). Eye contact does not facilitate detection in children with autism. Cognition, 89(1), B43–B51. Senju, A., Tojo, Y., Dairoku, H., & Hasegawa, T. (2004). Reflexive orienting in response to eye gaze and an arrow in children with and without autism. Journal of Child Psychology and Psychiatry, 45(3), 445–458. Senju, A., Tojo, Y., Yaguchi, K., & Hasegawa, T. (2005). Deviant gaze processing in children with autism: An ERP study. Neuropsychologia, 43(9), 1297–1306.
Eye Movement Desensitization and Reprocessing (EMDR) Therapy in Children and Adults with Autism Ella Lobregt-van Buuren1, Liesbeth Mevissen2 and Ad De Jongh3,4,5,6 1 Dimence Institute of Mental Health, Deventer, The Netherlands 2 Trajectum, (Forensic) Treatment Facility for Adults with Intellectual Disabilities, Zwolle, The Netherlands 3 Department of Social Dentistry and Behavioral Sciences, Academic Centre for Dentistry Amsterdam (ACTA), University of Amsterdam and VU University Amsterdam, Amsterdam, The Netherlands 4 School of Health Sciences, Salford University, Manchester, UK 5 Institute of Health and Society, University of Worcester, Worcester, UK 6 School of Psychology, Queen’s University, Belfast, Ireland
Definition Eye movement desensitization and reprocessing (EMDR) therapy is a first-choice psychological treatment for posttraumatic stress disorder
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(PTSD) in the general population (World Health Organization 2013; ISTSS 2019). Although there is growing evidence that the scope of EMDR therapy is broader than treatment of full blown PTSD, little is known about the feasibility and potential effectiveness of EMDR in children and adults with autism spectrum disorder (ASD) who suffer from the consequences of exposure to adverse events and trauma. There are indications that these consequences often are overlooked and misinterpreted as autistic features and therefore remain untreated. This entry provides an overview of the current knowledge about the feasibility and effectiveness of EMDR therapy in individuals with ASD and trauma-related symptoms and its clinical and scientific implications. EMDR therapy is a protocolized treatment, aimed to resolve symptoms resulting from disturbing and unprocessed life experiences. EMDR therapy is an evidence-based treatment for PTSD and may be supportive in the treatment of a wide variety of other mental health problems like anxiety disorders, depressive disorders, and obsessive-compulsive disorders (Shapiro 2018; Valiente-Gómez et al. 2017). EMDR therapy starts with history taking and a case conceptualization. Therapy continues with a short introduction about how EMDR therapy works, shortly after which the client focusses on the traumatic memory. The therapist then asks the client to bring up the memory and to focus on the most distressing image, eliciting the dysfunctional negative cognition (NC) of oneself in relation to the image, as well as the accompanying emotions and the body disturbance that go along with it. Next, the therapist moves his or her fingers back and forth in front of the client’s eyes as fast as the client can follow. Repeatedly, the client is asked to report about emotional, cognitive, somatic, and/or imagistic experiences that arise. A new set of eye movements follows, and this procedure is repeated until the disturbance related to the memory reaches a SUD (Subjective Unit of Disturbances scale) of zero out of ten and an adaptive and positive statement about oneself (PC, Positive Cognition) is rated as fully believable on a VoC (Validity of Cognition) scale. The end of the session is dedicated to closing down the session positively and preparing the
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Eye Movement Desensitization and Reprocessing (EMDR) Therapy in Children and Adults with Autism
client for the interim in between sessions. A core feature of the procedure is carrying out a sufficient demanding bilateral working-memory-task which is accomplished by the rapid eye movements (De Jongh et al. 2013).
Historical Background Children and adults with ASD are at elevated risk of experiencing a history of adverse events and revictimization (Hoover 2015; Kildahl et al. 2019). It has been argued that exposure to adverse events inhibits the ability to detect violations and exacerbates already impaired emotion regulation problems in youth with ASD (Kerns et al. 2015). These factors may negatively influence the ability to cope with future stressors and elevate the risk of revictimization. PTSD in children and adolescents with ASD co-occurs at a similar or greater rate compared to general population estimates (Rumball 2019). Individuals with ASD are known to have specific difficulties in resolving unprocessed life experiences, making them susceptible to psychosocial consequences of exposure to adverse events (e.g., Roberts et al. 2015). ASD can be characterized as a different way of sense-making and as a problem with self-regulation, which is reflected in problems in social communication and interaction and restricted and repetitive patterns of behavior or interests. Adverse events that are considered to be mildly annoying for individuals without ASD may be perceived as distressing or even traumatic by individuals with ASD and vice versa (Taylor and Gotham 2016). Wood and Gadow (2010) hypothesized that ASD-related sensory hyper-reactivity to daily stimuli, social confusion, incomprehension, and rejection by others may lead to clinically significant anxiety, which affects resilience to cope with stressors. Several authors reported a risk of overlooking a history of exposure to adverse events and symptoms of PTSD in persons with ASD. There may be several reasons for this phenomenon. First, the different sense-making may prevent them from recognizing risky circumstances and
communicating about their experiences (Kerns et al. 2015). The risk of overlooking is even stronger in case of persons with ASD and an intellectual disability (Kildahl et al. 2019). Second, symptoms attributed to ASD might in fact be stress reactions to adverse events or trauma, a phenomenon termed diagnostic overshadowing. For example, hyperarousal and numbing (symptoms of PTSD) overlap with ASD related hyper- and hypo-reactivity to sensory stimuli. Feelings of detachment of others – a symptom of PTSD – partly overlap with deficits in social-emotional reciprocity. A reduced ability to mentalize and recognize emotions is seen in both people with PTSD and in people with ASD. Also, there is an overlap between the ASD-related symptoms of perseveration and rumination and the criterion negative cognitions and mood due to PTSD (DSM-5 2013). Especially people with severe PTSD symptoms not fulfilling PTSD DSM-5 criteria (e.g., because they do not meet DSM-5 criterion A for PTSD) are at risk to be excluded for trauma treatment. Hence, both trauma and trauma-related symptoms can be overlooked or overshadowed by autistic features and therefore remain untreated.
Current Knowledge Little is known about the feasibility and potential effectiveness of EMDR therapy in individuals with ASD and trauma-related symptoms. This may partly be caused by the problem of overlooking and overshadowing as mentioned above. Another impeding factor may be that individuals with ASD are often excluded from participating in research (Spinazzola et al. 2005). This entry gives a short overview of what is known about EMDR in autistic children and adults with and without intellectual disabilities. EMDR Therapy in Children with ASD Preliminary findings from a few case reports suggest EMDR therapy for PTSD to be feasible and potentially effective for children with ASD (e.g., Ipci et al. 2017) also in case of comorbid intellectual disabilities (Mevissen et al. 2011). Controlled studies are lacking.
Eye Movement Desensitization and Reprocessing (EMDR) Therapy in Children and Adults with Autism
EMDR Therapy in Adults with ASD Several case reports have been published describing the treatment of trauma with EMDR therapy in adults with ASD (Kosatka and Ona 2014) and in adults with ASD and intellectual disabilities (Barol and Seubert 2010; Mevissen et al. 2011), with promising results. Until now, one controlled study has been published describing the feasibility and effectiveness of EMDR therapy for PTSD symptoms in adults with ASD (Lobregt-van Buuren et al. 2019). The study investigated whether EMDR is a feasible therapy for adults with ASD and a history of adverse events and whether it is associated with reductions in symptoms of PTSD, psychological distress, and autism. The study had a non-randomized add-on design consisting of three phases, in which participants were their own controls. In the first phase participants received treatment as usual (TAU) during 6–8 weeks while on the waiting list for EMDR therapy. The second phase consisted of up to 8 sessions EMDR in addition to TAU. The third phase comprised of a follow-up period with the TAU only condition. TAU consisted of the most common treatments for adults with ASD aimed at coping with ASD, psychological distress, and comorbid problems. TAU included pharmacotherapy, psychoeducation, supportive counseling, job
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coaching, support with housekeeping, and so-called case management. Results showed a significant reduction of symptoms of posttraumatic stress (measured with the Impact of Event Scale-Revised: d¼1.16; see Fig. 1), psychological distress (measured with the Brief Symptom Inventory: d¼0.93; see Fig. 2), and autistic features (Social Responsiveness Scale-Adult version: d¼0.39; see Fig. 3). Moreover, the participants experienced a significantly lower level of daily life impairment related to the traumatic events following EMDR therapy (thermometer card of the Adapted Anxiety Disorders Inventory Scale for children (ADIS-C) section PTSD: d¼1.81; see Fig. 4). The positive results were maintained at follow-up. The results suggest EMDR therapy added to TAU to be a feasible and potentially effective treatment for individuals with ASD who suffer from the consequences of exposure to distressing events. It should be noted that the study was based upon a small sample of participants (n¼21). The significant reduction of autistic features concerning social motivation and communication following EMDR therapy, and at follow-up, albeit with a small effect size, is remarkable. A possible explanation for this finding might be that the clinical manifestation of autistic symptoms decreases when individuals with ASD experience
Eye Movement Desensitization and Reprocessing (EMDR) Therapy in Children and Adults with Autism, Fig. 1 Mean scores IES-R at four moments in time
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Eye Movement Desensitization and Reprocessing (EMDR) Therapy in Children and Adults with Autism
Eye Movement Desensitization and Reprocessing (EMDR) Therapy in Children and Adults with Autism, Fig. 2 Mean scores BSI at four moments in time
Eye Movement Desensitization and Reprocessing (EMDR) Therapy in Children and Adults with Autism, Fig. 3 Mean scores SRS-A at four moments in time
less trauma-related stress and psychological distress, such as somatization, depression, and obsessive-compulsive symptoms. Hence, it is conceivable that trauma-related symptoms are a moderator for the severity of ASD symptoms, such that exposure to trauma and other adverse events exacerbate autistic core features like deficits in social-emotional reciprocity (e.g., reduced
sharing of interests, emotions, or affect; Wood and Gadow 2010). Another possible explanation for the observed reduction of autistic features in the study is that symptoms ascribed to phenotypic features of ASD may in fact be symptoms of PTSD, the phenomenon termed diagnostic overshadowing. For example, hyperarousal as a consequence of exposure to trauma may be
Eye Movement Desensitization and Reprocessing (EMDR) Therapy in Children and Adults with Autism
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Eye Movement Desensitization and Reprocessing (EMDR) Therapy in Children and Adults with Autism, Fig. 4 Mean scores ADIS thermometer card at four moments in time
interpreted as an autistic feature similar to hyperreactivity to sensory stimuli. Also, trauma-related avoidance of social situations can be confused with autistic features similar to problems in social communication. In other words, symptoms of PTSD can be masked by symptoms of autism, therefore symptoms of PTSD, previously seen as features of ASD, might have declined as a result of EMDR therapy.
Future Directions Clinical Implications The following practical recommendations based on current knowledge can be made for future clinical practice.
– Awareness of the impact of adverse events and trauma in individuals with ASD is important to prevent undertreatment. – There is a high prevalence of being bullied in individuals with ASD (Hoover 2015; Maïano et al. 2016). Exposure to bullying is associated with severe psychiatric outcomes in adulthood. Bullying does not comply with the (i.e., A-criterion) definition of trauma in the PTSD section of DSM-5. When there is no formal
PTSD-classification, there is an extra risk of undertreatment of the trauma-related symptoms. Disturbing memories of being bullied (e.g., social situations where participants felt excluded and intimidated) can be reprocessed with EMDR therapy. – Trauma history and associated trauma-related symptoms should be routinely assessed in individuals with ASD who present to clinical services, taking into account the social communication impairments. What is not explicitly asked for often remains undisclosed, because of inability to spontaneously share relevant information. This issue can be addressed by making use of the concrete, visualized, and structured way in which potentially traumatic events and trauma-related symptoms are probed by the Diagnostic Interview Trauma and Stressors-Intellectual Disability (DITS-ID; Mevissen et al. 2016, 2017, 2018, in press), such that also in children and adults with ASD unprocessed memories can be identified. The DITS-ID is the latest DSM-5 based version of the Adapted Anxiety Disorders Inventory Scale for children (ADIS-C) section PTSD. All documents (only in Dutch language) belonging to the DITS are available and can be downloaded for free. See https://www.accare.nl/childstu dycenter/opleidingen/bijscholing/dits-lvb/ aanvragen-documenten-dits-lvb.
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Eye Movement Desensitization and Reprocessing (EMDR) Therapy in Children and Adults with Autism
– Trauma-related symptoms and PTSD in individuals with ASD can be treated successfully with EMDR therapy, taking into account aspects of their specific information processing. The EMDR procedure for children can be used, when the language of the EMDR protocol for adults is too abstract for people with ASD (Dutch version, De Roos et al. 2014). Individuals with ASD seem to need more time to become familiar with the therapist and the procedures and to fill out questionnaires. In case of obstacles when administering EMDR therapy to individuals with ASD, see the link to the following document: Guidelines and tips for EMDR treatment for Autism Spectrum Disorders (Special Interest Group EMDR – ASD November 2013, https:// psycho-trauma.nl/wp-content/uploads/2014/ 01/GuidelinesEMDRASD-1.pdf). These preliminary recommendations are based on wellestablished and evidence-based practices for working with this population. – Based on clinical observations, EMDR therapy seems difficult to perform in individuals with ASD who have problems with activating the traumatic memories because of fear of affects, in combination with severe problems in self- and emotional regulation, a cognitive, rigid, and extremely negative mindset. These individuals are often exposed to prolonged and repeated trauma such as childhood sexual abuse and domestic violence (complex PTSD) and/or are exposed to prolonged and repeated overload and relational mismatches between the needs of the individual and the possibilities of the environment. The iatrogenic effects of inadequate treatments due to delayed diagnostics or inadequate facilities should not be underestimated in these subgroups. – EMDR embedded in treatment as usual seems appropriate and necessary given the comorbid psychiatric conditions and/or structural problems at home, school, or work in individuals with ASD. Scientific Implications Controlled trials with sufficient power to detect differences are greatly needed to confirm the
present findings and to test the hypothesis that EMDR therapy is more effective than treatment as usual in reducing trauma- and stressor-related symptoms. Investigation and experimentation into other evidence-based trauma treatments like cognitive behavioral therapy with a trauma focus (CBT-T), narrative exposure therapy for individuals with ASD, and trauma-related symptoms are strongly recommended. More generally, the issue as to how PTSD and other trauma-related disorders manifest or may be masked by symptoms of ASD is an intriguing one which should be explored in further research.
See Also ▶ Diagnostic Overshadowing ▶ Emotional Regulation ▶ Posttraumatic Stress Disorder
References and Reading American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders, DSM-5 (5th ed.). Washington, DC: American Psychiatric Association. Barol, B. I., & Seubert, A. (2010). Journal of EMDR Practice and Research, 4(4), 156–169. https://doi.org/ 10.1891/1933-3196.4.4.156. De Jongh, A., Ernst, R., Marques, L., & Hornsveld, H. (2013). The impact of eye movements and tones on disturbing memories involving PTSD and other mental disorders. Journal of Behavior Therapy and Experimental Psychiatry, 44, 477–483. De Roos, C., Beer, R., De Jongh, A., & Ten Broeke, E. (2014). EMDR-protocol voor kinderen tot 18 jaar. In E. Ten Broeke, A. De Jongh, & H. J. Oppenheim (Eds.), Praktijkboek EMDR: Casusconceptualisatie en specifieke patiëntengroepen [bijlage V]. Amsterdam: Harcourt. Hoover, D. W. (2015). The effects of psychological trauma on children with autism spectrum disorders: A research review. Review Journal of Autism and Developmental Disorders, 2, 287–299. https://doi.org/ 10.1007/s40489-015-0052-y. International Society for Traumatic Stress Studies. (2019). Posttraumatic stress disorder prevention and treatment guidelines. Methodology and recommendations. Retrieved from: http://www.istss.org/treating-trauma/ new-istss-prevention-and-treatment-guidelines.aspx Ipci, M., Inci, S. B., Ardic, U. A., & Ercan, E. S. (2017). A case of Asperger syndrome with comorbidity of
Eyeblink Reflexes posttraumatic stress disorder and selective mutism: Significant remission with the combination of aripiprazole and eye movement desensitization and reprocessing. Journal of Clinical Psychopharmacology, 37(1), 109–110. Kerns, C. M., Newschaffer, C. J., & Berkowitz, S. J. (2015). Traumatic childhood events and autism spectrum disorder. Journal of Autism and Developmental Disorders, 45, 3475–3486. https://doi.org/10.1007/ s10803-015-2392-y. Kildahl, A. N., Bakken, T. L., Iversen, T. E., & Helverschou, S. B. (2019). Identification of posttraumatic stress disorder in individuals with autism spectrum disorder and intellectual disability: A systematic review. Journal of Mental Health Research in Intellectual Disabilities, 12, 1–2. https:// doi.org/10.1080/19315864.2019.1595233. Kosatka, D., & Ona, C. (2014). Eye movement desensitization and reprocessing in a patient with Asperger’s Disorder: case report. Journal of EMDR Practice and Research, 8(1), 13–18. Lobregt-van Buuren, E., Sizoo, B., Mevissen, L., & De Jongh, A. (2019). Eye movement desensitization and reprocessing (EMDR) therapy as a feasible and potential effective treatment for adults with Autism Spectrum Disorder (ASD) and a history of adverse events. Journal of Autism and Developmental Disorders, 49(1), 51–164. https://doi.org/10.1007/s10803-0183687-6. Maïano, C., Normand, C. L., Salvas, M. C., Moullec, G., & Aimé, A. (2016). Prevalence of school bullying among youth with autism spectrum disorders: a systematic review and meta-analysis. Autism Research, 9, 601–615. Mevissen, L., Lievegoed, R., & De Jongh, A. (2011). EMDR treatment in people with mild ID and PTSD: 4 cases. Psychiatric Quarterly, 82, 43–57. Mevissen, L., Didden, R., Korzilius, H., & De Jongh, A. (2016). Assessment of PTSD in children with mild to borderline intellectual disabilities. European Journal of Psychotraumatology, 7, 29786. https://doi.org/10. 3402/ejpt.v7.29786. Mevissen, L., Didden, R., Korzilius, H., & De Jongh, A. (2017). Eye movement desensitisation and reprocessing therapy for posttraumatic stress disorder in a child and an adolescent with mild to borderline intellectual disability: A multiple baseline across subjects study. Journal of applied research in intellectual disabilities, 00, 1–8. https://doi.org/10.1111/jar.12335. Mevissen, L., Didden, R., & De Jongh, A. (2018). DITSLVB Handleiding. Diagnostisch Interview Trauma en Stressoren – Licht Verstandelijke Beperking [DITSMID Manual. Diagnostic interview trauma and stressors – Mild intellectual disability]. Accare Child Study Center. https://www.accare.nl/childstudycenter/ opleidingen/bijscholing/dits-lvb/aanvragendocumenten-dits-lvb Mevissen, L., Didden, R., de Jongh, A., & Korzilius, H. (in press). Assessing posttraumatic stress disorder in
1929 adults with mild intellectual disabilities or borderline intellectual functioning. Journal of Mental Health Research in Intellectual Disabilities. Roberts, L. A., Koenen, K. C., Lyall, K., Robinson, E. B., & Weisskopf, M. G. (2015). Association of autistic traits in adulthood with childhood abuse, interpersonal victimization and posttraumatic stress. Child abuse & neglect, 45, 135–142. Rumball, F. (2019). A systematic review of the assessment and treatment of posttraumatic stress disorder in individuals with autism spectrum disorders. Journal of Autism and Developmental Disorders, 6, 294–324. https://doi.org/10.1007/s40489-018-0133-9. Shapiro, F. (2018). Eye movement desensitization and reprocessing (EMDR) therapy: Basic principles protocols, and procedures. New York: Guilford Press. Spinazzola, J., Blaustein, M., & Van der Kolk, B. A. (2005). Posttraumatic stress disorder treatment outcome research: the study of unrepresentative samples? Journal of Traumatic Stress, 18(5), 425–436. Taylor, J. L., & Gotham, K. O. (2016). Cumulative life events, traumatic experiences, and psychiatric symptomatology in transition-aged youth with autism spectrum disorder. Journal of Neurodevelopmental Disorders, 8, 28. https://doi.org/10.1186/s11689-0169160-y. Valiente-Gómez, A., Moreno-Alcázar, A., Treen, D., Cedrón, C., Colom, F., Pérez, V., & Amann, B. L. (2017). EMDR beyond PTSD: A systematic literature review. Frontiers in Psychology, 8, 1668. https://doi. org/10.3389/fpsyg.2017.01668. Wood, J. J., & Gadow, K. D. (2010). Exploring the nature and function of anxiety in youth with autism spectrum disorders. Clinical Psychology. Science and Practice, 17(4), 281–292. World Health Organization. (2013). Guidelines for the management of conditions specifically related to stress. Geneva: WHO. Retrieved from http://www. who.int/mental_health/emergencies/stress_guidelines/ en/.
Eyeblink Reflexes Lindsey Sterling Department of Psychiatry, Jane and Terry Semel Institute for Neuroscience and Human Behavior UCLA, Los Angeles, CA, USA
Synonyms Corneal reflex; Startle response
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Definition
Eye-to-Eye Gaze The first and most reliable component of the startle reflex in humans. The blink reflex is an involuntary blinking of the eyelids elicited when the cornea is stimulated by touch, bright light, loud sounds, or other peripheral stimuli. The evolutionary purpose of this involuntary response is believed to be a survival-related function, to protect the eyes from potentially harmful stimuli (i.e., threat). When a stimulus is presented to the cornea of the eye, sensory information is carried to the trigeminal nucleus and relayed to the accessory abducens and abducens motor nuclei, initiating a motor response. The eyeblink consists of a rapid contraction of the orbicularis oculi muscle surrounding the eye; this is the most sensitive component of the blink. Strength of the response can be measured by electromyographic activity (EMG) of the orbicularis muscle during the eyeblink, or blink magnitude. Because EMG is quantifiable, it is the behavioral variable often used in startle response experiments designed to investigate neurological indices of fear and anxiety (i.e., amygdala activation).
See Also ▶ Startle Reflex
References and Reading Blumenthal, T. D., Cuthbert, B. N., Filion, D. L., Hackley, S., Lipp, O. V., & van Boxtel, A. (2005). Committee report: Guidelines for human startle eyeblink electromyographic studies. Psychophysiology, 42, 1–15. Dichter, G. S., Benning, S. D., Holtzclaw, T. N., & Bodfish, J. W. (2010). Affective modulation of the startle eyeblink and postauricular reflexes in autism spectrum disorder. Journal of Autism and Developmental Disorders, 40, 858–869.
Eyes Test ▶ Reading the Mind in Eyes Test
▶ Mutual Gaze
Eye-Tracking Frederick Shic School of Medicine, Yale Child Study Center, Yale University, School of Medicine, New Haven, CT, USA
Definition Eye-tracking is a technique in which one or more of a subject’s eyes are tracked with the intent of inferring, in a moment-by-moment fashion, what the individual is attending to in his or her visual world.
Historical Background Eye-tracking has had a long history in psychological research and vision science. Some of the earliest reported work using eye-tracking appeared in the late 1800s and early 1900s, focusing primarily on understanding and optimizing visual processes during reading (e.g., the work of Huey, 1898, 1910). Later work, by Guy Buswell (1935), Alfred Yarbus (1967), and others, would use eye-tracking to study more natural visual scanning processes during the viewing of complex scenes such as those found in paintings or photographs. From these early experiments, it was noted that typical individuals, both young and old, when presented visually with scenes with people, and unless given instructions to the contrary, would spend much of their time looking at the people portrayed. Furthermore, it was noted that given a face to view, subjects would largely attend to the eyes and mouth of the face, despite the fact that these static stimuli never changed. Yarbus noted, in his treatise on eye movements: “The observer’s attention is frequently drawn to
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elements which do not give important information but which, in his opinion, may do so.” This early work, which advanced the primary of social information processing in natural viewing for typically developing individuals, set the stage for modern investigations of the visual strategies employed by individuals with autism spectrum disorders (ASD).
Current Knowledge The use of eye-tracking for understanding neuropsychiatric conditions and special populations in general is attractive for a number of reasons. First, at its essence, eye-tracking is a method for seeing how others see, providing a robust, quantitative measure of where someone is looking. Since where someone is looking is highly correlated to where that person’s attention is deployed, eyetracking can be used to understand temporally changing cognitive processes as they unfold (with caveats; see section “Limitations of Eye-Tracking”). Second, eye-tracking is today a relatively noninvasive and easily tolerated experimental technology. As compared to the original systems of the early nineteenth century, which were often so uncomfortable that the eye had to be anesthetized with cocaine, modern desktopmounted video oculography eye-tracking systems (see section “Types of Eye-Trackers”) can obtain precise measurements of the eye without ever touching any part of the participant. Third, the oculomotor system is early maturing and highly preserved neurophysiological control system. This means that eye-tracking can be an applicable and appropriate technique across the vast cognitive, behavioral, and physiological heterogeneity found in many neuropsychiatric conditions. The earliest published work using eye-tracking to study individuals with ASD appeared in 2002 (Klin et al. 2002; Pelphrey et al. 2002; van der Geest et al. 2002a; van der Geest et al. 2002b). The general pattern of this work suggested that in many cases, the visual scanning strategies of individuals with ASD are significantly different than that of controls. These initial reports found, for example, that older adolescents and adults with
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ASD tended to look more at the mouth and less at the eyes in comparison to control individuals. They also highlighted relationships between communicative ability and scanning patterns, with the somewhat surprising finding that individuals with ASD who looked more at the mouth were actually more communicatively capable than those who looked less. Additional studies have largely confirmed the atypical nature of visual scanning strategies in individuals with ASD, especially under conditions where choices in attentional selection must be made between social and nonsocial stimuli. Results of several studies show, for instance, decreased attention toward social actors, people in general, and faces, and increased attention toward nonsocial background elements of scenes (e.g., Jones et al. 2008; Klin et al. 2002; Pierce et al. 2011; Riby and Hancock 2009; Shic et al. 2011). Other studies also highlight decreased sensitivity in individuals with ASD to communicative and social aspects of interactions, such as gaze cues and shared activities (Freeth et al. 2009; Riby and Doherty 2011; Shic et al. 2011). Finally, eyetracking studies have related inefficient social information–gathering strategies to poor performance in face recognition tasks in both adults (Spezio et al. 2007) and toddlers and children with ASD (Chawarska and Shic, 2009). Taken together, these studies provide evidence for atypical visual exploratory behaviors in individuals with ASD and promote the use of eye-tracking technology in efforts toward understanding the mechanisms underlying atypically developing social skills in individuals with ASD. However, it is important to note that the use of eye-tracking in studying ASDs is a developing and emerging field. While originally there was a great deal of enthusiasm regarding atypical distributions of attention toward specific elements of the face (e.g., proportions of mouth and eye scanning), recent reports have highlighted the complex interrelationships between cognitive, social, and behavioral phenotypes and visual exploratory behavior (Chawarska and Shic 2009; Norbury et al. 2009). Indeed, several studies did not replicate atypical patterns of looking at faces or social scenes, even as early as 2002 (van der Geest et al. 2002a, b). The reasons for these disagreements are
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currently active areas of exploration. However, there are likely several factors involved, such as heterogeneity of sample populations, variety of experimental paradigms and contexts, and inconsistencies across research sites in use of preprocessing methodology and analytical techniques. Nevertheless, eye-tracking has been and continues to be a powerful methodology for understanding the nature of ASD; as more research using eye-tracking is conducted, its application will likely extend further toward questions regarding the heterogeneity of behavioral and visual exploratory activities in ASD. Types of Eye-Trackers There are several types of eye-tracking technologies still in widespread use (Duchowski 2007). However, recently, psychological and behavioral studies of individuals with ASD and other neuropsychiatric conditions have largely begun to rely exclusively on the use of remote video oculography (VOG) methods. In modern video oculography, a video camera or multiple video cameras are positioned so as to be able to obtain a clear image of the participant’s eye. These cameras, typically linked up to either specialized hardware or a computer, employ sophisticated computational algorithms in order to extract from the video the position of the participant’s pupil(s). In the most common methodology, infrared filters are used to remove contaminating light sources which otherwise complicate signal analysis extraction of the pupil location. In these infrared systems, additional infrared light sources, which are largely invisible to the human eye, are used in order to generate “glints” or corneal reflections which serve as a reference coordinate system for the pupil that is more invariant to participant motion. Additionally, the eye and face of the participants can be lit by on-camera-axis lighting or off-camera-axis lighting. On-camera-axis lighting generates a form of infrared video oculography called “bright-pupil” eye-tracking. In bright-pupil eye-tracking, illumination of the eye causes a “red-eye effect” (similar to what occurs when a flash is reflected off someone’s eye in a camera photograph). This makes the pupil appear bright. In off-camera-axis lighting,
Eye-Tracking
the pupil remains dark because infrared light is not reflected, resulting in a “dark pupil.” Though some manufacturers suggest one form of lighting is superior for certain individuals (e.g., for those with light blue eyes as compared to those with dark brown eyes), there have been no independent scientific studies showing one method is actually superior in practical applications and modern use. Another form of eye-tracking which has been in many ways superseded by video oculography methods, at least in behavioral studies of individuals with neuropsychiatric disorders, is electrooculography (EOG). Electrooculography uses conductive electrodes placed nasally and temporally to the eye, together with a reference electrode, in order to measure the potential difference between the front of the eye and the back of the eye (the corneofundal potential; see Brown et al. 2006; Marmor and Zrenner 1993). This method of eye-tracking typically is noisier than commercial video oculography systems, and the head must remain relatively immobilized, as compared to video oculography techniques which can accommodate relatively larger amounts of head movements. However, EOG systems are relatively inexpensive compared to VOG eyetracking systems, making their use a viable alternative in situations where precision is not at a premium and participants are very compliant to instructions and immobilization. Finally, a form of eye-tracking used primarily in primate and animal research employs scleral coils. With scleral coils, a conducting ring is placed directly on the sclera of the eye and an enclosing alternating magnetic field induced around the head of the participant. As the eye changes position, voltages generated within the coil give the position of the eye, giving extremely precise recordings of eye position. The main drawback of this technique is the physical discomfort associated with inserting the coil directly into the eye and the risk of corneal abrasion. Still, recent work has identified the lead from the coil to the measuring device as a major source of discomfort for scleral coils, and strides have been made using wireless scleral coils which may make the technology more usable for fine-
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grained detection of eye movements in the future (Roberts et al. 2008). Mechanics of Eye Movements and Common Eye-Tracking Outcome Variables Primary classifications of eye movements include saccades, fixations, and smooth pursuit. Saccades, a term coined by French psychologist Louis Javal in 1879, are rapid, ballistic movements of the eye. Research has shown that awareness of the visual world is suppressed during saccades, an effect neatly summarized by Dodge (1900). Dodge’s observation was that when an individual stands in front of a mirror, looking at different positions on his or her own eyes, that individual does not detect any movement of the eyes. However, an observer standing immediately to the side of the person in the mirror will see the eyes moving rapidly. This effect, known as saccadic suppression, is associated with decreased conscious awareness of the visual world during saccades. Fixations are the complement of saccades and represent periods in which the pupil is focused relatively stably and steadily on a particular location. By contrast, smooth pursuits are indicated by slower, sweeping motions of the eye and are found when the eye is used employed to visually track a moving target. Interestingly, smooth pursuits cannot be elicited voluntarily in the absence of a target and require a slow-moving target to manifest. Fixation/saccade identification algorithms have been advanced in order to segregate saccades from fixations in eye-tracking data. However, it is unclear if these algorithms are effective in drawing clear distinctions between saccades and fixations at modern eye-tracking speeds (60 Hz) and resolutions (Shic et al. 2008a, b). Nevertheless, these fixation identification algorithms are in widespread use and are supported by most eyetracking manufacturers. By comparison, smooth pursuit recognition, which would affect the interpretation of studies employing dynamic stimuli, is currently not a standard analytical technique. Perhaps the most common technique used in analysis of eye-tracking data is region-of-interest (ROI) analysis. In ROI analysis, the scene presented to the participant is divided into a series of
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not necessarily (but usually) nonoverlapping regions, and the amount of time spent by the participant looking at each of these regions is calculated. As studies of natural activities have shown that people tend to be looking for information they need, it is inferred that more examined areas correspond to areas found by observers to be more important. Typically, for comparability purposes, looking times are expressed as a proportion of the trial time. Some researchers choose to exclude from analyses eye-tracking data acquired during saccades due to diminished awareness of the scene by the participant, but others choose to incorporate all data into proportion data and/or looking time reports. Additional measures employed in eye-tracking research are extensive (Jacob and Karn 2003), and there exists a large body of research on sophisticated techniques for processing eye-tracking data (see Duchowski 2007; see Holmqvist et al. 2011a for reference). While many of these techniques are theoretically and conceptually interesting, it is also important to note that, in many cases, the relationships between derived eye-tracking variables and physiological or behavioral constructs is unclear. A notable exception is the detection of blinks. The inhibition of blinks has recently been shown to correspond to decreased attribution of salience to social scenes in toddlers with ASD (Shultz et al. 2011). Concerted and rigorous explorations of the space of possible eye-tracking measures may thus more firmly ground our understanding of outcome eye-tracking measures, from psychological, neurophysiological, and behavioral perspectives, in the years to come. Limitations of Eye-Tracking Interpretations of the results of eye-tracking experiments often involve assumptions regarding the concurrent relationship between the position of eye and the allocation of attention. However, attention and eye movements are not always coupled; indeed, research suggests that the movement of attention toward a particular point in the world precedes an eye movement toward that location by 100–120 ms. In addition, attention can be moved covertly, i.e., without an explicit, overt eye movement. Research has shown,
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however, that when a movement of the eye is made, it must be preceded by focal attention to that location. Thus, the occurrence of an eye movement suggests that attention has already been deployed to the target of that eye movement; on the other hand, when the eye is relatively still, standard interpretations of eye-tracking data rely on an assumption that the point of fixation is the same as the point of attention. For practical purposes, this is often the case (Duchowski 2003; Holmqvist et al. 2011; Kowler 1990).
Future Directions Keith Rayner, one of the pioneers of eye-tracking research, especially in studies of reading, notes that there have been four main eras of eyetracking research (Rayner 2009). In the first era (early 1900s), many of the basic properties about eye movements were discovered. In the second era (1930s to 1950s), experimental work in eyetracking research took a behaviorist position, classifying and quantifying, in context, the myriad behaviors comprising eye movements. In the third era (1970s to 1990s), the cognitivist revolution brought new perspectives for delving deeper into the mechanisms and cognitive processes underlying eye movements. Rayner notes that we are now in the fourth era of eye-tracking research, one where quantifiable and predictive models of eye movements, indexing underlying cognitive substrates, can be developed. While Rayner’s summary mainly refers to studies of reading, the same classification of eras can be applied to eye-tracking research in social visual information processing, with the understanding that this field lags research in reading by about a decade. Currently, there is a great deal of interest in using eye-tracking technology for understanding autism, and much of this effort is aimed at understanding the specific individual behavioral and cognitive characteristics that affect visual scanning of social information in autism. Once this heterogeneity of behavior can be decoded, it is hoped that eye-tracking research in autism will also enter into its fourth era, where the accurate models of eye movements can help us
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both understand individual variability as well as predict those forms of intervention that will be most efficacious. The fourth era of eye-tracking research also corresponds with the proliferation and technological development of eye-tracking technology as a whole. In many ways, eye-tracking is now appearing as an almost requisite adjunct methodology for social information processing paradigms that directly target underlying neural substrates, such as functional magnetic resonance imaging and electroencephalography. Combining these multiple sources of information will provide a more complete picture of the multifaceted complexities in ASDs. Given the wealth of information, an increased focus on computational and statistical approaches toward integrating and unifying eye-tracking data with other sources of information will be critical to future research. Finally, while atypical scanning of social scenes in individuals with ASD has been found by several research groups employing eyetracking, there is still much work left to do in terms of pinpointing specific behavioral markers of ASD. It is yet unknown whether atypical gaze patterns in ASD are a secondary manifestation of social perceptual disadvantages and to what extent the compounding effects of the atypical experience reflected by these gaze patterns diminish prototypical social development. Current and future work with eye-tracking will thus continue to isolate autism-specific developmental processes and deepen our appreciation of the roles of typical and atypical gaze patterns in shaping the behavioral phenotype of individuals with ASD.
See Also ▶ Eye Gaze ▶ Visual Processing
References and Reading Brown, M., Marmor, M., Vaegan, Zrenner, E., Brigell, M., & Bach, M. (2006). ISCEV standard for clinical electro-oculography (EOG) 2006. Documenta
Eye-Tracking Ophthalmologica, 113(3), 205–212. https://doi.org/10. 1007/s10633-006-9030-0. Buswell, G. T. (1935). How people look at pictures: A study of the psychology of perception in art. Chicago: The University of Chicago Press. Chawarska, K., & Shic, F. (2009). Looking but not seeing: Atypical visual scanning and recognition of faces in 2 and 4-year-old children with autism spectrum disorder. Journal of Autism and Developmental Disorders, 39(12), 1663–1672. https://doi.org/10.1007/s10803009-0803-7. Dodge, R. (1900). Visual perception during eye movement. Psychological Review, 7(5), 454–465. https:// doi.org/10.1037/h0067215. Duchowski, A. T. (2003). Eye tracking methodology: Theory and practice (1st ed.). New York: Springer. Duchowski, A. T. (2007). Eye tracking methodology: Theory and practice. New York: Springer. Freeth, M., Chapman, P., Ropar, D., & Mitchell, P. (2009). Do gaze cues in complex scenes capture and direct the attention of high functioning adolescents with ASD? Evidence from eye-tracking. Journal of Autism and Developmental Disorders, 40(5), 534–547. Holmqvist, K., Nystrm, M., Andersson, R., Jarodzka, H., & van de Weijer, J. (2011a). Eye-tracking data and dependent variables. Oxford: Oxford University Press. Holmqvist, K., Nyström, M., Andersson, R., Dewhurst, R., Jarodzka, H., & Van de Weijer, J. (2011b). Eye tracking: A comprehensive guide to methods and measures. New York: Oxford University Press Inc. Huey, E. B. (1898). Preliminary experiments in the physiology and psychology of reading. The American Journal of Psychology, 9(4), 575–586. Retrieved July 21, 2008. Huey, E. B. (1910). The psychology and pedagogy of reading: With a review of the history of reading and writing and methods, texts, and hygiene in reading. New York: Macmillan. Jacob, R. J. K., & Karn, K. S. (2003). Eye tracking in human-computer interaction and usability research: Ready to deliver the promises (section commentary). In J. Hyona, R. Radach, & H. Deubel (Eds.), The mind’s eye: Cognitive and applied aspects of eye movement research (pp. 573–605). Oxford, UK: Elsevier Science. Jones, W., Carr, K., & Klin, A. (2008). Absence of preferential looking to the eyes of approaching adults predicts level of social disability in 2-year-old toddlers with autism spectrum disorder. Archives of General Psychiatry, 65(8), 946–954. Klin, A., Jones, W., Schultz, R., Volkmar, F., & Cohen, D. (2002). Visual fixation patterns during viewing of naturalistic social situations as predictors of social competence in individuals with autism. Archives of General Psychiatry, 59(9), 809. Kowler, E. (1990). The role of visual and cognitive processes in the control of eye movement. Reviews of Oculomotor Research, 4, 1–70.
1935 Marmor, M. F., & Zrenner, E. (1993). Standard for clinical electro-oculography. Documenta Ophthalmologica, 85(2), 115–124. https://doi.org/10.1007/BF01371127. Norbury, C. F., Brock, J., Cragg, L., Einav, S., Griffiths, H., & Nation, K. (2009). Eye-movement patterns are associated with communicative competence in autistic spectrum disorders. Journal of Child Psychology and Psychiatry, 50(7), 834–842. https://doi.org/10.1111/j. 1469-7610.2009.02073.x. Pelphrey, K. A., Sasson, N. J., Reznick, J. S., Paul, G., Goldman, B. D., & Piven, J. (2002). Visual scanning of faces in autism. Journal of Autism and Developmental Disorders, 32(4), 249–261. https://doi.org/10.1023/ A:1016374617369. Pierce, K., Conant, D., Hazin, R., Stoner, R., & Desmond, J. (2011). Preference for geometric patterns early in life as a risk factor for autism. Archives of General Psychiatry, 68(1), 101. Rayner, K. (2009). Eye movements in reading: Models and data. Journal of Eye Movement Research, 2(5), 1–10. Riby, D. M., & Doherty, M. J. (2011). Tracking eye movements proves informative for the study of gaze direction detection in autism. Research in Autism Spectrum Disorders, 3(3), 723–733. https://doi.org/10.1016/j. rasd.2009.02.001. Riby, D., & Hancock, P. J. B. (2009). Looking at movies and cartoons: Eye-tracking evidence from Williams syndrome and autism. Journal of Intellectual Disability Research, 53(2), 169. Roberts, D., Shelhamer, M., & Wong, A. (2008). A new wireless search-coil system. In Proceedings of the 2008 Symposium on Eye Tracking Research & Applications (pp. 197–204). ACM. Shic, F., Chawarska, K., & Scassellati, B. (2008a). The incomplete fixation measure. In Proceedings of the 2008 Symposium on Eye Tracking Research & Applications (pp. 111–114). Savannah, Georgia: ACM. https://doi.org/10.1145/1344471.1344500. Shic, F., Chawarska, K., & Scassellati, B. (2008b). The amorphous fixation measure revisited: With applications to autism. In 30th Annual Meeting of the Cognitive Science Society. Washington, DC: Presented at the 30th Annual Meeting of the Cognitive Science Society. Shic, F., Bradshaw, J., Klin, A., Scassellati, B., & Chawarska, K. (2011). Limited activity monitoring in toddlers with autism spectrum disorder. Brain Research, 1380, 246–254. https://doi.org/10.1016/j. brainres.2010.11.074. Shultz, S., Klin, A., & Jones, W. (2011). Inhibition of eye blinking reveals subjective perceptions of stimulus salience. Proceedings of the National Academy of Sciences, 108(52), 21270–21275. https://doi.org/10.1073/ pnas.1109304108. Spezio, M., Adolphs, R., Hurley, R., & Piven, J. (2007). Abnormal use of facial information in high-functioning autism. Journal of Autism and Developmental Disorders, 37(5), 929–939. https://doi.org/10.1007/s10803006-0232-9.
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Eye-Tracking pervasive developmental disorder toward human faces: A fixation time study. Journal of Child Psychology and Psychiatry, 43(5), 669–678. https://doi.org/10.1111/ 1469-7610.00055. Yarbus, A. L. (1967). Eye movements and vision. New York: Plenum Press.
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Face Blindness ▶ Pareidolic Faces
Face Perception Tracey Ward1 and Raphael Bernier2 1 Simons Autism Family Collaboration, University of Washington, Autism Research Center, Seattle, WA, USA 2 Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA
Definition Facial perception refers to the ability to rapidly recognize and understand information from faces. The ability to perceive faces and to use that information to guide and direct behavior plays a critical role in interpreting and forming representations from the social world and in the acquisition and understanding of reciprocal social interaction. Early facial recognition research suggests that humans innately attend to faces and are able to rapidly recognize faces remarkably early in life (Goren et al. 1975). Within 9 min after birth, infants turn their head to a 90° angle to preferentially attend to faces or face-like images. At 42 min after birth, infants
can imitate facial expressions; and by 2 days old, infants can recognize the face of their mother. One-month-old infants show greater pupillary dilation to faces than to nonsocial stimuli suggesting greater interest and arousal in infants when looking at faces. By the end of the first year of life, infants are able to respond to directional gaze, emotional expression, and facial gestures. These early infant preferential face-directed behaviors mark the beginning of a highly protracted development in which repertoires for the face processing system become more sophisticated over time. The examination of facial perception in typical and atypical development, such as in autism spectrum disorders (ASD), has elucidated face perception processes across behavioral, anatomical, and neurophysiological domains and revealed differences in functioning in individuals with ASD and typical development in face perception.
Historical Background Research with children with ASD has consistently revealed deficits in facial perception surfacing within the first 3 years of life. Children with ASD show impairments in social behaviors related to the perception and processing of information from faces. These impairments include reduced eye contact, joint attention, and social orienting, deficits in the imitation of faces and in
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face recognition, and attenuated responses to emotional displays of others (Dawson et al. 2005). The retrospective examination of joint attention and gaze monitoring of toddlers through home videos of first birthday parties has provided insight into facial perception in ASD. In novel and uncertain situations, typically developing young children will seek affective cues from others’ faces. Given that a child’s first birthday can be a highly social, yet novel event, the setting provides the opportunity for a range of observable social behavior. Osterling and Dawson collected videotapes of 1st year birthday parties from children who were later diagnosed with ASD and from children with typical development. The tapes were rated by coders blind to the child’s subsequent diagnostic status. The authors found that the children later diagnosed with ASD looked at and attended to faces significantly less than the typically developing children. The authors determined that the single most critical factor in recognizing early signs of ASD was a failure to look at others and attend to faces in a typical manner, at or before 12 months old (1994).
Current Knowledge The dynamic processes related to facial perception can be examined from behavioral, neuroanatomical, and neurophysiological perspectives. Much of our understanding of facial perception has been derived from clinical work and research with individuals with ASD. These impressions suggest that individuals with autism, compared to typical peers, perform worse on face discrimination, recognition, and memory tasks when behaviorally assessed. Additionally, individuals with ASD display atypical development and anatomical activation of the brain structures involved in face processing and exhibit delayed and differential neurophysiological responses to faces. Behavioral Perspectives on Face Perception Very early in life, typically developing individuals show an attentional preference for faces and are equipped for the specialization of face processing.
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These specialized abilities involve recognizing upright faces better than inverted faces and interpreting complex interactions such as direction of gaze, emotional expressions, and facial gestures. Into middle childhood, the specialization for facial perception grows in complexity. Typically, individuals show better memory for faces than objects. They also derive meaningful social-emotional information from faces through preferential attention toward the eyes and perceive faces as a holistic unit rather than isolating individual facial features (Joseph and Tanaka 2003). In contrast, individuals with ASD attend to inverted faces for the same amount of time as upright faces and recognize inverted faces better than typical individuals by focusing on isolated features of the face rather than the face as a whole (van der Geest et al. 2002). Further, when viewing faces, individuals with ASD fixate on the mouth region rather than the eyes. Accordingly, they exhibit better performance memory for the lower portion of the face than the upper portion of the face but show a decrease in attention to the entire internal region of the face. As a result, individuals with ASD perform in the below average range on tasks of overall face memory ability. Further, individuals with ASD show impairments in the ability to use information from faces to guide behavior, such as failing to monitor a gaze shift and follow a gaze to share attention. Studies of facial discrimination and recognition using eye tracking suggest that individuals with ASD employ atypical strategies during facial perception tasks. First, individuals with ASD attend to other objects in the environment more often than the face. For example, a pioneering study using eye tracking found that when watching a videotape of the movie Who’s Afraid of Virginia Woolf, individuals with ASD attended two times more to the actors’ mouth, bodies, and objects in the scenes than to the actors’ eyes (Klin et al. 2002). This suggests that individuals with ASD do not seek social information and cues from naturalistic exchanges in the way that typical individuals seek social information. Subsequent studies have shown that individuals with ASD utilize atypical spatial distribution fixation strategies exemplified by fixating to, or avoiding specific
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regions of the face, focusing on the mouth, rather than the typical direction toward the eyes. A second atypical face perception strategy that individuals with ASD appear to use involves processing faces utilizing a piecemeal (i.e., featurebased) strategy (Chawarska and Shic 2009). In contrast, typical individuals employ the piecemeal processing strategy when looking at objects but not faces. Typical individuals attend to faces holistically by utilizing a complex-looking strategy called configural processing (Dawson et al. 2005). Configural processing is the perception and encoding of faces as a single holistic unit by incorporating the major features (i.e., eyes, nose, mouth), in respect to other features throughout the face. To explore the limitations of the configural strategy employed by the typical population, researchers alter the configuration of faces by inverting and partially obscuring the images. Typical individuals are adept at distinguishing similarities between faces that are upright; however, when the face is inverted, it is more difficult to discern facial feature differences. Inversion appears to alter the perception of the face by challenging the relational facial features (i.e., eye, nose, mouth), which contests the configural processing looking strategy producing what is called the inversion effect. While inverting faces results in the inversion effect, altering a face by forcing one to focus on individual facial features results in the decomposition effect. Even though typical individuals are adept at perceiving faces, once the face is altered from its original composition (e.g., inverted or presented in sections), the accuracy in which the face is identified is challenged. By challenging the configural processing strategy in these two ways, typical individuals have to abandon a configural processing strategy when looking at faces and adopt a piecemeal strategy as if they were looking at an object. This concept provides support that face perception is a highly specialized and sensitive processing system, which when challenged, forces individuals to compensate using differential processing strategies and neuroanatomical mechanisms that are not specialized for face perception tasks.
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From studies exploring the configural processing strategy, the decomposition effect and the inversion effect, researchers have found that typical individuals attend to inverted faces for longer lengths of time, are better at recognizing parts of the face when presented in the context of the entire face, and are better at recognition of the eyes compared to the mouth than their peers with ASD (van der Geest et al. 2002). When presented with the inversion face perception task, individuals with autism spend an equal amount of time looking at upright and inverted faces. This is similar to the way typical individuals attend to upright and inverted objects. By employing the object specific, piecemeal processing strategy, individuals with ASD are better at perceiving specific features of the face or partially exposed sections of the face with emphasis on local detail rather than global detail. In fact, individuals with ASD are in some cases better at feature-based matching, especially around the mouth, because they are not inhibited by the implementation of decomposition effect. These behavioral-based face perception studies suggest that typically developing individuals use the configural processing strategy to perceive faces and the piecemeal processing to perceive objects while individuals with ASD appear to apply the piecemeal strategy to process both faces and objects. The benefits of configural processing strategies are reflected in better performance on face discrimination and recognition tasks and better memory for faces relative to objects for typical individuals. In contrast, children with ASD perform equally to typical peers and in some cases better on nonface memory recognition tasks that include objects such as buildings, animals, houses, and bicycles (Blair et al. 2002). Neuroanatomical Perspectives on Face Perception Much of our understanding of the neuroanatomical structures related to face perception has been driven by research with individuals with impairments in face perception. Studies of the brain in individuals with prosopagnosia, a disorder characterized by intact object recognition ability, but
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impairment in the ability to recognize faces, provide a model for face perception that can be applied to ASD. Neuroanatomical findings in individuals with prosopagnosia, who exhibit behavioral face perception impairments similar to those found in ASD, indicate damage to particular cortical regions, such as the fusiform gyrus, indicating a critical role in face perception for this neural region (De Renzi et al. 1994). By utilizing functional magnetic resonance imaging (fMRI), structural magnetic resonance imaging (MRI), and positron-emission tomography (PET), neuroimaging research has provided further evidence that occipitotemporal cortical and other neural structures are functionally abnormal in individuals with ASD during facial perception. The regions identified as playing a primary role in face processing include the fusiform gyrus, the posterior superior temporal sulcus, and the amygdala. This dynamic human face processing system that is linked to the structural encoding of facial features, processing emotional expressions, and interpreting biological movement is critical to understanding the fundamental neural circuitry of the “social brain” (Pelphrey et al. 2007). Fusiform Gyrus Research over the last decade has verified a region of the fusiform gyrus that activates more strongly to faces than any other visual stimuli and appears to be specialized for face processing and facial expression discrimination. The fusiform face area (FFA), located in the medial-lateral region of the fusiform gyrus, responds almost twice as strongly to faces than wide varieties of nonface objects (Kanwisher 2000). However, additional research adds complexity to the role of the FFA region. After finding that the FFA is activated while viewing objects that fall in an individual’s area of expertise (e.g., car experts display FFA activation when viewing images of automobiles), Gauthier, Tarr, Anderson, and Skudlarski proposed that the FFA region is not only responsible for face perception but is responsible for the mediation of specific visual expertise. Gauthier and colleagues supported this conclusion by showing that training a group of college students to identify
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computer generated 3-D objects (called Greebles) results in an increase of activity of the FFA (1999). Rather than increased activity in the fusiform gyrus when looking at faces as seen in typical development, neuroimaging findings suggest that individuals with ASD display reduced activation of the fusiform gyrus while looking at faces (Schultz et al. 2000). Further, individuals with ASD exhibit an increase in activation in the inferior temporal gyri, a surrounding anatomical region specialized for object recognition. While for typical individuals, viewing objects of expertise, such as faces, results in FFA activation, individuals with ASD show FFA activation only when looking at images of their specialized interest. While the fusiform gyrus is an area linked to facial perception in typical individuals, for individuals with ASD, this “social brain region” fails to activate to faces in the same manner. Amygdala While there is strong research to support the FFA role in the perception of faces, the amygdala plays a significant role in detecting emotional expressions on faces. The amygdala is a fast-responding structure that plays a critical role in notifying other brain regions of emotional arousal through a mediated reward system. This structure quickly and reliably reacts to salient environmental stimuli, especially fear, and plays a role in assigning significance and constructing social judgments, such as inferring personality characteristics from pictures of the face or facial features (LeDoux 1996). The allure of the amygdala in ASD research was initially recognized in postmortem cases of individuals with ASD who appeared to have limited dendritic tree development resulting in an amygdala that was small and densely packed compared to typical individuals (Bauman and Kemper 1985). Adolphs found that individuals with ASD demonstrated similar impairments to those with focal lesions to limbic structures, including the amygdala. While individuals with ASD are able to form representations of faces and understand basic knowledge of emotions, they are unable to link the perception of faces to social judgments that are often revealed by facial expressions.
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Neuroimaging studies of individuals with ASD have reported hypoactivation of the amygdala when viewing images of emotional and mental states of another individual. For example, Pelphrey and colleagues demonstrated that when emotional faces were presented naturalistically through a dynamic video display, all three components of the broader social brain – the amygdala, the superior temporal sulcus, and the fusiform gyrus – were hypoactive (2007). A study examining the perception of emotionally expressive faces in individuals with amygdala and hippocampal lesions found that there was a strong correlation between the extent of amygdala lesions and hypoactivation of the FFA, suggesting that the amygdala plays a direct role on the FFA when interpreting emotionally expressive face (Vuilleumier et al. 2004). Superior Temporal Sulcus (STS) The STS region of the brain is responsible for the detection and processing of biological motion (Pelphrey et al. 2007). In synchrony with the amygdala and fusiform gyrus, this neural network is important for linking structural encoding of facial features and emotions to biological motion. Perception of biological motion is thought to underlie impairments in attention and imitation, skills that are strongly linked to language and social development. Since past research examined the face processing system through the display of emotional expressions using still image stimuli, Pelphrey et al. (2007) were interested in how presenting dynamic facial expressions affects the face processing system. They found that individuals with ASD display reduced activation in the STS, fusiform, and amygdala when faces contain dynamic information, such as movement, relative to static snapshots of the same emotional expressive face. These results were specific to the social brain and were not observed outside of the human face processing system (i.e., STS, fusiform gyrus, and amygdala). The STS also plays a role in comprehending intentions and detecting “errors” in social situations (Pelphrey et al. 2007). This region modulates tasks involving the attribution of intention
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in the context of dynamic social situations. During situations in which an individual’s actions do not fulfill the expectation (e.g., a character in a story is not looking in the direction that the viewer would think he/she should), there was an increase in blood flow to the STS in a typical observer. However, for individuals with ASD, there was reduced activity in the STS region. The lack of observed activity in the STS during attribution tasks in ASD provides further evidence that the STS region plays a strong role in the social dysfunction in ASD. Neurophysiological Perspectives on Facial Perception By utilizing neurophysiological measures such as event-related potentials (ERPs), scientific studies have provided insight into the neuropsychological processes underlying face perception and face perception impairment in ASD. While fMRI studies have allowed researchers to efficiently investigate abnormal regional brain activity during facial performance tasks, electroencephalogram (EEG) and event-related potentials (ERPs) have allowed researchers to examine the temporal characteristics and strength of neural activity by assessing oscillatory activity in response to viewing faces. Unlike fMRIs, which cannot determine the timing of neuronal firing, EEG/ERPs are able to monitor the timing of the summation of a collection of neuronal postsynaptic potentials firing at the resolution of milliseconds. Most studies utilizing EEG data concentrate on the amplitude, scalp topography, and latency. Amplitude is considered a representation of the amount of neuronal resources recruited for a particular process. Scalp topography distribution differences are thought to represent the location of the originally generated neuronal activity, and the timing of information processing speed is represented through the wavelike display of troughs and peaks, known as latency. Because simply passive viewing of stimuli is needed for EEG/ERP studies, valuable information can be gathered from children with significant social communication impairments through the use of EEG/ERP to effectively and noninvasively address fundamental questions
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about the neural basis of face processing in individuals with ASD. Electrophysiology studies are particularly salient in face processing research because of the discovery of a face-sensitive component that occurs with a negative slope at approximately 170 milliseconds poststimuli exposure. The N170 represents one stage in a series of stages of face processing; the P100, a positive going peak around 100 milliseconds, reflects neural activity in basic visual processing; the N170 reflects structural encoding of the face; and the N250 reflects higher order recognition such as affect or identity recognition. The N170 is recorded over the posterior temporal lobe and is greater in the right hemisphere than the left hemisphere. The N170 responds quicker to face and eye stimuli alone, rather than objects or inverted faces. Slight changes in face composition or inversion alter the latency and amplitude of this component. In typical children, the N170 undergoes a prolonged period of development and does not reach full maturation until late adolescence. Between 4 and 15 years of age, the precursor to the N170, identified as the prN170, is measured from the same electrode configuration as the N170 in adults but is more positive (does not extend beyond the baseline) than the fully maturated N170 component in adults. The prN170 in children ages 3–6 years is shown to have a faster response to faces. Similar to adults, this component is shown to have preferential responses to faces than objects. McPartland, Dawson, Webb, and Panagiotides documented the first report of an altered N170 pattern in adolescents and adults with ASD (2004). Individuals with ASD showed a slower latency when looking at faces than furniture and failed to show the face inversion effect. Additionally, researchers observed a slower speed of information processing which disrupts early structural encoding. These differences in the timing of cortical processing suggest that individuals with ASD have abnormal circuitry or delayed neural processes that modulate the face processing system. Children with ASD also show disruptions to this physiological measure of face processing.
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They demonstrate longer prN170 responses to faces than objects. By the age of 6, compared to typically developing peers who continue to show faster responses to faces over objects, children with ASD show no latency differences to faces versus objects (Webb et al. 2006). In addition, research indicates that children with ASD display structural encoding of faces that is disrupted and slowed. While typically developing children exhibit a faster prN170 response to faces than objects, individuals with ASD exhibit a slower prN170 response to faces than objects. Differential activation patterns have been observed using ERP studies in typical children comparing familiar and unfamiliar stimuli (i.e., caregivers to strangers, familiar objects to unfamiliar objects) as early as 6 months. This suggests early preferential neurological responses to familiar over unfamiliar faces and objects (de Haan and Nelson 1999). Typically developing infants and young children display ERP differences when viewing familiar and unfamiliar faces and objects at the P400 component recorded from posterior electrodes. Children with ASD, however, fail to show a differential P400 component when looking at familiar versus unfamiliar faces. Instead, children with ASD display an enhancement in the P400 component when looking at favorite objects versus unfamiliar objects. These results provide evidence that children with ASD have specific impairment in processing faces, but not objects. Children with ASD also show abnormal electrophysiological responses when viewing emotional expressions. Compared to typical peers, children with ASD show a slower and more positive N300 wave in response to fear and exhibit abnormal scalp topography. This slower information processing for faces in the N300 for emotional stimuli is associated with greater severity in social domains such as joint attention and social orienting (Dawson et al. 2004). Neurophysiological studies in early childhood provide insight into the structural and neurological mechanisms that are involved in the development of face processing. Face processing impairments examined at the neurophysiological level provide insight into social brain functioning
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and the face processing system in individuals with ASD from infancy and throughout development.
Future Directions Faces hold special significance and convey valuable social information early in life. Face perception provides a foundation for the development of social cognitive skills such as sharing attention and understanding emotions. Given the prominent role of face perception in social cognition, the study of ASDs has provided insight into the behavioral, neuroanatomical, and neurophysiological components of face perception. Behavioral studies of face perception in individuals with ASD have yielded insight into visual scanning, face processing strategies, and the role of expertise of processing information. Imaging studies have identified structures related to the processing of faces, such as the fusiform gyrus, superior temporal sulcus, and amygdala, and demonstrated that the structures of the “social brain system” involved in face perception appear to be functioning differently in ASD. Neurophysiological studies have elucidated face-specific brain waves and revealed temporal delays in the processing of faces in ASD. The ability to process and gather information from facial expressions is crucial for successful social interactions. This unique ability allows humans to understand affective cues in uncertain situations, such as situations that may be dangerous and important for survival, and to accurately read the emotions of another individual. This ability provides attentional and directive focus through gaze monitoring of others and provides a catalyst to overtly and covertly mimic the facial expressions and affective states of others, such as states that offer the opportunity for bonding or shared reciprocal experience (Chawarska and Shic 2009). While face perception provides immediate insight and interpretation of the social world, over time, and development, it is clear that early developing face processing skills set the foundation for the maintenance of exchanges and social relationships.
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References and Reading Adolphs, R., Sears, L., & Piven, J. (2001). Abnormal processing of social information from faces in autism. Journal of Cognitive Neuroscience, 13(2), 232–240. Bauman, M., & Kemper, T. L. (1985). Histoanatomic observations of the brain in early infantile autism. Neurology, 35(6), 866–874. Blair, R., Frith, U., Smith, N., Abell, F., & Cipolotti, L. (2002). Fractionation of visual memory: Agency detection and its impairment in autism. Neuropsychologia, 40, 108–118. Chawarska, K., & Shic, F. (2009). Looking but not seeing: Atypical visual scanning and recognition of faces in 2 and 4-year-old children with autism spectrum disorder. Journal of Autism and Developmental Disorders, 39, 1663–1672. Dawson, G., Webb, S., Carver, L., Panagiotides, H., & McPartland, J. (2004). Young children with autism show atypical brain responses to fearful versus neutral facial expressions. Developmental Science, 7, 340–359. Dawson, G., Webb, S. J., & McPartland, J. (2005). Understanding the nature of face processing impairment in autism: Insights from behavioral and electrophysiological studies. Developmental Neuropsychology, 27(3), 403–424. de Haan, M., & Nelson, C. A. (1999). Brain activity differentiates face and object processing in 6-month-old infants. Developmental Psychology, 35, 1113–1121. De Renzi, E., Perani, D., Cariesimo, G. A., Silveri, M. C., & Fazio, F. (1994). Prosopagnosia can be associated with damage confined to the right hemisphere – An MRI and PET study and review of the literature. Neuropsychologia, 32(8), 893–902. Gauthier, I., Tarr, M. J., Anderson, A. W., Skudlarski, P., & Gore, J. C. (1999). Activation of the middle fusiform “face area” increases with expertise in recognizing novel objects. Nature Neuroscience, 2, 568–573. Goren, C., Sarty, M., & Wu, P. (1975). Visual following and pattern discrimination of face-like stimuli by newborn infants. Pediatrics, 56, 544–549. Joseph, R. M., & Tanaka, J. (2003). Holistic and part-based face recognition in children with autism. Journal of Child Psychology and Psychiatry, 44, 529–542. Kanwisher, N. (2000). Domain specificity in face perception. Nature Neuroscience, 3, 759–763. Klin, A., Jones, W., Schultz, R., Volkmar, F. R., & Cohen, D. J. (2002). Visual fixation patterns during viewing of naturalistic social situations as predictors of social competence in individuals with autism. Archives of General Psychiatry, 59(9), 809–816. LeDoux, J. (1996). The emotional brain. New York: Simons & Schuster. McPartland, J., Dawson, G., Webb, S., Panagiotides, H., & Carver, L. (2004). Event-related brain potentials reveal anomalies in temporal processing of faces in autism spectrum disorder. Journal of Child Psychology and Psychiatry, 45, 1235–1245.
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Osterling, J., & Dawson, G. (1994). Early recognition of children with autism: A study of first birthday home videotapes. Journal of Autism and Developmental Disorders, 24, 247–257. Pelphrey, K., Morris, J., McCarthy, G., & LaBar, K. (2007). Perception of dynamic changes of facial affect and identity in autism. Social Cognitive and Affective Neuroscience, 2, 140–149. Schultz, R., Gauthier, I., Klin, A., Fulbright, R., Anderson, A., Volkmar, F., et al. (2000). Abnormal ventral temporal cortical activity during face discrimination among individuals with autism and asperger syndrome. Archives of General Psychiatry, 57, 331–340. van der Geest, J. N., Kemner, C., Verbaten, M. N., & van Engeland, H. (2002). Gaze behavior of children with pervasive developmental disorder toward human faces: A fixation time study. Journal of Child Psychology and Psychiatry, 43, 669–678. Vuilleumier, P., Armony, J. L., Driver, J., & Dolan, R. J. (2004). Distant influences of the amygdala lesion on visual cortical activation during emotional face processing. Nature Neuroscience, 7(11), 1271–1278. Webb, S. J., Dawson, G., Bernier, R., & Panagiotides, H. (2006). ERP evidence of atypical face processing in young children with autism. Journal of Autism and Developmental Disorders, 36(7), 881–890.
Face Processing in Autism: Active Avoidance of the Eyes Versus Passive Indifference James W. Tanaka1, Patrick Dwyer1,2 and Hidemi Kyotani1 1 Centre for Autism Research, Technology and Education, Department of Psychology, Victoria, Canada 2 Department of Psychology, University of California, Davis, Davis, CA, USA
Definition A person’s face is the source of their identity, a reflection of their internal emotions, and the means by which they communicate thoughts, feelings, and intentions to others. Most people are “expert” face perceivers – able to identify a familiar face, interpret a facial expression, and follow a person’s gaze, instantaneously, effortlessly, and without conscious forethought. However, many individuals on the autism spectrum experience
difficulties with face processing and struggle to understand the perceptual and social cues conveyed by the human face.
Current Knowledge If individuals on the autism spectrum have difficulties recognizing faces and understanding the emotional and social cues in a face, this could contribute to problems in the navigation of everyday interpersonal interactions. In this article, we will discuss the face processing strategies employed by people on autism spectrum and their problems with recognizing face identities and expressions, gaze following, and eye contact. We reason that impairments in face processing abilities have cascading effects on the social and emotional functioning of people on the autism spectrum that further exacerbate their difficulties with social communication and interaction. We propose that the pattern of face impairment in autism is best explained by a failure to attend to the eye information in a face. To account for this eye deficit, we will compare two accounts: the “Eye Avoidance” hypothesis, in which it is proposed that people on the autism spectrum actively avoid the eye region, and the “Eye Indifference” hypothesis, which claims that people on the autism spectrum do not show preferential attention to the socially-relevant information conveyed by the eyes. Deficits in Facial Identity The human face is the physical manifestation of one’s identity and the primary source of how people individuate one another as conspecifics, each with a distinct personality and personal history. Although it is not a defining characteristic of the disorder, many people on the autism spectrum are impaired in their ability to perceive and recognize facial identity. Research has shown that persons on the autism spectrum have difficulties with the perceptual discrimination of different facial identities (Behrmann et al. 2006; Tantam et al. 1989; Wallace et al. 2008), are impaired in their recognition of familiar faces (Boucher and Lewis 1992), and have problems with learning to
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recognize unfamiliar faces (Blair et al. 2002; Boucher and Lewis 1992; Gepner et al. 1996; Hauck et al. 1998; Klin et al. 1999). These impairments do not reflect a breakdown in general perceptual processing, because individuals on the autism spectrum perform equivalently or even sometimes better than control participants on tasks that involve the perception and recognition of nonface objects, such as cars and houses (Wallace et al. 2008; Wolf et al. 2008; Weigelt et al. 2013). However, not all individuals on the autism spectrum have difficulties with face recognition. In their extensive review of the literature, Weigelt et al. (2012) found that about half of the reviewed studies (N ¼ 46) supported differential impaired face processing in autism, whereas the other half (N ¼ 44) showed no difference between ASD and non-ASD control groups. Nevertheless, in the studies demonstrating face deficits, systematic breakdowns in face processing were identified. Face deficits in autism are most pronounced when the task involves either immediate or longterm memory for faces. Furthermore, when identifying unfamiliar faces, children on the autism spectrum process eye information differently than typically developing children. On a part/ whole task intended to measure holistic strategies, children on the autism spectrum relied less on the eye features than neurotypical children (Wolf et al. 2008). On a series of face matching tasks, participants with autism performed more poorly than neurotypically developing children and children diagnosed with Williams Syndrome on items that tested the upper eye region of the face, but performed on par with comparison groups on items that tested the lower mouth region (Riby et al. 2009). One implication of the converging research evidence is that individuals on the autism spectrum might not have a basic impairment in face recognition, but that they employ face recognition strategies that are different from neurotypical individuals. Deficits in Emotion Facial expressions play a key role in our everyday interactions with others because they are our moment-to-moment manifestations of our internal emotions or the emotions that we wish to project
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to others (Ekman and Friesen 1971). Therefore, success in social interactions relies on the capacity to recognize and interpret facial emotions in a social context. In typically developing children, the ability to discriminate basic facial expressions, such as anger, fear, sadness, happiness, and surprise, appears by approximately 4 months (Walker-Andrews 1998) and between 8 and 10 months, infants begin to use the emotional expressions of parents or caregivers to form their responses to events in their environment (Camras and Shutter 2010). If individuals on the autism spectrum struggle in their ability to perceive and interpret facial expressions, this would invariably lead to difficulties in everyday social exchanges. Although there is a clear connection between the accurate recognition of facial expressions and everyday social competence, there is inconsistent empirical evidence concerning whether individuals with autism are differentially impaired in their ability to perceive and interpret facial emotions. On the one hand, there are studies providing results consistent with impaired emotion perception and recognition in autism populations (Corbett et al. 2009; Davies et al. 1994; Loveland et al. 2008; Tantam et al. 1989; Tanaka et al. 2012). Some studies show that individuals on autism spectrum perform worse than control participants on tasks requiring the labeling of primary basic emotions, such as anger, disgust, and sadness (Bölte and Poustka 2003; Boraston et al. 2007). On the other hand, many studies report no differences between typical and autism population participants in their perception and recognition of facial expressions (Baron-Cohen et al. 1997; Castelli 2005; Jones et al. 2011; Lacroix et al. 2009; Ozonoff et al. 1990; Spezio et al. 2007). However, when the expression studies were combined in a recent meta-analysis of 48 papers, Uljarevic and Hamilton (2013) found that people on the autism spectrum performed below control participants, with a calculated mean effect size of 0.80, and an effect size of 0.41 after correction for publication bias. Relative to neurotypical groups, recognition of happiness was only marginally impaired in autism. Individuals on the autism spectrum performed most poorly on their recognition of fear, and the
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difference between their recognition of fear and happiness was marginally reliable. The relative sparing of the happy expression and the selective impairment of the fear expression is informative, because whereas the perceptual cues for happiness are centered on the mouth region (i.e., upturned smile), fear requires attention to cues contained in the upper eye region (i.e., widened eye and arched eyebrows) (Dimberg and Petterson 2000; Dimberg and Thunberg 1998; Smith et al. 2005). Thus, evidence from the identity and expression recognition studies suggests that individuals on the autism spectrum might be predisposed to ignore information in the upper eye region. Cognitive Strategies in Face Processing and Autism What is the source of the face processing deficits in autism? A likely candidate is the failure to process information in the eye region of the face. The eye region is a perceptually rich area of the face that provides critical cues for recognizing identity (Schyns et al. 2002; Vinette et al. 2004) and expression (Calder and Jansen 2007; Smith et al. 2005) If individuals on the autism spectrum fail to attend to eye information, it follows that they would be impaired in face identity and expression recognition tasks and have difficulty interpreting social cues in their daily lives. Eye tracking is a powerful technique for revealing cognitive strategies and it provides insights into the perceptual strategies guiding face processing in autism (Black et al. 2017; Boraston and Blakemore 2007). When a photograph of a face is presented to neurotypical participants, the majority of their looking behaviors (70–80%) are devoted to the eye region of the face (Vinette et al. 2004; Walker-Smith et al. 1977). In contrast, when adults on the autism spectrum look at a face, they look less at the eye, nose, and mouth features of the face and more at the external features (hair, chin, clothing) (Pelphrey et al. 2002). Critically, when individuals with autism do attend to the facial features, their gaze patterns are directed more to the mouth area; they spend less time inspecting the eyes (Dalton et al. 2005; Pelphrey et al. 2002; Spezio et al. 2007).
In the real world, faces are not static objects, but are dynamic entities that constantly move and change in reaction to social events in the environment. To capture dynamic social situations, eye movements were recorded while individuals with autism and control participants viewed a film depicting two actors engaged in a highly emotional conversation. Whereas neurotypicals spent the majority of time fixated on the eyes of the actors, individuals on the autism spectrum spent more time looking at the mouths of the actors and unrelated objects in the scene and less time on actors’ eyes (Klin et al. 2002). In a replication study, Speer et al. (2007) also found that individuals on the autism spectrum spent significantly less time looking at the eyes than did their typically developing peers. Interestingly, a link was found between social competence as measured by the Social Rating Scale (SRS) and scanning behaviors, such that as social competence decreased, the participant’s fixation time on the eyes also decreased. The authors suggested that the amount of time an individual spends fixating on others’ eyes in social interactions may reflect the degree to which they are able to interpret social information as conveyed through facial cues. Developmentally, eye neglect in autism emerges early and provides a reliable predictor of social impairment for toddlers who are later diagnosed with autism (Jones et al. 2008; Klin and Jones 2008). However, the precise age at which these eye contact differences first emerge is unclear. A prospective study using dynamic, but relatively still, face stimuli suggests that infants with autism could be less likely to look at the eyes as early as 3 months of age (Rutherford et al. 2015), but another prospective study using dynamic videos of an individual interacting with viewers found that infants later diagnosed with autism showed normative patterns of eye looking at 2 months of age, with fixation on the eyes declining gradually over subsequent observations (Jones and Klin 2013). Eye Avoidance or Eye Indifference? There are two accounts addressing why individuals on the autism spectrum might not show a
Face Processing in Autism: Active Avoidance of the Eyes Versus Passive Indifference
preference for the eyes. The “Eye Avoidance” hypothesis asserts that people on the autism spectrum actively divert their attention away from the eyes as a strategy to reduce and regulate social interactions. In contrast, the “Eye Indifference” hypothesis claims that individuals on the autism spectrum are insensitive to the social information conveyed by the eyes, which therefore are perceived to be a region of the face that is not particularly informative, engaging, or adaptively useful. Whereas the “Eye Avoidance” account regards the eyes as aversive and as having a negative valence, the “Eye Indifference” account regards the eyes as a nonthreatening and neutral stimulus. In this section, we will discuss the research relevant to the “Eye Avoidance” and “Eye Indifference” accounts and provide an explanation that attempts to reconcile these perspectives. Eye Avoidance Hypothesis Despite the importance of the eyes for recognition of identity and expression, the eyes present the most threatening area of the face to many individuals on the autism spectrum. Eye contact conveys a social message of approach and can be interpreted as an invitation for social engagement and intimacy (Kleinke 1986). In everyday face-toface encounters, social communication is initiated and regulated through the “language of our eyes.” Therefore, for individuals with autism, avoiding eye contact may be an adaptive strategy for deflecting, discouraging, or coping with stressful social interactions. Indeed, people with autism anecdotally comment that looking into another person’s eyes is an unpleasant, even painful experience (Robison 2007). These anecdotal observations have been validated by the self-reports of older children and adults indicating the threat and anxiety associated with looking at the eyes (Corden et al. 2008; Tottenham et al. 2014). Physiological measures provide an indirect measure of emotional reactivity. It is well established that increased skin conduction is a measure of increased emotional arousal, and therefore, serves as a good biomarker for examining a possible link between eye gaze and emotion in autism (Bradley et al. 2001). Consistent with an Eye Avoidance account, children with autism
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exhibit a higher skin reactivity to direct gaze faces than averted gaze faces, suggesting the direct eye contact is perceived as more threatening by participants. In contrast, the skin reactivity of neurotypical children does not change across direct and averted gaze conditions (Kylliäinen and Hietanen 2006). In an important ecological extension of this finding, Kaartinen et al. (2012) measured the skin conductance response of participants on the autism spectrum and neurotypical control participants using live models. Participants viewed the models displaying either an eyes closed, gaze averted, or direct gaze pose through a liquid crystal shutter. Although no group differences were found in skin reactivity between the participants on the autism spectrum and neurotypical participants, the magnitude of arousal for children on the autism spectrum was reliably associated with their social skill abilities (e.g., language, social communication skills, nonverbal play) in the direct gaze condition, but not in the eyes closed or averted gaze conditions, suggesting that engaging in eye contact promotes social competence. Gaze may interfere with face recognition ability in autism, insofar as skin reactivity to direct gaze faces was negatively correlated with face recognition performance; that is, high skin reactivity to direct gaze faces was correlated with poor recognition (Joseph et al. 2008). Although researchers have not always found a reliable link between skin reactivity and eye gaze in autism (Louwerse et al. 2013), null findings might be related to variations in stimulus type (static, videos, live models), task demands, age, and level of functioning of the participants. As a whole, the bulk of the skin conductance evidence indicates that individuals on the autism spectrum are more emotionally aroused when they look at the eyes of faces and this response is accentuated when the eyes of the faces are looking back at them. At the neuroanatomical level, a network of cortical and subcortical brain structures that includes the fusiform gyrus, the amygdala, and the superior temporal sulcus is central to social and emotional processing of faces. Whereas the fusiform gyrus is selectively sensitive to faces compared to nonface objects (Kanwisher et al. 1997; Puce et al. 1995)
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and to familiar faces compared to unfamiliar faces (Lehmann et al. 2004; Rotshtein et al. 2005), the amygdala is sensitive to emotional information in a face (Kawashima et al. 1999; Morris et al. 2002; Whalen et al. 2004). In autism, activation of amygdala is strongly and positively correlated with the time spent fixating on the eyes, relative to neurotypical participants (Dalton et al. 2005; Hadjikhani et al. 2004; Hadjikhani et al. 2017; Kliemann et al. 2012). Although they are not predisposed to look at the eyes, when individuals on the autism spectrum are cued to the eye region, they demonstrate greater bilateral amygdala activation than neurotypical individuals (Hadjikhani et al. 2004). Tottenham et al. (2014) showed that amygdala activity could be increased through an experimental manipulation to the eyes and that those participants with autism who made the fewest natural eye movements toward the eyes exhibited the highest amygdala response when their gaze was experimentally driven toward the eyes. The researchers hypothesized that those individuals on the autism spectrum who found direct gaze to be the most aversive may have modulated their amygdala-mediated arousal by averting their gaze away from direct eye contact. For many people on the autism spectrum, looking into the eyes of another person is a threatening experience, one that provokes feelings of anxiety and trepidation. As the foregoing research indicates, subjective feelings of discomfort are manifested in physiological responses that include heightened skin conductance and increased amygdala brain activity and these responses are accentuated by direct eye contact. According to the Eye Avoidance hypothesis, choosing not to look at someone’s eyes is an adaptive strategy that allows people on the autism spectrum to regulate their social interactions and manage potential feelings of discomfort that these encounters might elicit. Gaze Indifference Account In contrast to the Eye Avoidance hypothesis, the Gaze Indifference hypothesis maintains that individuals on the autism spectrum are insensitive to social cues and, consequently, are indifferent to the role that the eyes play in social
communication. Whereas the Eye Avoidance hypothesis assumes an awareness on the part of people on the autism spectrum to the potential social threat of the eyes, the Gaze Indifference hypothesis claims that they have no awareness of the social meaning or significance of eye information. According to this account, insensitivity to eye gaze is not specific to face processing, but reflects a general lack of interest in the social behaviors of others. Consistent with the Gaze Indifference account, neuroimaging studies have demonstrated that individuals on the autism spectrum show either hypoactivation or nonselective activation in the amygdala to social stimuli (Pelphrey et al. 2005), to faces (Pierce et al. 2001), and to the eyes (Pelphrey et al. 2005). Thus, in contrast to previously discussed findings (Dalton et al. 2005; Tottenham et al. 2014), these studies indicate that for people on the autism spectrum, the affective valence of a stimulus has little effect on the brain structures that are most closely associated with emotional processing. To support the Gaze Indifference hypothesis, Moriuchi et al. (2017) monitored the eye movements of 2-year-old toddlers on the autism spectrum while they watched a video of an actress speaking into the camera. When cued to the eyes of the actress, the 2-year-olds with autism did not shift their gaze away from the eyes as predicted by the Eye Avoidance account. Unlike the toddlers from the control group, the eye movements of toddlers on the autism spectrum were unaffected by the social cues of the actress, such as when she made eye contact and spoke directly into the camera. Hence, while the toddlers on the autism spectrum did not find the actress’ eyes particularly noxious or aversive, they did not respond to the social cues conveyed through her eyes. The results are consistent with a Gaze Indifference account in which the 2-year-olds on the autism spectrum displayed a passive insensitivity to the social cues conveyed by the eyes of others. Although the foregoing results are consistent with the Gaze Indifference interpretation, the findings are not necessary incompatible with the Eye Avoidance account. Moriuchi and colleagues
Face Processing in Autism: Active Avoidance of the Eyes Versus Passive Indifference
speculated that whether an individual on the autism spectrum responds passively or aversively to the eyes of others might depend on factors related to the age and social and cognitive development of the child. Previous eye gaze reports of atypical amygdala activation (Dalton et al. 2005; Tottenham et al. 2014), heightened anxiety (Corden et al. 2008), and atypical skin reactivity (Kaartinen et al. 2012; Kylliäinen and Hietanen 2006) have examined the responses in older children and adults on the autism spectrum, in contrast to the eyetracking study by Moriuchi et al., which was conducted with young, 2-year-old toddlers. If children on the autism spectrum learn that attending to the eyes is associated with a potential social threat, then the eye contact-social threat connection would require time, experience, and awareness. The frequency of anxiety symptoms in autism increases with age (van Steensel et al. 2011) and is moderated by cognitive functioning (White et al. 2009). It is not surprising then that the eye contact-social threat contingency is not fully established at 2 years of age. One model proposes that people on the autism spectrum could develop anxiety as a result of stressful experiences, including the stressful experience of navigating unpredictable social interactions (Wood and Gadow 2010). Hence, Eye Avoidance behaviors would not be expected to appear until later childhood and early adulthood, when the connection between social anxiety and eye threat is more firmly established.
Future Directions Summary In this article, we discussed the face processing abilities and strategies of individuals on the autism spectrum. Although impairments in the recognition of face identities and expressions are not defining characteristics of the autism spectrum, the converging evidence indicates that many individuals on the autism spectrum have difficulty in these areas. Because their recognition abilities for nonface objects are on par with or superior to those of neurotypical individuals, the deficits cannot be attributed to low-level or perceptual visual impairments but are selective to
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face processing. Cognitive and eye-tracking experiments indicate that in contrast to neurotypical individuals, individuals on the autism spectrum fail to attend to and encode the information in the eyes – a region of the face that is critical for recognizing identity and expression. Two hypotheses have been advanced to explain why individuals on the autism spectrum fail to look at the eyes of another person. According to the “Eye Avoidance” hypothesis, individuals on the autism spectrum divert their attention away from the eyes as a strategy to regulate the social threat of eye contact. In contrast, the “Gaze Indifference” hypothesis posits that individuals on the autism spectrum regard the eyes as a socially neutral stimulus that neither attracts nor repels their attention. Although the autonomic, neuroimaging, and eye-tracking results are inconclusive, it is plausible that the indifference and avoidance responses reflect behaviors at different developmental time points. Gaze indifference behavior might be evident in early childhood (e.g., 2 years of age) when the young child on the autism spectrum is relatively insensitive to the social cues in their world. Eye avoidance behaviors may appear later in childhood (e.g., school age) as a result of increased social interaction and a developing sense of selfawareness. As a test of this developmental account, longitudinal studies could be conducted to monitor for a possible “indifference-to-aversion” shift in eye-tracking behaviors. If the “eyes are windows into the soul,” then it is little surprise that individuals on the autism spectrum should struggle with understanding the emotions and intentions of other people. Although there might be good reasons why people on the autism spectrum choose not to attend to the eyes of others, understanding the source of this strategy is a rich area for future research and potential interventions.
See Also ▶ Gaze ▶ Joint attention ▶ Social Language Development Test
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Face Processing Tests 1517–1526. https://doi.org/10.1007/s10803-0121695-5. van Steensel, F. J. A., Bögels, S. M., & Perrin, S. (2011). Anxiety Disorders in Children and Adolescents with Autistic Spectrum Disorders: A Meta-Analysis. Clinical Child and Family Psychology Review, 14(3), 302–317. https://doi.org/10.1007/s10567-011-0097-0. Vinette, C., Gosselin, F., & Schyns, P. G. (2004). Spatiotemporal dynamics of face recognition in a flash: It’s in the eyes. Cognitive Science, 28(2), 289–301. https:// doi.org/10.1016/j.cogsci.2004.01.002. Walker-Andrews, A. S. (1998). Emotions and social development: Infants’ recognition of emotions in others. Pediatrics, 102(E1), 1268–1271. Retrieved from http://pediatrics.aappublications.org/content/102/Sup plement_E1/1268. Walker-Smith, G. J., Gale, A. G., & Findlay, J. M. (1977). Eye movement strategies involved in face perception. Perception, 6(3), 313–326. https://doi.org/10.1068/ p060313n. Wallace, S., Coleman, M., & Bailey, A. (2008). Face and object processing in autism spectrum disorders. Autism Research, 1(1), 43–51. https://doi.org/10.1002/aur.7. Weigelt, S., Koldewyn, K., & Kanwisher, N. (2012). Face identity recognition in autism spectrum disorders: A review of behavioral studies. Neuroscience and Biobehavioral Reviews, 36(3), 1060–1084. https://doi.org/ 10.1016/j.neubiorev.2011.12.008. Weigelt, S., Koldewyn, K., & Kanwisher, N. (2013). Face recognition deficits in autism are both domain specific and process specific. PLoS One, 8(9), e74541. https:// doi.org/10.1371/journal.pone.0074541. Whalen, P. J., Kagan, J., Cook, R. G., Davis, F. C., Kim, H., Polis, S., et al. (2004). Human amygdala responsivity to masked fearful eye whites. Science, 306(5704), 2061. https://doi.org/10.1126/science.1103617. White, S. W., Oswald, D., Ollendick, T., & Scahill, L. (2009). Anxiety in children and adolescents with autism spectrum disorders. Clinical Psychology Review, 29(3), 216–229. https://doi.org/10.1016/j.cpr. 2009.01.003. Wolf, J. M., Tanaka, J. W., Klaiman, C., Cockburn, J., Herlihy, L., Brown, C., et al. (2008). Specific impairment of face-processing abilities in children with autism spectrum disorder using the Let’s Face It! skills battery. Autism Res, 1(6), 329–340. https://doi.org/10. 1002/aur.56. Wood, J. J., & Gadow, K. D. (2010). Exploring the nature and function of anxiety in youth with autism spectrum disorders. Clincial Psychology: Science and Practice, 17(4), 281–292. https://doi.org/10.1111/j.1468-2850. 2010.01220.x.
Face Processing Tests ▶ Facial Recognition Tests
Face Recognition
Face Recognition Cora Mukerji1, Danielle Perszyk1 and James C. McPartland2 1 Yale Child Study Center, New Haven, CT, USA 2 School of Medicine, Child Study Center, Yale University, New Haven, CT, USA
Definition Face recognition is an important dimension of face processing in which specific information about identity is derived. The capacity to recognize individual faces is critical to socialization throughout development and adulthood, providing important social cues that guide interactions. While no scientific consensus exists on whether autistic populations show behavioral impairment in the ability to recognize faces, substantial evidence indicates that individuals with autism may use atypical visual and neural mechanisms to determine facial identity. By disrupting the extraction of important social information from the face, these abnormalities in processing facial recognition may contribute to the social deficits that characterize autism spectrum disorders (ASD).
Historical Background Faces are highly relevant visual stimuli, conveying information critical to social understanding and effective communication. The ability to accurately recognize others’ faces, which allows for quick identification of others and retrieval of information associated with them, is particularly critical to social function. Developmental studies indicate that face recognition facilitates engagement with others from infancy; from the first days of life, infants demonstrate the capacity to recognize their mothers’ faces among others (Pascalis et al. 1995). Further studies of face recognition ability in children have revealed that the basic capacity to differentiate faces matures over the course of development, following a
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developmental trajectory distinct from general pattern recognition or memory (Carey et al. 1980). Neuropathological findings have emphasized the importance of face recognition over the lifespan, suggesting that circumscribed regions of the brain are specialized for differentiating faces. Patients with prosopagnosia, a disorder of face perception, exhibit normal recognition of common objects in spite of severe impairment in recognizing faces (Whiteley and Warrington 1977), while individuals demonstrating severe impairment in recognizing common objects retain normal face recognition (Moscovitch and Winocur 1997). Together, these findings suggest a double dissociation between face and object recognition, i.e., that face and object recognition rely upon distinct neural networks in the brain. Subsequent neuroimaging studies have indicated that the region of the temporal lobe known as the fusiform gyrus is activated preferentially during face recognition tasks, suggesting the specialization of this brain region for face processing (e.g., Kanwisher et al. 1997). However, recent studies reveal that nonface objects with which an individual has expertise and novel face-like objects also animate this brain region, calling this interpretation into question (Grelotti et al. 2005). In addition, early research into the effects of inversion upon recognition accuracy has indicated that face and object recognition rely upon distinct processing strategies. Yin (1969) discovered that inversion of faces impairs recognition in typical adults to a greater degree than does inversion of common objects; this robust phenomenon, known as the “inversion effect,” suggests that adults use a different visual strategy to identify faces versus objects. Subsequent research indicates that, whereas object recognition depends upon discrimination of individual features, accurate face recognition relies more heavily upon the configuration of the face, which is disrupted during inversion. This processing strategy, however, may not be specific to faces but more generally applied to stimuli that an individual is expert at recognizing; individuals with expertise in nonface objects demonstrate an inversion effect for objects in their domain of expertise comparable to that for faces (Diamond and Carey 1986).
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The first experimental investigations of face recognition in autism were carried out by Langdell (1978) to assess whether disruption of the typical visual mechanisms underlying face recognition might contribute to the social impairment observed in children with autism; this research indicated that children and young adults with autism are proficient in recognizing faces but employ atypical visual strategies in order to do so. While children with autism did not exhibit behavioral deficits in recognizing photographs of their peers, they demonstrated a greater reliance on features located in the lower parts of the face (i.e., the mouth region) than did typical peers. In contrast, typically developing children relied more heavily on the upper part of the faces (i.e., the eye region) to recognize peers, replicating previous findings. Langdell (1978) also found that older children with autism failed to show an inversion effect for faces, while typically developing children did. Similarly, Hobson, Ouston, and Lee (Hobson et al. 1988) found that adolescents and young adults with autism were significantly better than typical peers at recognizing inverted faces, which they were able to identify as well as upright faces, suggesting that individuals with autism employ different processing strategies to determine facial identity than do typically developing individuals.
Current Knowledge Initial studies of face recognition ability in autism indicated that children and adolescents with autism demonstrate behavioral proficiency in recognizing faces but atypical patterns of visual processing. Current research continues to explore the question of whether individuals with autism are impaired in face recognition. However, the focus of this line of investigation has largely shifted from reporting behavioral performance to clarifying the visual mechanisms and neural networks that underlie the distinct processing strategies for face recognition in ASD. To date, no consensus exists as to whether individuals with autism demonstrate significant deficits in face recognition relative to typically
Face Recognition
developing peers matched on cognitive and verbal ability. While several recent studies report behavioral impairment in face recognition in children with ASD that is independent of overall functioning, many have failed to report such impairment (see Klin et al. 1999 for a review). Although most research to date indicates that recognition deficits in autism are specific to faces, some studies have reported more general impairment in processing both face and nonface stimuli (e.g., Davies et al. 1994). Klin et al. (1999) suggest that these inconsistencies in the literature are attributable to differences in age and functioning in the subject populations, control group criteria, and experimental tasks employed across studies of face recognition in ASD; in addition, Klin et al. (1999) hypothesize that abnormalities in the way facial identity is determined in autism may not always translate into impaired performance because children with autism may develop compensatory strategies for face processing to overcome difficulties in the critical domain of face recognition. While controversy exists as to whether individuals with autism demonstrate behavioral impairment in the specific domain of face recognition, an emerging body of research supports the hypothesis that atypical visual mechanisms underlie face recognition processing in ASD. Recent studies have provided direct evidence that the facial inversion effect observed in typical adults reflects the use of a configural visual strategy (i.e., one that relies upon the spatial relationship between individual features) for face recognition that is distinct from the feature-based strategies employed in object recognition (Freire et al. 2000). Given the lack of inversion effect in autism, this suggests that facial recognition in autism relies less upon the configural strategies and more upon the feature-based strategies characteristic of object recognition in typical development. However, subsequent work has indicated that high-functioning children with ASD do employ holistic strategies for face recognition (i.e., evaluating the face as a whole rather than part by part) in instances where accurate face recognition depends upon the mouth region of the face (Joseph and Tanaka 2003); this suggests
Face Recognition
that atypical patterns of face recognition in autism cannot be fully explained by impairment in configural processing of faces. Current research confirms that individuals with autism rely upon different regions of the face to determine facial identity. While typically developing children are more proficient in recognizing faces based upon the eyes compared to the mouth, high-functioning children with ASD demonstrate the reverse pattern (Joseph and Tanaka 2003). Eye-tracking studies have provided further evidence that, while observing and encoding faces, individuals with autism may preferentially attend to different face regions than do typically developing peers. Prior research has indicated that adults with high-functioning autism (HFA) spend more time scanning the external features of the face than core internal features (Pelphrey et al. 2002). Similarly, adults with HFA have been found to spend more time viewing mouths, bodies, and objects than eyes while viewing video clips of dynamic social interactions (Klin et al. 2002). In comparison, typically developing subjects spend more time scanning the eye region of faces, indicating that abnormal patterns of viewing faces in autism, characterized by reduced attention to the socially informative eye region of faces and enhanced attention to mouths, may underlie atypical face recognition in ASD (Klin et al. 2002). Neuroimaging techniques have recently been applied to shed light on the neural mechanisms that support the distinct processing of facial identity in autism. While brain imaging studies of typically developing individuals have consistently revealed activation in the fusiform face area (FFA) to viewing faces (e.g., Kanwisher et al. 1997), individuals with autism demonstrate atypical patterns of neural activation during face processing (Pierce et al. 2001; Schultz et al. 2000). Work in this field has revealed evidence that face processing can occur outside the FFA, at individual-specific sites, in ASD (Pierce et al. 2001). This difference in where face processing occurs in the brain may be associated with distinct strategies for face recognition in this population. Furthermore, individuals with autism and Asperger disorder have shown weak activation
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in the FFA and heightened activation of the inferior temporal gyrus during a face discrimination task, the same pattern of brain activity observed when typically developing individuals performed a nonface object discrimination task (Schultz et al. 2000). This supports the idea that individuals with ASD may use feature-based processing strategies to discriminate among both faces and objects, showing a lack of typical specialization of face processing strategies in this population. Further studies have revealed key differences in how the brain response to faces is mediated in real time in typical and atypical development, suggesting that children with autism show impaired neural processing of faces at multiple stages of face recognition. Two distinct temporal markers of brain activity (the N400 and Pc) have been found to differentiate between familiar and unfamiliar faces and familiar and unfamiliar objects in typically developing 3- and 4-yearolds; in contrast, same-age peers with autism show differentiation between familiar and unfamiliar objects but not between familiar and unfamiliar faces at these two markers, suggesting that impairment in processing facial recognition emerges early in life (Dawson et al. 2002). Disruption of typical mechanisms for discriminating faces at this early age may derail subsequent brain and behavioral development by affecting how children with autism attend to faces in the world around them and how effective they are at gathering critical social information from faces (Dawson et al. 2002). Additional research has revealed that individuals with autism also show significant delay at a key temporal marker of face processing, the N170, compared to typically developing individuals, reflecting slowed neural speed of face processing in autism only 170 ms after viewing a face (McPartland et al. 2004). Slowed speed of processing has been correlated with poorer performance on tests of facial recognition in autism; this suggests that greater delays in neural processing of faces may contribute to difficulties in face discrimination. While the N170 is delayed in typical development to inverted versus upright faces, this inversion effect is not observed in ASD, providing further
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support for the idea that individuals with autism may rely upon configural rather than typical holistic strategies for processing faces (McPartland et al. 2004).
Future Directions While an emerging body of eye-tracking and neuroscientific research has provided evidence that individuals with autism employ atypical visual and neural mechanisms for discriminating faces, the exact nature of the relationship between these divergent mechanisms and social impairment in autism remains unknown. Atypical mechanisms for recognizing faces may serve as both a cause and an effect of social impairment in autism. For example, reduced attention to faces early in development may disrupt typical specialization of visual strategies and neural circuitry for discriminating faces; the resulting abnormalities in processing facial recognition may further deprive individuals of critical social inputs necessary for the development of typical patterns of social behavior (Dawson et al. 2002). Additional work is needed to clarify the connection between divergent patterns of visual and neural processing of facial identity and the social difficulties that characterize autism in order to design effective interventions.
See Also ▶ Face Perception ▶ Fusiform Gyrus (FG)
References and Reading Carey, S., Diamond, R., & Woods, B. (1980). Development of face recognition: A maturational component? Developmental Psychology, 16(4), 257–269. https://doi.org/ 10.1037/0012-1649.16.4.257. Davies, S., Bishop, D., Manstead, A. S. R., & Tantam, D. (1994). Face perception in children with autism and Asperger’s syndrome. Journal of Child Psychology and Psychiatry, 35(6), 1033–1057. https://doi.org/10. 1111/j.1469-7610.1994.tb01808.x.
Face Recognition Dawson, G., Carver, L., Meltzoff, A. N., Panagiotides, H., McPartland, J., & Webb, S. J. (2002). Neural correlates of face and object recognition in young children with autism spectrum disorder, developmental delay, and typical development. Child Development, 73(3), 700–717. https://doi.org/10.1111/1467-8624.00433. Diamond, R., & Carey, S. (1986). Why faces are and are not special: An effect of expertise. Journal of Experimental Psychology. General, 115(2), 107. Freire, A., Lee, K., & Symons, L. A. (2000). The faceinversion effect as a deficit in the encoding of configural information: Direct evidence. Perception, 29(2), 159–170. https://doi.org/10.1068/p3012. Grelotti, D. J., Klin, A. J., Gauthier, I., Skudlarski, P., Cohen, D. J., Gore, J. C., et al. (2005). fMRI activation of the fusiform gyrus and amygdala to cartoon characters but not to faces in a boy with autism. Neuropsychologia, 43(3), 373–385. https://doi.org/10.1016/j. neuropsychologia.2004.06.015. Hobson, R. P., Ouston, J., & Lee, A. (1988). What’s in a face? The case of autism. British Journal of Psychology, 79(4), 441–453. https://doi.org/10.1111/j.20448295.1988.tb02745.x. Joseph, R. M., & Tanaka, J. (2003). Holistic and part-based face recognition in children with autism. Journal of Child Psychology and Psychiatry, 44(4), 529–542. https://doi.org/10.1111/1469-7610.00142. Kanwisher, N., McDermott, J., & Chun, M. M. (1997). The fusiform face area: A module in human extrastriate cortex specialized for face perception. Journal of Neuroscience, 17(11), 4302–4311. Klin, A., Sparrow, S. S., de Bildt, A., Cicchetti, D. V., Cohen, D. J., & Volkmar, F. R. (1999). A normed study of face recognition in autism and related disorders. Journal of Autism and Developmental Disorders, 29(6), 499–508. https://doi.org/10.1023/ a:1022299920240. Klin, A., Jones, W., Schultz, R., Volkmar, F., & Cohen, D. (2002). Visual fixation patterns during viewing of naturalistic social situations as predictors of social competence in individuals with autism. Archives of General Psychiatry, 59(9), 809–816. https://doi.org/10.1001/ archpsyc.59.9.809. Langdell, T. (1978). Recognition of faces: An approach to the study of autism. Journal of Child Psychology and Psychiatry, 19(3), 255–268. https://doi.org/10.1111/j. 1469-7610.1978.tb00468.x. McPartland, J., Dawson, G., Webb, S. J., Panagiotides, H., & Carver, L. J. (2004). Event-related brain potentials reveal anomalies in temporal processing of faces in autism spectrum disorder. Journal of Child Psychology and Psychiatry, 45(7), 1235–1245. https://doi.org/10. 1111/j.1469-7610.2004.00318.x. Moscovitch, M., & Winocur, G. (1997). What is special about face recognition? Nineteen experiments on a person with visual object. Journal of Cognitive Neuroscience, 9(5), 555. Pascalis, O., de Schonen, S., Morton, J., Deruelle, C., & Fabre-Grenet, M. (1995). Mother’s face recognition by
Facial Inversion Effect neonates: A replication and an extension. Infant Behavior & Development, 18(1), 79–85. https://doi.org/10. 1016/0163-6383(95)90009-8. Pelphrey, K. A., Sasson, N. J., Reznick, J. S., Paul, G., Goldman, B. D., & Piven, J. (2002). Visual scanning of faces in autism. Journal of Autism and Developmental Disorders, 32(4), 249–261. https://doi.org/10.1023/ a:1016374617369. Pierce, K., Muller, R. A., Ambrose, J., Allen, G., & Courchesne, E. (2001). Face processing occurs outside the fusiform ‘face area’ in autism: Evidence from functional MRI. Brain, 124(10), 2059–2073. https://doi. org/10.1093/brain/124.10.2059. Schultz, R. T., Gauthier, I., Klin, A., Fulbright, R. K., Anderson, A. W., Volkmar, F., et al. (2000). Abnormal ventral temporal cortical activity during face discrimination among individuals with autism and Asperger syndrome. Archives of General Psychiatry, 57(4), 331–340. https://doi.org/10.1001/archpsyc.57.4.331. Whiteley, A. M., & Warrington, E. K. (1977). Prosopagnosia: A clinical, psychological, and anatomical study of three patients. Journal of Neurology, Neurosurgery & Psychiatry, 40(4), 395–403. https://doi.org/ 10.1136/jnnp.40.4.395. Yin, R. K. (1969). Looking at upside-down faces. Journal of Experimental Psychology, 81(1), 141–145. https:// doi.org/10.1037/h0027474.
Face Tests ▶ Facial Recognition Tests
Face Validity
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problems in which the test taker has to add and subtract numbers may be considered to have strong face validity. The test items appear, at face value, to measure what one is seeking to measure.
See Also ▶ Validity
References and Reading Cicchetti, D. V. (2008). From Bayes to the just noticeable difference to effect sizes: A note to understanding the clinical and statistical significance of oenologic research findings. Journal of Wine Economics, 3, 185–193. Cicchetti, D. V. (2011). On the reliability and accuracy of the evaluative method for identifying evidence-based practices in autism, Chapter 3. In B. Reichow, P. Doehring, D. V. Cicchetti, & F. R. Volkmar (Eds.), Evidence-based practices and treatments for children with autism. New York: Springer. Cicchetti, D. V., & Rourke, B. P. (2004). Methodological and biostatistical foundations of clinical neuropsychology and medical and health disciplines (2nd ed.). London: Psychology Press/Taylor & Francis. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale: Lawrence Erlbaum Associates. Tsatsanis, K. D., Saulnier, C., Sparrow, S. S., & Cicchetti, D. (2011). The role of adaptive behavior in evidencebased practices for ASD: Translating intervention into functional success, Chapter 11. In B. Reichow, P. Doehring, D. V. Cicchetti, & F. R. Volkmar (Eds.), Evidence-based practices and treatments for children with autism. New York: Springer.
Ellen Johnson Section of Social Work, Mayo Clinic, Rochester, MN, USA
Facial Imitation Definition Face validity refers to the extent to which a test appears to measure what it is intended to measure. A test in which most people would agree that the test items appear to measure what the test is intended to measure would have strong face validity. For example, a mathematical test consisting of
▶ Oral-Facial Imitation
Facial Inversion Effect ▶ Upright/Inverted Figures
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Facial Recognition Tests
Facial Recognition Tests Faja Susan Developmental Medicine, Boston Children’s Hospital/Harvard Medical School, Boston, MA, USA
Abbreviations ADT ASD BTFR CFMT CFMT-K CFPT CMS CNB FME K-ABC-II, FR LFI RMT WMS
Age differentiation test Autism spectrum disorder Benton test of facial recognition Cambridge face memory test Cambridge face memory test-kids Cambridge face perception test Children’s memory scales Penn computerized neurocognitive battery Facial memory test Kaufman Assessment Battery for Children-II, Face Recognition Subtest Let’s Face It! Warrington recognition memory test Wechsler memory scales
Synonyms Face processing tests; Face tests
Description A variety of tests have been developed to measure the ability to recognize faces. In order to recognize a face, one must have the basic perceptual ability to distinguish that face from others, often over time and from multiple vantage points. Tests that measure this ability provide information about the accuracy with which the face is represented, recognized, and distinguished from others. A range of skills are critical for face recognition including the ability to analyze the face and its features, understand the relationship of these features within the overall configuration of the face,
perceive the identity of the face across changes in viewpoint and expression, and explicitly recognize the faces of familiar people. Recognition of faces is critical throughout social development, because faces are used to identify others in social situations more than any other physical characteristic. The face provides a unique way to identify individuals and comes to serve as a representation of that person and their personal characteristics. Infants distinguish faces of familiar individuals shortly after they are born. Without the early ability to recognize and remember faces or to make use of the nonverbal information they contain, many other subsequent learning processes may be derailed, including the development of coordinated eye contact, joint referencing, and emotional reciprocity. Impaired face recognition in individuals with autism spectrum disorder (ASD) has been demonstrated with a wide range of experimental tests. On average, groups with ASD are less accurate in their ability to recognize the faces of others. This is true for discrimination of facial identities, recognition of familiar faces, and immediate recognition of newly learned faces. Impairment in face recognition appears to be somewhat specific because individuals with ASD are typically unimpaired in their recognition of objects. On an individual level, there appears to be variability in the ability of people with ASD to recognize faces. There is evidence of an association between the degree of severity of face processing impairments and the severity of more general social symptoms. For this reason, tests of facial recognition are important in classifying the degree of difficulty individuals experience in this perceptual domain in clinical and research settings. Standardized face recognition tests are also useful when describing group characteristics of a research sample, because they provide a common means for comparison across studies and for interpreting experimental measures of face processing.
Historical Background Early reports of individuals who experienced difficulty with recognizing faces were made in the
Facial Recognition Tests
late 1800s, though these difficulties were usually associated with other visual impairments. Bodamer (1947) coined the term prosopagnosia (i.e., “loss of knowledge of faces”) to describe individuals who had greater impairments with faces than objects. In order to evaluate patients with frank impairments in face recognition, tests of facial recognition were developed. For example, in the 1960s, Benton and Van Allen developed a face-matching test and De Renzi and colleagues developed a same-different face test. The former became the Benton Facial Recognition Test (Benton et al. 1983). Warrington (1984) developed a test that involved learning a set of new faces followed by a series of target-distracter face pairs to test recognition memory for faces, although this measure has not been widely used with autism and nonface information is contained in the pictures that may aid in the discrimination of targets. In more recent years, similar face recognition tests have been included with standardized batteries of memory and neuropsychological functioning. The Wechsler Memory Scales-III (Wechsler 1997) included the Faces subtest, which is appropriate for adults. A very similar task is included in the Children’s Memory Scales (Cohen 1997), and is appropriate for children. Also appropriate for younger children are the Face Recognition subtest of the Kaufman Assessment Battery for Children (K-ABC-2; Kaufman and Kaufman 2004) and the Memory for Faces subtest of the NEPSY-II (Korkman et al. 2007). In recent years, a new set of facial recognition tests has been developed to increase the sensitivity, validity, and reliability of available measures including the Cambridge, Caledonian, Penn, and Philadelphia facial tests. Most of these tests, along with experimental tasks, have been used to assess face recognition in ASD. The face processing of individuals with ASD has been studied extensively since Langdell (1978) first offered evidence of atypical strategies in the way young children with autism use information to identify familiar faces. Langdell proposed that face processing deficits may explain the social symptoms associated with autism including impaired eye contact and reduced social reciprocity. Throughout the 1980s and 1990s, a
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series of studies followed that revealed impaired or unusual patterns of face recognition in children, adolescents, and adults with autism. In many cases, these studies did not use standardized measures of face recognition, and instead used tasks developed for the particular study being conducted. Most investigations of face recognition have also been limited by small sample size with a few exceptions, thus limiting the ability to generalize conclusions about the integrity of the face recognition system and determine the expected range of function for individuals with ASD. In an effort to address this problem, the Face Recognition subtest of the K-ABC was administered to over 100 children with ICD-10 defined autistic disorder, pervasive developmental disorder-not otherwise specified, and a comparison group without ASD (Klin et al. 1999). Children with autistic disorder (i.e., the most severe cases) recognized fewer faces than the other two groups, even when matched on verbal and nonverbal mental age. Based on this investigation, it was concluded that children with autism exhibited impairments in face recognition that could not be accounted for by more generalized cognitive impairment. Further, this study suggests face recognition difficulty corresponds with autism severity. Most studies have been also limited by use of a single measure of face processing or recognition. As a result, it has been difficult to compare results across studies, and the ability to determine the nature and relative contribution of various processing deficits is limited. Additionally, while standardized measures provide a means of comparison across studies, they are limited in their sensitivity to the specific challenges with face processing exhibited by some individuals with ASD (i.e., a focus on features within the face rather than the face as a whole, decreased visual attention to the eye region of the face, and reduced disruption in performance when faces are presented upside down). To address these limitations, Tanaka and Schultz developed the Let’s Face It! (LFI) Skills Battery of computerized face recognition tasks aimed at children that includes measures of short-term memory, feature processing and configural perception, and holistic processing
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of faces. Many LFI tasks require children to recognize and match individual faces across changes in orientation, different emotional expressions, and with certain regions of the face obscured. LFI also includes tasks that measure processing and short-term memory for objects. Compared with a group without ASD, the LFI Skills Battery is sensitive to the specific ASD-related impairments in face memory and matching, a processing advantage for the mouth region of the face, and unimpaired object processing.
Psychometric Data All tests presented are individually administered, use still photographs of unfamiliar faces, and are thought to measure facial recognition. The Benton Test of Facial Recognition (BTFR; Benton et al. 1983) is a measure of the ability to perceive and match unfamiliar faces without a memory component. Normative samples span a wide range of ages (6–74 years). Black and white photos are presented, with the target at the top of the page and six test faces at the bottom of the page. Photos vary based on orientation and lighting condition across three conditions: (1) an identical photograph, (2) the same face with different lighting, and (3) three-quarter views of the same face. Initially, there is only one target face, but in the latter portion of the task, three of the six faces have the same identity (i.e., there are three correct responses). Administration time typically ranges from 5–15 min, although the items are untimed. The BTFR has a short and long form (i.e., a subset of the first 13 items versus the full set of items). Scores are obtained by a simple summation of correct responses. Normative data is available for 286 adults and 229 children (Benton et al. 1994). The psychometric properties of the long form are adequate at best, and the short form has poor psychometric properties. In particular, evidence challenging the construct validity comes from work by Duchaine and colleagues who found adults with typical development were able to score in the normal range when administered a version of the BTFR that presented only the
Facial Recognition Tests
eyebrows and hairline of faces. These authors concluded that the simultaneous presentation of targets and test faces permits the use of featurebased strategies. The same group showed individuals with developmental prosopagnosia could score in the normal range – a challenge to the BTFR’s predictive validity. The Cambridge Face Memory Test (CFMT; Duchaine and Nakayama 2006; Bowles et al. 2009) is a standardized assessment of face processing, recognition, and memory of unfamiliar faces with good psychometric properties and sensitivity to face impairments. The CFMT first establishes familiarity with six study images of male faces from three angles (left, frontal, and right) for 3 s each, then tests recognition from multiple viewpoints, different poses and lighting conditions, and with visual noise from a set of forced-choice groupings. Administration lasts between 10–15 min. There are four phases of the task (Practice with Cartoons, Introduction with the Same Images, Novel Images, and Novel Images with Noise). During the Same Images phase, three test items that are identical to the study images are presented for each face. In the Novel Images phase, a brief review of the study images is followed by presentation of 30 test items that vary by lighting or pose. Finally, in the Novel Images with Noise phase, study images are again briefly reviewed followed by 24 test items with different levels of Gaussian noise added to each image. The CFMT also assesses the effect of face inversion, which is thought to disrupt specialized face processing mechanisms. The CFMT is appropriate for adults, and was developed and initially tested with 50 college students and 9 older adults. A long form with 30 additional difficult trials was developed to identify individuals for whom face recognition and memory is a particular strength (Russell et al. 2009). Initial data support the validity of the CFMT, and normative data and reliability measurements are available from 240 healthy adults (Bowles et al. 2009) including measures of acceptable reliability between blocks and good internal consistency based on data from 124 individuals. Of note, normative data demonstrate that norms must be derived from participants with similar race and ethnicity as the stimuli.
Facial Recognition Tests
A Cambridge Face Memory Test-Kids (CFMT-K) has also been developed for children (Croydon et al. 2014). The CFMT-K stimuli are images of children, and it was validated with 401 children between 5 and 12 years of age to demonstrate age-related developmental sensitivity for upright and inverted face recognition and memory. The Cambridge Face Perception Test (CFPT; Duchaine et al. 2007; Russell et al. 2012) does not require face memory. Rather it requires participants to rank order a set of six faces according to their similarity to a target face. There are eight upright trials and eight inverted trials, with 1 min allowed per set. The target face is presented with a three-fourth view and the test faces are morphed to include 88, 76, 64, 52, 40, and 28% of the original target face. Scoring accounts for the magnitude of the error (i.e., distance from correct sort position). Norms are available, but there is significant variability in performance of the initial sample. Adequate internal consistency was reported for a different sample of 126 adults, as well as a high level of correlation with the CFMT suggesting they assess largely overlapping but partially separable abilities (Bowles et al. 2009). Cambridge face measures (CFMT, CFMT-K, CFPT) are available for research. The Caledonian face test (Logan et al. 2016) requires participants to determine the odd-one-out from a group of four faces. That is, they must select the one face that is different from the three distracters. The task is untimed and presented on a computer. Completion time is 3–4 min. Stimuli are synthetic faces with predictable differences between targets and distracters. The face with the mean value across dimensions occurs in every trial and is the target for half of the trials, whereas the other face is selected from a pool of 80 faces, which allows the task to be readministered without learning effects. The difference between faces on each trial has a varying magnitude that is individually controlled for each participant using a QUEST algorithm. The psychometrics of the Caledonian test were determined in a sample of 80 young adults. An inversion effect was present and face discrimination thresholds had comparable sensitivity to
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other lab-based measures, both confirmations of the validity of the more efficient Caledonian test. The measure has good sensitivity including detection of individual differences in performance among nonclinical adults. It was also more sensitive to the impairments of an individual with developmental prosopagnosia, with sensitivity almost threefold greater than two other measures of face processing. Finally test-retest on the same day had good reliability. The Caledonian test is available for research. The Children’s Memory Scales (CMS) Faces subtest (Cohen 1997) measures face recognition and memory with unfamiliar faces. The test is appropriate for children aged 5–16 years and can be administered in 10 min plus a 25- to 35-min delay. A series of color photographs of unfamiliar faces are presented one at a time for 2 s (either 12 or 16 faces depending on age). Immediately a series of test faces are presented one at a time and the child is asked to respond to each by indicating whether the face was viewed in the learning phase (or not). After a delay, a second series of test images is presented and children are asked to respond in the same manner. Correct responses for the Immediate and Delayed Faces subtests are totaled to attain two scaled scores. Normative data were obtained from a representative sample of children in the United States during the 1990s. Stability of the assessment across administrations is adequate. The Glasgow Face Matching Test (Burton et al. 2010) requires matching in the same view but stimuli are taken with different cameras, which introduce variability in the images, and is particularly relevant in forensic or security identification tasks. The test contains 168 pairs of adult faces with neutral expressions and takes about 15 min to complete. Half the pairs are same-face trials. The test was normed on 300 adults aged 18–80. Split-half reliability is high and performance related to face processing ability on other common manipulations such as recognition memory for faces supports the validity of the test as a measure of face processing ability. A short version with 40 of the most difficult face pairs was also examined using 194 new adult volunteers aged 18–46 and takes about 3–4 min to complete. Short and long versions are available for research.
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The Kaufman Assessment Battery for Children-II (K-ABC-II) Face Recognition Subtest (Kaufman and Kaufman 2004) is standardized for ages 3–18; however, psychometric data for the Face Recognition test is available only for ages 3–5 years. The test involves viewing and attending one or two photographs of faces with a brief exposure. Then, children are asked to select the correct face or faces from a group. The task requires children to recognize the target face(s) across changes in pose. In the revision of the K-ABC, new items were added to the subtest and distracting features in the background of photos were removed. The K-ABC-II Face Recognition subtest is standardized on a national sample of children and has excellent psychometric properties. The Let’s Face It! Skills Battery (Wolfe et al. 2008) is a battery of tasks designed to measure specific aspects of face recognition and memory that are particularly relevant for children with ASD. Administration of the battery occurred over 2 days in the initial validation and requires more time for completion than other individual tests described in this section. The battery contains five facial identity tests, three emotion processing tests, and two object processing tests. Face recognition subtests include: Matching Identity Across Expression, Matching Identity Across Masked Features, Featural and Configural Face Dimensions, Parts/Whole Identity, and Immediate Memory for Faces. The LFI Skills Battery is not standardized, but has been administered to a relatively large sample of children, adolescents, and young adults with ASD (85 total, with >60 responding to any given subtest) and 130 with typical development. The age range of participants with ASD was 5.1–20.1 years (mean age ¼ 11.58) and cognitive functioning was high (mean full scale IQ of 99.74). Effect sizes for individual subtests were moderate to large. The largest effect sizes were found for the Matching Identity across Expression subtest (effect size ¼ 1.00) and for the Immediate Memory for Faces subtest (effect size ¼ 1.00). Moderate effect sizes were also reported for task conditions in which the eye region was tested (Parts/whole identity, Face dimensions).
Facial Recognition Tests
The NEPSY-II Memory for Faces Subtest (Korkman et al. 2007) is thought to measure encoding, discrimination, and recognition of unfamiliar faces. Children are first presented with a series of photos of faces, and the child is asked to identify the gender of each face as a way to increase attention to the stimuli. Immediately after presentation, test trials require children to select the target face from a group of three faces (a target and two distracters). Fifteen to twenty minutes later, children are asked to select the same set of targets from a new set of three faces. The longer delay assesses long-term facial memory. The subtest yields three scores: Memory for Faces Total Score (immediate recognition), Memory for Faces Delayed Total Score (delayed memory for faces), and Memory for Faces vs. Memory for Faces Delayed Contrast Scaled Score (contrast between the first two). The task is standardized for ages 5–16 years. Normative data were collected between 2004 and 2006 from across the United States, and included 1200 children aged 3–16 years. The revision process also included clinical studies of children with ASD. The validity of the subtest is supported by moderate correlations between the Memory and Delayed Memory scores, between the NEPSY-II and NEPSY, between the NEPSY-II Memory for Faces and CMS Faces subtests, and by diminished performance by children with ASD. Concurrent validity data suggest that processing speed and visual discrimination are also involved, and inattention, impulsivity, and repetitive behaviors may influence performance. Internal consistency ranges from adequate to high and has adequate stability over time. Of interest, the NEPSY-II battery also includes other measures of social function including Affect Recognition and Theory of Mind subtests. The Penn Computerized Neurocognitive Battery (CNB) Facial Memory Test (FME) and Age Differentiation Test (ADT) (Moore et al. 2015) are brief web-based computerized measures of functioning validated with functional neuroimaging. Scores for accuracy as well as reaction time for correct responses are available. Good reliability is reported for FME and ADT response speed, adequate reliability for ADT accuracy, and
Facial Recognition Tests
questionable reliability for FME accuracy. The FME begins with 20 images of faces presented for 1 sec that participants are told they will be asked to remember. Then, a series of 40 test faces is presented including 20 targets and 20 distracters matched on age, ethnicity, and gender. Participants must decide if the face is one that has been viewed previously using a four-point scale (definitely not, probably not, probably yes, or definitely yes). The test takes 4 min to administer. The FME is administered immediately and again after a delay. The ADT takes 3 min to administer and simultaneously presents neutral face pairs; participants must determine which face is older. Stimuli were generated from faces morphed in age in order to control the level of difficulty. The psychometric properties and validity of the CNB have been evaluated with thousands of adults and children in the USA and the Netherlands (Gur et al. 2010; Moore et al. 2015; Swagerman et al. 2016). It is available online for research. The Philadelphia Face Perception Battery (Thomas et al. 2008) measures matching based on similarity, evaluation of attractiveness, gender classification, and identification of older faces. Stimuli were created in software allowing modification of 114 parameters and refined via pilot studies. To develop the tests, 124 adults participated. A target face is presented for matching with two forced choice options in the Similarity test (more/less similar faces) and the Gender test (male/female labels). Two faces are presented with a question for the Beauty test (who is more attractive?) and Age test (who is older?). Testretest was adequate only for the Similarity subtest based on the performance of 19 participants. A patient with prosopagnosia performed two standard deviations below the healthy adult sample. This test is available for research. The Warrington Recognition Memory Test (Warrington 1984) tests memory of faces after a short delay. It begins by asking participants to view a set of faces and words and judge whether each is pleasant or unpleasant. Then, after a delay, participants must decide which face or word in a target-distracter pair they previously saw. Originally standardized with 300 adults, additional
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psychometric testing was conducted with 72 patients with traumatic brain injuries. Internal consistency was adequate; however, as with other face memory tests, the Recognition Memory Test has been criticized because poor performance could be due to general memory impairment. The Wechsler Memory Scales-III (WMS-III) Faces subtest (Wechsler 1997) is a measure of face memory and recognition for use with individuals aged 16–89 years. As with the CMS, this subtest may be administered in 10 min plus the 25- to 35-min delay. Twenty-four photographs of unfamiliar faces are presented one at a time for 2 sec, followed immediately by a test phase of 48 faces. Adults are asked to respond to each item by indicating whether the face was viewed in the learning phase. After a delay, a second test series is presented in the same way. Correct responses for the Immediate and Delayed faces are totaled to arrive at two scaled scores (immediate and delayed). The scales were normed on a representative sample of over 1000 individuals throughout the United States during the 1990s, with good reliability. The Faces subtest was not included in the most recent edition (WMS-IV) of the battery.
Clinical Uses Measurement of face recognition in a clinical setting may provide useful information about social function as well as memory for social versus nonsocial information. Subtests within larger batteries provide a means for such comparisons and offer standardized scores for comparison with the population. For preschoolers, the K-ABC-II Face Recognition subtest provides a useful measure. Older children may be assessed with tests such as the NEPSY-II Memory for Faces subtest or Children’s Memory Scales Faces subtest. The Weschler Memory Scales-III Faces subtest provides a similar measure for adults. These tests provide a more global assessment of face recognition, but may be influenced by motivation, attention, and repetitive or impulsive responding. As well, they do not provide insight into why face recognition is disrupted. An emerging research
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literature with tests such as the Let’s Face It! Skills Battery and Cambridge Face Memory Test, which provide more focused assessment of face recognition and perception, and current psychometric investigations of biological measures of facial recognition, suggests that such tests may ultimately be available.
See Also ▶ Face Perception ▶ Face Recognition ▶ Memory Assessment
References and Reading Benton, A. L., Sivan, A. B., Varney, N. R., & Spreen, O. (1983). Facial recognition: Stimulus and multiple choice pictures. New York: Oxford University Press. Benton, A. L., Sivan, A. B., Hamsher, K. de. S., Varney, N. R., & Spreen, O. (1994). Facial recognition. In A. L. Benton (Ed.), Contributions to neuropsychological assessment: A clinical manual (2nd ed., pp. 517–552). New York: Oxford University Press. Bodamer, J. (1947). Die Prosopagnosie. Archiv fur Psychiatrie und Zeitschrift fur Neurologie, 179, 6–54. Bowles, D. C., McKone, E., Dawel, A., Duchaine, B., Palermo, R., Schmalzl, L., et al. (2009). Diagnosing prosopagnosia: Effects of ageing, sex, and participant– stimulus ethnic match on the Cambridge face memory test and Cambridge face perception test. Cognitive Neuropsychology, 26, 423–455. Burton, A. M., White, D., & McNeill, A. (2010). The Glasgow face matching test. Behavior Research Methods, 42, 286–291. Cohen, M. J. (1997). Children’s memory scale. San Antonio: The Psychological Corporation. Croydon, A., Pimperton, H., Ewing, L., Duchaine, B. C., & Pellicano, E. (2014). The Cambridge face memory test for children (CFMT-C): A new tool for measuring face recognition skills in childhood. Neuropsychologia, 62, 60–67. Duchaine, B., Germine, L., & Nakayama, K. (2007). Family resemblance: Ten family members with prosopagnosia and within-class object agnosia. Cognitive Neuropsychology, 24, 419–430. Duchaine, B., & Nakayama, K. (2006). The Cambridge face memory test: Results for neurologically intact individuals and an investigation of its validity using inverted face stimuli and prosopagnosic participants. Neuropsychologia, 44, 576–585. Gur, R. C., Richard, J., Hughett, P., Calkins, M. E., Macy, L., Bilker, W. B., et al. (2010). A cognitive
Facial Recognition Tests neuroscience-based computerized battery for efficient measurement of individual differences: Standardization and initial construct validation. Journal of Neuroscience Methods, 187, 254–262. Kaufman, A. S., & Kaufman, N. L. (2004). Kaufman assessment battery for children (2nd ed.). Circle Pines: American Guidance Service. Klin, A., Sparrow, S. S., de Bildt, A., Cicchetti, D. V., Cohen, D. J., & Volkmar, F. R. (1999). A normed study of face recognition in autism and related disorders. Journal of Autism and Developmental Disorders, 29, 499–508. Korkman, M., Kirk, U., & Kemp, S. L. (2007). NEPSY II. Clinical and interpretative manual. San Antonio: Psychological Corporation. Langdell, T. (1978). Recognition of faces: An approach to the study of autism. Journal of Child Psychology and Psychiatry, 19, 255–268. Logan, A. J., Wilkinson, F., Wilson, H. R., Gordon, G. E., & Loffler, G. (2016). The Caledonian face test: A new test of face discrimination. Vision Research, 119, 29–41. Moore, T. M., Reise, S. P., Gur, R. E., Hakonarson, H., & Gur, R. C. (2015). Psychometric properties of the Penn computerized neurocognitive battery. Neuropsychology, 29, 235–246. Russell, R., Duchaine, B., & Nakayama, K. (2009). Superrecognisers: People with extraordinary face recognition ability. Psychonomic Bulletin & Review, 16, 252–257. Russell, R., Chatterjee, G., & Nakayama, K. (2012). Developmental prosopagnosia and super-recognition: no special role for surface reflectance processing. Neuropsychologia, 50, 334–340. Swagerman, S. C., de Geus, E. J. C., Kan, K. J., van Bergen, E., Nieuwboer, H. A., Koenis, M. M., et al. (2016). The computerized neurocognitive battery: Validation, aging effects, and heritability across cognitive domains. Neuropsychology, 30, 53–64. Tanaka, J., Lincoln, S., & Hegg, L. (2003). A framework for the study and treatment of face processing deficits in autism. In H. Leder & G. Swartzer (Eds.), The development of face processing. Berlin: Hogrefe Publishers. Thomas, A., Lawler, K., Olson, I. R., & Aguirre, G. K. (2008). The Philadelphia face perception battery. Archives of Clinical Neuropsychology, 23, 175–187. Volkmar, F. R., Sparrow, S. S., Rende, R. D., & Cohen, D. J. (1989). Facial perception in autism. Journal of Child Psychology & Psychiatry, 30, 591–598. Warrington, E. K. (1984). Recognition memory test. Windsor: NFER-Nelson. Wechsler, D. (1997). Wechsler memory scale (3rd ed.). San Antonio: The Psychological Corporation. Wolfe, J. M., Tanaka, J. W., Klaiman, C., Cockburn, J., Herlihy, L., Brown, C., South, M., McPartland, J., Kaiser, M. D., Phillips, R., & Schultz, R. T. (2008). Specific impairment of face-processing abilities in children with autism spectrum disorder using the Let’s Face It! Skills battery. Autism Research, 1, 329–340.
Facilitated Communication
Facilitated Communication Maureen Nevers Center on Disability and Community Inclusion, University of Vermont, Burlington, VT, USA Augmentative Communication Consultant, Center on Disability and Community Inclusion, Burlington, VT, USA
Definition Facilitated communication (FC) is an intervention for individuals with a disability that involves pointing to letters or other materials to communicate using the support of a partner. The supporting partner, or facilitator, may provide emotional, physical, or communicative support as needed by the person with a disability (“FC user”) to produce messages. Although FC can be used with different targets, messages are most often generated by typing on a keyboard.
Historical Background Facilitated communication was first recognized in the 1970s in Australia with the work of Rosemary Crossley. Crossley began to use physical support to help individuals with significant communication and motor impairments to type (Biklen 1990). In 1990 Douglas Biklen published his experience with facilitated communication while at the Dignity through Education and Learning Communication (DEAL) Center in Australia. He shared his changed perspective about the assumptions of the cognitive abilities of individuals with autism and the possibility that their speech challenges were a function of motor expression (Biklen 1990). In 1992 Biklen helped to establish the Facilitation Communication Institute (FCI) at Syracuse University (Syracuse University Institute for Community Inclusion). In the years that followed, FC use expanded, as did the professional community’s concerns about this unproven intervention. In 1992 Cummins et al. published a critical response
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to Biklen’s article, challenging many of Biklen’s claims. They concluded their commentary by saying “to imply in the absence of empirical validation that this technique can break through the severe communication difficulties of children with autistic or other severe developmental disabilities raises serious ethical concerns” (Cummins and Prior 1992, p. 240). Even as the use of FC was growing in the 1990s, it continued to be surrounded by controversy related to its validity. Over the next nearly 30 years, numerous studies demonstrated the influence of the facilitator on the messages produced (Hemsley et al. 2018a). Researchers showed again and again that the messages were being authored by facilitator and not the FC user and were therefore denying the person their right to communicate (Hemsley et al. 2018a). By 2020 many professional organizations had officially denounced FC as a valid communication intervention (American Speech-Language-Hearing Association n.d.). FC, or typing to communicate, continues to be supported by the Institute for Community Inclusion (ICI, formerly FCI) and still practiced by some individuals.
Rationale or Underlying Theory In her 1994 book on facilitated communication, Rosemary Crossley explained that “a partner (facilitator) helps the communication aid user overcome physical problems and develop functional movement patterns” (p. 3).
Goals and Objectives The training materials associated with FC (Institute for Community Inclusion Training Standards Manual 2000) indicate that the goal of FC is independent typing, which may include speech or reading of the typed message aloud. The Standards state that FC user should be able to demonstrate that they are the source of the typed messages, so that there is no suspicion that it is the facilitator who is influencing the content. Some of the ways described in the Manual include
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having the FC user type a message not known to the facilitator or completing some typing tasks independently. The FC user and facilitator are expected to regularly practice activities to build independence. A successful, independent communicator would need to access their communication support, initiate communication, and produce accurate messages without the support of the facilitator (ICITSM 2000).
Treatment Participants According to information from the Institute for Community Inclusion (ICI) at Syracuse University, people who have significant communication impairment resulting in limited or impaired speech and who are not able to point reliably without support would be potential candidates for FC. The individuals who use this method are referred to as FC user, typer, or communication aid user. Information from the ICI Training Standards Manual (2000) recommends that consideration for the use of FC begin with an assessment which looks at present and past communication, movement, and support strategies. An experienced facilitator trained in assessment would help determine the benefit of FC and possibly develop recommendations for its use (ICI Training Standards 2000).
Treatment Procedures According to the Institute for Community Inclusion’s website entry on supported typing, FC involves the use of a facilitator to help the person with a disability to communicate by pointing at a target. The target can be an object or picture, but most often it is a letter. The letters are accessed from a paper-based or electronic communication display. The ICI Training Standards Manual (ICI) recommends that multiple people be trained as facilitators to increase the validity of the process. The person who provides the support to the person typing is known as the facilitator. Facilitators are expected to receive training on their role, learning about the specific methods of
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assistance that are part of FC. The Institute for Community Inclusion’s Training Standards Manual provides the information below regarding the individualized physical, emotional, or communicative support provided by facilitators. The facilitator’s physical support is usually provided at the hand, wrist, arm, or shoulder. The facilitator provides resistance, stability, and movement to increase the accuracy of the FC user’s point. Emotional support includes encouragement, motivation, and sharing an expectation of competence. It also promotes focus and attention. Communicative support by the facilitator includes monitoring and providing prompts and feedback to make sure the user’s selections and message are clear. A wide range of support is acceptable with FC, and ultimately the specific format of assistance is based on the FC user’s motor and emotional needs. Depending on the nature and severity of the persons’ motor and emotional needs, it may take considerable time for FC to be successful even with the supports described.
Efficacy Information Questions about the validity of facilitated communication have focused primarily on authorship, determining whether the typed messages are produced by the FC user or if they are a product of the facilitator (Hemsley et al. 2018a). In a review of the FC research, Hemsley et al. (2018a) noted that the primary method for testing the authenticity of the supported typing process is to see if the person typing can share information that is unknown to the facilitator. In 2014 Schlosser et al. published the results of their comprehensive review of peer-reviewed research establishing the source of messages generated through facilitated communication. The review was conducted by a committee of members of the International Society of Augmentative and Alternative Communication (ISAAC) in order to inform the organization’s Position Statement on Facilitated Communication (Schlosser et al. 2014). Relevant materials for the review were initially identified for the use of the term facilitated
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communication or equivalent (e.g., supported typing, assisted typing) (Schlosser et al. 2014). Materials were then compared to a checklist of features to identify those that met the committee’s established criteria for inclusion in the review. Many of the materials originally identified did not focus on FC or were not published in a peerreviewed journal and therefore were excluded (Schlosser et al. 2014). In addition, papers needed to include conditions that allowed for the identification of the source of the typed messages. They had to be able to establish authorship of the communication to be included in the review. A total of seven products, both systematic reviews and individual studies, met the checklist criteria and were included in the analysis. Schlosser et al. reported that the results of every one of the reviewed sources established that the facilitator was the source of the typed messages. The messages generated were influenced by the facilitator and did not reflect the communication of the person with a disability. Considered together, the reviewed materials contained strong evidence for the conclusion that the facilitator, and not the FC user, was the author of the messages (Hemsley et al. 2018a). The results of this review were used as the basis of ISAAC’s Facilitated Communication Position Statement of 2014. This paper reads “ISAAC does not support FC as a valid form of AAC. . .The weight of evidence does not support FC and therefore it cannot be recommended for use in clinical practice” (ISAAC 2014, p. 358). In 2017–2018 the American Speech Language Hearing Association (ASHA) created an Ad Hoc Committee to review the research related to authorship to inform an updated Position Paper on Facilitated Communication. The Committee found that since the work of Schlosser et al. in 2014, no new research had been conducted that included conditions for establishing authorship using facilitated communication (Hemsley et al. 2018a). Schlosser et al. had stated that “Results indicated unequivocal evidence for facilitator control: messages generated through FC are authored by the facilitators rather than the individuals with disabilities. Hence, FC is a technique that has no validity” (Schlosser et al. 2014, p. 2). In 2018 ASHA updated its 1995 Position Paper on
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Facilitated Communication to reflect a stronger statement. The 2018 Position Paper ASHA stated: Facilitated Communication (FC) is a discredited technique that should not be used. There is no scientific evidence of the validity of FC, and there is extensive scientific evidence—produced over several decades and across several countries—that messages are authored by the “facilitator” rather than the person with a disability. (ASHA 2018)
In addition, ASHA’s Position Paper (2018) expressed concerns over the use of FC and identified sources of potential harm. When FC is inaccurately used to represent the person’s communication they are denied the right to communicate their own thoughts. In addition, focus on FC can reduce the time and financial resources available to pursue other AAC tools and interventions which have been proven effective. ASHA cautions that SLPs have a responsibility to share the issues related to FC and share other AAC options (ASHA 2018). A number of other professional organizations and advocacy groups have officially denounced the practice of FC based on lack of research to support its claims of authorship. These include American Academy of Pediatrics, American Psychological Association, Association for Behavior Analysis, Association for Science in Autism Treatment, Speech-Language and Audiology Canada, and American Association on Intellectual and Developmental Disabilities (ASHA n.d.)
Outcome Measurement In the ICI Training Standards Manual (2000), the ICI indicates a range of factors which can affect the FC user’s progress toward independence, including those related to the user (e.g., motor skills), the facilitator (their skill, experience, availability), access to the right tools and environments, and practice opportunities. The ICI Training Standards Manual (2000) offers the following suggestions for documentation as indicators of valid application of FC: similar patterns of spelling and typographical errors across facilitators; typing of similar topics and
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Qualifications of Treatment Providers Facilitators are expected to receive training on the proper way to provide support without influencing the FC user. “All facilitators should participate in a supervised training process so that they learn the appropriate and correct skills for supporting a person in his or her communication” (ICI Training Standards Manual 2000, p. 11). The ICI Training Standards Manual (2000) provides information for facilitators on best practices and methods of moving toward independence.
See Also ▶ Augmentative and Assistive Technology ▶ Nonverbal Communication ▶ Visual Supports
References and Reading American Association on Intellectual and Developmental Disabilities. (2019). Facilitated communication and rapid prompting method: Position statement of the AAIDD board of directors. https://www.aaidd.org/ news-policy/policy/position-statements/facilitated-com munication-and-rapid-prompting-method American Speech-Language-Hearing Association. (2018). Facilitated communication [Position statement]. www. asha.org/policy/ American Speech-Language-Hearing Association. (n.d.). A number of organizations caution against use of FC and RPM. https://www.asha.org/SLP/CautionsAgainst-Use-of-FC-and-RPM-Widely-Shared/ Biklen, D. (1990). Communication unbound: Autism and praxis. Harvard Educational Review, 60(3), 291–314. ProQuest Central.
Faciogenital Dysplasia Biklen, D., Winston Morton, M., Gold, D., Berrigan, C., & Swaminathan, S. (1992). Facilitated communication: Implications for individuals with autism. Topics in Language Disorders, 12(4), 1–28. Crossley, R. (1994). Facilitated communication training. New York: Teachers College Press. Cummins, R. A., & Prior, M. P. (1992). Autism and assisted communication: A response to Biklen. Harvard Educational Review, 62(2), 228–241. https://doi. org/10.17763/haer.62.2.p86j205360177322. Hemsley, B., Bryant, L., Schlosser, R. W., Shane, H. C., Lang, R., Paul, D., & Ireland, M. (2018a). Systematic review of facilitated communication 2014–2018 finds no new evidence that messages delivered using facilitated communication are authored by the person with disability. Autism & Developmental Language Impairments. https://doi.org/10.1177/2396941518821570. Hemsley, B., Shane, H. C., Todd, J. T., Schlosser, R. W., & Lang, R. (2018b). It’s time to stop exposing people to the dangers of facilitated communication. https:// theconversation.com/its-time-to-stop-exposing-peopl e-to-the-dangers-of-facilitated-communication-95942 Institute on Community Inclusion. (2000). ICI training standards manual. Retrieved January 21, 2020, from Syracuse University’s website: https://ici.syr.edu/wpcontent/uploads/2018/09/Training_Standards_Manual. pdf Institute on Community Inclusion. (n.d.). https://ici.syr. edu/ International Society for Augmentative and Alternative Communication. (2014). ISAAC position statement on facilitated communication. Augmentative and Alternative Communication, 30(4), 357–358. https://doi. org/10.3109/07434618.2014.971492. Schlosser, R., Balandin, S., Hemsley, B., Iacono, T., Probst, P., & von Tetzchner, S. (2014). Facilitated communication and authorship: A systematic review. Augmentative and Alternative Communication, 30(4), 359–368. Singer, G., Horner, R., Dunlap, G., & Wang, M. (2015). Standards of proof: TASH, facilitated communication, and the science-based practices movement. Research and Practice for Persons with Severe Disabilities, 39, 178–188. https://doi.org/10.1177/1540796914558831.
Faciogenital Dysplasia ▶ Aarskog Syndrome
Fact Memory ▶ Explicit Memory
Factor Analysis
Factor Analysis Stelios Georgiades1, Thomas Frazier2,3 and Eric Duku1 1 Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada 2 Autism Speaks, New York, NY, USA 3 Cleveland Clinic Children’s, Cleveland, OH, USA
Definition Factor analysis refers to statistical methods that attempt to represent a set of correlated observed variables (questionnaire items, autism symptoms, etc.) using a smaller number of unobserved (latent) variables, also called factors or dimensions. The ultimate goal of this technique is to determine the number and composition of factors or dimensions that can parsimoniously represent a larger set of variables.
Historical Background Factor analytic methods began with Charles Spearman’s observation that specific measures of children’s academic performance and cognitive skills were correlated. He developed a theory that the correlations between different measures of ability were due to a single underlying common factor or dimension that he called “g” or general intelligence (Spearman 1904). Subsequent uses of factor analysis examined whether multiple components of intelligence can be identified, such as verbal and nonverbal abilities. As factor analytic techniques became more popular, their use expanded to other sets of variables, including psychiatric symptoms. Factor analytic studies of psychiatric symptoms have been helpful in identifying the “boundaries” of psychiatric conditions and separating important core markers and components from peripheral or associated symptoms. One of the earliest uses of factor analysis in autism was a study by Freeman et al. (1980)
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examining the factor structure of a behavior observation scale. Since then, numerous studies have used factor analysis methods in samples of individuals with autism. A search of the terms autism and factor analysis in PubMed yields more than 800 hits. The majority of studies examine autism and/or other behavioral symptoms, often using specific measures designed to assist in screening, diagnosing, or tracking of symptoms in individuals with autism. Existing literature suggests that the primary use of factor analysis has been to better understand which autism symptoms group/cluster together to form broader symptom domains. The other major use has been to evaluate how many dimensions are captured by a specific instrument, typically a measure of autism symptoms. Most instruments do not include all autism symptoms or include some items that are not considered core and/or unique aspects of autism (e.g., selfinjurious behavior). Thus, results of factor analytic studies examining specific instruments are mainly useful for scoring the instrument rather than understanding the core symptom domains in autism. The exceptions are factor analyses of measures which comprehensively cover autism symptoms, such as the Autism Diagnostic Interview-Revised (Lord et al. 1994). There are two general categories of factor analyses: exploratory and confirmatory factor analyses. The main difference in these categories is that, in exploratory analyses, variables are used with no preconceived notion or hypothesis of which variables will measure which factor. In confirmatory factor analysis, the researcher specifies, a priori, which variables will measure which factors and whether factors will be correlated. Researchers can be even more specific by explicitly “fixing” how an item or symptom will load on a given factor. Thus, confirmatory methods are more rigorous in that a researcher must have a hypothesis prior to conducting the analysis. Confirmatory methods are also useful in that multiple models that correspond to different hypotheses can be specified and tested. Direct comparisons of the fit of these models to the data provide evidence of the relative plausibility of the hypotheses. However, because not all possible
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Factor Analysis
models can be tested in confirmatory factor analyses, the better fitting model is not viewed as the “truth” but rather a better approximation of reality. Both exploratory and confirmatory approaches consist of multiple methods for estimating the factor structure, including estimating model fit to the observed variables. Norris and Lecavalier (2010) provide an excellent overview of exploratory factor analytic methods and recommendations for their use in autism and other developmental disorders. Tabachnick and Fidell (2016) provide a very accessible introduction to both exploratory and confirmatory methods. Although principal components analysis (PCA) is not strictly a factor analysis method, results are often highly similar, and this method is included when discussing previous factor analytic studies of autism. Conceptual differences are that PCA involves analyzing all the variance in the indicators whereas factor analysis involves analyzing the common variance (covariance) in the indicators. Differences between the two methodologies are discussed in detail in Tabachnick and Fidell (2016).
Current Knowledge Defining the Autism Symptom Phenotype Factor analytic methods have helped to clarify the nature and underlying structure of autism
symptoms. Although there is not perfect agreement across studies, a consensus has emerged as to the number and composition of autism symptom dimensions. Based on accumulating evidence from factor analytic studies (Frazier et al. 2008; Snow et al. 2009; van Lang et al. 2006), the new diagnostic criteria for ASD include two domains (DSM-5) instead of three domains (DSM IV) – a combined social communication domain and a restricted, repetitive behavior domain (American Psychiatric Association 2013). Several investigations have since used factor analytic techniques to evaluate the underlying structure of the new criteria (2-factor model; see Fig. 1). Since these analyses were based on a priori hypotheses, confirmatory factor analysis was used in most studies (Guthrie et al. 2013; Sipes and Matson 2014; Mandy et al. 2012). Although these types of studies can be informative, results are mostly driven by the samples used; and in the absence of new samples assembled using the DSM-5 criteria, these results need to be interpreted with caution and replicated in future studies. As Kim et al. (2017) note, instead of continuing to debate the optimal number of factors at a given level of the ASD phenotype, perhaps there is value in examining the underlying structural hierarchy in its symptomatology. Such a hierarchical approach would inform our understanding on how features from differing levels (core diagnostic domains, subdomains, related and/or comorbid phenotypes) come together to
ASD Domain B Restricted, Repetitive Beahvior
Domain A Social Communication Subdomain A.1 Symptom A.1.1
Symptom A.1.2
Subdomain A.2 Symptom A.2.1
Symptom A.2.2
Factor Analysis, Fig. 1 Schematic of the ASD factor structure with diagnosis at the top level, followed by the two primary diagnostic symptom domains (as per DSM-5) and subdomains and individual symptoms. Due to the lack
Subdomain B.1 Symptom B.1.1
Symptom B.1.2
Subdomain B.2 Symptom B.2.1
Symptom B.2.2
of agreement across studies regarding the exact number and composition of subdomains and symptoms, those are not specified/named in this figure
Factor Analysis
make up the complex clinical profiles seen in individuals with ASD. Assessment Tools Used in Autism As previously noted, factor analysis has also been used in studies evaluating the number and composition of factors/dimensions captured by a specific instrument, typically a measure of autism symptoms (Constantino et al. 2004; Slappendel et al. 2016). There are hundreds of published studies describing the use of factor analytic methods to evaluate the structure of different tools measuring autism-related constructs. Since this section is not meant to serve as a review of the literature, we highlight here an example of a tool – the Social Responsiveness Scale (SRS) – that has triggered constructive debate among researchers over the past decade or so. Several studies examining the SRS, a quantitative trait measure of autism symptoms, have identified only a single autism symptom factor with predominantly social difficulties represented (Cheon et al. 2016; Constantino et al. 2004; Constantino and Gruber 2005). This discrepancy between a single symptom severity model and the 2-factor model in the DSM-5 (also see Fig. 1) is most likely due to the predominance of social indicators examined on the Social Responsiveness Scale and the focus on population samples rather than mixed clinical or autism-only samples. Specifically, the discrepancy between a single autism factor and separate social communication and restricted/ repetitive dimensions is at least partly due to the fact that, in population samples, the majority of healthy individuals will show less variation in symptom levels, while a small minority of mostly affected individuals will show very large variations in symptom levels. This pattern exaggerates correlations among social communication and restricted/repetitive behavior symptoms, resulting in a single symptom severity factor. In a more recent study of the SRS-2, Frazier et al. (2014) showed that both a two-factor structure (as per DSM-5) and a three-factor structure (DSM-5 but with restricted, repetitive behavior
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factor breaking into two factors, insistence on sameness and repetitive mannerisms; see also Georgiades et al. 2007) provided acceptable model fits using confirmatory factor analysis. Of note were the intercorrelations among symptom factors/domains, something that suggests that understanding their co-emergence remains a high priority in conceptualizing common clinical profiles and causal mechanisms underlying ASD (Frazier et al. 2014). Grouping Variables and Individuals One of the recent and promising methodological advances in the field has been the use of novel techniques that simultaneously examine categorical and dimensional models of ASD. By using factor mixture modeling, a method that includes factor analysis and latent class analysis, researchers are able to move away from the simplistic debate of “categories versus dimensions” and examine phenotypic models of ASD that integrate the two. By doing so, empirical methods can be used to begin to disentangle the observed heterogeneity in ASD and examine how both, variables and individuals, come together to form more homogeneous dimensions and subgroups within the autism spectrum (Frazier et al. 2010; Georgiades et al. 2013).
Future Directions In an era where Big Data is becoming more and more prevalent, factor analytic studies may prove to be very useful in “reducing” information without losing the necessary components that can describe the nature and structure of the ASD phenotype. Such an approach, combined with new methods used for the identification of homogeneous subgroups (Frazier et al. 2010; Georgiades et al. 2013) within the spectrum, has the potential to inform the development of more precise diagnostics and interventions. To truly understand the causes, diagnosis, and prognosis of ASD, prospective factor analytic studies are needed to examine the hierarchical structure of the revised DSM-5 criteria in relation
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to time (Georgiades et al. 2017). Such studies can also examine the associations and potential interactions between the core autism domains and symptoms (see Fig. 1) and the “clinical specifiers” (e.g., intellectual and language impairment, comorbid disorders, etc.) introduced in the DSM-5. Factor analytic methods could also be expanded to better understand underlying neurobiological characteristics. For example, different methods may be useful for reducing genetic variants and/or imaging signals variants to a smaller set of dimensions that may correspond with specific symptom and/or behavior profiles. Future work using factor analyses and related methods would benefit from using empirically supported approaches for determining the number and composition of dimensions. It is clear that a large number of previous studies did not use empirically recommended approaches to conducting analyses. In future work, it will be important that researchers follow general recommendations for implementing both exploratory and confirmatory methods (McCutcheon 2002; Muthén 2001; Norris and Lecavalier 2010; Ruscio et al. 2006).
See Also ▶ Latent Variable Modeling ▶ Statistical Approaches to Subtyping
References and Reading American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: APA Press. Cheon, K. A., Park, J. I., Koh, Y. J., Song, J., Hong, H. J., Kim, Y. K., . . . Paik, K. C. (2016). The social responsiveness scale in relation to DSM IVand DSM5 ASD in Korean children. Autism Research, 9(9), 970–980. Constantino, J. N., & Gruber, C. P. (2005). Social responsiveness scale: Manual. Los Angeles: Western Psychological Services. Constantino, J. N., Gruber, C. P., Davis, S., Hayes, S., Passanante, N., & Przybeck, T. (2004). The factor structure of autistic traits. Journal of Child Psychology and Psychiatry, 45(4), 719–726.
Factor Analysis Frazier, T. W., Youngstrom, E. A., Kubu, C. S., Sinclair, L., & Rezai, A. (2008). Exploratory and confirmatory factor analysis of the autism diagnostic interview- revised. Journal of Autism and Developmental Disorders, 38(3), 474–480. Frazier, T. W., Youngstrom, E. A., Sinclair, L., Kubu, C. S., Law, P., & Rezai, A. (2010). Autism spectrum disorders as a qualitatively distinct category from typical behavior in a large, clinically ascertained sample. Assessment, 17(3), 308–320. Frazier, T. W., Ratliff, K. R., Gruber, C., Zhang, Y., Law, P. A., & Constantino, J. N. (2014). Confirmatory factor analytic structure and measurement invariance of quantitative autistic traits measured by the Social Responsiveness Scale-2. Autism, 18(1), 31–44. Freeman, B. J., Schroth, P., Ritvo, E., Guthrie, D., & Wake, L. (1980). The behavior observation scale for autism (BOS): Initial results of factor analysis. Journal of Autism and Developmental Disorders, 10(3), 343–346. Georgiades, S., Szatmari, P., Zwaigenbaum, L., Duku, E., Bryson, S., Roberts, W., . . . Mahoney, W. (2007). Structure of the autism symptom phenotype: A proposed multidimensional model. Journal of the American Academy of Child & Adolescent Psychiatry, 46(2), 188–196. Georgiades, S., Szatmari, P., Boyle, M., Hanna, S., Duku, E., Zwaigenbaum, L., . . . Smith, I. (2013). Investigating phenotypic heterogeneity in children with autism spectrum disorder: A factor mixture modeling approach. Journal of Child Psychology and Psychiatry, 54(2), 206–215. Georgiades, S., Bishop, S. L., & Frazier, T. (2017). Editorial perspective: Longitudinal research in autism–introducing the concept of ‘chronogeneity’. Journal of Child Psychology and Psychiatry, 58(5), 634–636. Guthrie, W., Swineford, L. B., Wetherby, A. M., & Lord, C. (2013). Comparison of DSM-IV and DSM-5 factor structure models for toddlers with autism spectrum disorder. Journal of the American Academy of Child & Adolescent Psychiatry, 52(8), 797–805. Kim, H., Keifer, C. M., Rodriguez-Seijas, C., Eaton, N. R., Lerner, M. D., & Gadow, K. D. (2017). Structural hierarchy of autism spectrum disorder symptoms: an integrative framework. Journal of Child Psychology and Psychiatry. https://doi.org/10.1111/jcpp.12698 Lord, C., Rutter, M., & Le Couteur, A. (1994). Autism Diagnostic Interview-Revised: A revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. Journal of Autism and Developmental Disorders, 24(5), 659–685. Mandy, W. P., Charman, T., & Skuse, D. H. (2012). Testing the construct validity of proposed criteria for DSM-5 autism spectrum disorder. Journal of the American Academy of Child & Adolescent Psychiatry, 51(1), 41–50. McCutcheon, A. L. (2002). Basic concepts and procedures in single- and multiple-group latent class analysis.
Factors Affecting Outcomes In J. A. Hagenaars & A. L. McCutcheon (Eds.), Applied latent class analysis (pp. 56–85). Cambridge, UK: Cambridge University Press. Muthén, B. O. (2001). Second-generation structural equation modeling with a combination of categorical and continuous latent variables: New opportunities for latent class/latent growth modeling. In A. Sayer & Collins (Eds.), New methods for the analysis of change (pp. 291–322). Washington, DC: American Psychological Association. Norris, M., & Lecavalier, L. (2010). Evaluating the use of exploratory factor analysis in developmental disability psychological research. Journal of Autism and Developmental Disorders, 40(1), 8–20. Ruscio, J., Haslam, N., & Ruscio, A. M. (2006). An introduction to the taxometric method: A practical guide. Mahwah: Lawrence Erlbaum Associates. Sipes, M., & Matson, J. L. (2014). Factor structure for autism spectrum disorders with toddlers using DSM-IV and DSM-5 criteria. Journal of Autism and Developmental Disorders, 44(3), 636–647. Slappendel, G., Mandy, W., van der Ende, J., Verhulst, F. C., van der Sijde, A., Duvekot, J., . . . Greaves-Lord, K. (2016). Utility of the 3Di short version for the diagnostic assessment of autism spectrum disorder and compatibility with DSM-5. Journal of Autism and Developmental Disorders, 46(5), 1834–1846. Snow, A. V., Lecavalier, L., & Houts, C. (2009). The structure of the autism diagnostic interview-revised: Diagnostic and phenotypic implications. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 50(6), 734–742. Spearman, C. (1904). “General intelligence,” objectively determined and measured. The American Journal of Psychology, 15(2), 201–292. Tabachnick, B. G., & Fidell, L. S. (2016). Using multivariate statistics (6th ed.). Boston: Pearson/Allyn & Bacon. van Lang, N. D. J., Boomsma, A., Sytema, S., Bildt, A. A., Kraijer, D. W., & Ketelaars, C. (2006). Structural equation analysis of a hypothesized symptom model in the autism spectrum. Journal of Child Psychology and Psychiatry, 47, 37–44.
Factor Mixture Modeling ▶ Latent Variable Modeling
Factors Affecting Outcome ▶ Course of Development
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Factors Affecting Outcomes Patricia Howlin and Sarah Savage Institute of Psychiatry, Psychology and Neuroscience King’s College, London, UK
Definition The outcome for individuals with autism is very variable and depends not only on the characteristics of the individual but on many family and environmental factors and external support systems. Childhood factors that are associated with later outcome include early language development, IQ, severity of autism symptoms, and access to appropriate autism-specific intervention. In adolescence and adulthood, the onset of comorbid mental health problems and the lack of adequate support and interventions are associated with poorer outcomes.
Historical Background It has been evident since the very earliest followup studies of Eisenberg (1956), Rutter et al. (1967), Kanner (1973), and others that outcome in autism is highly variable. Many individuals remain highly dependent on support throughout their lives; others are able to live independently and some go on to make significant achievements. Both Asperger (1944) and Kanner (1971) were struck by the wide heterogeneity in outcome among individuals with autism. Kanner (1971), for example, noted that “The results of the followup after about 30 years . . . invite serious curiosities about the departures from the initial likeness, ranging all the way from complete deterioration to a combination of occupational adequacy with limited, though superficially smooth social adjustment.” Similarly, Asperger (1944) commented that although the social integration of individuals with autism might be expected to be extremely difficult, if not impossible, this was the case only for a minority and “almost exclusively in
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those people with considerable intellectual retardation in addition to autism . . . This is not so with intellectually intact autistic individuals.” Indeed, he suggested that “able autistic individuals can rise to eminent positions and perform with such outstanding success that one may even conclude that only such people are capable of certain achievements.” The factors affecting outcome, and the reasons why some individuals are able to make very good progress while others continue to have significant problems throughout their lives, has been the focus of considerable research over the years.
Current Knowledge Although many follow-up studies have indicated a relatively poor outcome for individuals with autism, the results of such research cannot be interpreted without detailed information on the characteristics of the individuals involved. Kanner (1943) and Asperger (1944) were among the first to note a possible association between higher intellectual ability and greater achievements in adulthood. Subsequent follow-up studies have confirmed that childhood IQ is highly correlated with adult outcome. For example, in a series of studies conducted in the UK in the late 1960s and early 1970s, Rutter and colleagues (Lockyer and Rutter 1969; Rutter et al. 1967) found that individuals with an IQ below 60 were far more likely to have a “poor” or “very poor” outcome than those with an IQ above 60. Individuals with an IQ of at least 70 (i.e., at the lower end of the normal range) tended to be rated as having a “fair” outcome, while those with a “good outcome” had an average IQ above 80. More recent studies, involving individuals across the IQ range (i.e., from severe cognitive impairment to superior intelligence), have also noted a strong association between IQ and outcome, with very few individuals with a childhood IQ below 75 living independently as adults (see Howlin and Magiati 2017; Magiati et al. 2014; Steinhausen et al. 2016 for reviews). However, childhood IQ alone is not the only factor affecting outcome (Bal et al. 2015; Lord et al. 2015), and many individuals
Factors Affecting Outcomes
with a high IQ in childhood still do poorly in adulthood. Thus, it seems that while only individuals with a childhood IQ in the average range or above do well in adulthood, even among those with an IQ above 70, outcome can still vary markedly (Steinhausen et al. 2016). The range of early cognitive abilities may also be important, in that children who are able to score only on nonverbal tests, or on a very limited range of subtests – even if their scores on these assessments are within the normal range – tend to have a poorer prognosis than those able to score on both verbal and nonverbal tests in early childhood (Howlin et al. 2014). Another crucial indicator of later outcome is early language development and it is now well established that there is a strong link between early language abilities and subsequent development (Magiati et al. 2014). Thus, the majority of individuals who go on to do well as adults have usually developed at least some useful speech by the age of 5 years. However, there are exceptions to this, and some children with significant early delays in language later catch up and may make good progress more generally. Overall, however, language impairments, particularly if coupled with low IQ, are the factors most strongly associated with a poorer adult outcome. In adulthood, individuals with good verbal comprehension, good spoken language, and a verbal IQ in the normal range are significantly more likely to be functioning well socially than those who are impaired in these areas (Howlin et al. 2014). There also appears to be an association between the severity of early autistic symptomatology and later outcome, although findings here are inconsistent. Indeed, predicting later adult outcomes from early child characteristics remains highly challenging (Howlin and Magiati 2017; Lord et al. 2015). There is a need to focus more research on different patterns of developmental trajectories in autism, as such knowledge may be important for identifying different genetic aetiologies as well as having implications for more individually tailored interventions (Lord et al. 2015). Although many follow-up studies suggest that the severity of autistic symptoms tends to reduce
Factors Affecting Outcomes
with age, the overall level of autistic symptomatology in adulthood continues to affect social and economic independence (Gillberg et al. 2016; Howlin et al. 2013; Lord et al. 2015). The presence of mental health problems can also have a very negative impact on outcome, as well as proving difficult to treat effectively (Howlin and Magiati 2017). Although estimates of the frequency of mental health problems in autism vary widely, it is estimated that at least of half of all individuals with autism suffer from significant emotional and psychiatric difficulties (Croen et al. 2015; Russell et al. 2016; Moss et al. 2015). The most frequent problems relate to stress, anxiety, and depression, but attention deficit disorders and obsessive compulsive disorders are also common. Physical disorders, including epilepsy, are more frequent than in the general population (Croen et al. 2015) and these too can have a negative impact on adult functioning. Data on the effects of ageing remain limited. Although overall severity of autism symptoms tends to reduce between early childhood and early adulthood, there are few studies of trajectories of change in older individuals. Nevertheless, some small-scale studies suggest that certain cognitive skills (notably visual and working memory) may be less prone to decline in elderly people with autism than in the general population (Lever and Geurts 2016). There is also some indication that quality of life in ASD is less affected by age than in the general population (van Heijst and Geurts 2015), but to date, studies on ageing in ASD are small scale and involve few participants aged over 60. The role of gender remains uncertain since most follow-up studies have involved so few females. There are some suggestions that women with autism have poorer social outcomes, especially with respect to employment (Taylor et al. 2015) and quality of life (Bishop-Fitzpatrick et al. 2016). However, other studies (Kirby et al. 2016; Woodman et al. 2015) have identified no significant impact of gender. The role of external, environmental factors on outcome is yet another area in need of much greater research. Although, to date, there is no evidence that early, highly intensive behavioral
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or other interventions make a significant difference to functioning in adulthood, it is likely that the quality of early intervention, access to appropriate education programs, and support for families will have an important impact on later life. There are indications, too, that mental health difficulties in adulthood may be associated with environmental disturbance, such as the transition from school, difficulties in coping with college work or employment, family disruption and loss, and change and unpredictability in daily life. The paucity of appropriate adult services, especially for individuals of average IQ, is also likely to be another reason why adults with ASD are more economically, educationally, and socially disadvantaged than adults with other developmental or intellectual disabilities (Roux et al. 2013). Recent studies also suggest the importance of a wide range of other child and adult factors associated with social functioning and autism symptom severity in adulthood. These include bullying, extent of inclusion in school, maternal warmth/quality of mother–child relationships, physical health, independence in daily living skills, executive function, stress, physical health, and adaptive behavior skills (see Howlin and Magiati 2017 for review).
Future Directions Long-term follow-up studies over the past 50 years have taught us a great deal about the factors that are associated with outcome in adulthood. However, many issues still remain to be resolved. Thus, although there is good evidence that language, IQ, and severity of autism are all linked to longer-term prognosis, we still know relatively little about how these factors interact, or how far environmental, genetic, family, social, educational, racial, or other variables may ameliorate or exacerbate their impact. The role of temperament and personality is almost unexplored, and almost nothing is known about factors that influence functioning in the later years of life. Research into effective treatments has led to major improvements in early intervention for
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many children. However, how long the effects of such interventions endure remains unknown, and much longer-term follow-up studies are needed if we are to judge the true benefit of these (often very costly) programs. In summary, although research into children with autism has made significant strides forward over recent years, leading to major advances in diagnosis, understanding of causes, and treatment, research into adulthood still has a long way to go. Follow-up studies of individuals identified and fully assessed as children and followed up periodically from decade to decade are essential if we are to understand the factors that influence prognosis and how these factors exert their effects.
See Also ▶ Adulthood, Transition To ▶ Asperger Syndrome Follow-Up Studies ▶ Employment ▶ Employment in Adult Life ▶ Factors Affecting Outcome ▶ Living Arrangements in Adulthood ▶ Psychotic Disorder
References and Reading Asperger, H. (1944). “Autistic psychopathy” in childhood (U. Frith, Trans., and annotated in Autism and Asperger syndrome (1991)). In U. Frith (Ed.), Autism and asperger syndrome (1st ed., pp. 37–92). Cambridge: Cambridge University Press. Bal, V. H., Kim, S. H., Cheong, D., & Lord, C. (2015). Daily living skills in individuals with autism spectrum disorder from 2 to 21 years of age. Autism, 19(7), 774–784. Bishop-Fitzpatrick, L., Hong, J., Smith, L. E., et al. (2016). Characterizing objective quality of life and normative outcomes in adults with autism spectrum disorder: An exploratory latent class analysis. Journal of Autism and Developmental Disorders, 46(8), 707–719. Croen, L. A., Zerbo, O., Qian, Y., et al. (2015). The health status of adults on the autism spectrum. Autism, 19(7), 814–823. Eisenberg, L. (1956). The autistic child in adolescence. The American Journal of Psychiatry, 112, 607–612. Gillberg, I. C., Helles, A., Billstedt, E., & Gillberg, C. (2016). Boys with Asperger syndrome grow up:
Factors Affecting Outcomes Psychiatric and neurodevelopmental disorders 20 years after initial diagnosis. Journal of Autism and Developmental Disorders, 46(1), 74–82. Howlin, P., & Magiati, I. (2017). Autism spectrum disorder: Outcomes in adulthood. Current Opinion in Psychiatry, 30(2), 69–76. Howlin, P., Moss, P., Savage, S., & Rutter, M. (2013). Social outcomes in mid to later adulthood among individuals diagnosed with autism and average non-verbal IQ as children. Journal of the American Academy of Child and Adolescent Psychiatry, 52(6), 572–581. Howlin, P., Moss, P., Savage, S., Tempier, A., & Rutter, M. (2014). Cognitive and language skills in adults with autism: A 40 year follow-up. Journal of Child Psychology and Psychiatry, 55(1), 49–58. Kanner, L. (1943). Autistic disturbances of affective contact. The Nervous Child, 2, 217–250. Kanner, L. (1971). Follow-up study of eleven autistic children originally reported in 1943. Journal of Autism and Childhood Schizophrenia, 1(2), 119–145. Kanner, L. (1973). How far can autistic children go in matters of social adaptation? In L. Kanner (Ed.), Childhood psychosis: Initial studies and new insights (pp. 189–213). New York: V. H. Winston. Kirby, A. V., Baranek, G. T., & Fox, L. (2016). Longitudinal predictors of outcomes for adults with autism spectrum disorder systematic review. OTJR, 36(2), 55–64. Lever, A. G., & Geurts, H. M. (2016). Age-related differences in cognition across the adult lifespan in autism spectrum disorder. Autism Research, 9, 666–676. Lockyer, L., & Rutter, M. (1969). A five- to fifteen-year follow-up study of infantile psychosis. III. Psychological aspects. The British Journal of Psychiatry, 115, 865–882. Lord, C., Bishop, S., & Anderson, D. (2015). Developmental trajectories as autism phenotypes. American Journal of Medical Genetics Part C: Seminars in Medical Genetics, 169(2), 198–208. Magiati, I., Tay, X. W., & Howlin, P. (2014). Cognitive, language, social and behavioural outcomes in adults with autism spectrum disorders: A systematic review of longitudinal follow-up studies in adulthood. Clinical Psychology Review, 34, 73–86. Moss, P., Howlin, P., Savage, S., Bolton, P., & Rutter, M. (2015). Self and informant reports of mental health difficulties among adults with autism: Findings from a long-term follow-up study. Autism, 19(7), 832–841. Roux, A. M., Shattuck, P. T., Cooper, B. P., et al. (2013). Postsecondary employment experiences among young adults with an autism spectrum disorder. Journal of the American Academy of Child & Adolescent Psychiatry, 52(9), 931–939. Russell, A. J., Murphy, C. M., Wilson, E., et al. (2016). The mental health of individuals referred for assessment of autism spectrum disorder in adulthood: A clinic report. Autism, 20(5), 623–627. Rutter, M., Greenfield, D., & Lockyer, L. (1967). A five to fifteen year follow-up study of infantile psychosis.
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II. Social and behavioural outcome. The British Journal of Psychiatry, 113, 1183–1199. Steinhausen, H. C., Mohr Jensen, C., & Lauritsen, M. B. (2016). A systematic review and meta-analysis of the long-term overall outcome of autism spectrum disorders in adolescence and adulthood. Acta Psychiatrica Scandinavica, 133(6), 445–452. Taylor, J. L., Henninger, N. A., & Mailick, M. R. (2015). Longitudinal patterns of employment and postsecondary education for adults with autism and average-range IQ. Autism, 19(7), 785–793. van Heijst, B. F., & Geurts, H. M. (2015). Quality of life in autism across the lifespan: A meta-analysis. Autism, 19(2), 158–167. Woodman, A. C., Smith, L. E., Greenberg, J. S., & Mailick, M. R. (2015). Change in autism symptoms and maladaptive behaviors in adolescence and adulthood: The role of positive family processes. Journal of Autism and Developmental Disorders, 45(1), 111–126.
repeat them until he does so without error. The adult then instructs the child to spell his name and starts by saying only the first letter out loud and then only “mouthing” (silently moving lips) the remaining letters using no voice. Eventually, the teacher asks the child to spell his name, and he does so without additional prompts. The removal of the verbal prompts illustrates the process of fading.
Fading
References and Reading
Thomas Zane1,3, Traci Lanner2 and Michelle Myers2 1 Van Loan School of Graduate and Professional Studies, Endicott College, The Institute for Behavioral Studies, Beverly, MA, USA 2 The School at Springbrook, Oneonta, NY, USA 3 Department of Applied Behavior Science, University of Kansas, Lawrence, KS, USA
Harper, C. B., Symon, J. B. G., & Frea, W. D. (2008). Recess is time-in: Using peers to improve social skills of children with autism. Journal of Autism and Developmental Disorders, 38(5), 815–826. Krantz, P. J., & McClannahan, L. E. (1998). Social interaction skills for children with autism: A script-fading procedure for beginning readers. Journal of Applied Behavior Analysis, 31, 191–202. Miltenberger, R. (2001). Behavior modification: Principles and procedures (2nd ed.). Belmont: Wadsworth. Patel, M., Piazza, C., Kelly, M., Ochsner, C. A., & Santana, C. (2001). Using a fading procedure to increase fluid consumption in a child with feeding problems. Journal of Applied Behavior Analysis, 34, 357–360.
Synonyms
See Also ▶ Prompt Fading ▶ Prompts
Prompt fading; Stimulus fading
Definition
FAFSA
Fading is the process of reducing assistance (i.e., cues, prompts, supports) until no longer needed so that the skill or response being taught is exhibited independently. In behavioral instruction, fading generally refers to gradually removing supports put in place during training programs so that the target behavior eventually occurs independently. For example, when teaching a child with autism how to spell his name, the teacher starts with stating each letter out loud and having the child
Ernst O. VanBergeijk Vocational Independence Program, New York Institute of Technology, Central Islip, NY, USA Threshold Program, Lesley University, Cambridge, MA, USA
Synonyms Financial aid; Grants; Scholarships
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Definition FAFSA is an acronym which stands for Free Application for Federal Student Aid. FAFSA is the mechanism by which the government distributes grants, loans, and student work study funds. More specifically, it is the gateway by which college students and their families access Federal Pell Grants, Stafford and Perkins Loans, Federal Supplemental Education Opportunity Grants, and Federal Work-Study program funds. By completing the FAFSA form, families learn what their expected family contribution (EFC) will be. The EFC is the amount of money the family is expected to contribute to the student’s college education. Prior to 2008, only students enrolled full time in a degree-bearing program at an Institution of Higher Education (IHE) were eligible to complete the FAFSA application. With the passage of the Higher Education Opportunity Act (HEOA) of 2008 (P.L. 110-315), the regulations governing financial aid and the FAFSA process changed to make it easier for students with intellectual disabilities (ID) to gain access to postsecondary education. The term ID is broadly defined and can include students with autism spectrum disorders or cognitive impairments. According to HEOA, a student with an intellectual disability is one who has mental retardation or a significant cognitive impairment and is or was eligible for a Free and Appropriate Public Education (FAPE) under the Individuals with Disabilities Education Act (IDEA). This can include students who were homeschooled or attended private schools. The college is responsible for the determination and supporting documentation of the student’s ID (Bergeron et al. 2010). In order for a student with an ID to be eligible, he or she must be enrolled in an approved Comprehensive Transition and Postsecondary Program (CTP). He or she also must meet all of the general student eligibility requirements except the following: (1) he or she does not have to be enrolled for the purpose of obtaining a degree or certificate; (2) he or she is not required to have a high school diploma or have passed an ability-tobenefit test; and (3) he or she must maintain
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satisfactory academic progress under school’s policy for students in the CTP (Finkel et al. 2010). After a public commentary period, the US Department of Education began accepting applications from IHE, for approval of their Comprehensive Transition Programs (CTP) in the latter part of 2010. An eligible CTP must be offered by a college that already has an established financial aid program for its general student body. It also must be designed to support students with intellectual disabilities (ID) and include an advising and curriculum structure. The CTP must require students with ID to participate in courses and activities with students without disabilities (Bergeron et al. 2010). Students with ID will be eligible for Federal Pell Grants, Federal Supplemental Education Opportunity Grants, and Federal Work-Study programs funds only (Finkel et al. 2010). It is important to note that students with ID currently are not eligible for Perkins or Stafford loans. US DOE-approved CTPs are listed at the National Coordinating Center and include the Consortium for Postsecondary Education for Individuals with Developmental Disabilities and the Center for Postsecondary Education for Individuals with Intellectual Disabilities.
See Also ▶ Comprehensive Transition Program ▶ Free Appropriate Public Education ▶ Individuals with Disabilities Education Act (IDEA) ▶ Transition Planning
References and Reading Bergeron, D., Murray, B., Shanley, J. L., & Daley, R. (2010). U.S Department of Education state of the art conference on postsecondary education for students with intellectual disabilities. Comprehensive transition and postsecondary (CTP) program: The process and lessons learned. Fairfax: George Mason University. College Preparation Checklist: http://studentaid.ed.gov/ PORTALSWebApp/students/english/checklist.jsp.
False-Belief Task Federal student aid: Funding education beyond high school: The guide to federal student aid. http:// studentaid.ed.gov/students/publications/student_ guide/index.html. Finkel, J., Anderson, H., & Shanley, J. L. (2010). U.S Department of Education state of the art conference on postsecondary education for students with intellectual disabilities. Eligibility of students with intellectual disabilities to receive federal student aid: The FAFSA and more! Fairfax: George Mason University. Free Application for Federal Student Aid. http://www. fafsa.gov Heath Resource Center: Online clearinghouse on post secondary education for individuals with disabilities. New postsecondary programs for students with intellectual disabilities. Student Aid on the Web (http://www.studentaid.ed.gov, www.studentaid.ed.gov) – The U.S. Department of Education’s federal student aid information website. http://www.heath.gwu.edu/.
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References and Reading Denham, S. A., Wyatt, T. M., Bassett, H. H., Echeverria, D., et al. (2009). Assessing social-emotional development in children from a longitudinal perspective. Journal of Epidemiology and Community Health, 63(Suppl 1), i37–i52. Johnson, S., Hollis, C., Hennessy, E., Kochhar, P., et al. (2011). Screening for autism in preterm children: Diagnostic utility of the Social Communication Questionnaire. Archives of Disease in Childhood, 96, 73–77. Neyman, J., & Pearson, E. S. (1967) [1928]. On the use and interpretation of certain test criteria for purposes of statistical inference, Part I. Joint Statistical Papers. Cambridge University Press, pp. 1–66. Posserud, M.-B., Lundervold, A. J., & Gillberg, C. (2009). Validation of the autism spectrum screening questionnaire in a total population sample. Journal of Autism and Developmental Disorders, 39(1), 126–134.
False-Belief Task False Positive
Nirit Bauminger-Zviely School of Education, Bar-Illan University, Ramat-Gan, Israel
Samantha Huestis Yale Child Study Center, New Haven, CT, USA
Definition Synonyms Alpha (α) error; Type I error; Wrong decision
Definition A false positive occurs when the null hypothesis is true and rejected erroneously. That is, a difference is observed when there is no true difference. An individual may be diagnosed with a condition, for example, that he does not have. A test that produces a substantial number of false positives (i.e., Type 1 Errors) is unable to systematically rule out disorders and diagnoses, and consequently has poor specificity.
See Also ▶ Sensitivity and Specificity
False-belief task is based on false-belief understanding which is the understanding that an individual’s belief or representation about the world may contrast with reality. False-belief task is a frequently used methodology to examine theory of mind (i.e., child’s ability to construct people in terms of internal mental states such as their beliefs, Wellman 1993). It is considered as litmus test of theory of mind, in that in such cases, it becomes possible to distinguish unambiguously between the child’s (true) belief and the child’s awareness of someone else’s different (false) belief (Dennett 1978). First-order falsebelief tasks involve attribution about other’s false belief with regard to real events; whereas, secondorder false-belief tasks are related with what people think about other people’s thoughts. In second-order false-belief tasks, the child is required to attribute the false belief of one person based on the thoughts of another (Perner and Wimmer 1985). An example of a commonly used first-order false-belief task is the “Smarties,” in which the child is required to predict another child’s perception about the content of a box
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of candies (that actually includes a pencil) (Gopnik and Astington 1988). A commonly used secondorder false-belief task is the Perner and Wimmer (1985) “ice-cream van story” (or John and Marry tasks). In this story, Marry is asked to predict John’s thoughts about the location of the ice-cream man. Only Marry knows the actual location (in the school), but John knows about his former intention to stay in the park. Thus, Marry needs to take into consideration John’s thoughts about the location of the ice-cream man, based on the ice-cream man’s original intentions.
See Also ▶ Theory of Mind
References and Reading Dennett, D. (1978). Brainstorms: Philosophical essay on mind and psychology. Montgomery: Harvester Press. Gopnik, A., & Astington, J. W. (1988). Children’s understanding of changes in their mental states. Child Development, 62, 98–110. Perner, J., & Wimmer, H. (1985). John thinks that Mary thinks that: Attribution of second-order beliefs by 5-to 10-year-old children. Journal of Experimental Child Psychology, 60, 689–700. Wellman, H. M. (1993). Early understanding of mind: The normal case. In S. Baron-Cohen, H. Tager-Flusberg, & D. J. Cohen (Eds.), Understanding of other minds: Perspectives from autism (pp. 10–39). Oxford: Oxford University Press.
Family Accommodation in Autism Spectrum Disorder Judah Koller1 and Eli R. Lebowitz2 1 Seymour Fox School of Education, Clinical Child Psychology, The Hebrew University of Jerusalem, Jerusalem, Israel 2 Yale School of Medicine, Child Study Center, Yale University, New Haven, CT, USA
Definition Ways in which family members, mostly parents, modify their behavior to help a child avoid or
Family Accommodation in Autism Spectrum Disorder
alleviate distress and negative affect associated with mental health problems (Lebowitz and Bloch 2012; Lebowitz et al. 2014).
Historical Background A sizable body of literature indicates that family accommodation is common among families of children with obsessive-compulsive disorders (OCD) and anxiety disorders. Such accommodation is associated with both proximal and distal negative outcomes. High levels of family accommodation in these populations are associated with increased severity of anxiety or OCD symptoms, poorer psychosocial functioning, and elevated parental distress (Caporino et al. 2012; Lebowitz and Bloch 2012; Lebowitz et al. 2013, 2014; Storch et al. 2007). Additionally, higher levels of family accommodation are predictive of poorer subsequent response to treatment in children with OCD and anxiety disorders (Lebowitz et al. 2016; Kagan et al. 2016). These findings have already yielded clinical benefit through the development of an efficacious parent-based intervention that targets the reduction of family accommodation (Lebowitz et al. in press). This work raises the possibility that the study of family accommodation in children with other disorders could yield similar contributions. Several findings point to a partial phenotypic overlap between autism spectrum disorders (ASD), OCD, and anxiety disorders, in addition to a possible partial overlap in their etiology. The phenotypic overlap exists with regard to the restricted and repetitive behaviors (RRBs) inherent to a diagnosis of ASD. RRBs are defined in the Diagnostic and Statistical Manual of Mental Disorders 5 (DSM5) (APA 2013) as “restricted, repetitive patterns of behavior, interests, or activities. . .” Within OCD, compulsions are defined as “repetitive behaviors or mental acts.” While not synonymous, these two concepts bear resemblance to one another (Jacob et al. 2009; Wood and Gadow 2010). In addition to the partial phenotypic overlap, high comorbidity between these diagnostic constructs has been noted, with greater prevalence of anxiety disorders and OCD
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in children with ASD than in typically developing children (van Steensel et al. 2011). Children of parents with OCD or ASD are at greater risk of ASD or OCD, respectively (Meier et al. 2015), and the likelihood that an individual will be diagnosed with OCD or anxiety disorders is elevated in relatives of children with ASD (Jacob et al. 2009), pointing to some degree of shared heritability. Given the above-noted links between OCD, anxiety disorders, and the RRBs of children with ASD, as well as the potential biological links between the disorders, an examination of family accommodation in the context of autism, specifically regarding RRBs, was undertaken.
Current Knowledge Building on the data related to family accommodation in OCD and anxiety disorders, an examination of family accommodation in the context of RRBs in ASD was deemed worthwhile (Feldman et al. 2019). Applying the concept of accommodation to RRBs, however, is complicated by several factors. Research has suggested the existence of different sub-groups of RRBs, including repetitive motor and sensory behavior, insistence on sameness, ritualistic behavior, compulsive behavior, circumscribed interests, and self-injurious behavior (Honey et al. 2012; Leekam et al. 2011). Notably, different types of RRBs have been linked to different psychiatric symptoms (e.g., anxiety, depression, and oppositionaldefiant symptoms; Lidstone et al. 2014; Stratis and Lecavalier 2013). These and other findings (e.g., Leekam et al. 2011) suggest that the various types of RRBs, and perhaps even different specific RRBs within a single sub-group, may differ in both their etiologies and functions. Furthermore, RRBs may also vary in function and in degree of adaptiveness, indicating that accommodation of these RRBs may be either potentially helpful or detrimental to the child and family system. Certain RRBs may allow the individual to occupy themselves, regulate hyper- or hypo-sensory arousal, or reduce anxiety (Leekam et al. 2011). In some cases, these behaviors can
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even provide a source of income and employment (Attwood 2003; Howlin 2003). Other RRBs, however, such as self-injurious behaviors, are unambiguously harmful. Studies link certain types of RRBs, including preoccupation with object parts, sensory interests, and stereotyped motor behaviors, to poorer reasoning skills, lower adaptive functioning at a later age, and increased caregiver stress (Harrop et al. 2016; Troyb et al. 2016). Further complicating the matter, findings suggest that the same RRBs may have differing etiologies and serve different functions for different children (Leekam et al. 2011; Stratis and Lecavalier 2013). Particularly relevant to the issue of family accommodation in the context of ASD is the role of RRBs in regulating arousal and in alleviating anxiety and distress, similar to the role of compulsive behaviors in OCD (Leekam et al. 2011; Lidstone et al. 2014). It has been proposed that family accommodation of children’s OCD symptoms increases the severity of these symptoms over time by enabling avoidance of distressinducing stimuli and hampering the development of more adaptive strategies for independent regulation of negative arousal (Lebowitz 2013; Storch et al. 2007). To the extent that RRBs might serve to alleviate distress in children with ASD, family accommodation of RRBs may function in a similar manner and could potentially lead to decreased self-regulation. Prior to Feldman et al. (2019), limited work had been done examining the role of family accommodation in ASD. Russel et al. (2013) examined a small group of adolescents and adults (n ¼ 23) with comorbid ASD and OCD who received cognitive behavioral therapy for OCD. In that study, higher reported family accommodation prior to treatment was associated with poorer response to the treatment. Storch et al. (2015) examined the prevalence and correlates of family accommodation of anxiety symptoms in 40 children with ASD and comorbid anxiety disorders. In line with previous findings on family accommodation in anxiety disorders, family accommodation of anxiety symptoms in the context of ASD was found to be highly prevalent and was correlated with the severity of the children’s
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anxiety symptoms. The same study also found that family accommodation decreased following cognitive behavioral therapy, with the reduction in family accommodation associated with the improvement in the children’s anxiety symptoms. Of note, both of the aforementioned studies focused on accommodation of the anxiety or OCD symptoms and did not investigate accommodation of RRBs. The first study to examine family accommodation of core ASD symptoms, specifically RRBs, piloted the use of the Family Accommodation Scale for Restricted and Repetitive Behaviors (FAS-RRB; Feldman et al. 2019). This study found that accommodation of RRBs is highly prevalent, with 80% of parents engaging in family accommodation at least once a month and 55% reporting daily accommodation. These rates of accommodation are high, but lower than those reported in pediatric OCD (Lebowitz et al. 2016) and anxiety disorders (Lebowitz et al. 2013, 2014, 2016; Thompson-Hollands et al. 2014), as well as those reported for anxiety symptoms in children with ASD (Storch et al. 2015). The most common accommodations of RRBs reported by parents of children with ASD were (1) providing symptom-related items, (2) participating in symptom-related actions, and (3) assisting in the avoidance of symptomrelated stimuli. In this study, higher levels of accommodation were strongly associated with higher RRB severity (r ¼ 0.820, p < 0.001). Additionally, higher levels of accommodation were associated with poorer communication (r ¼ 0.258, p ¼ 0.029) and daily living skills (r ¼ 0.407, p < 0.001) in the children, and a majority of parents report feeling distress due to the accommodation. Most parents also reported their children responding aggressively when parents do not accommodate. These findings parallel previous work focused on children with OCD and anxiety disorders (Lebowitz et al. 2013, 2016; Thompson-Hollands et al. 2014). While certain similarities exist between family accommodation of RRBs in ASD and accommodation of OCD and anxiety disorders, there
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are also significant differences. Every child with ASD presents with some RRBs. However, the frequency, severity, and nature of these behaviors vary widely. Some children with ASD experience difficulty primarily in the area of social communication and interaction while presenting with relatively few RRBs. In such cases, parents may have fewer RRBs to accommodate compared with cases in which the RRBs are a more prominent clinical feature. In the context of OCD and anxiety, the accommodation can relate to almost any clinical feature of the disorder. This may partially explain the lower rates of accommodation reported by parents of children with ASD relative to those reported in OCD and anxiety disorders. Additionally, in light of the heterogeneity in form and function of RRBs, it is yet to be determined whether a reduction of accommodation of RRBs would be invariably beneficial or whether clinical recommendations must be informed by a functional assessment of the child’s symptoms.
Future Directions Further research on the directions of associations and the causal links that may exist between accommodation and severity of ASD is required. RRB severity and lower adaptive functioning may lead to increased family accommodation. It is alternatively plausible that, as in OCD and anxiety disorders, family accommodation leads to more severe child symptomatology and to greater functional impairment in the children. A third possibility is that the relationship is bidirectional or moderated by variables not yet taken known. Longitudinal research is critically needed in order to address these questions. An additional area worthy of attention is the motivation of parents engaging in family accommodation of RRBs. When asked to provide a qualitative description of their child’s RRBs, some parents used language expressing a belief that these behaviors were not optional, such as “must” or “have to,” in describing both the RRBs
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and their own accommodations. As such, parents of children with ASD may be providing accommodations due to their belief that no other alternatives exist. Children’s aggressive responses to not being accommodated, reported by a majority of parents, are another likely contributor to the maintenance of such parental accommodations. Family accommodation may offer a useful target for clinical interventions for individuals with ASD and their parents or families. Future work elucidating the role of accommodation should aim to determine how best to support parents in this context. In both OCD and anxiety disorders, current interventions emphasize the supportive reduction of family accommodation as a path to increasing independent coping in the child (Lebowitz 2015; Kagan et al. 2016; Lebowitz et al. 2018a, b; Salloum et al. 2018). Recent work supports the efficacy of SPACE (Supportive Parenting for Anxious Childhood Emotions), a parent-based intervention that reduces family accommodation of childhood anxiety and OCD. In a recent randomized controlled trial, SPACE was as efficacious as CBT in treating child anxiety disorders, and it led to greater reduction in family accommodation (Lebowitz et al. 2019). A similar approach may be beneficial in cases of ASD. The development and evaluation of such an intervention program targeting the accommodation of RRBs would be novel and could represent an important step forward in autismspecific interventions.
References and Reading American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (DSM-5 ®). American Psychiatric Pub. Attwood, T. (2003). Understanding and managing circumscribed interests. In M. R. Prior (Ed.), Learning and behavior problems in Asperger syndrome (pp. 126–147). New York: Guilford Press. Caporino, N. E., Morgan, J., Beckstead, J., Phares, V., Murphy, T. K., & Storch, E. A. (2012). A structural equation analysis of family accommodation in pediatric obsessive-compulsive disorder. Journal of Abnormal Child Psychology, 40(1), 133–143.
1983 Feldman, I., Koller, J., Lebowitz, E. R., Shulman, C., Itzchak, E. B., & Zachor, D. A. (2019). Family accommodation in autism spectrum disorder. Journal of Autism and Developmental Disorders, 49, 1–9. Harrop, C., McBee, M., & Boyd, B. A. (2016). How are child restricted and repetitive behaviors associated with caregiver stress over time? A parallel process multilevel growth model. Journal of Autism and Developmental Disorders, 46(5), 1773–1783. Honey, E., Rodgers, J., & McConachie, H. (2012). Measurement of restricted and repetitive behaviour in children with autism spectrum disorder: Selecting a questionnaire or interview. Research in Autism Spectrum Disorders, 6(2), 757–776. Howlin, P. (2003). Longer-term educational and employment outcomes. In M. R. Prior (Ed.), Learning and behavior problems in Asperger syndrome (pp. 269– 293). New York: Guilford Press. Jacob, S., Landeros-Weisenberger, A., & Leckman, J. F. (2009). Autism spectrum and obsessive–compulsive disorders: OC behaviors, phenotypes and genetics. Autism Research, 2(6), 293–311. Kagan, E. R., Peterman, J. S., Carper, M. M., & Kendall, P. C. (2016). Accommodation and treatment of anxious youth. Depression and Anxiety, 33(9), 840–847. Lebowitz, E. R. (2013). Parent-based treatment for childhood and adolescent OCD. Journal of Obsessive-Compulsive and Related Disorders, 2, 425–431. Lebowitz, E. R. (2016). Treatment of extreme family accommodation in a youth with obsessive-compulsive disorder. In E. A. Storch, & A. B. Lewin (Eds.), Clinical handbook of obsessive-compulsive and related disorders (pp. 321–335). Cham: Springer. Lebowitz, E. R., & Bloch, M. (2012). Family accommodation in pediatric obsessive-compulsive disorder. Expert Review of Neurotherapeutics, 12(2), 229–238. Lebowitz, E. R., Scharfstein, L. A., & Jones, J. (2014). Comparing family accommodation in pediatric obsessive-compulsive disorder, anxiety disorders, and nonanxious children. Depression and Anxiety, 31(12), 1018–1025. Lebowitz, E. R., Panza, K. E., & Bloch, M. H. (2016). Family accommodation in obsessive-compulsive and anxiety disorders: A five-year update. Expert Review of Neurotherapeutics, 16(1), 45–53. Lebowitz, E. R., Gee, D. G., Pine, D. S., & Silverman, W. K. (2018a). Implications of the Research Domain Criteria project for childhood anxiety and its disorders. Clinical Psychology Review, 64, 99–109. Lebowitz, E. R., King, R. A., & Silverman, W. K. (2018b). Anxiety disorders of childhood and adolescence. In M. Ebert, J. F. Leckman, & I. Petrakis (Eds). Current diagnosis and treatment psychiatry (3rd ed, pp. 554– 564). New York: McGraw-Hill Education. Lebowitz, E. R., Marin, C., Martino, A., Shimshoni, Y., & Silverman, W. K. (2019). Parent-based treatment as efficacious as cognitive-behavioral therapy for
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1984 childhood anxiety: A randomize noninferiority study of supportive parenting for anxious childhood emotions. Journal of the American Academy of Child and Adolescent Psychiatry. Leekam, S. R., Prior, M. R., & Uljarevic, M. (2011). Restricted and repetitive behaviors in autism spectrum disorders: A review of research in the last decade. Psychological Bulletin, 137(4), 562. Lidstone, J., Uljarević, M., Sullivan, J., Rodgers, J., McConachie, H., Freeston, M., Le Couteur, A., Prior, M., & Leekam, S. (2014). Relations among restricted and repetitive behaviors, anxiety and sensory features in children with autism spectrum disorders. Research in Autism Spectrum Disorders, 8(2), 82–92. Meier, S. M., Petersen, L., Schendel, D. E., Mattheisen, M., Mortensen, P. B., & Mors, O. (2015). Obsessive-compulsive disorder and autism spectrum disorders: Longitudinal and offspring risk. PloS One, 10(11), e0141703. Salloum, A., Andel, R., Lewin, A. B., Johnco, C., McBride, N. M., & Storch, E. A. (2018). Family accommodation as a predictor of cognitive-behavioral treatment outcome for childhood anxiety. Families in Society, 99(1), 45–55. Storch, E. A., Geffken, G. R., Merlo, L. J., Jacob, M. L., Murphy, T. K., Goodman, W. K., Larson, M. J., Fernandez, M., & Grabill, K. (2007). Family accommodation in pediatric obsessive–compulsive disorder. Journal of Clinical Child and Adolescent Psychology, 36(2), 207–216. Storch, E. A., Zavrou, S., Collier, A. B., Ung, D., Arnold, E. B., Mutch, P. J., Lewin, A. B., & Murphy, T. K. (2015). Preliminary study of family accommodation in youth with autism spectrum disorders and anxiety: Incidence, clinical correlates, and behavioral treatment response. Journal of Anxiety Disorders, 34, 94–99. Stratis, E. A., & Lecavalier, L. (2013). Restricted and repetitive behaviors and psychiatric symptoms in youth with autism spectrum disorders. Research in Autism Spectrum Disorders, 7(6), 757–766. Thompson-Hollands, J., Kerns, C. E., Pincus, D. B., & Comer, J. S. (2014). Parental accommodation of child anxiety and related symptoms: Range, impact, and correlates. Journal of Anxiety Disorders, 28(8), 765– 773. Troyb, E., Knoch, K., Herlihy, L., Stevens, M. C., Chen, C. M., Barton, M., Treadwell, K., & Fein, D. (2016). Restricted and repetitive behaviors as predictors of outcome in autism spectrum disorders. Journal of Autism and Developmental Disorders, 46(4), 1282– 1296. Van Steensel, F. J., Bögels, S. M., & Perrin, S. (2011). Anxiety disorders in children and adolescents with autistic spectrum disorders: A meta-analysis. Clinical Child and Family Psychology Review, 14(3), 302–317. Wood, J. J., & Gadow, K. D. (2010). Exploring the nature and function of anxiety in youth with autism spectrum disorders. Clinical Psychology: Science and Practice, 17(4), 281–292.
Family Burden
Family Burden Peter Doehring Foundations Behavioral Health, Doylestown, PA, USA ASD Roadmap, Chadds Ford, PA, USA
Definition The physical, emotional, and economic impact directly associated with the responsibility of caring for a person with ASD, including the secondary impact on all family members. These can include: (a) the economic cost of care (Montes and Halterman 2008), including lost opportunities for income (Jabrink 2007); (b) quality of life (Lee et al. 2008; Khanna et al. 2010); and (c) the impact on siblings (Harris and Glasberg 2003). Unlike the typical costs associated with child rearing, this burden often extends – and may increase – into adulthood, for those families who elect guardianship. It is reasonable to expect that this burden may vary as a function of the family’s race, ethnicity, income, and education, given other findings suggesting disparities in accessing health services, and cultural differences in attitudes toward disability. It is generally believed that various types of supports, including parent training, respite care, and wraparound services, can mitigate some aspects of the family burden.
See Also ▶ Family-Centered Care, Second Edition ▶ Guardianship ▶ Health Disparities ▶ Parent Training ▶ Respite Care ▶ Wraparound Services
References and Reading Harris, S. L., & Glasberg, B. A. (2003). Siblings of children with autism: A guide for families (2nd ed.). Bethesda: Woodbine House.
Family Therapy Jabrink, K. (2007). The economic consequences of autistic spectrum disorder among children in a Swedish municipality. Autism, 11, 453–463. Khanna, R., Madhavan, S. S., Smith, M. J., Patrick, J. H., Tworek, C., & Becker-Cottrill, B. (2010). Assessment of health-related quality of life among primary caregivers of children with autism spectrum disorders. Journal of Autism and Developmental Disorders, 41(9), 1214–1227. Lee, L. C., Harrington, R., Louie, B., & Newschaffer, C. (2008). Children with autism: Quality of life and parental concerns. Journal of Autism and Developmental Disorders, 38(6), 1147–1160. Montes, G., & Halterman, J. S. (2008). Association of childhood autism spectrum disorders and loss of family income. Pediatrics, 121, e821–e826.
Family Therapy Guus van Voorst Clinical Psychology, Center for Autistic Disorders, GGZ Centraal, Amersfoort, Netherlands
Definition Family-based treatments attempt to decrease interactions between family members that contribute to psychiatric disorders and to increase interactions that protect them from these problems. The psychoeducational model is most commonly used in conjunction with psychiatric disorders. Family life is all about relationships and communication. Autism spectrum disorders are about communication challenges, misunderstanding of social cues, and lack of emotional understanding, thus affecting relationships in families. The family burden for parents and siblings is very high in families with a member on the spectrum, and the divorce rate is also high in families with children who have autism. In marriage, if one of the partners is on the spectrum, there will be more problems than just the normal marriage conflicts. Collaborative family work is directed to meet the needs of people with autism and their families and should address problems in each developmental phase of the family life cycle. Parents of a young autistic child are perplexed when they realize that the child is not developing properly and
1985
may feel rejected or unimportant when the child does not seek their attention. Getting a diagnosis and services may be stressful, as well as the process of adaptation: accepting the diagnosis and informing other family members and friends. At school age, the family has to adapt in many ways: confrontation with outside world, dealing with aversive reactions of peers, specific ways of rearing, arranging for child care, and dealing with professionals. In adolescence, families have to cope with the chronicity of the child’s disability, with issues of aggression, odd behavior, sexuality, and addiction. Parents have to delay the developmental phase of launching and moving on and are confronted with the perspective of endless care. Marriages with an autistic member have a serious risk of running into trouble. The spouse with ASD misses relational skills, there will be an asymmetry in the relation, and the non-ASD spouse will feel lonely and neglected. A diagnosis of an autism spectrum disorder is likely to be experienced as ambiguous loss in a family, because resolution of the situation is not possible and the outcome is not predictable, especially when the individual with ASD may give an outward appearance of health, thus raising too high expectations for his functioning. Sometimes, families have several autistic members, for instance, one or two siblings, or a father and a son both have the condition, complicating family function and rearing practices even more. Family interventions have been less effective in reducing core ASD symptoms, yet they do contribute to reducing the comorbid family and behavior problems associated with the disorder in children and adolescents. A biopsychosocial approach with a treatment package (e.g., family treatment, behavioral therapy, and pharmacology) is common in most clinical settings. Some models and schools of family therapy should not be applied in families with an autistic member, because they focus primarily on context, meaning, and mentalizing processes. Moreover, family and couples’ therapists should adapt their style, taking into account that family members may have problems with imagination, generalizing, and they need basic knowledge about ASD in order to work effectively with these families.
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1986
Family Therapy
Historical Background
See Also
For decades following Kanner’s description of the disorder, it was wrongly believed that the parents, especially the mother, were responsible for causing the disorder by emotional neglect. Family psychoeducation started in the 1970s of last century in the treatment of schizophrenia. It was based on the premise that the patient has a brain disorder and that families need to be supported in their care of the mentally ill person. The emphasis on the biological aspect of the illness is intended to correct that families somehow cause the illness. Family psychoeducation nowadays is commonly used in various chronic childhood and adult illnesses.
▶ Family Burden
Current Knowledge Nowadays, the association of genetic factors in the etiology of autism spectrum disorders is well established. Problems in parenting and family dysfunction may be a response to the child and adolescent psychopathology. A psychoeducational approach in families is the treatment of choice in clinical practice. It is defined as the systematic administration of information to both patient and family (significant others) about symptoms, diagnosis, treatment, and prognosis. Its aim is behavioral change and not just teaching for the sake of increasing knowledge. It must be given in doses, as the recipients are able to hear it and given repetitively over time. Other goals are improving family skills, positive communication development, and increased social involvement for the family. Family psychoeducation includes the provision of emotional support and resources during periods of transition and crisis.
Future Directions Early diagnosis and treatment of the child and its family may have positive effects for some children. There is no cure for ASD, yet various treatments are used to treat the effects of the disorder.
References and Reading Aston, M. (2004). Asperger in love: Couples relationships and family affairs. London: Jessica Kingsley. Baker-Ericzén, M. J., Brookman-Frazee, L., & Stahmer, A. (2005). Stress levels and adaptability in parents of toddlers with and without autism spectrum disorders. Research and Practice for Persons with Severe Disabilities, 30, 194–204. Bolman, W. (2006). The autistic family life cycle: Family stress and divorce. Retrieved 15, July 2006 from http:// Asa.confex.om/asa/2006/tehprograms/s1940.htm Carr, A. (Ed.). (2002). Prevention: What works with children and adolescents? Sussex: Brunner-Routledge. Diamond, G., & Josephson, A. (2005). Family-based treatment research: A 10-year update. Journal of the American Academy of Child and Adolescent Psychiatry, 44(9), 872–887. Hastings, R. P., Kovshoff, H., Ward, N. J., Degli Espinosa, F., Brown, T., & Remington, B. (2005). Systems analysis of stress and positive perceptions in mothers and fathers of pre-school children with autism. Journal of Autism and Developmental Disorders, 35(5), 635–644. Keitner, G. I., Heru, A. M., & Glick, I. D. (2010). Clinical manual of couples and family therapy. Washington, DC: American Psychiatric Publishing. McGoldrick, M., & Carter, B. (2003). The family life cycle. In F. Walsh (Ed.), Normal family processes (pp. 375–399). New York: The Guilford Press. O’Brien, M. (2007). Ambiguous loss in families of children with autism spectrum disorders. Family Relations, 56(2), 135–146. Rolland, J. S. (2003). Mastering family challenges in illness and disability. In F. Walsh (Ed.), Normal family processes. New York: The Guilford Press. Seligman, M., & Darling, B. (2007). Ordinary families, special children: A systems approach to childhood disability. New York: The Guilford Press. Sicile-Kira, C. (2008). The affects of Autism in Families and in Partner Relationships. Family Therapy Magazine, pp. 19–23. Solomon, M., Ono, M., Timmer, S., & Goodlin-Jones, B. (2008). The effectiveness of parent–child interaction therapy for families of children on the autism spectrum. Journal of Autism and Developmental Disorders, 38(9), 1767–1776. Van Voorst, A. J. P. (2008). Relatieproblemen bij personen met een autisme spectrum stoornissen (couple therapy with one partner having an autism spectrum disorder). Systeemtherapie, 20(3), 152–164. Van Voorst, A. J. P., Jansma, H. B. M., van de Wiel, N. H. M., & Matthys, W. (2005). Kinderpsychiatrische stoornissen en gezinsbelasting (Psychiatric disorders in children and family stress). Kind en Adolescent, 25(3), 181–244.
Family-Centered Care, Second Edition
Family-Centered Care, Second Edition Laura Foran Lewis College of Nursing and Health Sciences, University of Vermont, Burlington, VT, USA
Synonyms Family-centered practice; Patient- and family-centered care
Definition Family-centered care is an approach to care that is based on the understanding that the family is a child’s primary source of strength and support (American Academy of Pediatrics 2012). As such, the family is recognized as an integral part of the team in collaboration with service providers to best support the care of the child. Service providers view the family as a unit that includes the child, and this unit is the primary focus of care (Coyne et al. 2018). The family unit may include parents, extended family, and/or other caregivers who are determined to be most meaningful and supportive to the child in time of need (Baas 2012). Core attributes of family-centered care include collaboration, evidenced by empowering families to participate in care by building partnerships that provide dignity and respect; communication, evidenced by honest and open informationsharing, providing choices, and including families in shared decision-making; negotiation, evidenced by educating and building family confidence in care, sharing expectations of roles and responsibilities with family, and assessing families’ needs to provide care that is holistic and individualized; and support, evidenced by providing care that is appropriate to the cultural and social context of the family, including formal and informal support (Coyne et al. 2018). Family-centered care is mutually beneficial for the child, family, and service providers. Research
1987
has shown that family-centered care leads to improved health outcomes, decreased health-care costs, more effective allocation of resources, and increased child, family, and staff satisfaction (American Academy of Pediatrics 2012). Youth on the autism spectrum who receive familycentered care are also more likely to experience successful transition from pediatric to adult primary care (Rast et al. 2018). Families experience increased coping, adjustment, confidence, and competence as caregivers and decision-makers when family-centered care is utilized. Inclusion of the child as developmentally appropriate encourages the child to take responsibility for his or her own health and assist with transition to adult services (American Academy of Pediatrics 2012; Christon and Myers 2015). In the care of children on the autism spectrum, families are constant in the child’s life and therefore can provide a unique perspective and information to service providers who transition in and out of a child’s life when planning, delivering, and evaluating interventions. Including families in creating goals of care and incorporating familycenteredness throughout care is a particularly effective tool in managing medical concerns for children on the autism spectrum.
See Also ▶ Culture and Autism ▶ Interdisciplinary Team
References and Reading American Academy of Pediatrics Committee on Hospital Care and Institute for Patient- and Family-Centered Care. (2012). Policy statement: Patient- and familycentered care and the pediatrician’s role. Pediatrics, 129(2), 394–404. Baas, L. S. (2012). Patient- and family-centered care. Heart & Lung: The Journal of Acute and Critical Care, 41(6), 534–535. https://doi.org/10.1016/j.hrtlng. 2012.08.001. Christon, L. M., & Myers, B. J. (2015). Family-centered care practices in a multidisciplinary sample of pediatric professionals providing autism spectrum disorder services in the United States. Research in Autism Spectrum
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1988 Disorders, 20, 47–57. https://doi.org/10.1016/j.rasd. 2015.08.004. Coyne, I., Holmström, I., & Söderbäck, M. (2018). Centeredness in healthcare: A concept synthesis of familycentered care, person-centered care and child-centered care. Journal of Pediatric Nursing, 42, 45–56. https:// doi.org/10.1016/j.pedn.2018.07.001. Rast, J. E., Shattuck, P. T., Roux, A. M., Anderson, K. A., & Kuo, A. (2018). The medical home and health care transition for youth with autism. Pediatrics, 141(Supplement 4), S328–S334. https://doi.org/10.1542/peds. 2016-4300J.
Family-Centered Practice ▶ Family-Centered Care, Second Edition
Family-Centered Programming ▶ Parent-Professional Partnership
Family-Centered Practice
FBA ▶ Functional Behavior Assessment
Fear ▶ Anxiety
Febrile Convulsions Jennifer M. Kwon Department of Neurology and Pediatrics (SMD), University of Rochester, School of Medicine and Dentistry, Rochester, NY, USA
Synonyms Febrile seizures
Fanatrex
Definition
▶ Gabapentin
Febrile convulsions are a common type of seizure seen in children between the ages of 6 months and 6 years. These seizures are triggered by fever, (temperature > 38 °C) but with no evidence of encephalitis or meningitis or prior history of afebrile seizures. Children who have febrile seizures typically have an acute intercurrent viral or bacterial illness such as an ear infection or sinus infection. The seizures are not associated with any systemic metabolic abnormalities such as hypoglycemia. Febrile seizures are commonly categorized as simple or complex. Simple febrile seizures last less than 15 min (typically only last a minute or two) and have no focal features. Two or more simple febrile seizures can occur in succession, but the total duration of the seizures should be less than 30 min. A “complex” febrile seizure is one that has focal features, may last more than 15 min, or, when they occur in a series, may last more than 30 min. Rarely, febrile seizures can present with status epilepticus.
FAST (Functional Assessment Screening Tool) ▶ Functional Analysis Screening Tool ▶ Functional Assessment Screening Tool (FAST)
FAS-Test ▶ Verbal Fluency
FazaClo ▶ Clozapine
Federal Rules of Evidence
Febrile seizures are a type of “provoked seizure.” Seizures can also be “provoked” or “triggered” by head injury or withdrawal from medications.
See Also ▶ Electroencephalogram (EEG) ▶ Epilepsy ▶ Seizures
References and Reading Patel, N., Ram, D., Swiderska, N., Mewasingh, L. D., Newton, R. W., & Offringa, M. (2015). Febrile seizures. BMJ, 351:h4240.
Febrile Seizures ▶ Febrile Convulsions
Fecal Impaction ▶ Constipation
Fecal Incontinence ▶ Encopresis
Federal Rules of Evidence Edward McNulty Quinnipiac University School of Law, Hamden, CT, USA
Definition In General The Federal Rules of Evidence are the rules governing the admissibility of evidence at civil
1989
and criminal trials in federal courts. The Federal Rules of Evidence generally comprise a set of restraints that the courts place on lawyers in an attempt to manage the risks associated with the adversarial process in a trial setting. The Federal Rules of Evidence were adopted in 1975, and as of 2009, 42 states had adopted codes based on the federal model. Admission of Relevant Evidence Federal Rule of Evidence 402 provides for the admissibility of relevant evidence unless the rule provides otherwise. Federal Rule of Evidence 403 gives the trial judge discretion to exclude relevant evidence where its probative value is substantially outweighed by the danger of unfair prejudice, confusion of the issues, or misleading the jury, or by considerations of undue delay, waste of time, or needless presentation of cumulative evidence. Because exclusion occurs only where the probative value is “substantially outweighed,” the rule favors admissibility. Witness Federal Rule of Evidence 601 serves the purpose of removing a considerable number of the common law grounds for disqualifying witnesses. In particular, a section of Federal Rule of Evidence 601 states that “[e]very person is competent to be a witness except as otherwise provided in these rules.” Federal Rule of Evidence 602 requires that before a witness can testify, evidence must support that the witness has personal knowledge and memory of the matter. Thus, one may not testify to knowledge obtained from what others told them. Expert Witnesses Under Federal Rule of Evidence 702, only experts may testify on matters that are scientific, technical, or specialized in nature. In Daubert v. Merrell Down Pharmaceuticals, the Supreme Court held that (1) “general acceptance” is not necessary precondition to admissibility of scientific evidence under Federal Rule of Evidence and (2) under the Federal Rules of Evidence, the trial judge is assigned the task of ensuring that any and all scientific testimony or evidence admitted is not only relevant but reliable. In Daubert and similar
F
1990
cases, the United States Supreme Court set forth a number of factors to consider in determining whether to admit expert testimony under Federal Rule of Evidence 702. These factors are neither exclusive nor dispositive. For instance, the US Supreme Court stated in Daubert that courts may consider whether the theory or technique employed by the expert is generally accepted in the scientific community, whether it has been subjected to peer review and publication, whether it can be and has been tested, whether the known or potential rate of error is acceptable, and the existence and maintenance of standards and controls. Sources Daubert v. Merrell Dow Pharmaceuticals, Inc., 509 U.S. 579 (1993). Federal Rules of Evidence (As amended to December 1, 2011) Federal Rule of Evidence 402. General Admissibility of Relevant Evidence Relevant evidence is admissible unless any of the following provides otherwise • • • • •
the United States Constitution a federal statute these rules; or other rules prescribed by the Supreme Court.
Feedback Error-Related Negativity (fERN)
Awitness may testify to a matter only if evidence is introduced sufficient to support a finding that the witness has personal knowledge of the matter. Evidence to prove personal knowledge may, consist of the witness’ own testimony. This rules does not apply to a witness's export testimony under rule 703 Federal Rule of Evidence 702. Testimony by Expert Witnesses A witness who is qualified as an expert by knowledge, skill, experience, training, or education may testify in the form of an opinion or otherwise, if: (a) the expert's scientific, technical, or other specialized knowledge will help the trier of fact to understand the evidence or to determine a fact in issue (b) the testimony is based on sufficient facts or data; (c) the testimony is the product of reliable principles and methods; and (d) the expert has reasonably applied the principles and methods to the facts of the case.
References and Reading Garner, B. A. (2009). Black’s law dictionary (9th ed.). St. Paul: West Publishing Company. Mueller, C. B., & Patrick, L. C. K. (2009). Evidence (9th ed.). New York: Aspen Publishers.
Feedback Error-Related Federal Rule of Evidence 403. Excluding Rel- Negativity (fERN)
evant Evidence for Prejudice, Confusion, Waste of Time, or Other Reasons The court may exclude relevant evidence if its probative value is substantially outweighed by the danger of one or more of the following: unfair prejudice, confusing the issues, misleading the jury, undue delay, wasting time, or needlessly presentating of cumulative evidence. Federal Rule of Evidence 601. Competency to Testify in General Every person is competent to be a witness unless these rules provide otherwise. But in a civil case, state law governs the witness’s competency regarding a claim or defense for which state law supplies the rule of decision. Federal Rule of Evidence 602. Need for Personal Knowledge
▶ Feedback-Related Negativity
Feedback on Provider Work Performance S. Michael Chapman TEACCH Autism Program, University of North Carolina Chapel Hill, Chapel Hill, NC, USA
Synonyms Employee development; Employee performance; Performance reviews for direct care staff; Staff training and development
Feeding Disorder
1991
Definition
Definition
Refers to the various methods used to encourage personnel working with individuals with autism spectrum disorders to improve the strategies they use in their everyday interactions, teachings, and supports they provide. Though various methods have been tested (Brown et al. 1981; Quilitch 1975), it was found that the most successful method for increasing work performance was a combination of feedback and praise based upon the ability of the staff member to assist the individual under their care meet their specific goals/outcomes (Kreitner et al. 1977; Montegar et al. 1977).
The feedback-related negativity or FRN is an electrical brain signal measured with an electroencephalogram. Detectable at the scalp via the event-related potential (ERP), the FRN occurs when an individual receives external feedback (visual, auditory) indicating that performance is worse than expected in a given context. Such contexts include monetary loss and feedback about performance in simple games. The FRN occurs approximately 250 ms after feedback and is typically observed at central to frontal-central scalp regions. The most likely neural generator of the FRN is the anterior cingulate cortex (Gehring and Willoughby 2002; Luu et al. 2003).
See Also ▶ Individuals with Disabilities Education Act (IDEA)
References and Reading
See Also
Brown, K. M., Willis, B. S., & Reid, D. H. (1981). Differential effects of supervisor verbal feedback and feedback plus approval on institutional staff performance. Journal of Organizational Behavior Management, 3, 57–68. Kreitner, R., Reif, W. E., & Morris, M. (1977). Measuring the impact of feedback on the performance of mental health technicians. Journal of Organizational Behavior Management, 1, 105–109. Montegar, C. A., Reid, D. H., Madsen, C. H., & Ewell, M. D. (1977). Increasing institutional staff-to-resident interactions through in-service training and supervision approval. Behavior Therapy, 8, 533–540. Quilitch, H. R. (1975). A comparison of three staff management procedures. Journal of Applied Behavior Analysis, 7, 217–221.
▶ Anterior Cingulate ▶ Cingulate Cortex ▶ ERN ▶ Error-Related Negativity
Feedback-Related Negativity Michael J. Crowley Developmental Electrophysiology Laboratory, Yale Child Study Center, New Haven, CT, USA
References and Reading Gehring, W. J., & Willoughby, A. R. (2002). Medial prefrontal cortex and rapid processing of monetary gains and losses. Science, 295, 2279–2282. Luu, P., Tucker, D. M., Derryberry, D., Reed, M., & Poulsen, C. (2003). Electrophysiological responses to errors and feedback in the process of action regulation. Psychological Science, 14, 47–53. Nieuwenhuis, S., Holroyd, C. B., Mol, N., & Coles, M. G. (2004). Reinforcement-related brain potentials from medial frontal cortex: Origins and functional significance. Neuroscience and Biobehavioral Reviews, 28(4), 441–448.
Synonyms
Feeding Disorder Feedback error-related negativity (fERN); Medial frontal negativity (MFN)
▶ Pica
F
1992
Feeding Problems Allison Bean Ellawadi Speech and Hearing Science, The Ohio State University, Columbus, OH, USA
Definition Feeding problems are delays and/or disorders in the development of eating and drinking skills including disordered placement of food in the mouth, difficulty in appropriately manipulating food when it is in the mouth, difficulty chewing, and/or difficulty swallowing (American SpeechLanguage-Hearing Association 2001). Feeding problems also include deficits in “any aspect of taking nutritional elements that result in under nutrition, poor growth or stressful mealtimes for children and their caregivers”.
Historical Background In the early 1900s, doctors were cognizant of the fact that behaviors such as vomiting could be a symptom of feeding problems including overfeeding or too frequent feeding, medical conditions such as pyloric obstruction, and feeding of improper foods (Lowenburg 1912). By 1935, clinicians were addressing feeding problems that occurred as the result of structural anomalies with equipment such as the modified Asepto syringe and medicine dropper. In fact, the Brecht feeder was specifically developed to assist in feeding infants with structural abnormalities and/or physical and mental handicaps (Hess 1946). Feeding problems began to appear in physicians’ descriptions of developmental disabilities, such as cerebral palsy in the 1950s (Ingram 1954; Perlstein and Barnett 1952). During this time, it was suggested that feeding problems be classified into one of two groups: organic feeding problems and feeding problems in children who are emotionally disturbed and show tension and anxiety (Shwartz 1958). By the 1970s, reports suggested that feeding problems occurred frequently in preschool
Feeding Problems
handicapped children and were a “significant clinical entity” (Palmer et al. 1975). However, no system was consistently being used to classify these difficulties; a problem that continues to exist today. Feeding problems during this time were attributed to neuromotor dysfunction or physical abnormalities. In cases where neuromotor dysfunction and physical abnormalities were ruled out, the feeding problems were determined to have a behavioral cause (Thompson and Palmer 1974). Interventions to address feeding problems included techniques designed to treat feeding problems caused by mechanical difficulties (as cited by Thompson & Palmer). These techniques often included positioning recommendations aimed at facilitating movement and/or prescribed exercises to normalize tone and oral sensitivity. Treatment of behavioral feeding problems often included psychotherapy, using hunger as a means to motivate the child to eat a specific food and gradually increasing texture as a means to introduce solid foods into a child’s diet, and/or behavioral modification strategies (Benotvin 1970; Bernal 1972; Jones 1982; Thompson and Palmer 1974; Palmer et al. 1975). Diagnosis and treatment of feeding problems is still in its infancy. The treatment strategies used during the 1960s and 1970s are similar to those used now (for a review of early strategies, see Palmer et al. 1975). One of the most significant changes in treatment of feeding problems since the 1970s is awareness that feeding is a give-andtake experience that relies on both the child and caregiver. Thus, treatment today often includes consideration of child-caregiver interactions during mealtimes.
Current Knowledge Successful feeding involves (1) acceptance of a wide variety of developmentally appropriate foods and (2) development of motor skills that enable efficient and safe sucking, chewing, propelling, and swallowing. However, normal feeding development is not restricted to the physical act of eating. It also includes the successful integration of a range of physical functions and interpersonal relationships. Both physical factors
Feeding Problems
(e.g., integration of swallowing and respiration, hand-eye coordination, normal posture, and tone development) and social factors (e.g., cultural patterns and social factors within a family) influence feeding development (Arvedson and Brodsky 2002). Disruption in one or more of these areas can result in a feeding problem (Bryant-Waugh et al. 2010). Feeding problems are defined as delays and/or disorders in the development of eating and drinking skills. This includes the motor aspects of eating (e.g., difficulty chewing and difficulty swallowing) as well as deficits in taking nutritional elements, which causes poor growth or stressful mealtimes (American Speech-Language-Hearing Association 2001). Feeding problems manifest themselves in a variety of ways, ranging from an inability to swallow without choking to restricted diets. Approximately 25–40% of infants and toddlers are reported to have feeding problems (Reau et al. 1996). Many early feeding problems are transient and resolve without significant clinical intervention (BryantWaugh et al. 2010). However, others fail to resolve without intervention. These feeding disorders often have multifactorial causes, which may include structural abnormalities, and a substantial behavioral component (Arvedson and Brodsky 2002; Bernard-Bonnin 2006). A consistent classification system is not yet used for feeding problems. The Diagnostic and Statistical Manual of Mental Disorder-4th Edition (DSM-IV) classifies feeding disorders into three diagnostic categories: feeding disorders, pica, and rumination disorders. However, a number of eating disorders commonly seen in early childhood have no place in the DSM-IV classification system (Bryant-Waugh et al. 2010). The organic and nonorganic dichotomy has also been used to classify feeding problems. Organic feeding problems occur as a result of structural anomalies, neuromuscular issues, or other known physiological causes (e.g., gastroesophageal reflux), whereas nonorganic feeding problems occur due to disruptive environments or have emotional underpinnings. While this dichotomy is helpful, feeding problems often have more than one cause (for a review, see Burklow et al. 1998). The American Speech-Language-Hearing Association has
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adopted a more in-depth classification system (American Speech-Language-Hearing Association 2007). While this classification may prove to be more useful than the organic/nonorganic dichotomy, it does not address the issue that most children with feeding problems often have multiple diagnoses (Arvedson and Brodsky 2002) and, as such, may fall under more than one category. For example, organic factors may disrupt the typical development of eating which results in the development of maladaptive behavioral patterns by the child and/or the caregiver (Burklow et al. 1998). Given the complexity of many feeding problems, an interdisciplinary approach to addressing these problems is recommended. Interdisciplinary feeding teams may include a speech-language pathologists, developmental pediatrician, otolaryngologist, gastroenterologist, nutritionist/dietitian, occupational therapist, and social worker or nurse. Speech-language pathologists conduct feeding and swallowing evaluations and develop feeding plans. Developmental pediatricians provide diagnoses of pediatric and neurodevelopmental disabilities and manage overall patient care. Otolaryngologists examine the integrity of the structural mechanisms of swallowing and manage medical and surgical treatment of issues related to drooling, aspiration, and gastroesophageal reflux. Gastroenterologists conduct gastrointestinal evaluations and manage gastrointestinal diseases. The nutritionist/dietitian assesses and manages the child’s nutritional needs, and the occupational therapist evaluates the child’s posture, tone, and sensory system. Social workers or nurses coordinate home care and other appointments (Arvedson and Brodsky 2002). Regardless of who is on the team, diagnosis and management of feeding problems follow the same steps: (1) define the feeding and/or swallowing problem, (2) identify etiology(ies), (3) determine appropriate diagnostic tests, (4) plan approach to patient/family, (5) teach about problem, (6) implement treatment, monitor progress, and (7) evaluate progress (Arvedson and Brodsky 2002). Determining the underlying etiology is a critical aspect of any successful intervention program because disturbances in feeding and eating behavior with similar clinical presentations
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may differ in their underlying etiologies and require different interventions (Bryant-Waugh et al. 2010). Certain medical conditions and developmental disabilities are associated with specific feeding problems. Neurological conditions and anatomical anomalies are most often associated with skills deficits such as oral motor delays, whereas medical conditions such as gastroesophageal reflux are most often associated with food refusal among children with Down syndrome, autism, and cerebral palsy (Field et al. 2003; Williams et al. 2005). Feeding programs often have multiple components. For example, programs may address oral sensorimotor issues and posture and positioning during feeding. Oral sensorimotor programs are designed to address deficits in the structures that support feeding and swallowing. Goals of these programs may include improving jaw, lip, and/or tongue movements; developing coordinated movements of the mouth; and normalizing sensory oral experiences during mealtimes. Therapy may also be aimed at developing transitional feeding skills including spoonfeeding, cup-drinking, straw-drinking, and chewing. In addition to targeting skills that directly support feeding, interventionists may also address positioning and posture. Positioning is especially important for children with abnormal muscle tone. Management of behavioral feeding problems may focus on weight gain, initiation of oral feedings, weaning from tube feeds, increased oral intake, improved cooperation and reduced stress during mealtimes, and/or acceptance of a wider variety of flavors and textures (Arvedson and Brodsky 2002). Because mealtimes offer the opportunity for parent-child interactions, it is important that management of feeding and swallowing problems occurs within the context of social and communicative activities associated with mealtime for a parent and child (Arvedson and Brodsky 2002).
Future Directions The field of feeding problems is still in its infancy, especially with regard to feeding and swallowing
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problems in children (Arvedson and Brodsky 2002). Although research in the area of feeding and typical swallowing and swallowing disorders has increased, continued research is needed. In the field of feeding problems, there are still inconsistencies in terminology and the detail of description of a number of eating problems, which hampers research. Thus, it is critical that an internationally recognized and accepted classification is established to inform clinical interventions for particular disorders and move the field forward (Bryant-Waugh et al. 2010). To date, few research studies have attempted to examine prognosis, course, outcome, and treatment response in feeding disorders using a formal, widely accepted classification system. There is little evidence to guide clinical determination of what constitutes a clinically significant feeding problem and to help clinicians distinguish feeding problems that are more serious from those that are likely to be short lived. In order to better assess prognosis and outcome, researchers should employ a set of consistently used standardized assessment measures (Bryant-Waugh et al. 2010). In addition to directly examining feeding problems, research should continue to examine the outcomes of feeding assessment and intervention in schools and the overall impact on education. Finally, there needs to be an expansion of education programs focused on delivery of feeding and swallowing services in the school (American SpeechLanguage-Hearing Association 2007).
See Also ▶ Gastrointestinal Disorders and Autism ▶ Nutritional Interventions ▶ PICA
References and Reading American Speech-Language-Hearing Association. (2007). Guidelines for speech-language pathologists providing swallowing and feeding services in schools [Guidelines]. Available from http://www.asha.org/policy American-Speech-Hearing-Association. (2001). Roles of speech-language pathologists in swallowing and feeding disorders: Technical report. Retrieved 4 Mar 2011
Feingold Diet from http://www.asha.org/docs/html/TR2001-00150. html Arvedson, J. C., & Brodsky, L. (2002). Pediatric swallowing and feeding: Assessment and management second edition. Clifton Park: Thompson Delmar Learning. Benotvin, A. (1970). A clinical approach to feeding disorders of childhood. Journal of Psychosomatic Research, 14, 267–276. Bernal, M. E. (1972). Behavioral treatment of a child’s eating problem. Journal of Behavior Therapy and Experimental Psychiatry, 3, 45–50. Bernard-Bonnin, A. (2006). Feeding problems in infants and toddlers. Canadian Family Physician, 52, 1247–1251. Bonafede, J. P., Lavertu, P., Wood, B. G., & Eliachar, I. (1997). Surgical outcome in 87 patients with Zenker’s diverticulum. Laryngoscope, 107, 720–725. Bryant-Waugh, R., Markham, L., Kreipe, R. E., & Walsh, T. B. (2010). Feeding and eating disorders in childhood. International Journal of Eating Disorders, 43, 98–111. Burklow, K. A., Phelps, A. N., Schultz, J. R., McConnell, K., & Rudolph, C. (1998). Classifying complex pediatric feeding disorders. Journal of Pediatric Gastroenterology and Nutrition, 27, 143–147. Field, D., Garland, M., & Williams, K. (2003). Correlates of specific childhood feeding problems. Journal of Pediatrics and Child Health, 39, 299–304. Hess, J. G. (1946). A modification of the Brecht feeder. The Journal of Pediatrics, 29, 233–234. Ingram, T. T. S. (1954). A study of cerebral palsy in the childhood population of Edinburgh. Archives of Diseases in Childhood, 30, 85–98. Jones, W. (1982). Treatment of behavior- related eating problems in retarded students: A review of the literature. Monographs of the American Association on Mental Deficiency, 5, 3–26. Lowenburg, H. (1912). The significance and treatment of vomiting in infants and young children. Journal of American Medical Association, 3, 180–183. Palmer, S., Thompson, R. J., & Linscheid, T. R. (1975). Applied behavior analysis in treatment of childhood feeding problems. Developmental Medicine and Child Neurology, 17, 333–339. Perlstein, M. A., & Barnett, H. E. (1952). Nature and recognition of cerebral palsy in infancy. Journal of the American Medical Association, 148, 1389–1397. Reau, N. R., Senturia, Y. D., Lebailly, S. A., & Christofell, K. K. (1996). Infant and toddler feeding patterns and problems: Normative data and a new direction. Journal of Developmental and Behavioral Pediatrics, 17, 149–153. Shwartz, S. (1958). Eating problems. Pediatric Clinics of North America, 5, 596–611. Thompson, R. J., & Palmer, S. (1974). Treatment of feeding problems – A behavioral approach. Journal of Nutrition Education, 6, 63–36. Williams, K. E., Bridget, B. G., & Schreck, K. A. (2005). Comparing selective eaters with and without developmental disabilities. Journal of Developmental and Physical Disabilities, 17, 299–309.
1995
Feingold Diet Susan Hyman Developmental and Behavioral Pediatrics, Division Chief Neurodevelopmental and Behavioral Pediatrics, University of Rochester Golisano Children’s Hospital, Rochester, NY, USA
Definition Feingold diet is a dietary intervention popularized for symptoms of inattention, impulsivity, and hyperactivity by Benjamin Feingold, M.D., a pediatric allergist who clinically observed improvement in behavior in his patients for whom he prescribed dietary restrictions for management of symptoms of allergy in the 1960s. It calls for the elimination of synthetic food colorings and synthetic preservatives made from petroleum-based products, synthetic sweeteners, and foods containing high levels of natural salicylates (related to the chemical compound in aspirin). The Feingold diet eliminates: • Synthetic food coloring made from petroleum products (e.g., Red #40; Yellow #5) • Synthetic food preservatives made from petroleum products (e.g., BHT-butylated hydroxytoluene) • Foods that may be high in natural salicylates (e.g., apples, almonds, berries, cucumbers, grapes, and oranges, among other fruits and vegetables) • Artificial sweeteners (e.g., aspartame) Foods could be reintroduced as tolerated. Later modifications to the diet included removal of the preservatives Sodium Benzoate and BHA (butylated hydroyanisole). While other dietary approaches to hyperactivity have become popular since the initial dissemination of the Feingold diet, the additional assertions that sugar, yeast, milk, or other foods result in behavioral change were not part of the Feingold diet.
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Historical Background It was initially reported in the 1940s that some foods high in natural salicylates and FD and C Yellow #5 (a food coloring approved for food, drug, and cosmetics) produced similar allergic symptoms. Dr. Feingold was the chief of pediatrics of Cedar Sinai Medical Center in Los Angeles and subsequently the Chief of Allergy for Kaiser Permanente Medical Center in San Francisco. In 1965, Dr. Feingold prescribed a diet low in foods containing salicylates to a patient with hives whose psychiatric symptoms subsequently improved (Feingold 1985). While he initially called the regimen for management of hyperactivity the K-P Program standing for Kaiser Permanente, it became known in the press as the Feingold diet or program. He clinically observed that the dietary regimen of removal of foods he believed contained high levels of natural salicylates and contained synthetic food dyes and sweeteners resulted in subjective behavioral improvement in up to 30–50% of his patients. By 1979, he further recommended elimination of synthetic preservatives with subjective reports of improvement in 60–70% of his patients. While Dr. Feingold recommended limiting sweets, he at no time recommended elimination of sugar in the diet. His concern was about the additives in processed foods and recommended that families make treats and sweets from scratch that should be provided as part of a healthy diet. He presented his observations regarding food and food additives and behavior to the American Medical Association in 1973. Much controversy ensued, with small clinical trials both reporting improvement in children on the restricted diet and other studies not being able to confirm these findings (Mattes and Gittelman 1981). The National Institutes of Health convened a consensus development conference in 1983 to examine the data concerning defined diets and hyperactivity. The expert panel concluded that the data were insufficient to recommend defined diets for the treatment of hyperactivity but allowed that a diet trial could be considered by families after appropriate neurobehavioral evaluation and diagnosis took place with consideration of evidence-based
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options (National Institutes of Health Consensus Development Panel 1983). Since that time, additional studies have been undertaken examining the behavioral effects of component parts of the Feingold diet. Wolraich et al. demonstrated that the artificial sweetener aspartame (Wolraich et al. 1994) does not impact attention. Studies on the effects of synthetic food coloring have been more controversial (reviewed in Schab and Trinh 2004). Synthetic food coloring use is restricted in European countries, but the scientific data have been interpreted to allow for continued use in the USA (Advisory Committee (March 30–31, 2011)). Dr. Feingold’s book – Why is Your Child Hyperactive? – was last published in 1985. The Feingold Foundation maintains an active society to support families who elect to pursue this dietary intervention (www.feingold.org).
Rationale or Underlying Theory The original theory proposed for behavioral improvement with the Feingold diet was related to the elimination of allergens, leading to improved attention, learning, and behavior. Proponents point toward the introduction of increasing amounts of artificial flavors and colors in the diets of children as paralleling the increase in diagnosis of Attention Deficit Hyperactivity Disorder (ADHD). A hypothesis related to inhibition of neurotransmitter uptake of dopamine by FDC Red #3 was proposed but not confirmed in subsequent animal and human studies (National Institutes of Health Consensus Development Panel 1983). The lack of documentation of benefit in clinical trials and biologic mechanisms to explain clinical observations impacted the FDA decision to continue to allow synthetic food coloring (Advisory Committee (March 30–31, 2011).
Goals and Objectives The Feingold diet eliminates specific types of foods in the diet reported to be high in natural salicylates and processed foods containing
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synthetic food colorings, sweeteners, and preservatives to promote behavioral improvement in attention, hyperactivity, irritability, and learning. Dr. Feingold managed over 600 of his patients with this approach and reported that up to twothirds of the children improved. He reported that many children could be taken off of stimulant medications used to treat symptoms of ADHD once the diet was initiated. Dr. Feingold advised Conners et al. (1976) in the first clinical trial testing his hypotheses. Prospective studies attempted to examine the behavioral effects of the Feingold diet, artificial sweeteners, and/or food dyes and preservatives on the behavior of children in the learning laboratory, the classroom, and the home. Studies were designed to examine both behavioral change with implementation of the diet and the response to double-blind placebocontrolled challenges of specific agents. The studies are difficult to compare because of varied design features and the different goals and outcome measures of the individual studies. Some address only components of the diet (e.g., aspartame, specific food dyes). There is no consistency to the dosage of exposure of synthetic food coloring across studies.
Treatment Participants None of the published studies specifically evaluates the utility of the Feingold diet for children with ASD. The studies evaluate children with ADHD, children with symptoms of hyperactivity, and/or community samples. There are several different sets of studies that examined components of the Feingold program that all approached identification of the study population in different ways. Many selectively recruited children whose families made the observation that their child improved behaviorally on the dietary intervention (Schab and Trinh 2004). Because of the difficulty in maintaining a restricted diet and cost of clinical trials, the double-blind placebo-controlled studies were all of relatively small size. Studies that set out to evaluate the effect of the Feingold diet including all four components included the original trial by Conners et al.
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(1976) that compared 15 children with ADHD to themselves in a 1-month crossover design. A crossover design was used to assess 36 children with hyperactivity without medication by Harley et al. (1978). While not the original Feingold diet, oligoantigenic diets where a few nonprocessed foods are initially permitted then other foods are sequentially added back have been studied more recently (Pelser et al. 2010) in children with behavioral symptoms. Other studies examined the effects of sucrose or artificial sweeteners like aspartame on behavior. Wolraich et al. (1994) demonstrated no effect on cognition or attention from double-blind placebo-controlled challenges of aspartame in 25 typical preschool children and 23 school-aged children who were reported to have behavioral effects with sugar. In another report, Wolraich et al. (1995) summarized studies examining the effect of sugar an attention that included a total of 535 children. No significant effect was sustained across studies. A third set of studies had as their goal the assessment of the behavioral effects of food dyes and preservatives on children. Mattes and Gittelman (1981) reviewed the literature and reported on 11 children with symptoms of ADHD who were recruited as responders to the Feingold diet. A meta-analysis of studies including 136 participants in randomized double-blind placebo-controlled studies that examined the effects of artificial food coloring was published by Schab and Trinh (2004). McCann et al. (2007) examined the behavioral response to two different concentrations of synthetic food dyes and sodium benzoate in two groups of children: 153 3-yearolds and 144 8- and 3-year-olds drawn from a diverse community sample and not selected for hyperactivity. Lok et al. (2013) compared the effect on attention in 130 8-year-old children in Hong Kong randomized to food coloring, sodium benzoate, or placebo delivered by capsule. There was no difference between groups studied. A fourth type of study investigating the effect of increasing the nutritional value of foods provided a large population of public school students that included provision of foods with fewer synthetic ingredients (Schoenthaler et al. 1986).
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Treatment Procedures The study procedures are varied. Diet introduction and maintenance: Most studies introduced the diet as part of the study or evaluated children already on the diet. Harley et al. (1978) provided all the foods to the families for the child under study to ensure that the diet was stringently followed. Some studies restricted other medications or supplements the children ingested. None formally analyzed and reported on the dietary sufficiency of the diet consumed or looked for other nutritional effects on behavior, although several commented on the overall sufficiency of the diet. Dietary exposure to challenges: The studies that used a double-blind placebo-controlled approach to examine the behavioral effect of exposures typically provided a snack or beverage that contained synthetic food coloring, preservative, and/or aspartame at a standard time or times through the day. Some provided for repeat daily exposure for a week or more; others provided single challenges. At least one study examined the effect of time of day. Allowance for washout was not typically addressed and might impact the results of otherwise carefully done trials (Schab and Trinh 2004). Measurement of change in behavior: Outcome measures will be discussed below. The study designs included subjective rating scales from parents, teachers, and research team members; laboratory tests of attention and learning; and objective measures of behavior largely in class settings.
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in attention or hyperactivity as reported by laboratory tasks of inattention and impulsivity, clinician ratings, or teacher ratings with the defined diets that eliminate food dyes and food preservatives or alter the fruit and vegetable composition of the diet. • There may be an impact on behavior in parent rating scales related to food dye exposure. It is unknown if this may be related to specific food dyes and preservatives, exposure, and/or age. • Aspartame does not impact attention or hyperactivity. • A defined diet should only be considered after diagnostic evaluation for ADHD and the implementation of educational and behavioral interventions. Families considering a trial of dietary intervention should have adequate information to weigh the potential benefit from conventional medications and should discuss their plan with their health-care provider. Data collection regarding change in target behaviors will help families determine if their behavioral goals are met by this intervention. No controlled trials on the effects of the Feingold Diet have been completed in children with autism spectrum disorders. A recent review of dietary treatments by Millichap and Yee (2012) reiterates that dietary intervention may be preferred by some families but does not have the scientific support to endorse general use.
Outcome Measurement Efficacy Information The report of the NIH consensus development panel in 1982 was an accurate summary of the evidence available at that time and remains an appropriate summary of the efficacy information accrued over the subsequent 35 years: • There are clinical reports of children whose parents observe that dietary restriction improved symptoms of hyperactivity. • The scientific literature overall does not demonstrate a statistically significant improvement
The response to dietary interventions across studies is difficult to compare because different measures were used that capture different aspects of behavioral response. Subjective response as reported by parents: Studies used a variety of parent report measures designed to capture the core symptoms of ADHD: impulsivity, inattention, and motor hyperactivity. The Conners scales have been validated and widely used by researchers studying hyperactivity (Schab and Trinh 2004). Other investigators compromised the interpretation of their studies
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by developing their own measures that were not well validated (reviewed in Schab & Trinh). Subjective response as reported by teachers: Teacher reports using the Conner’s scales were common to many studies. This questionnaire is well validated and was widely used in the time period that many of these studies were completed. Schab and Trinh (2004) point out that teachers often rate children as more hyperactive than parents because of the tasks required during the school day. Laboratory measures of attention and activity: Laboratory measures of vigilance and attention were used in several studies. Tests such as the Conner’s Continuous Performance Test II were used by investigators to provide data on sustained attention and response inhibition on standard tasks that are impacted by inattention, impulsivity, and hyperactivity in children with ADHD. Combination of outcome measures: While most investigators reported on individual measures by examiner, McCann et al. (2007) created a Global Hyperactivity Index to capture the results of the laboratory attention task, classroom observation and rating, and parent rating in a single score. The participants in their study were not selected for symptoms of hyperactivity and represented a community sample. Measurement of behaviors other than ADHD: Few studies examined behaviors other than those related to attention and hyperactivity. Rowe and Rowe (1994) attempted to capture parent rating of irritability and sleep difficulties which were elevated in their report on exposure to food coloring. When parent report data are separated from the data collected by schools and clinicians, exposure to synthetic food coloring may be associated with increased behavioral symptoms (McCann et al. 2007; Schab and Trinh 2004). It may be that behavioral effects other than ADHD core symptoms are captured in the parent report data.
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The Feingold Foundation publishes a list of common products that meet their specifications, so implementation of the diet should be neither expensive nor onerous. Removal of a large number of the fruits and vegetables consumed in the American diet might have a nutritional impact if the necessary nutrients are not consumed in other forms. This has not been investigated. The diet recommends substitution of other foods such as bananas, cashews, and pears that the Feingold Foundation suggests are lower in natural salicylates. Reading food labels for processed foods for the presence of food dyes and preservatives may be challenging. Families who are considering a trial of dietary intervention should consult with their primary health-care provider. It may be helpful for families to be referred to a Registered Dietitian, a licensed professional with training that helps them understand the nutritional content of foods, for counseling regarding diet change and how to provide an adequate diet if supplemented, processed foods are eliminated. Not all fruits and vegetables are nutritionally equal, so children with food selectivity might require targeted supplementation. A family considering a dietary intervention might also consult with their child’s educational or behavioral providers to collect data on the behaviors they wish to change. This may be very helpful in determining if there is a behavioral effect that is clinically significant enough to continue intervention. Since the dietary intervention targets symptoms of ADHD, formal rating scales for ADHD in use in the school setting could be considered.
See Also ▶ Aberrant Behavior Checklist ▶ Gluten-Free Diet
References and Reading Qualifications of Treatment Providers The advocates of this diet suggest that there is no harm in the removal of synthetic food colorings and preservatives and artificial sweeteners by the provision of a diet containing fewer processed foods.
Advisory Committee. (March 30–31, 2011). Background document for food advisory committee: Certified color additives in food and possible association with attention deficit hyperactivity disorder in children. http:// www.fda.gov/downloads/AdvisoryCommittees/Comm itteesMeetingMaterials/FoodAdvisoryCommittee/UC M248549.pdf.
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2000 Conners, C. K., Goyette, C. H., Southwick, D. A., Lees, J. M., & Andralous, P. A. (1976). Food additives and hyperkinesis: A controlled double blind experiment. Pediatrics, 58(2), 154–166. Feingold, B. F. (1985). Why is your child hyperactive? New York: Random House. ISBN 0-394-73426-2. Harley, J. P., Ray, R. S., Tomasi, L., Eichman, P. L., Matthews, C. G., Crun, R., Cleelard, C. S., & Transmar, E. (1978). Hyperkineses and food activities: Testing the Feingold hypothesis. Pediatrics, 61(6), 18–28. http://www.feingold.org/overview.php Lok, K. Y., Chan, R. S., Lee, V. W., Leung, P. W., Leung, C., Leung, J., & Woo, J. (2013). Food additives and behavior in 8- to 9-year-old children in Hong Kong: A randomized, double-blind, placebo-controlled trial. Journal of Developmental and Behavioral Pediatrics, 34(9), 642–650. PMID: 24217026. Mattes, J. A., & Gittelman, R. (1981). Effects of artificial food colorings in children with hyperactive symptoms. Archives of General Psychiatry, 38, 714–718. McCann, D., Barrett, A., Cooper, A., Grumpler, D., Daler, L., Grimshaw, K., Kitanin, E., Lok, K., Porteaes, L., Prince, E., Sonuga-Barke, E., Warner, J. O., & Steverson, J. (2007). Food additives and hyperactive behavior in 3 year old and 8/9 year old children in the community: A randomized, double blinded, placebo controlled trial. Lancet, 5, 5. Millichap, J. G., & Yee, M. M. (2012). The diet factor in attention deficit hyperactivity disorder. Pediatrics, 129(2), 330–337. https://doi.org/10.1542/peds.20112199. Epub 2012 Jan 9. PMID: 22232312. National Institutes of Health Consensus Development Panel. (1983). National Institutes of Health consensus development conference statement: Defined diets and childhood hyperactivity. American Journal of Clinical Nutrition, 37, 161–165. Pelser, L. M., Frankena, K., Buitelaar, J. K., & Rommelse, N. N. (2010). Effects of food on physical and sleep complaints in children with ADHD: A randomized controlled pilot study. European Journal of Pediatrics, 169, 1129–1138. Rowe, K. S., & Rowe, K. J. (1994). Synthetic food coloring and behavior: A dose response effect in a double blind placebo controlled repeated measures study. Journal of Pediatrics, 125(50+1), 691–698. Schab, D. W., & Trinh, N. D. (2004). Do artificial food colors promote hyperactivity in children with hyperactive syndromes? A meta analysis of double blind placebo controlled trials. Journal of Developmental and Behavioral Pediatrics, 25, 423–443. Schoenthaler, S. J., Doraz, W. E., & Wakefield, J. A. (1986). The impact of a low food additive and sucrose diet on academic performance in 803 New York City public schools. International Journal of Biosocial Research, 8(2), 185–195. Weber, W., & Newmark, S. (2007). Complementary and alternative medical therapies for attention deficit/hyperactivity disorder and autism. Pediatric Clinics of North America, 54, 987–1006.
Fenfluramine Wender, E. H. (1986). The food additive free diet in the treatment of behavior disorders: A review. Journal of Developmental and Behavioral Pediatrics, 7(1), 35–42. Wolraich, M. L., Lindgren, S., Stumbo, P., Stegink, L. D., Applebaunr, I., & Kiritsy, M. L. (1994). Effects of diets high in sucrose or aspartame on the behavior and cognitive performance of children. The New England Journal of Medicine, 330, 301–307. Wolraich, M. L., Wilson, D. B., & White, J. W. (1995). The effect of sugar and behavior or cognition in children. Journal of the American Medical Association, 274(20), 1617–1621.
Fenfluramine Lawrence David Scahill Nursing and Child Psychiatry, Yale Child Study Center, Yale University School of Nursing, New Haven, CT, USA Marcus Autism Center, Children’s Healthcare of Atlanta, Atlanta, GA, USA Department of Pediatrics, Emory University, Atlanta, GA, USA
Synonyms Pondimin
Definition Fenfluramine is a drug with an incompletely understood mechanism of action. It was originally introduced for the treatment of obesity. It promotes the release of serotonin acutely but may have a serotonin-depleting effect over time. Given the replicated finding that at least a subgroup of children with autism are hyperserotonergic, investigators considered fenfluramine for the treatment of autism. Side effects were noteworthy. A randomized trial conducted in the mid-1980s showed that fenfluramine was no better than placebo. Since then, fenfluramine was removed from the market in 1997 when there were reports of pulmonary hypertension and damage to heart valves.
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See Also ▶ Serotonin
References and Reading Ritvo, E. R., Freeman, B. J., Yuwiler, A., Geller, E., Schroth, P., Yokota, A., et al. (1986). Review group(s): Cochrane child health field. Psychopharmacology Bulletin, 22(1), 133–140. Volkmar, F. R., Paul, R., et al. (1983). Irritability in autistic children treated with fenfluramine. New England Journal of Medicine, 309(3), 187.
Feral Children Lorna Wing Centre for Social and Communication Disorders, Bromley, Kent, UK
Definition The term “feral” children is used to refer to children who are known, or are believed, to have been deprived of contact with other human beings or to have been neglected or treated inhumanely by a human being since infancy. They have had little or no experience of human love or care and no experience of human communication through spoken language. Some are thought to have been reared by animals. Accounts of such children can be found in myths, legends, fiction, and in stories believed to be true, though some have turned out to be hoaxes. The relevance for autism spectrum conditions lies in the inability to understand and speak and the odd behavior of many of the children and young adults, who are known or thought to have been deprived of human love and communication in their early years.
Historical Background The accounts of such children in myths, legends, and fiction do not, however, have any link with
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autism. The legend of Romulus and Remus is one example. They were twin brothers, sons of the god Mars. When very young, they were abandoned by the banks of the river Tiber, but were found by a she-wolf who fed them with her milk and sustained them until they were able to feed themselves. Later, they were found and given a home and care by a shepherd, and they grew up to be strong and clever. They decided to build a city on the place where the shepherd had found them – the legendary origin of the city of Rome. After it was built, they quarreled over who should be in charge, and Romulus killed Remus. Romulus became King of Rome, which he named after himself. The most famous “feral” characters in fiction are Mowgli and Tarzan. Mowgli was a character in stories written by Rudyard Kipling. Mowgli’s parents were killed by a tiger in the Indian jungle when Mowgli was still a baby. He was found and reared by a mother and father wolf in a pack of wolves. He developed remarkable skills in hunting and tracking. He had close relationships with a black panther and a brown bear and was able to communicate with them. In the end, he returned to human society and had no problems communicating in human speech. Tarzan was the fictional character invented by Edgar Rice Burroughs (although it has been suggested that he was influenced by the Mowgli stories). Tarzan’s parents died when he was a year old, and he was adopted by a group of great apes, the mangani, which is a species unknown to science! Tarzan’s jungle upbringing gave him remarkable abilities, beyond those acquired by most human beings. He was strong, agile, and well able to wrestle with all kinds of animals. He learned languages rapidly, including the language of the apes who reared him and other jungle animals and English, French, German, Latin, and ancient Greek, among others. He met and fell in love with a young American woman, Jane, whom he eventually married. They tried living together in England but found civilization hypocritical, so they returned to live in Africa. The stories of Mowgli and Tarzan have been and are enormously popular and have led to many more books, as well as films, DVDs, television
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programs, and even Disney cartoons. There seems to be something about these stories that people all over the world find endlessly fascinating. However, the fictional pictures of highly intelligent, gifted individuals who have learned languages and can communicate with animals and with human beings stand in stark contrast to the known facts concerning real “feral” children. Those in fiction bear no relationship to autism, unlike many of those, who, in real life, in their early years, were deprived of care and love from other human beings (Frith 2003a).
Current Knowledge Another inappropriate use of the term “feral” children is to refer to those who live on the streets of cities. They are also known as “street” children. Such children have been deprived of family care because of neglect, or abuse, or parental death, or abandonment by poor parents who cannot afford to care for them. Large numbers of such children can be found in great cities throughout the world. Professor Martin Patt (2010) has created a website on which it is possible to find details of street children in different countries throughout the world. Patt emphasizes that such children can fall under the control of traffickers, who may disfigure the children in order to increase the money they can make by begging. The street children are also vulnerable to sexual abuse. The suggestion of a link with autism is not appropriate especially for those street children who do not fall under the control of criminal adults. Typically, such children form their own gangs, with their own social rules and hierarchies to support each other and survive through various forms of criminal behavior. (See note on Romanian street children, Patt (2010) below.) They appear in fiction, for example, as the young pickpockets organized by Fagin in Charles Dickens’ book “Oliver Twist” or the street gangs, the “Baker Street Irregulars” who carried out various tasks for Sherlock Holmes in the stories by Sir Arthur Conan Doyle. They are quite different from the isolated child deprived of any human concern, care, and communication.
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The children sometimes referred to as “feral,” whose history and behaviors are most relevant for any discussion of autism spectrum conditions, are those who have been deprived of human companionship and loving care for all or most of their infancy and childhood. There are accounts of children who are known or believed to have been kept in solitary confinement from very early in life, perhaps from infancy. These children have been given minimal basic feeding and minimal care by a human being who does not show them any love and companionship and does not communicate beyond simple orders. One of these unfortunate children was Kaspar Hauser, who appeared in Nuremberg in 1828, at the age of 16–17 years. A detailed account of Kaspar and his behavior was written by Anselm von Feuerbach (Feuerbach and Von Ritter 1833), then President of the Bavarian Court of Appeal at Anspael near Nuremberg. It seems that, during his first 16 years, Kaspar had been kept in a dark cellar. He had been fed on bread and water and had never seen his keeper. His only companion was a toy horse. When found in Nuremberg, he was able to say only a few phrases and fragments of speech. He preferred darkness to daylight and liked to sit on the ground with his legs stretched out in front of him. He had problems with distance vision, which could be linked to the fact that his vision had been limited by the darkness and walls of the cellar he had lived in. His story is one of those discussed by Uta Frith (2003b). She notes that Kaspar rapidly learned to spell. He also showed social interest and affection towards children and adults who became close to him. He loved the toy horse he was given to replace the one he used to have in the cellar, and offered it a share of his food. He continued to have an odd gait, and he showed a marked preference for cleanliness and order for his possessions. Despite these continuing oddities, Professor Frith concluded that Kaspar was certainly not autistic – his social attachments and rapid improvement being strong indicators that he was not affected by this developmental disorder. Kaspar survived one attempt by a stranger to kill him. However, he was assassinated 5 years after his arrival in Nuremberg. The mysteries of his
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origin, why he was kept in a cellar, why he was assassinated, and who killed him have never been solved, though there were unverified rumors that he was the unwanted child of a royal person. A much more recent account of a child deprived of contact with loving human care and the outside world from early childhood is that of the girl known as Genie (not her real name). The psycholinguist who studied her, Susan Curtis (1977), referred to her as a modern-day “wild child.” Genie was born in the USA, the daughter of a violent, difficult father and a sad, frightened mother. She was the fourth and last child in the family, and was delivered by cesarean section. The first had died in infancy because his father objected to his crying and so kept him in the unheated garage where he developed pneumonia, which caused his death. The second child also died. The third, a boy, at 5 years, was taken into the care of his paternal grandmother, where his development, previously retarded, improved markedly, and he finally returned to his parents. At the age of 14 months, Genie developed pneumonitis and was seen by a pediatrician, who suggested that she showed signs of retardation, though the pediatrician could not be sure because Genie had a high fever. When Genie was 20 months old, her paternal grandmother died in a road accident. Genie’s father was intensely disturbed by this. He moved the family into his mother’s house and isolated them from the outside world. Genie was kept in a small bedroom, tied by a harness to a potty chair. At night, she was placed in a sleeping bag, which severely restricted her movements. Genie was kept secluded and had no toys to play with or radio to listen to. Her father hated any sort of noise, so conversation in the home was kept to a minimum and at a low volume. Genie’s father did not speak to her but barked and growled at her, and then beat her if she made any noises. She was fed baby foods that were stuffed into her mouth. This pattern of life for Genie lasted until she was 13½ years old, when her parents had a violent argument and Genie’s mother left her husband and home, taking Genie with her. After spending 3 weeks with the maternal grandmother, Genie was seen by a social worker, who sensed that
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there were serious problems. Genie was admitted to hospital with severe malnutrition. The police were involved and charges brought against the parents. On the day of the trial, the father killed himself. For 5 years from November 1970, when Genie was released from virtual captivity and isolation, her skills and disabilities were studied in detail by Curtiss and other colleagues. At first, she was very underweight and undersized and had major problems of self-care, movement, and distance vision. She was silent, even when sobbing, apart from a high-pitched whimpering and a few words. To quote from Susan Curtis (1977), “. . .she salivated copiously, spitting onto anything at hand. Genie was unsocialized, primitive, hardly human.” Susan Curtiss analyzed in detail aspects of Genie’s ability with understanding and using language. Among other problems, Genie consistently confused you and me. She used entire phrases as labels, she failed to acknowledge questions, requests, etc., addressed to her. These are features also seen in children with autism spectrum disorders. Despite all this, Curtiss reports that Genie was alert and curious, avidly exploring her new surroundings. She was intensely eager for human contact and made good eye contact. These aspects of her behavior counteract any suggestion that she had an autism spectrum disorder. Susan Curtiss concluded that Genie appeared to be a “right hemisphere thinker.” She suggested that this was due to the deprivation of language experienced in her childhood. Many questions relevant for a possible diagnosis of autism remain unanswerable. Was the pediatrician right in thinking that Genie was retarded when he or she saw her at 14 months, before her isolation? Did Genie show any of the odd hand and arm movements associated with autism, as well as her odd gait and posture? What was the underlying cause of Genie’s father’s bizarre behavior to his family – did he have a developmental disorder? There is no way of knowing. Very relevant to the subject of children deprived of loving personal care and human communication from infancy or early childhood are the stories of the Romanian orphans. Nicolae Ceausescu was president of Romania from 1974
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to 1989. During his presidency, birth control was made illegal in order to boost the population. The result was that many families had more children than they could possibly afford to care for, so these children were placed in state-run institutions. The care provided was minimal and often appallingly bad, and the children were kept in cots, deprived of human love, social interaction, and communication as well as lacking adequate nutrition and other health needs. In addition, during the Ceausescu regime, children who did not pass the state-organized physical and psychological assessments carried out on preschool children were also placed in institutions. After the fall of Ceausescu and his regime, the sad life of the children in such institutions was publicized outside Romania. Some children from Romanian institutions were adopted by nonRomanian families, including some in the UK. A sample of 144 children who had experienced institutional care in Romania and who were later adopted by UK parents were assessed at ages 4, 6, and 11 years by Professor Michael Rutter et al. (2007; O’Connor et al. 2000; Rutter et al. 1999) They were compared with 52 non-deprived UK children adopted in the UK and placed before 6 months of age. The children from Romania, when they arrived in England, showed marked signs of physical and psychological deprivation. They had been kept in their cots with no social or intellectual stimulation of any kind. The majority of the group showed remarkable catch-up by the time they were seen at 4 years of age. However, 16 children (11.1% of the sample of 144 children) showed “quasiautism.” Three of the 16 had IQs below 50, the rest having IQs above this level. These 13 children were studied in detail. Their pattern of symptoms was called “quasiautism” for specific reasons. While their scores on the ADI-R assessment at age 4 years were similar to a group of children with typical autism studied using the Autism Diagnostic Observation Schedule (Lord et al. 1994), their scores at age 6 years were significantly lower (less autistic) than those of the typically autistic children. There was also a tendency for the IQ scores to rise. They showed more flexibility in communication. Some showed
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substantial social approaches, though of an abnormal kind, some being indiscriminately friendly. Those with “quasi-autism” had a much greater degree of cognitive impairment than the remainder of the adoptees from Romania. The sex ratio in the “quasi-autistic” children was approximately equal, compared with an excess of boys in the typically autistic children. Unlike the typically autistic children, the head circumference was not raised in those with “quasi-autism.” The followup at 12 years showed that one quarter of the affected children had lost their autistic-like features by age 11 years. Rutter and his team considered the possible causes of the “quasi-autistic” behavior, particularly in those (the majority) who did not have IQs below 50. They were significantly older than the rest when leaving institutional care to come to the UK. To quote from Rutter et al. (1999), “The quasi-autistic pattern seems to be associated with a prolonged experience of perceptual and experiential privation, with a lack of opportunity to develop attachment relationships and with cognitive impairment.” The strongest predictor was the children’s age of entry to the UK. Those adopted before 6 months showed almost complete physical and cognitive catch-up by 4 years. Those adopted between 6 and 24 months had lower scores on cognitive tests than those adopted before 6 months. Those adopted between 24 and 42 months had the lowest scores of all and a general developmental impairment. Rutter and his colleagues concluded that children adopted before 2 years of age were likely to show considerable resistance and to catch-up in cognitive and physical development. The research team also found another 12 children who had some autistic-like features but not enough to qualify for “quasi-autism.”
Future Directions Dr. Sue Sheppard, a chartered specialist educational psychologist who is working in the field of developmental disorders and is a consultant to the NAS Diagnostic Centre, went to Romania in 1992 as one of the group of professionals whose
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aim was to help the children in the state institutions. Among the children in the institutions, Sue Sheppard (personal communication, in conversation with the author in December 2010) found a small minority who quite clearly had autism spectrum disorders. This is not surprising, since parents who sent or abandoned children to institutions would be likely to select those whose behavior was challenging or whose development was clearly abnormal. Furthermore, stateimplemented preschool screening tests would identify those with developmental disorders. In addition to those who were clearly in the autism spectrum, there was another minority who had some autistic traits, especially odd movements and other repetitive behaviors, and who were not socially responsive, though they did not present the full picture of an autism disorder. They closely resembled the children Rutter and colleagues described and called “quasi-autistic” (see above). Unlike those with clearly diagnosable autism, these children showed definite improvement over a period of time when given loving care and help by people who understood their problems, who came from relief agencies from other countries, sent in response to the problems children were having in Romania. These children’s physical health and intelligence levels also improved markedly. The majority of the institutionalized children did not show either of the above abnormal behavior patterns, seeming to be highly resistant despite the deprivation suffered. This was most likely to be the case if, as Rutter and his ream also found, the children were under 2 years old when their living conditions were improved. Sue Sheppard also observed another group of children, often described as “wild” or as “bandits.” These were children who lived in gangs on the streets and were sociable according to the values of the group they were with. They had become “street children” for various reasons, including having run away from abusive parents or institutions. They could smile sweetly and act in a charming way in order to get near enough to someone to steal from them. Although their behavior was antisocial from the point of view of society as a whole, as a group, they had their
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own social system, which certainly does not fit with a diagnosis of autism spectrum disorder. The last group of “feral” children to be discussed here are those who are given the name “feral” because they are thought to have been reared by animals in their early childhood years. Stories of such individuals found wandering, captured, and reintroduced into human society can be traced back to the 1300s. Lucien Malson (1972) gives a list of 53 “feral” children recorded from 1344 up to 1961. Wikipedia adds another 10 in the years from 1961 to 1999 and 8 from 2000 to 2009. The animals alleged to have cared for the children include wolves, cows, pigs, sheep, leopards, bears, chimpanzees, monkeys, dogs, goats, and gazelles. A few children were actually found with animals (most such accounts are of children found with packs of dogs), but there is no direct evidence of them being suckled by the animals in their infancy. Several of the stories are known or thought to be fictitious, including the two that described children reared by gazelles. Where sufficient contemporary information is available, it is clear that those thought to have been reared by animals lacked basic social skills. The great majority of those reported did not learn to speak or else managed only a few words; they had problems learning to walk upright and to use a toilet and had no interest in other human beings or human activity. The one “feral” child concerning whom we have a considerable amount of information is the “wild boy of Aveyron.” This boy was found in 1798 wandering naked and wild in the woods in south-central France. He was captured and eventually given into the care of the physician Dr. Jean Marc Gaspard Itard and his house keeper, Madam Guerin. Itard wrote two detailed reports in 1801 and 1806 (see English translations, Itard 1962) describing the boy’s behavior and his progress in learning the skills that Itard tried to teach him. When captured, Victor (the name given him by Itard) was thought to be about 12 years old, though small for his age. He had many scars on his body and limbs including one across his throat. He had been glimpsed in the forest on occasions for 2 years before his capture, so it seemed that he had lived in the wild at least from around age 10 up to 12 years.
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In her book on autism, Uta Frith (2003c) includes a detailed discussion of the evidence concerning the possibility that Victor had autism. The relevant aspects of his behavior, as described by Itard, are as follows. Victor showed no marked social attachment to anyone. He showed some attachment to his caretakers, Dr. Itard and Madame Guerin, which seemed to be due to the fact that they provided his food. He showed no sympathy and no evidence that he had any concept of other people’s ideas, wishes, or feelings. He had no ability to conform to any social rules. Victor made various noises but did not learn to speak. He did learn that the word “lait” referred to milk, but never used it to request milk, only as a label when he was given milk. He used limited nonverbal ways of communicating the few messages he wished to communicate. For instance, he enjoyed having Dr. Itard touch and stroke his head and would take Itard’s hand and place it on his head to indicate what he wanted. He never showed any evidence of pretend play or imaginative skills. As is typical in autism, Victor did respond in unusual ways to various kinds of sensory input (Leekam et al. 2007). He ignored most sounds, however sudden and loud, but turned at once to the sound of a nut cracking (nuts being a favorite food), however faint the sound. He seemed oblivious of heat, cold, or pain. This was one of the possible reasons why he could exist for at least 2 years in the wild. He would wake early in the morning, wrap his head and body in his blanket and rock himself back and forth. He would lie down from time to time and then rock again until it was time for breakfast. He was fascinated by the moonlight, and would sit and watch the moon while rocking himself. Despite lack of language skills, Victor was very efficient at shelling and preparing beans for cooking, including picking out any that were bad. He learned to carry out some other practical domestic tasks that did not require language to perform. These behaviors indicate impairment of social interaction, communication, and imagination, and a preference for repetitive routines (rocking),
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which are diagnostic of an autism spectrum disorder. Uta Frith considers that it is highly likely that Victor had such a disorder. The present writer agrees with her, though, from such a distance in time, there can be no absolute certainty. Harlan Lane (1979), professor of social psychology, does not accept that Victor could have had an autism spectrum disorder. However, the aspects of Victor’s behavior he lists as evidence that would exclude a diagnosis of autism could be seen in many children with this diagnosis. Lane notes that Victor was not totally aloof and indifferent to others. For example, as already mentioned above, Victor did show some signs of attachment to Dr. Itard and to Madame Guerin, who were his careers. There is an account of him weeping when reproached by Itard. But many children with typical autism spectrum disorders are not totally aloof – their social behavior is limited and can be inappropriate. Lane appears to be aware only of the most severe manifestations of autism and not the wide range of behavior across the whole autism spectrum and the changes that can occur with increasing age and the positive effects of education and of loving care. While it is highly likely that Victor was in the autism spectrum and a few other “feral” children were clearly not autistic, for the great majority of cases of “feral” children, a diagnosis of autism or at least autistic traits appears to be a possibility, but there is rarely enough evidence to be certain. A question that inevitably arises in any discussion of “feral” children is whether the autism spectrum disorder is due to biological causes and was present from birth (Feinstein 2010; Frith 2003d), leading to the child’s loss or abandonment, or whether the lack of loving human care was the cause (Bethelheim 1967). The available information on “feral” children cannot provide an answer to this question. The known cases of “feral” children are just a minute fraction of all those with autism spectrum disorders. The latest prevalence studies suggest that around 1% or more of children and adults have some form of this disorder (Gillberg and Wing 1999; Wing 2004; Wing and Potter 2002). Research into the nature and origins of autism has shown that autism spectrum disorders are most likely to be due to
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genetic or other biological causes (Feinstein 2010; Frith 2003d). The amount of progress possible for those with autism has been shown to be linked to their innate level of ability, as measured by IQ tests, though the prognosis even for those with IQ over 70 is very variable (Beadle-Brown et al. 2006; Howlin et al. 2004). Another question that has not been answered concerns whether there is a “critical” or “sensitive” period for the acquisition of language. The critical period hypothesis holds that there is a limited time in early childhood when language can be acquired in response to exposure to facilitative speech presented by caretakers. If a child is deprived of this essential experience during the appropriate age period, he or she will not learn to understand and speak (Lenneberg 1967). It has been suggested that the histories of “feral” children support this theory. The findings of Rutter et al. concerning the importance of removal to a supportive environment before 2 years, described above, do provide some support. The problem is that there is no way of knowing whether or not the “feral” children who do not speak suffered from autism, or a developmental language disorder, or intellectual disability, as the reason why they did not speak and did not learn language when education was provided. The sad truth is that the stories of “feral” children in all their variation, in most cases, present fascinating but insoluble mysteries. The questions they raise can only be solved (if at all) by appropriate modern research.
See Also ▶ Atypical Autism ▶ Child Abuse in Autism ▶ Factors Affecting Outcomes ▶ Romanian Adoptive Children
References and Reading Beadle-Brown, J., Murphy, G., & Wing, L. (2006). The Camberwell Cohort, 25 years on; characteristics and changes in skills over time. Applied Research in Intellectual Disabilities, 19, 317–329.
2007 Bethelheim, B. (1967). The empty fortress. New York: The Free Press. Candland, D. K. (1993). Feral children and clever animals: Reflections on human nature. Oxford: Oxford University Press. Curtis, S. (1977). Genie: A psycholinguistic study of a modern-day wild child. New York: Academic Press. Feinstein, A. (2010). A history of autism: Conversations with the pioneers. Chichester: Wiley-Blackwell. Feuerbach, A. R., & Von Ritter, P. (1833). Kaspar Hauser: An account of an individual kept in a dungeon separated from all communication with the world from early childhood until about the age of 17. London: Simpkin & Marshall. Frith, U. (2003a). Lessons from history. In U. Frith (Ed.), Autism explaining the enigma (2nd ed., pp. 34–57). Oxford: Blackwell. Frith, U. (2003b). The case of Kaspar Hauser: Lessons from history. In U. Frith (Ed.), Autism explaining the enigma (2nd ed., pp. 43–49). Oxford: Blackwell. Frith, U. (2003c). The case of the wild boy of Aveyron: Lessons from history. In U. Frith (Ed.), Autism explaining the enigma (2nd ed., pp. 35–43). Oxford: Blackwell. Frith, U. (2003d). Seeing the brain through a scanner: Lessons from history. In U. Frith (Ed.), Autism explaining the enigma (2nd ed., pp. 1820–1824). Oxford: Blackwell. Gillberg, C., & Wing, L. (1999). Review: Autism is not an extremely rare disorder. Acta Psychiatrica Scandinavica, 99, 399–406. Howlin, P., Goode, S., Hutton, J., & Rutter, M. (2004). Adult outcome for children with autism. Journal of Child Psychology and Psychiatry, 45(2), 212–229. Itard, J. M. G. (1962). The wild boy of Aveyron. New York: Appleton. Reports published in French in1801 and 1807, translated into English by G. Humphrey & M. Humphrey. Lane, H. (1977). The wild boy of Aveyron: The dramatic account of a wild boy of nature and a young French doctor who shaped the modern education of retarded, deaf and pre-school children (pp. 176–178). London: George Alan & Unwin. Lane, H. (1979). The wild boy of Aveyron. Cambridge, MA: Harvard University Press. Leekam, S. R., Nieto, C., Libby, S., Wing, L., & Gould, J. (2007). Describing the sensory abnormalities of individuals with autism. Journal of Autism and Developmental Disorders, 37, 894–910. Lenneberg, E. H. (1967). Biological foundations of language. Chichester: Wiley. Lord, C., Rutter, M., & Le Couteur, A. (1994). Autism diagnostic interview-revised: A revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. Journal of Autism and Developmental Disorders, 24(5), 659–685. Lord, C., Rutter, M., Dihavore, P. C., & Risi, S. (2001). Autism diagnostic observation schedule manual. Los Angeles: Western Psychological Services.
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2008 Malson, L. (1972). Wolf children. New York: Monthly Review Press. O’Connor, T. G., Rutter, M., Beckett, C., Keaveney, L., Kreppner, J. M., & The English & Romanian Adoptees Study Team. (2000). The effectives of global severe privation on cognitive competence: Extension and longitudinal follow-up. Child Development, 71(2), 376–390. Patt, M. (2010). Street children: The prevalence, abuse & exploitation of street children. Internet address. http:// gvnet.com/streetchildren/. Accessed 25 June 2012. Rutter, M., Wood, L. A., Beckett, C., Bredenkamp, D., Castle, J., Groothues, C., et al. (1999). Quasi-autistic patterns following severe early global privation. Journal of Child Psychology and Psychiatry, 40(4), 537–549. Rutter, M., Kreppner, J., Croft, L., Murin, M., Colvert, E., Beckett, C., et al. (2007). Early adolescent outcomes of institutionally deprived and non-deprived adoptees III Quasi-autism. Journal of Child Psychology and Psychiatry, 48(12), 1200–1207. Wing, L. (2004). The spectrum of autistic disorders. Hospital Medicine, 65, 542–545. Wing, L., & Potter, D. (2002). The epidemiology of autistic spectrum disorders: Is the prevalence rising? Mental Retardation and Developmental Disabilities Research Reviews, 8, 151–161.
Ferster, C.B. A. Charles Catania Department of Psychology, UMBC (University of Maryland, Baltimore County), Baltimore, MD, USA
Name and Degrees
Ferster, C.B.
Ferster, Charles Bohris (Nov. 1, 1922–Feb. 3, 1981). B.S. (Rutgers University, 1947), M.A. (Columbia University, 1948), Ph.D. (Columbia University, 1950).
Major Appointments (Institution, Location, Dates) • US Army Air Force, 1943–1946 • Research Fellow under B. F. Skinner, Harvard University, 1950–1955 • Postdoctoral Research, Yerkes Laboratory, Emory University, 1955–1957 • Postdoctoral Research, Indiana University Medical Center, 1957–1962 • Institute for Behavioral Research (Silver Spring, MD), 1962–1968, as Executive Director (1962–1963), Associate Director (1963–1965), Senior Research Associate (1965–1968) • Professor of Psychology, Georgetown University (Washington, DC), 1967–1968 • Professor of Psychology, American University (Washington, DC), 1969–1981 (Department Chair, 1970–1973)
Major Honors and Awards • First Editor and Executive Editor of the Journal of the Experimental Analysis of Behavior, 1958–1961.
Landmark Clinical, Scientific, and Professional Contributions
Charles Fester
With B. F. Skinner, developed the experimental methods of the experimental analysis of behavior, including the design of apparatus and procedures, especially with regard to schedules of reinforcement. Participated in the founding of the first major journal in the field, the Journal of the Experimental Analysis of Behavior. Extended the basic principles of behavior analysis to clinical problems and to education, in areas
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such as clinical depression, psychotherapy, and individual instruction. Distinguished between the natural consequences of behavior and the arbitrary consequences that must be arranged when for some reason the natural consequences are not effective. Opened the way to the treatment of autism by demonstrating that the behavior of the autistic child is sensitive to its consequences.
Short Biography Charles B. Ferster was a pioneer in the experimental analysis of behavior and its extension to areas of application. His undergraduate career at Rutgers University was interrupted by service in the US Army Air Force during World War II. Upon completing his Rutgers B.S. in 1947, he entered the graduate program in Psychology at Columbia University. There Fred S. Keller, with W. N. Schoenfeld, had established a curriculum based on B. F. Skinner’s analysis of behavior (Keller and Schoenfeld 1949, 1950). Keller referred Ferster to Skinner at Harvard University, and Ferster, completing his Ph.D. in 1950, moved to Harvard as Skinner’s research assistant. Once there, he developed many innovations in methodology and in the automation of Skinner’s pigeon laboratory, summarized in a journal article that became a guide for many other researchers (Ferster 1953). By 1957, he and Skinner had published a seminal book, “Schedules of Reinforcement” (Ferster and Skinner 1957), that detailed the properties and the results of these novel procedures in the analysis of behavior. Skinner was sufficiently impressed by Ferster’s contributions to the research enterprise that he gave Ferster first authorship. Behavior is maintained by its consequences, as when a pigeon’s key peck or a rat’s lever press occurs frequently because it produces access to food. This property of behavior is called reinforcement; reinforcement is not a theory but rather is simply a name for a ubiquitous phenomenon. What Skinner discovered about reinforcement in his earlier research (Skinner 1938), and what he and Ferster explored in depth, was that the maintenance of behavior by reinforcement depends
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crucially on the contingencies arranged for responses, their schedules of reinforcement. In the real world as well as in the laboratory, not every response is reinforced. In what is called a ratio schedule, for example, only some fraction of responses is reinforced, as when some percentage of bets pays off; one must play to win, but one will not win every time. In what is called an interval schedule, however, whether a response is reinforced depends on when the response occurs rather than on how many responses have occurred, as when one finds mail in one’s mailbox only if checking it after the mail has been delivered; checking more often will not affect when the mail comes. A full account of reinforcement schedules, which include both fixed and variable versions of ratio and interval schedules as well as other categories of contingencies, is beyond the scope of this article. For the present purposes, the crucial point is that different schedules produce and maintain very different patterns of behavior. For example, ratio schedules typically maintain substantially higher rates of behavior than do interval schedules, and details of the schedules determine the patterning of behavior over time (e.g., some schedules produce relatively constant responding, others produce gradual increases, and still others produce abrupt shifts from not responding to high rates of responding). Schedules of reinforcement soon became commonplace procedures for the study of a broad range of topics, such as the analysis of sensory processes, effects of psychopharmacological agents, and motivational operations. One procedure studied by Ferster (1954) involved using a blackout, turning off the lights in the pigeon chamber and therefore eliminating the pigeon’s opportunity to produce food. The name was later changed to time-out, and it was discovered that time-outs were sometimes useful because they could be used to reduce behavior, as when errors in some visual task produced timeouts from positive reinforcement. The use of timeout in some behavioral programs and eventually by some parents and teachers evolved from this early pigeon research. In those early days of operant research, extending the basic results from the animal
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laboratory to significant human problems had just begun. Examples were Fuller’s (1949) demonstration that reinforcers could be effective even on the behavior of a vegetative human, work by Lindsley (1956) showing the effects of schedules of reinforcement on the behavior of adult psychotic patients, and Bijou’s (1957) extensions to the study of child development. After moving to a postdoctoral position at the Medical Center at Indiana University in 1957, Ferster undertook an extension of his work with schedules of reinforcement to the behavior of autistic children. In those days, diagnostic criteria were highly variable, and such children were often described in terms of early-stage schizophrenia. More important, the classification of autism implied individuals who were virtually confined within themselves, impervious to events in their environments. Ferster, in collaboration with DeMyers (Ferster and DeMyer 1961, 1962), arranged laboratory settings functionally similar to those used in the analyses of reinforcement schedules and found that when appropriately arranged food and candy and trinkets could become effective reinforcers of the behavior of these children. Other studies examined ways in which these reinforcers might eventually provide a foundation for building social reinforcers, which are typically ineffective with these children (DeMyer and Ferster 1962). The research also showed that food could be used to make coins effective as conditioned reinforcers and that reinforcers could be arranged so as to enhance the attention of these children to some of the stimuli around them. Beyond the fundamental observation that the behavior of these children could be reached through reinforcement schedules, albeit more slowly than with children without these deficits, this research provided the precedent for extensions that were soon to follow, including work by Lovaas and others (e.g., Lovaas et al. 1965, 1966). In his basic research, Ferster had shown that relatively simple and small changes in the contingencies arranged by reinforcement schedules could produce profound changes in ongoing behavior, and he brought these observations to bear in his subsequent work, which moved to analyses of other clinical deficits and pathologies,
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such as clinical depression (Ferster 1965, 1972, 1973). Some additional contributions Ferster has made to the analysis of behavior have been described by Keller (1981) and by Skinner (1981).
See Also ▶ Applied Behavior Analysis (ABA) ▶ Behavior Analysis ▶ Contingencies of Reinforcement ▶ Operant Behavior ▶ Schedule of Reinforcement
References and Reading Bijou, S. W. (1957). Methodology for an experimental analysis of child behavior. Psychological Reports, 3, 243–250. DeMyer, M. K., & Ferster, C. B. (1962). Teaching new social behavior to schizophrenic children. Journal of the American Academy of Child Psychology, 1, 443–461. Ferster, C. B. (1953). The use of the free operant in the analysis of behavior. Psychological Bulletin, 50, 263–274. Ferster, C. B. (1954). Use of the blackout in the investigation of temporal discrimination in fixed-interval reinforcement. Journal of Experimental Psychology, 47, 69–74. Ferster, C. B. (1961). Positive reinforcement and behavioral deficits of autistic children. Child Development, 32, 437–456. Ferster, C. B. (1965). Classification of behavioral pathology. In L. Krasner & L. P. Ullman (Eds.), Research in behavior modification (pp. 6–26). New York: Holt, Rinehart & Winston. Ferster, C. B. (1970). Schedules of reinforcement with Skinner. In P. B. Dews (Ed.), Festschrift for B. F. Skinner (pp. 37–46). New York: Appleton-Century-Crofts. Ferster, C. B. (1972). The experimental analysis of clinical phenomena. Psychological Record, 22, 1–16. Ferster, C. B. (1973). A functional analysis of depression. American Psychologist, 28, 857–870. Ferster, C. B., & DeMyer, M. K. (1961). The development of performances in autistic children in an automatically controlled environment. Journal of Chronic Diseases, 13, 312–345. Ferster, C. B., & DeMyer, M. K. (1962). A method for the experimental analysis of the behavior of autistic children. Journal of Orthopsychiatry, 32, 89–98. Ferster, C. B., & Skinner, B. F. (1957). Schedules of reinforcement. New York: Appleton-Century-Crofts. Fuller, P. R. (1949). Operant conditioning of a vegetative human organism. American Journal of Psychology, 62, 587–590.
Fetal Alcohol Spectrum Disorder Keller, F. S. (1981). Charles Bohris Ferster (1922–1981), An appreciation. Journal of the Experimental Analysis of Behavior, 36, 299–301. Keller, F. S., & Schoenfeld, W. N. (1949). The psychology curriculum at Columbia College. American Psychologist, 4, 165–172. Keller, F. S., & Schoenfeld, W. N. (1950). Principles of psychology. New York: Appleton-Century-Crofts. Lindsley, O. R. (1956). Operant conditioning methods applied to research in chronic schizophrenia. Psychiatric Research Reports, 6, 118–139. Lovaas, O. I., Freitag, G., Kinder, M. I., Rubinstein, B. D., Schaeffer, B., & Simmons, J. Q. (1965). Establishment of social reinforcers in two schizophrenic children on the basis of food. Journal of Experimental Child Psychology, 4, 109–125. Lovaas, O. I., Berberich, J. P., Perloff, B. F., & Schaeffer, B. (1966). Science, 151, 705–707. Skinner, B. F. (1938). The behavior of organisms: An experimental analysis. New York: Appleton-CenturyCrofts. Skinner, B. F. (1981). Charles B. Ferster-A personal memoir. Journal of the Experimental Analysis of Behavior, 35, 259–261.
Fetal Alcohol Effects ▶ Fetal Alcohol Spectrum Disorder
Fetal Alcohol Spectrum Disorder John Thorne Department of Speech and Hearing Sciences, University of Washington Fetal Alcohol Syndrome Diagnostic and Prevention Network, Seattle, WA, USA
Synonyms Alcohol-related neurodevelopmental disorder; Fetal alcohol effects; Fetal alcohol syndrome
Short Description or Definition Used to refer to the range of negative outcomes associated with prenatal ethyl-alcohol exposure,
2011
fetal alcohol spectrum disorders (FASD) is an umbrella term that describes a heterogeneous set of permanent birth defects. When an FASD includes growth deficiency, a unique cluster of three minor facial anomalies (i.e., small eyes, a thin upper lip, and a smooth philtrum), and evidence of central nervous system (CNS) abnormalities, it is referred to as fetal alcohol syndrome (FAS). Other FASD can include any combination of these impairments in the context of confirmed prenatal alcohol exposure. CNS abnormalities are the most common impairment found in individuals with an FASD. FASD may also include ophthalmologic, otologic, cardiac, renal, and orthopedic abnormalities.
Categorization Diagnostic categories FAS, alcohol exposed FAS, alcohol exposure unconfirmed Partial FAS, alcohol exposed Sentinel physical finding(s) with static encephalopathy, alcohol exposed Static encephalopathy, alcohol exposed Sentinel physical finding(s) with neurobehavioral disorder, alcohol exposed Neurobehavioral disorder, alcohol exposed Sentinel physical finding(s), alcohol exposed
Epidemiology Although the impact of maternal drinking on the pre- and postnatal development of children was examined as early as the late nineteenth century (Sullivan 1899), teratogenic effects of prenatal ethyl-alcohol exposure were not characterized until the late 1960s (Lamache 1967) and not widely recognized until the work by Smith and colleagues in 1973 (Jones et al. 1973). The fetal alcohol syndrome (FAS) they described is now recognized internationally as a permanent birth defect syndrome resulting from prenatal alcohol exposure and is understood to include growth deficiency, a unique cluster of three minor facial anomalies (i.e., small eyes, a thin upper lip, and a
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smooth philtrum), and evidence of central nervous system (CNS) abnormalities. Subsequent work has shown that significant impairments are frequently found in children with prenatal alcohol exposure even in the absence of the full set of symptoms required for a diagnosis of FAS. Over time, the term fetal alcohol spectrum disorders (FASD) has come to be used to describe the full range of impairments associated with prenatal alcohol exposure. FAS is the most easily recognized FASD, largely because the distinctive FAS facial phenotype provides a specific diagnostic marker of prenatal alcohol exposure (Astley 2004a). With prevalence of FAS estimated at 1–5 cases per 1,000 live births, it is the leading known preventable cause of developmental and intellectual disabilities. Given lifetime costs for FAS estimated at two million dollars per case (Lupton et al. 2004), FAS is estimated to have an annual cost in the United States in excess of five billion dollars (Riley and McGee 2005). FASD that lack the telltale facial features of FAS are more difficult to diagnose, but share a similar range and severity of central nervous system impairments and social costs. With numbers approaching 1% of all children, other FASD are many times more prevalent than FAS (Bailey and Sokol 2008).
Natural History, Prognostic Factors, and Outcomes The impairments associated with prenatal ethylalcohol exposure are permanent birth defects that effect on the developing system throughout the life course of individuals with fetal alcohol spectrum disorders (FASD). The degree and extent of damage caused by prenatal alcohol exposure is both dose and timing dependent, and the central nervous system is the most vulnerable body system. Individuals with FASD are at risk for learning, behavioral, and mental health impairments throughout their lives. Protective factors that reduced the chances of negative outcomes in individuals with prenatal alcohol exposure and/or FASD include early diagnosis, stable home placement with quality care, protection from violence,
Fetal Alcohol Spectrum Disorder
absence of substance abuse in the home, and the presence of appropriate social services. Early intervention in a coordinated system of care that maximizes these protective factors may reduce the chances that children with FASD will experience negative outcomes and should support their chances of success as they grow to adulthood (Streissguth et al. 2004).
Clinical Expression and Pathophysiology The degree and extent of damage caused by prenatal ethyl-alcohol exposure is both dose and timing dependent. The central nervous system (CNS) is the most vulnerable body system, and various regions, developmental processes, and cell types in the CNS are differentially vulnerable to prenatal alcohol exposure at different stages of development. Prenatal alcohol exposure changes many ontogenetic processes in the developing CNS-disrupting or distorting neurulation and neural tube development, neuronal proliferation and migration, synaptogenesis, neuronal differentiation, and apoptosis. Prenatal alcohol exposure alters the hormone balance in the system as well as astroglial development and gene expression. Alcohol interferes with gliogenesis and development, delays myelination, and causes changes in the production of important neurotrophic factors (e.g., cell adhesion molecules such as the integrins and laminins) and neurotransmitters such as glutamate, NDMA, and serotonin. Neurological Abnormalities As expected, given its impact on developmental processes, prenatal alcohol exposure is associated with structural abnormalities throughout the CNS. These can include microcephaly, microgyria, gray and white matter defects, heterotopias, and regional hypoplasia. Imaging studies of the population with FASD are in their early stages, but some trends are emerging in the findings. Studies using functional magnetic resonance imaging and electroencephalography consistently find atypical results suggesting abnormal processing and development in individuals with prenatal alcohol exposure (e.g., D’Angiulli et al. 2006). The few
Fetal Alcohol Spectrum Disorder
magnetic resonance spectroscopy studies conducted have all found differences in neurochemical profiles associated with prenatal alcohol exposure (e.g., reduced choline; Astley et al. 2009). Prenatal alcohol exposure can result in reduced frontal lobe volume relative to overall brain volume particularly in the presence of FAS facial features (Astley et al. 2009). There is also emerging evidence that temporal and parietal lobes may also be disproportionately vulnerable to prenatal alcohol exposure with exposure resulting in reduced volume of these structures. The caudate nucleus of the basal ganglia seems particularly sensitive to prenatal alcohol exposure (Astley et al.), while the putamen, amygdala, and hippocampus seem relatively resistant to damage from prenatal alcohol exposure (Spadoni et al. 2007). Findings related to the volume of the cerebellar vermis (CV) are equivocal (e.g., Astley et al. 2009), making it unclear whether findings of reduced CV in children with FASD result from smaller overall brain size or not. However, when found in children with FASD, reduced volume of the CV is associated with deficits in verbal learning tasks. The effects of prenatal alcohol exposure on the corpus callosum (CC) are also not clear, but there is a consistent finding across studies of shorter CC, with some regions of the CC apparently more sensitive to prenatal alcohol exposure than others (Astley et al.). When they are found, CC abnormalities are associated with functional deficits. Neuropsychological Findings Neuropsychological impairments have been documented across a variety of domains in children with FASD. Deficits may include impairments of attention, motor and choice reaction time, visual spatial learning, fine/gross motor control and balance, response conditioning, executive function, working memory, mathematical reasoning, nonverbal inductive reasoning, and information processing (Kodituwakku 2007). Communication deficits are among the most frequently reported in FASD literature, and peripheral and central hearing impairments are common in FAS. Differences in IQ between children with
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prenatal alcohol exposure and their unexposed peers are well documented. However, in the FASD population, IQ scores cover the range from profoundly handicapped to above average intelligence. Although lower IQ scores have been shown to be associated with the presence of the face of FAS and increased prenatal alcohol exposure levels, even among children with FAS, approximately 75% have IQ scores above 70 (Riley and McGee 2005). Executive Functioning, Attention, and Behavioral Regulation: The caudate nucleus of the basal ganglia and the frontal lobes, structures which support executive functioning, seem particularly vulnerable to PAE, and impairments of executive function appear widespread in FASD. The most commonly reported concern is inattention. Deficits in planning, set shifting, problem solving, verbal and nonverbal fluency, and working memory are also commonly described. Deficits in social skills and adaptive functioning are widely reported, as are clinically significant problems maintaining appropriate behaviors (Kodituwakku 2007; Riley and McGee 2005). Communication: Communication deficits have been documented for children with FASD in all areas of language and communication. These include impairments in auditory processing, phonology, morphosyntax, semantics, and integrative language/pragmatics. Later-developing discourse-level communication skills such as narrative storytelling may be particularly vulnerable to prenatal alcohol exposure (e.g., Coggins et al. 2007). Sensory-Motor Development: Children with FASD frequently have deficits in fine motor, gross motor, and visual-motor abilities (Riley and McGee 2005). These can include delayed achievement of early motor milestones, tone abnormalities, tremulousness, and oral-motor (e.g., suck-swallow) difficulties. Older children with FASD may experience impaired balance, reduced fine motor control, visual spatial problems, and motor immaturities. The poor sensory modulation commonly described among clinicreferred children with FASD may play a role in motor and postural deficits and poor behavioral regulation (Jirikowic et al. 2008).
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Mental Health: Mental health and psychiatric problems are found at high rates among clinical samples of individuals with prenatal alcohol exposure. Indeed, even low levels of prenatal alcohol exposure have been shown to increase risk of clinically significant mental health issues in young girls. Other adverse pre-/postnatal risk factors commonly associated with prenatal alcohol exposure (e.g., polydrug exposure, abuse/ neglect) may exacerbate early biologic vulnerability and increase the risk for secondary disabilities later in life (Streissguth et al. 2004).
Evaluation and Differential Diagnosis Fetal alcohol spectrum disorder (FASD) diagnoses are based on the degree of presentation of (1) prenatal and/or postnatal growth deficiency, (2) the fetal alcohol syndrome (FAS) facial phenotype, and (3) central nervous system (CNS) abnormality, in the context of (4) prenatal alcohol exposure. There are several competing diagnostic systems used in the diagnosis of FASD. This discussion of FASD diagnosis will be structured around the most established system, the FASD 4-Digit Diagnostic Code (3rd edition, Astley 2004a: diagnostic guidelines, Lip-Philtrum Guides, and facial measurement software available at www.fasdpn.org). The other major diagnostic systems, the Canadian Guidelines for FASD Diagnosis (Chudley et al. 2005) and the Guidelines for Identifying FAS from the Centers for Disease Control (CDC; Bertrand et al. 2005), incorporate methods/tools from the 4-Digit Diagnostic Code but use different diagnostic criteria. In individuals being assessed for an FASD, as the presentation of key features of FAS move from not characteristic to highly characteristic of FAS, the 4-Digit Diagnostic Code quantifies the expression of these features using a set of four 4-point Likert ranks that move from 1 to 4. The 4-digit code is created by combining these four Likert ranks from left to right in the order GROWTH, FACE, CNS, and PRENATAL ALCOHOL EXPOSURE (PAE). This numeric code is then used to assign an individual the appropriate
Fetal Alcohol Spectrum Disorder
FASD diagnosis. A brief summary of how each Likert rank is determined is presented below. Growth In the 4-Digit Diagnostic Code, a GROWTH rank of 1 is given when both prenatal and postnatal height and weight are above the 10th percentile; a GROWTH rank of 2 indicates that either prenatal or postnatal height, weight, or both are above the 3rd percentile, but less than or equal to the 10th percentile; a GROWTH rank of 3 indicates that either prenatal or postnatal height or weight is less than or equal to the 3rd percentile; and a GROWTH rank of 4, severe growth deficiency, is given when both height and weight are less than or equal to the 3rd percentile during either the prenatal or postnatal period. The FAS Facial Phenotype Three key facial features are considered diagnostic of FAS: (1) small eyes with palpebral fissure length (PFL) more than 2 standard deviations (SD) below the mean for age; (2) smooth philtrum consistent with a rank 4 or 5 on the University of Washington Lip-Philtrum Guide; and (3) thin upper lip consistent with a rank 4 or 5 on the University of Washington Lip-Philtrum Guide. At these values, this constellation of features has high specificity for prenatal ethyl-alcohol exposure, and their presence can serve as a proxy indicator of prenatal alcohol exposure when exposure is unknown (Astley 2006). In the 4-Digit Diagnostic Code, a FACE rank of 4 indicates that all three facial features are present; a FACE rank of 3 indicates that deficits have been measured in all three features, but that one is only moderately impacted (e.g., PFL between 1 and 2 SD or a lip/philtrum Likert of 3); a FACE rank of 2 indicates that at least one feature is present; and a FACE rank of 1 indicates that there are no features present. Increased FACE rank is associated with increased risk/severity of CNS abnormality (Astley et al. 2009). Central Nervous System (CNS) Integrity The 4-Digit Diagnostic Code includes a nested ranking of CNS abnormalities. This ranking quantifies both evidence of structural abnormality and
Fetal Alcohol Spectrum Disorder
severity of functional impairment. A CNS rank of 4 indicates that there is direct structural/neurological evidence (e.g., microcephaly, seizures) of a “definite” abnormality in CNS structure; a CNS rank of 3 indicates that there is a “significant” functional impairment in three or more domains of brain function (i.e., a deficit >2 SD on a standardized test); a CNS rank of 2 indicates mild to severe impairment in some domain of functioning; and a CNS rank of 1 indicates that the individual does not meet criteria for CNS ranks of 2 through 4. Documenting Prenatal Ethyl-Alcohol Exposure (PAE)
The 4-Digit Diagnostic Code includes 4 exposure ranks. A PAE rank of 4 indicates a “confirmed exposure to high levels of alcohol.” Blood alcohol concentration greater than 100 mg/dL present on a weekly basis is given as a general guideline for defining “high levels” of exposure (Astley 2004a, p. 43). A PAE rank of 3 indicates “confirmed exposure” of either unknown quantity or of an amount that falls short of a PAE rank of 4. A PAE rank of 2, “unknown exposure,” indicates that exposure cannot be confirmed present nor absent. A PAE rank of 1 indicates a “confirmed absence of exposure from conception to birth.” Diagnostic Categories When combined, ranks for GROWTH, FACE, CNS, and PAE provide a 4-Digit Diagnostic Code. This code can then be converted into 1 of 22 possible diagnoses. Eight of these fall under the umbrella of FASD. Importantly, only a diagnosis of FAS implies a causal role for prenatal alcohol exposure in producing the impairments identified during assessment. This causal inference is grounded in the specificity of the FAS facial phenotype to prenatal ethyl-alcohol exposure. For this reason, both FAS diagnoses in the 4-Digit Diagnostic Code require a FACE rank of 4 (FAS ¼ GROWTH 2–4, FACE 4, CNS 3–4, PAE 2–4). The other 6 FASD diagnostic categories simply describe the degree and type of impairment and document prenatal alcohol exposure. The eight FASD categories from the 4-Digit Diagnostic Code are:
2015 Diagnostic category FAS, alcohol exposed FAS, alcohol exposure unconfirmed Partial FAS, alcohol exposed Sentinel physical finding(s) with static encephalopathy, alcohol exposed Static encephalopathy, alcohol exposed Sentinel physical finding(s) with neurobehavioral disorder, alcohol exposed Neurobehavioral disorder, alcohol exposed Sentinel physical finding(s), alcohol exposed
4-Digit code criteria GROWTH 2–4, FACE 4, CNS 3–4, PAE 3–4 GROWTH 2–4, FACE 4, CNS 3–4, PAE 2 GROWTH 1–4, FACE 3, CNS 3–4, PAE 3–4 GROWTH 3–4, FACE 1–2, CNS 3–4, PAE 3–4 GROWTH 1–2, FACE 1–2, CNS 3–4, PAE 3–4 GROWTH and/or FACE 3–4, CNS 2, PAE 3–4 GROWTH 1–2, FACE 1–2, CNS 2, PAE 3–4 GROWTH and/or FACE 3–4, CNS 1, PAE 3–4
In the Canadian Guidelines for FASD Diagnosis, there are four FASD diagnoses. These diagnoses are based on the 4-Digit Diagnostic Code and include two FAS categories: FAS, alcohol exposed; and FAS, alcohol exposure unconfirmed. The Canadian Guidelines use loosened facial criteria for FAS compared to the 4-Digit Diagnostic Code. In the Canadian system, FAS can be diagnosed with the following ranks: GROWTH 2–4, FACE 3–4, CNS 3–4, and PAE 2–4. The Canadian Guidelines diagnosis of Partial FAS is also a broader category than in the 4-Digit Diagnostic Code and includes a FACE rank of 2. The final diagnostic category in the Canadian system is alcohol-related neurodevelopmental disorder (i.e., ARND), which corresponds to the 4-Digit Diagnostic Code diagnoses of static encephalopathy with or without sentinel physical findings. The Centers for Disease Control’s Guidelines for Identifying FAS has the most liberal FAS diagnostic criteria. CDC guidelines relax requirements for eye size to include PFL as large as 10th percentile. The CDC guidelines also relax criteria for CNS impairment and only require mild functional impairments (i.e., one standard deviation from the mean below the 16th percentile) in three domains of functioning for a diagnosis of FAS.
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Treatment Evidence-based interventions for fetal alcohol spectrum disorders (FASD) are emerging in the literature (Premji et al. 2007). Currently, primary prevention and direct individualized instruction to remediate specific learning and/or behavior problems are the primary tools available for treating individuals with FASD. Because children with FASD commonly face disruptive challenges from their environment as well as complex neurophysiologic deficits (Coggins et al. 2007), successful treatment for this population demands multimodal services. These services should incorporate the knowledge and expertise of multiple disciplines (e.g., medicine, psychology, social work, special education, speech-language pathology, audiology, occupational therapy) in order to remediate both environmental barriers to success and central nervous system (CNS) dysfunction. Primary Prevention: FASD are entirely preventable, and simple, low-cost informative counseling directed at women who use alcohol and are pregnant or thinking about pregnancy can decrease FASD risk (Bailey and Sokol 2008). More intensive interventions for women at high risk who have already given birth to one child with prenatal alcohol exposure are also important. For example, the Parent–Child Assistance Program (Grant et al. 2005) provides participants with a paraprofessional advocate throughout their pregnancy and for 3 years after their child is born. The program helps mothers to decrease or eliminate their use of alcohol and/or to use family planning to prevent pregnancies when using alcohol. The program also promotes addiction recovery, supports economic and personal self-sufficiency, and helps participants connect to community resources. Screening: Screening in high-risk populations has also been demonstrated to be effective in reducing FASD (e.g., Astley 2004b). The state of Washington, for example, developed an effective program for screening children in the foster care system by using a review of medical records and facial photograph. In the program, those children with a positive screen for the FAS facial phenotype or for CNS damage (e.g., microcephaly) are
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invited for a diagnostic evaluation. Benefits from this kind of program include early diagnosis, earlier access to community supports and services, and increased caregiver knowledge regarding signs and symptoms of FASD. Medical Interventions: Medical providers play an important role in FASD prevention, screening, diagnosis, and treatment. Early referrals to appropriate service providers are of particular importance and should always include formal vision and audiological screenings. Because efficacy and safety data on psychotropic medications are not generally available for the FASD population (Premji et al. 2007), there is little guidance for providers on how to target specific psychiatric or behavioral symptoms associated with FASD. Children with FASD may be at risk for paradoxical reactions and can be more sensitive to dosing. While research using animal models has revealed a number of promising medical approaches for protecting against or even reversing the longterm impacts of alcohol exposure, a more rigorous evidence base needs to be established before these approaches can play a major role in FASD treatment. Avenues being pursued include perinatal choline supplementation, prenatal folic acid, exercise training, and postnatal aniracetam treatment. Educational and Behavioral Interventions: Because the impairments associated with prenatal ethyl-alcohol exposure are timing and dose dependent, the FASD population is extremely heterogeneous. As a result, no single behavioral or educational intervention approach will meet the needs of all those in the FASD population. Interventions need to be designed to target the specific needs of a particular individual with FASD in order to be most effective. Indeed, targeted and multimodal interventions are reported to make important changes across behavioral and learning domains for children with FASD while also improving caregiver outcomes (Kalberg and Buckley 2007). Effectiveness may be maximized when interventions include a functional behavioral analysis to identify problem behaviors and antecedent events leading to those behaviors. This analysis can then be paired with caregiver and teacher education to “reframe” problem behaviors from a neurodevelopmental
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perspective. Accommodations and environmental modifications to compensate for specific neurological deficits are also important pieces of any effective intervention program (c.f., Olson et al. 2005). Social skills training can be another important element in any comprehensive intervention plan and has been shown to be effective with school-age children with FASD. Targeted interventions for specific academic deficits are effective for children with FASD, and approaches that work with typically developing populations and children with other similar developmental disabilities should be effective interventions for individuals with FASD.
References and Reading Astley, S. J. (2004a). Diagnostic guide for fetal alcohol spectrum disorders: The four-digit diagnostic code (3rd ed.). Seattle: FAS Diagnostic and Prevention Network, University of Washington, electronic version available from http://fasdpn.org Astley, S. J. (2004b). Fetal alcohol syndrome prevention in Washington State: Evidence of success. Paediatrics & Perinatal Epidemiology, 18(5), 344–351. Astley, S. J. (2006). Comparison of the 4-digit diagnostic code and the hoyme diagnostic guidelines for fetal alcohol spectrum disorders. Pediatrics, 118(4), 1532–1545. Astley, S. J., Aylward, E. H., Olson, H. C., Kerns, K., Brooks, A., Coggins, T. E., et al. (2009a). Magnetic resonance imaging outcomes from a comprehensive magnetic resonance study of children with fetal alcohol spectrum disorders. Alcoholism, Clinical and Experimental Research, 33(10), 1671–1689. Astley, S. J., Richards, T., Aylward, E. H., Olson, H. C., Kerns, K., Brooks, A., et al. (2009b). Magnetic resonance spectroscopy outcomes from a comprehensive magnetic resonance study of children with fetal alcohol spectrum disorders. Magnetic Resonance Imaging, 27(6), 760–778. Bailey, B. A., & Sokol, R. J. (2008). Pregnancy and alcohol use: Evidence and recommendations for prenatal care. Clinical Obstetrics and Gynecology, 51(2), 436–444. Bertrand, J., Floyd, R., Weber, K., O’Connor, M., Riley, E., Johnson, K., & Cohen, D. (2005). National task force on fetal alcohol syndrome and fetal alcohol effect. Fetal alcohol syndrome: Guide-lines for referral and diagnosis. Atlanta: Centers for Disease Control and Prevention. Coggins, T. E., Timler, G. R., & Olswang, L. B. (2007). A state of double jeopardy: Impact of prenatal alcohol exposure and adverse environments on the social communicative abilities of school-age children with fetal
2017 alcohol spectrum disorder. Language, Speech, and Hearing Services in Schools, 38(2), 117–127. Chudley, A. E., Conry, J., Cook, J. L., Loock, C., Rosales, T., & LeBlanc, N. (2005). Fetal alcohol spectrum disorder: Canadian guidelines for diagnosis. Journal of Canadian Medical Association, 172. Suppl. 5). D’Angiulli, A., Grunau, P., Maggi, S., & Herdman, A. (2006). Electroencephalographic correlates of prenatal exposure to alcohol in infants and children: A review of findings and implications for neurocognitive development. Alcohol, 40(2), 127–133. Grant, T. M., Ernst, C. C., Streissguth, A. P., & Stark, K. (2005). Preventing alcohol and drug exposed births in Washington state: Intervention findings from three parent-child assistance program sites. The American Journal of Drug and Alcohol Abuse, 31(3), 471–490. Jirikowic, T., Carmichael-Olson, H., & Kartin, D. (2008). Sensory processing, school performance, and adaptive behavior among young school-aged children with FASD. Physical & Occupational Therapy in Pediatrics, 28(2), 117–136. Jones, K., Smith, D., Ulleland, C., & Streissguth, A. (1973). Pattern of malformation in offspring of chronic alcoholic mothers. Lancet, 301(7815), 1267–1271. Kalberg, W. O., & Buckley, D. (2007). FASD: What types of intervention and rehabilitation are useful? Neuroscience and Biobehavioral Reviews, 31(2), 278–285. Kodituwakku, P. W. (2007). Defining the behavioral phenotype in children with fetal alcohol spectrum disorders: A review. Neuroscience and Biobehavioral Reviews, 31(2), 192–201. Lamache, A. (1967). The progeny of alcoholics article in French. Bulletin de l'Académie Nationale de Médecine, 151(25), 517–521. Lupton, C., Burd, L., & Harwood, R. (2004). Cost of fetal alcohol spectrum disorders. American Journal of Medical Genetics. Part C, Seminars in Medical Genetics, 127(1), 42–50. Olson, H. C., Quamma, J., Brooks, A., Lehman, K., Ranna, M., & Astley, S. J. (2005). Efficacy of a new model of behavioral consultation for families raising schoolaged children with FASD and behavior problems. Alcoholism, Clinical and Experimental Research, 29(5), 247A. Abstract 243. Premji, S., Benzies, K., Serrett, K., & Hayden, K. A. (2007). Research-based interventions for children and youth with a fetal alcohol spectrum disorder: Revealing the gap. Child: Care, Health and Development, 33(4), 389–397. discussion 398–400. Riley, E. P., & McGee, C. L. (2005). Fetal alcohol spectrum disorders: An overview with emphasis on changes in brain and behavior. Experimental Biology and Medicine (Maywood, N.J.), 230(6), 357–365. Sullivan, W. C. (1899). A note on the influence of maternal inebriety on the offspring. Journal of Mental Sciences, 45, 489–503. Spadoni, A. D., McGee, C. L., Fryer, S. L., & Riley, E. P. (2007). Neuroimaging and fetal alcohol spectrum
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2018 disorders. Neuroscience and Biobehavioral Reviews, 31(2), 239–245. Streissguth, A. P., Bookstein, F. L., Barr, H. M., Sampson, P. D., O’Malley, K., & Young, J. K. (2004). Risk factors for adverse life outcomes in fetal alcohol syndrome and fetal alcohol effects. Journal of Developmental and Behavioral Pediatrics, 25(4), 228–238.
Fetal Alcohol Syndrome ▶ Fetal Alcohol Spectrum Disorder
Fetal Anticonvulsant Syndrome Usha Kini Consultant Clinical Geneticist, Oxford Radcliffe Hospitals NHS Trust University of Oxford, Oxford, UK
Synonyms Fetal antiepileptic drug syndrome (fetal AED syndrome); Fetal carbamazepine syndrome; Fetal phenytoin syndrome; Fetal valproate syndrome (FVS)
Short Description or Definition “Fetal anticonvulsant syndrome” or “FACS” is the term used to describe the adverse effects of in utero exposure to anticonvulsant medication (e.g., phenobarbitone, phenytoin, carbamazepine, valproate, lamotrigine, etc.) and may manifest as dysmorphic facial features, major and minor structural anomalies, learning difficulties, and/or behavioral problems. Specific health and developmental problems are noted following exposure to specific antiepileptic drugs, e.g., phenytoin, valproate; the terms fetal phenytoin syndrome and fetal valproate syndrome are then used.
Fetal Alcohol Syndrome
Epidemiology Epilepsy is a common neurological condition affecting 0.6–1% of the population, and about a third of these are women of reproductive age. The majority of these women will be treated with antiepileptic drugs. Hence, approximately 1 in 160–250 pregnancies are exposed to antiepileptic drugs. Phenobarbital was introduced for use as an antiepileptic drug (AED) in 1912, phenytoin in 1938, carbamazepine in 1961, and sodium valproate in 1964. In the early 1960s, there were reports from Germany (Janz and Fuchs 1964), followed shortly later by reports from England, describing a pattern of major and minor anomalies affecting orofacial, cardiac, and skeletal development in children with prenatal exposure to AEDs. Gradually, reports of cognitive deficits and behavioral problems emerged. The exact incidence of fetal anticonvulsant syndrome (FACS) is not clear, as not all individuals with in utero exposure to AEDs show the adverse effects. Also, variability in the manifestation of the adverse effects is noted even between individuals who have been exposed to the same dose of the same AED. Some AEDs like valproate may be used in the treatment of neuropsychiatric disorders such as bipolar disorders. Offspring of these women are also at a risk of developing the adverse effects, if exposed in utero.
Natural History, Prognostic Factors, and Outcomes Natural history: The diagnosis of FACS is first suspected when ultrasound scan abnormalities are identified in a pregnant woman taking AEDs. However, the absence of scan abnormalities is not reassuring, as minor structural abnormalities may not always be picked up on scan and also because the scans do not usually provide any clues to the neurodevelopmental problems that may develop later on in life. The rate of obstetric complications in women with epilepsy is thought to be increased, but no single complication has been identified as a
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Fetal Anticonvulsant Syndrome, Table 1 Congenital malformations associated with FACS Systems involved Neural tube defects Congenital heart defects Orofacial clefts Limb defects
Genitourinary defects Brain abnormalities Eye abnormalities Abdominal wall defects Respiratory tract abnormalities Craniosynotosis
Abnormalities Spina bifida, anencephaly
Additional information 1–2% risk of spina bifida with valproate, 0.5–1% with carbamazepine exposure Seen with exposure to any AED
Ventricular septal defects, atrial septal defects, patent ductus arteriosus Cleft lip and palate, cleft palate Radial ray abnormalities, split hand malformation, polydactyly, tibial hypoplasia, camptodactyly Hypospadias, renal hypoplasia, hydronephrosis Agenesis of corpus callosum, porencephaly, hydranencephaly Cataracts, optic nerve hypoplasia, iris defects Omphalocele
Cleft palate has been reported with valproate exposure, but not cleft lip Seen with exposure to valproate; camptodactyly may be seen with other AED exposure as well More common with valproate exposure Not frequently seen but have been reported Iris defects have been reported with exposure to valproate only
Laryngeal hypoplasia, abnormal lobation of lung, lung hypoplasia, tracheomalacia
More common with valproate exposure
Trigonocephaly
Seen with valproate exposure only and is caused by premature fusion of the metopic suture; it may need surgical intervention
common finding, and in most studies, the increased rate of obstetric complications has not been found to be statistically significant. An increased incidence of stillbirths has been reported but not proven to be significant statistically. The birth weight is usually average. There is an increased rate of admission to special care baby units due to withdrawal symptoms such as lethargy, irritability, poor feeding, jitteriness, and due to the presence of major malformations such as neural tube defects, heart defects, kidney abnormalities, etc. Congenital malformations: There is a 6–7% risk of structural abnormalities at birth compared to 2–3% in the general population. This risk may be as high as 9% in children exposed to valproate (Table 1). Minor anomalies: Inguinal hernia, joint laxity, club feet, capillary hemangiomas (birth marks), strabismus (squint), nail hypoplasia, digital hypoplasia, clinodactyly, and overlapping toes are more frequently seen compared to the general population.
Facial dysmorphism: There are subtle facial features that are described with exposure to different AEDs. There is an overlap in the facial features described with exposure to different drugs. Of these, the facial gestalt of valproate exposure is the most characteristic and is formed by the following features: thin arched eyebrows with medial deficiency, epicanthic folds, infraorbital grooves, broad nasal bridge, short anteverted nose, smooth long philtrum, and a thin upper lip (Kini et al. 2006). Childhood medical problems: Visual and hearing problems are more frequently seen. Myopia (short-sightedness) and squints are more common; hearing problems include recurrent otitis media with effusion. Developmental delay and learning difficulties: Global developmental delay may be seen, but speech delay alone is more common. Joint laxity and poor muscle tone contribute to poor coordination which may affect daily activities such as writing, dressing and undressing, riding a bike, swimming, etc. A large proportion of children
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have additional educational needs, and some of them may need one-to-one help much of the time (Adab et al. 2001). Many studies have shown that children exposed to AEDs such as carbamazepine, phenytoin, and even the newer drug, lamotrigine, have an IQ in the normal range (Gaily et al. 2004; Mawer et al. 2002; Dean et al. 2002; Meador et al. 2011). However, the verbal IQ of individuals with valproate exposure appears to be significantly lower (Adab et al. 2004). Behavioral problems: Autistic spectrum disorders have been reported more frequently with valproate exposure (Rasalam et al. 2005). Difficulty in developing peer relationships, sharing interests and enjoyment, using language in social communication, symbolic or imaginative play, changes to routine, and lack of emotional or social reciprocity have been reported commonly. Poor concentration and hyperactivity are other behavioral issues reported. Prognostic factors: Several factors influence the severity of FACS: (1) Type of AED – Valproate exposure causes the most severe adverse effects. (2) Number of AEDs – Polytherapy (exposure to more than one AED) has a worse outcome than exposure to monotherapy (one AED). (3) Dose of the drug – Although the adverse effects have not been found to be dose related with exposure to other AEDs, exposure to / ¼ 1,000 mg/day of valproate has been associated with more severe problems. Specific dose-related features of valproate are neural tube defects, radial ray abnormalities, obviously dysmorphic face, and a reduction in verbal IQ (Kini 2006). (4) Time of exposure – First trimester exposure is associated with structural abnormalities. It is possible however that AED exposure through the second and third trimesters may have an effect on neurodevelopment as the brain continues to grow throughout the pregnancy. (5) Parental and environmental factors – The child’s IQ is known to strongly correlate with the parental IQ and also with the socioeconomic status. Women with epilepsy may themselves have learning difficulties, contributing further to the phenotype. (6) Genetic factors – The teratogenic effects of AEDs differ between fetuses that have been exposed to the same dose of the same drug suggesting that
Fetal Anticonvulsant Syndrome
maternal genetic factors may contribute. Studies have shown the contribution of genetic factors such as MTHFR (Kini et al. 2007), epoxide hydrolase genes, etc.
Clinical Expression and Pathophysiology The clinical expression of FACS is very variable. The full spectrum of problems, i.e., dysmorphic facial features, major and minor malformations, developmental delay, childhood medical problems, learning difficulties, and behavioral problems, is rarely seen in a single individual with prenatal AED exposure. The presence of specific dysmorphic facial features may be a clue to early diagnosis. However, the facial gestalt is usually obvious in the most severely affected patients only. In fact, the facial features with exposure to some AEDs such as carbamazepine are very mild, adding to the difficulty of making a conclusive diagnosis. In addition, some individuals may present with subtle dysmorphic features and neurodevelopmental problems only. In these individuals, a diagnosis of “fetal anticonvulsant effects” should be considered.
Mechanisms of Teratogenicity The exact mechanism by which AEDs cause their teratogenic effects is not known. Several plausible theories have been proposed: 1. Folic acid deficiency: AEDs such as phenytoin, phenobarbitone, and carbamazepine interfere with the intestinal absorption of folic acid, while valproate acts on its metabolism. Embryonic folic acid deficiency may disrupt gene expression, increase oxidative stress, and cause changes in protein synthesis (Wegner and Nau 1992). In animal studies, the rate of neural tube defects was reduced in mice exposed to valproate, when high doses of folic acid and folinic acid were given. Alteration in expression of folate pathway genes has been demonstrated in mice following valproate exposure (Finnell et al. 1997). Certain genetic
Fetal Anticonvulsant Syndrome
polymorphisms in the folate pathway (C677T in the MTHFR gene) have been shown to be associated with a higher rate of malformations in AED-exposed children (Kini et al. 2007). 2. Oxidative stress: Intermediate metabolites of AEDs may increase oxidative stress in the developing embryo, whose antioxidant defense mechanism is immature. This may damage the developing organs of the embryo. Embryonic oxidative stress due to free radical formation following hypoxia from phenytoininduced embryonic bradycardia has been demonstrated in animal studies. 3. Arene oxide intermediates: Reactive intermediate metabolites of AEDs such as phenytoin and carbamazepine in the form of arene oxides may bind cell macromolecules affecting cell function and causing cell death. Arene oxides are detoxified by epoxide hydrolases; genetic variability in the activity of these enzymes may influence the susceptibility of the fetus to the adverse effects. 4. Histone deacetylase inhibition: Valproate is a potent histone deacetylase inhibitor. Histone deacetylases are enzymes that induce chromatin changes to enable transcription of genes. Histone deacetylase inhibition therefore leads to interruption in the cell cycle, growth arrest, and apoptosis (cell death). Valproate also causes demethylation of DNA which results in changes in gene expression and hence in congenital malformations.
Evaluation and Differential Diagnosis FACS is a difficult diagnosis to make as it is a diagnosis of exclusion. There are no specific tests by which the diagnosis of FACS can be confirmed. Hence, basic tests such as karyotyping, fragile X syndrome, and urine metabolic tests should be offered to the patients to rule out other causes. Other fetal insults such as exposure to alcohol, toluene, and maternal diabetes may cause similar features as FACS and should be kept in mind while taking the medical history. Malformations related to specific drug exposure such as radial ray defects and trigonocephaly with
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valproate exposure may give a more definitive diagnosis, but other dysmorphic syndromes such as Baller-Gerold syndrome which have similar features need to be ruled out. This differentiation is important as it will impact on the recurrence risk given to these families. Referral to a clinical geneticist is therefore recommended. In those with a history of AED exposure, who present mainly with behavioral problems such as autistic spectrum disorder, evaluation for other syndromes such as Rett syndrome, tuberous sclerosis, and fragile X syndrome should be considered.
Treatment Management The management of FACS is symptomatic. Specific structural abnormalities, such as heart defects, kidney abnormalities, etc., may need monitoring, medication, or surgical intervention. Children with developmental delay may need help in the form of physiotherapy, speech and language therapy, and occupational therapy. Surveillance for childhood medical problems such as myopia and recurrent otitis media is recommended. Assessment for special educational needs should be carried out early and appropriate help instituted early to allow the child to develop to his or her full potential. Behavioral therapy and sometimes medication may be helpful in dealing with behavioral issues such as autistic spectrum disorder and attention deficit/hyperactivity disorder. Prevention For women with epilepsy, prepregnancy counseling should be provided so as to reduce the risk of FACS. Every attempt should be made to reduce the dose (e.g., valproate should be reduced to ASD and US>ASD Trait activity: TD>ASD and TD>US Compensatory activity: US>ASD and US>TD
associated with object feature processing (the inferior temporal gyrus) when processing faces, as opposed to using the FG as seen in typically developing controls. Taken together, these findings suggest that individuals with autism process faces differently than do typically developing individuals. Research comparing the brain function of children with autism and their unaffected siblings against typically developing individuals is beginning to emerge. In one study, while viewing biological motion, children with autism showed
decreased activation in the PFC, STS, and FG as expected. However, their unaffected siblings, who were behaviorally indistinguishable from typically developing controls, also showed decreased activation in parts of the PFC and in the FG, likely reflecting the shared genetic vulnerability to developing autism. Unaffected siblings also exhibited unique patterns of activation in particular areas of the PFC and STS that were not activated in either affected or typically developing children (see Fig. 2). This activity could be a mechanism by which unaffected siblings
Functional Magnetic Resonance Imaging
compensate for genetic vulnerability to autism or could reflect genetic factors that served a protective function against disorder development. The existence of brain activation commonalities in children with autism and their siblings, and of compensatory activity in unaffected siblings, has been supported by other work. Decreased activation in the STS, FG, and PFC in response to happy faces was observed in both affected children and unaffected siblings compared to typically developing controls, indicating a potential common deficit in those at genetic risk for developing autism. Moreover, during a visual attention task, children with autism and their siblings both showed atypical, delayed activation in the frontocerebellar attention systems relative to controls, with the strongest activation in the unaffected siblings. This suggests that unaffected siblings are attempting to compensate for the delayed activation, which is likely a reflection of genetic vulnerability to autism. Studies of this design offer a unique and powerful opportunity to identify biomarkers, neuroimaging findings exclusive to, or shared between, each subgroup (affected, unaffected, and typical). Accurate identification of these biomarkers can assist in developing an endophenotype of autism, which details the anatomical, neuropsychological, and physiological features that reflect genetic risk for developing the disorder. Current MRI work has highlighted potential anatomical biomarkers of autism risk, but the use of functional MRI to identify differences in brain activation is less prevalent. The identification of a reliable endophenotype of autism could provide important insight into the mechanisms underlying the disorder toward the goal of presymptomatic diagnosis, which could then facilitate earlier implementation of intervention. Neural activation, as measured by fMRI, is starting to be utilized as a treatment outcome. Following 4 months of pivotal response treatment (PRT), a behavioral therapy designed to increase social motivation through naturalistic, play-based intervention, preschool and kindergarten-aged children with autism showed greater STS, FC, and PFC in response to biological motion, suggesting that treatment had increased (i.e., normalized)
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activation in regions used by typically developing children during social perception. These findings are particularly interesting in that they demonstrate that the social brain areas may be malleable to treatment beyond the commonly accepted “critical period” for intervention (2–4 years). In addition, after 10 months of treatment with diuretic bumetanide, a drug that reinforces GABAergic inhibition, young adults with autism exhibited enhanced activation in the brain areas involved in social and emotional information processing from faces, highlighting the potential for drug intervention to change brain function. Though further study is needed, these preliminary findings suggest that fMRI may be a useful tool for quantifying treatment outcomes, with the goal of eventually identifying biomarkers of treatment response. Once these biomarkers are reliably established, treatment response may be able to be predicted based on neuroimaging findings, which would then help facilitate the customization of treatment plans to meet each individual’s specific needs. While fMRI can be used to identify discrete areas of the brain that are active during certain cognitive processes, it can also be used to examine networks of brain regions involved in these processes, a method called functional connectivity analysis. For example, individuals with autism exhibited decreased synchronization between the inhibition network (anterior and middle cingulate gyrus and insula) and parts of the frontal and parietal regions relative to controls during an executive function task tapping response inhibition capabilities. Resting state functional connectivity has blossomed recently as a point of interest in the field. True to its name, resting state functional connectivity intends to locate neural networks in the absence of overt stimulation. This data is gathered during a “resting state” task, where the participant is asked to lie quietly and look at a blank screen. Resting state functional connectivity analyses have shown hypoconnectivity in many areas of the brain in children with autism, particularly in the temporal lobe and between hemispheres, as well as hyperconnectivity in subcortical areas such as the thalamus. Moreover, the mid- and posterior insula and posterior cingulate cortex have been identified as
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areas of dysfunction in children with autism. Connectivity abnormalities seen in the default network, or the areas of the brain (such as the medial PFC, superior frontal gyrus, temporal lobe, and posterior cingulate cortex) that are active in the absence of overt stimulation, are associated with poorer social functioning and more severe restricted and repetitive behaviors. Functional connectivity analyses can inform understanding of how areas of the brain communicate with one another and how this communication may be disrupted in children with autism, which may further reveal biomarkers or potential targets for intervention.
Future Directions Neuroimaging as a field, including the use and application of fMRI, is in its infancy relative to many other areas of study. The progress already made in advancing the knowledge of brain structure and function and the increasing complexity of the processes able to be studied highlights the incredible potential for impact in both research and clinical spheres. Scanning technology is improving constantly, from the gradient fields used to collect the images to the software that analyzes the data, underscoring the possibility of greater spatial resolution and a more precise delineation of neural activation. Methodological design in the field is also changing rapidly; it is now feasible to gather fMRI data and PET, ERP, MEG, or fNIRS data simultaneously. While this has been possible for nearly a decade, utilization costs have impeded widespread use until recently, particularly for combined fMRI-PET research, as these scans require a specially equipped MRI scanner. With the quality and capability of scanning technology increasing exponentially, it is likely that the use of these methods will flourish. Recent advances in data acquisition and processing techniques have also allowed for the use of real-time fMRI neurofeedback as a method by which to encourage the acquisition of control over certain cortical processes, such as emotion regulation. Results from the relatively small number of studies that have utilized this method are positive,
Functional Magnetic Resonance Imaging
highlighting the potential of this technique to inspire treatment progress. These advances may be particularly relevant to clinical practice, where the functionality of fMRI as a clinical tool has been slow to translate. The use of fMRI in diagnosing disorders before behavioral or physical symptoms develop, informing drug development and delivery, customizing treatment plans, and supporting treatment progress is bound to have a marked impact on the design and execution of clinical practice. Furthermore, translational work that combines the study of neurobiology, genetics, and behavior is beginning to appear in world-class research facilities across the globe. For example, a current Autism Centers of Excellence (ACE) project focuses specifically on the etiology of autism in girls using a multisite collaboration design that spans the United States. This project examines the genetics of the disorder by gathering samples from typically developing children and children with autism, as well as the siblings and parents of both groups. In addition, fMRI and ERP are used to quantify neural activation to stimuli evoking the various cognitive processes thought to contribute to autism-related deficits. Finally, extensive behavioral measures, including cognitive, language, adaptive functioning, and autism symptom assessments, are collected from all children involved. Once completed, the study will be one of the most comprehensive ever done on the etiology of autism in general and specifically on what autism looks like in girls. The study will assist in elucidating the differences in the neurobiological, genetic, and behavioral profiles of girls and boys with autism, of children with autism and their unaffected siblings, and of those with autism and typically developing individuals. As recognition of the importance of research that crosses disciplinary and methodological boundaries grows, the number of studies employing translational principles is bound to increase markedly. Functional magnetic resonance imaging is a powerful and widely applicable method for examining brain function. There is incredible potential for growth and progress within the field, and the possibility of widespread clinical use is in the foreseeable future. Specifically in autism, a better
Functional Routines (FR), Teaching
understanding of the differences in brain function of children with autism is crucial to understanding key autism-related deficits, how to ameliorate them, and, perhaps most importantly, how to prevent the disorder’s development altogether. Within the next few decades, more sophisticated technology and increased visibility of neuroimaging methods will hopefully inspire great strides in both research and clinical arenas.
See Also ▶ Magnetic Resonance Imaging ▶ Neural Mechanisms in Autism ▶ Superior Temporal Sulcus Region
References and Reading Ashwin, C., Baron-Cohen, S., Wheelwright, S., O’Riordan, M., & Bullmore, E. T. (2007). Differential activation of the amygdala and the ‘social brain’ during fearful face-processing in Asperger Syndrome. Neuropsychologia, 45, 2–14. Belmonte, M. K., Gomot, M., & Baron-Cohen, S. (2010). Visual attention in autism families:‘Unaffected’ sibs share atypical frontal activation. Journal of Child Psychology and Psychiatry, 51, 259–276. Cherkassky, V. L., Kana, R. K., Keller, T. A., & Just, M. A. (2006). Functional connectivity in a baseline restingstate network in autism. Neuroreport, 17, 1687–1690. Di Martino, A., Yan, C. G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K., & Milham, M. P. (2013). The autism brain imaging data exchange: Towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular Psychiatry, 19, 659–667. Hadjikhani, N., Zürcher, N. R., Rogier, O., Ruest, T., Hippolyte, L., Ben-Ari, Y., & Lemonnier, E. (2013). Improving emotional face perception in autism with diuretic bumetanide: A proof-of-concept behavioral and functional brain imaging pilot study. Autism. https://doi.org/10.1177/1232361313514141. Huettel, S. A., Song, A. W., & McCarthy, G. (2009). Functional magnetic resonance imaging (Vol. 2). Sunderland: Sinauer Associates. Kaiser, M. D., Hudac, C. M., Shultz, S., Lee, S. M., Cheung, C., Berken, A. M., & Pelphrey, K. A. (2010). Neural signatures of autism. Proceedings of the National Academy of Sciences, 107, 21223–21228. Kana, R. K., Keller, T. A., Minshew, N. J., & Just, M. A. (2007). Inhibitory control in high-functioning autism: Decreased activation and underconnectivity in inhibition networks. Biological Psychiatry, 62, 198–206. Monk, C. S., Peltier, S. J., Wiggins, J. L., Weng, S. J., Carrasco, M., Risi, S., & Lord, C. (2009).
2139 Abnormalities of intrinsic functional connectivity in autism spectrum disorders. NeuroImage, 47, 764–772. Raichle, M. E. (1998). Behind the scenes of functional brain imaging: A historical and physiological perspective. Proceedings of the National Academy of Sciences, 95, 765–772. Savoy, R. L. (2001). History and future directions of human brain mapping and functional neuroimaging. Acta Psychologica, 107, 9–42. Schultz, R. T., Gauthier, I., Klin, A., Fulbright, R., Anderson, A., Volkmar, F., et al. (2000). Abnormal ventral temporal cortical activity among individuals with autism and Asperger syndrome during face discrimination. Archives of General Psychiatry, 57, 331–340. Spencer, M. D., Holt, R. J., Chura, L. R., Suckling, J., Calder, A. J., Bullmore, E. T., & Baron-Cohen, S. (2011). A novel functional brain imaging endophenotype of autism: The neural response to facial expression of emotion. Translational Psychiatry, 1, e19. Voos, A. C., Pelphrey, K. A., Tirrell, J., Bolling, D. Z., Vander Wyk, B., Kaiser, M. D., & Ventola, P. (2013). Neural mechanisms of improvements in social motivation after pivotal response treatment: Two case studies. Journal of Autism and Developmental Disorders, 43, 1–10. https://doi.org/10.1007/s10803-012-1683-9.
Functional MRI ▶ Event-Related Functional Magnetic Resonance Imaging (MRI)
Functional Play ▶ Play
Functional Routines (FR), Teaching Katherine C. Holman Department of Special Education, Towson University, Towson, MD, USA
Definition Information-processing deficits in individuals with autism spectrum disorders (ASD) inhibit
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their ability to accurately attend to instructions, effectively participate in learning and social activities, and easily adapt to novel activities and change. Routines are effective at diminishing the information-processing load (Shatz 1983) by providing an inherent organizational structure that reduces variation and supports individual’s participation in them (Constable 1986). The teaching of functional routines can assist individuals with ASD to be more successful in engaging in and completing everyday activities. Routines have the benefit of being sequential (McClannahan and Krantz 1999; Siegel-Causey and Guess 1989; Yoder and Davies 1992) and predictable (Prizant et al. 2006; Synder-McLean et al. 1984) and are found in the natural environment (Pretti-Frontczak and Bricker 2004; Sussman 1999). Functional routines are predicable events that involve a chain of behaviors that are broken down into teachable units. They provide a reliable context that allows the individual to more independently participate or complete a specified activity. Functional routines instruction involves providing systematic prompts and cues to teach the child to participate independently in common school or home-based (self-care) routines (Arick et al. 2004). For example, the behaviors of arriving to school, hanging one’s coat up in the locker, traveling to desk, and taking out materials for the day are all integrated behaviors that must be completed to achieve the function of this routine. The routines will vary upon the setting in which they are taught. If it is during the school day, they could consist of transitioning and arrival, participating in circle-time or classroom activities, using the restroom, greeting a peer, eating lunch, etc. If the functional routines are being taught in the home environment, they may consist of improving daily living skills, such as independently dressing, eating meals, playing a social game with a caregiver, or making leisure choices.
Historical Background Routines have been described as a “scaffold” for children (Bruner 1975). Bruner and his
Functional Routines (FR), Teaching
colleagues focused their research on the development of joint-action routines which promote shared attention, cooperative activity, and language development, for example, peekaboo, book sharing, or object exchange (Ninio and Bruner 1978). This use of routines was later adapted by McLean and Snyder-McLean (1987) as part of a communication intervention that focused on highlighting the identifiable cycles and turn-taking opportunities that were present in everyday routines, such as feeding or bathing. The use of routines has historically been implicated in various language interventions (see Constable 1986; Nelson 1986). Functional skills are described as those that will have a meaningful impact on the individual’s life. The importance of explicitly teaching skills that are functional and using a routine as the platform for teaching these skills has been a critical intervention focus, particularly with individuals with severe disabilities (Brown et al. 1976; Snell 1983; Wilcox and Bellamy 1982). Historically, a task-analysis model was used to identify the skills and activities that are functional for a student and then further divide the activity or routine into teachable units of behavior. Brown et al. (1987) offered the component model of functional life routines, which provides a systematic structure for the specification of behaviors relevant to student competence. The model was proposed to provide a more comprehensive approach to the identification of behavioral sequences that will lead to a more successful and independent completion of routines by the individual with a disability. Specific research related to the use of functional routines with individuals with ASD is scarce; however, the concept has evolved from both language interventions (as described above) and the documented need to provide predictability and routine (Dawson and Osterling 1997) to promote participation and independence for individuals with ASD. Further, the use of routines is often a component in functional communication training, which is a common intervention used to improve the language and behavior of individuals with ASD (see Mancil 2006).
Functional Routines (FR), Teaching
Current Knowledge Limited research is available regarding the sole use of functional routines in teaching individuals with ASD. Often, the use of functional routines is naturally embedded as part of a comprehensive intervention program or classroom model (Arick et al. 2003; Dawson et al. 2010; Landa et al. 2011). The use of functional routines varies on the setting in which they are being used. In the school or workplace setting, they can be used as a generalization strategy after specific behaviors have been taught in a highly structured context (such as one-on-one using discrete trial training), and now they might be chained together in a more natural context (such as circle time) to promote participation or engagement in the routine. The STAR program, Strategies for Teaching Based on Autism Research, identifies functional routines as one of the three primary instructional strategies (the other two being discrete trial training and pivotal response training) that comprise the intervention. The STAR program utilizes functional routines to assist the young child with ASD to independently participate in common school (e.g., participation in circle-time routines) and self-care routines (e.g., using the bathroom). The use of functional routines in this program assists with creating a structured learning environment. Arick et al. (2003) researched the use of functional routines in a preliminary outcome study of the program and noted it to be one of the most common one-to-one instructional strategies utilized in the population studied. A study by Stahmer et al. (2015) looked at the fidelity of implementation of the evidence-based practices within the STAR program for teachers within an urban public school classroom. The researchers found that with consistent and ongoing coaching and support, teachers were able to implement functional routines with a high level of procedural integrity. In a follow up study investigated the fidelity of implementation of the individual components of the STAR program as associated with gains in cognitive ability, even though teachers once again demonstrated high fidelity implementing functional routines, this strategy was not directly related to gains in cognition (Pellecchia et al. 2015). However, as discussed
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below, the type of gains expected from the use of functional routines would be related to participation in activities and independence in self-care or school-based routines rather than a direct increase in cognitive abilities.
Rationale or Underlying Theory The underlying theory for teaching functional routines is grounded in the need for creating a context that is predictable, repeated often (offering multiple opportunities to practice and participation), and comprised of smaller segments of functional activities that can be broken down and taught part by part. The behaviors taught as part of the routine are functional to ensure more independence in the individual’s life. The routine can be conceptualized as a script (Bruner 1975) for the individual with ASD and reduce the cognitive load and improve their successful and independent participation in or completion of the activity (Nelson 1986).
Goals and Objectives When identifying the goals and objectives associated with a functional routine, one must consider the functionality of the skills targeted within the curriculum or those required for completion of important activities in the life of an individual with ASD. Focus should be on those skills that (a) are most likely to be useful in the student’s life to control his or her environment, (b) will increase the student’s independence and quality of life, and (c) will increase the student’s competent performance (Iovannone et al. 2003). The goal will most likely be to independently and successfully complete or participate in the specified functional routine. Each functional routine can be task analyzed into a number of core steps or behaviors, and these behaviors can be identified as the objectives. Independent mastery of each objective in succession will lead to the functional and independent completion of the routine. The ASD individual’s specific strengths and needs, the context, and the actual steps or components of the routine will determine the specific goals and objectives.
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Treatment Participants Traditionally, the teaching of functional routines has been used with individuals with cognitive impairments and those with ASD. They can be utilized with very young children, related to creating routines for caregivers and children (Kashinath et al. 2006; Kern et al. 2007) or with older individuals who need assistance completing functional life skills (Iovannone et al. 2003; Ogletree et al. 2007).
Treatment Procedures There is an inherent cycle for teaching functional routines, which includes (a) identification of a routine which is meaningful for the individual with ASD to complete independently, (b) task analysis of the steps and skills needed for a particular routine, (c) assessment of the individual’s independent completion of each step or use of each skill to successfully participate or complete the routine, (d) direct instruction of each behavior/ skill required in the functional routine, and (e) data collection on independence in participation or completion of the routine postinstruction. Further explanation of this cycle is below: 1. Identification of a functional routine. Determine what routine is meaningful for the individual with ASD to learn to complete independently. This could be to successfully arrive at work and prepare for the day, taking a bath at home, or participating in a songgesture routine at circle time. The specific routine will depend on the context and the individual needs of the person with ASD. 2. Task analyze the steps and skills needed for a particular routine. The adult who will be teaching the functional routine should break down the routine into specific sequential steps and skills and identify what behaviors need to be taught for successful participation or completion of the routine. 3. Assessment of the individual’s independent completion of each step of the routine. Prior to teaching the functional routine, the adult
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instructor should complete a baseline measurement of the routine and measure the ASD individual’s independence in completing each of the identified steps and skills in the functional routine. After collecting this information, the adult instructor should develop a lesson plan identifying the specific objectives (behaviors/skills) that need to be taught and the instructional procedures and strategies that will be used to teach the behaviors/skills for the routine. 4. Direct instruction of each behavior or skill in the functional routine. Depending on the needs of the individual with ASD, one might need to preteach each specific behavior individually using direct instruction. Once each step and behavior is mastered, then during the day, when the functional routine occurs, the instructor should provide the needed instruction and support for the individual to participate and complete each step of the routine. 5. Progress monitoring. Consistently monitor the ASD individual’s successful and independent completion of the functional routine. If the individual has mastered the routine/related skills, restart the process, and identify a new functional routine. If not, return to instruction and reevaluate the instructional strategies. The teaching of functional routines is typically carried out one-to-one or in a caregiver-coaching model. The particular routine that is taught will be determined by the functional needs of the individual within the particular context (e.g., home, school, or community).
Efficacy Information There is limited efficacy data available for teaching functional routines.
Outcome Measurement Outcome measurement for functional routines is related to the specific behaviors that are needed to successfully complete or participate in the routine.
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These behaviors have typically been identified through the task analysis completed prior to teaching the functional routine. See further information described in the section “Treatment Procedures.”
Qualifications of Treatment Providers There are no specific qualifications for treatment providers. Treatment providers can be teachers, speech-language pathologists, caregivers, counselors, behavior therapists, job coaches, or other significant adults who work and who are familiar with the individual with ASD.
See Also ▶ Functional Assessment and Curriculum for Teaching Everyday Routines ▶ Functional Communication Training
References and Reading Arick, J. R., Young, H. E., Falco, R. A., Loos, L. M., Krug, D. A., Gense, M. H., et al. (2003). Designing an outcome study to monitor the progress of students with autism spectrum disorders. Focus on Autism and Other Developmental Disabilities, 18, 75–87. Arick, J. R., Loos, L., Falco, R., & Krug, D. (2004). The STAR program: Strategies for teaching based on autism research. Austin: PRO-ED. Brown, L., Branston, M., Nietupski, S., Pumpian, I., Certo, N., & Gruenewald, L. (1976). A strategy for developing chronological-age-appropriate and functional curricular content for severely handicapped adolescents and young adults. The Journal of Special Education, 13, 81–90. Brown, F., Evans, I., Weed, K., & Owen, V. (1987). Delineating functional competencies: A component model. Journal of the Association for Persons with Severe Handicaps, 12(2), 117–124. Bruner, J. (1975). The ontogenesis of speech acts. Journal of Child Language, 2, 1–9. Constable, C. M. (1986). The application of scripts in the organization of language intervention contexts. In K. Nelson & A. van Kleeck (Eds.), Children’s language (Vol. 6, pp. 44–63). Hillsdale: Erlbaum. Dawson, G., & Osterling, J. (1997). Early intervention in autism. In M. Guralnick (Ed.), The effectiveness of early intervention (pp. 307–326). Baltimore: Brookes. Dawson, G., Rogers, S., Munson, J., Smith, M., Winter, J., Greenson, J., et al. (2010). Randomized, controlled trial
2143 of an intervention for toddlers with autism: The early start Denver model. Pediatrics, 125, e17–e23. Iovannone, R., Dunlap, G., Huber, H., & Kincaid, D. (2003). Effective educational practices for students with autism spectrum disorders. Focus on Autism and Other Developmental Disabilities, 18, 150–165. Kashinath, S., Woods, J., & Goldstein, H. (2006). Enhancing generalized teaching strategy use in daily routines by parents of children with autism. Journal of Speech, Language, and Hearing Research, 49, 466–485. Kern, P., Wolery, M., & Aldridge, D. (2007). Use of songs to promote independence in morning greeting routines for young children with autism. Journal of Autism and Developmental Disorders, 37, 1264–1271. Landa, R. J., Holman, K. C., O’Neill, A. H., & Stuart, E. A. (2011). Intervention targeting development of socially synchronous engagement in toddlers with autism spectrum disorder: A randomized controlled trial. Journal of Child Psychology and Psychiatry, 52, 13–21. Mancil, G. (2006). Functional communication training: A review of the literature related to children with autism. Education and Training in Developmental Disabilities, 41, 213–224. McClannahan, L. E., & Krantz, P. J. (1999). Activity schedules for children with autism: Teaching independent behavior. Bethesda: Woodbine House. McLean, J. E., & Snyder-McLean, L. (1987). Form and function of communicative behaviour among persons with severe developmental disabilities. Australia and New Zealand Journal of Developmental Disabilities, 13, 83–98. Nelson, K. (1986). Event knowledge: Structure and function in development. Hillsdale: Erlbaum. Ninio, A., & Bruner, J. (1978). The achievement and antecedents of labeling. Journal of Child Language, 5, 1–5. Ogletree, B. T., Oren, T., & Fischer, M. A. (2007). Examining effective intervention practices for communication impairment in autism spectrum disorder. Exceptionality, 15(4), 233–247. Pellecchia, M., Connell, J. E., Beidas, R. S., Xie, M., Marcus, S. C., & Mandell, D. S. (2015). Dismantling the active ingredients of an intervention for children with autism. Journal of Autism and Developmental Disorders, 45, 2917–2927. Pretti-Frontczak, K., & Bricker, D. (2004). An activitybased approach to early intervention (3rd ed.). Baltimore: Brookes. Prizant, B. M., Wetherby, A. M., Rubin, E., Laurent, A. C., & Rydell, P. J. (2006). The SCERTS model: A comprehensive educational approach for children with autism spectrum disorders (Programming planning and intervention, Vol. II). Baltimore: Brookes. Shatz, M. (1983). Communication. In P. H. Mussen (Ed.), Handbook of child psychology (Cognitive development, Vol. 3, pp. 841–889). New York: Wiley. Siegel-Causey, E., & Guess, D. (1989). Enhancing nonsymbolic communication interactions among learners with severe disabilities. Baltimore: Brookes.
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2144 Snell, M. E. (1983). Functional reading. In M. E. Snell (Ed.), Systematic instruction of the moderately and severely handicapped (pp. 445–487). Columbus: Merrill. Stahmer, A. C., Rieth, S., Lee, E., Reisinger, E. M., Mandell, D. S., & Connell, J. E. (2015). Training teachers to use evidence-based practices for autism: Examining procedural implementation fidelity. Psychology in Schoools, 52, 181–195. Sussman, F. (1999). More than words: A guide to helping parents promote communication and social skills. Toronto: Hanen Centre. Synder-McLean, L., Solomonson, B., Mclean, J. E., & Sack, S. (1984). Structuring joint action routines: A strategy for facilitating communication and language development in the classroom. Seminars in Speech and Language, 5, 213–228. Wilcox, B., & Bellamy, G. T. (1982). Design of high school programs for severely handicapped students. Baltimore: Paul Brookes. Yoder, P. J., & Davies, B. (1992). Do children with developmental delays use more frequent and diverse language in verbal routines? American Journal on Mental Retardation, 97, 197–208.
Functional Speech ▶ Verbal Communication
Functionally Equivalent Alternative Behavior Kristen D’Eramo The Center for Children with Special Needs, Glastonbury, CT, USA
Synonyms Replacement behavior
Definition Functionally equivalent alternative behaviors, or functionally equivalent replacement behaviors, are desirable/acceptable behaviors that achieve the same outcome as a less desirable problem behavior. For example, an individual may shout
Functional Speech
in order to gain his or her parent’s attention when the parent is otherwise occupied. An alternative behavior that could serve the same function might be to say “Excuse me.” In order to determine an appropriate alternative behavior, one must first understand the function of the problem behavior. This requires completion of a functional behavior assessment. Once the function of the problem behavior is clearly articulated, an appropriate alternative response, or repertoire of alternative responses, can be selected that are equal in function to the targeted challenging behavior. Alternative responses should be selected based on their social validity and should be more efficient than the challenging behavior. For example, saying “excuse me” and raising one’s hand are both socially acceptable ways to gain attention and thus represent potential alternative behaviors. On the other hand, circling a person three times silently may be another way to gain attention without shouting but should not be selected because the behavior lacks social validity. Once an alternative behavior is selected, the individual should be specifically taught to engage in the behavior and then reinforced for doing so. In the current example, the child would be taught to say “excuse me” and then reinforced for doing so. In order to make the alternative behavior more efficient than the problem behavior, the problem behavior should not be reinforced or should be reinforced less often. In the example cited, the parent would consistently provide attention to the child when he or she says “excuse me” but ignore him or her when he or she shouts. In another example, a learner who engages in hitting others (problem behavior) in order to escape work demands (function) may be taught a functionally equivalent alternative behavior of requesting a break. The behavior of requesting a break allows the individual to meet his or her need (escaping the demand) without engaging in a socially inappropriate response. Approaches to intervention that seek to eliminate problem behaviors without teaching functionally equivalent alternative behaviors are ill advised as the individual will ultimately select his or her own ways to meet the needs that the extinguished problem behavior was previously serving. For example, a child who
Fusiform Gyrus (FG)
shouts at his or her parent to gain attention could be taught to stop shouting for attention if the shouting is consistently ignored but over time he or she may begin hitting his or her parent in order to solicit attention if he or she is never taught what to do instead (e.g., say “excuse me”). Behavior support plans require explicit programming that addresses functionally equivalent alternative behaviors in order to best address individual needs. This includes specific teaching procedures for the alternative behaviors and plans to systematically reinforce the alternative behaviors when they occur.
See Also ▶ Differential Reinforcement ▶ Functional Behavior Assessment ▶ Functional Communication Training
References and Reading Cooper, J. O., Heron, T. E., & Heward, W. L. (2007). Applied behavior analysis (2nd ed.). Upper Saddle River: Pearson. Glasberg, B. A. (2008). Stop that seemingly senseless behavior! FBA-based interventions for people with autism. Bethesda: Woodbine House. Scott, T. M., et al. (2005). An examination of the relation between functional behavior assessment and selected intervention strategies with school-based teams. Journal of Positive Behavior Interventions, 7, 205–215.
Fusiform Gyrus (FG) Danielle Perszyk Yale Child Study Center, New Haven, CT, USA
Definition The fusiform gyrus (FG) is a brain region consisting of occipitotemporal cortex (temporal lobe) that corresponds to Brodmann area 37. It is involved in high-level visual processing, specifically object recognition and category identification, of complex stimuli such as faces. Individuals
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with ASD demonstrate deficits in certain cognitive abilities for which the fusiform gyrus has been implicated – most notably and socially relevant being face processing – as well as abnormalities in the fusiform gyrus itself.
Historical Background The fusiform gyrus is a brain region involved in high-level visual processing. Visual processing is organized hierarchically and involves many brain regions working together to transform information provided by the external world into internal representations. Early low-level visual processing occurs milliseconds after light hits the retina and distinguishes basic, isolated features such as orientation, spatial frequency, and color. Later highlevel visual processing combines information from lower levels of processing and culminates in identification, recognition, and categorization of objects and actions. Visual processing begins in occipital cortex and proceeds anteriorly via ventral and dorsal pathways; the highest levels of processing occur in temporal cortex. The ventral and dorsal pathways are hypothesized to represent the processing of “what” and “where/how” information, respectively, and are correspondingly associated with “perception” and “action” (Underleider and Mishkin 1982). The FG has been shown to be involved in numerous ventral stream processes, including high-level visual processes that entail semantic encoding. The initial discoveries concerning specific functions of the FG arose from studies of primates (Van Essen et al. 1992) and patients with brain lesions or psychological disorders (Mesulam 1994), both of which have revealed the anatomy of the mammalian visual system. First, single-unit recordings from the inferotemporal cortex in macaques found populations of neurons that responded specifically to faces (Gross et al. 1972). Then, electrophysiological studies in humans suggested similar cortical specialization. Recordings from the same brain region in epilepsy patients with implanted subdural electrodes showed large N200 potentials elicited by faces but not scrambled faces, cars, or butterflies
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(Allison et al. 1994). These studies were consistent with an older study of brain-injured patients that showed the same brain region seemed to be involved in recognizing faces but not houses (Yin 1970). Additional studies of patients with damage to occipitotemporal cortex of the right hemisphere, who had selectively lost the ability to recognize faces, further supported the notion that this region is involved in face processing (DeRenzi and Saetti 1997). At the same time, research in several areas, including cognitive psychology, neuropsychology, neurophysiology, and computational vision, converged to suggest the differentiation of face and object recognition as qualitatively different processes and perhaps even entailing distinct brain areas. As research continued building an understanding of the function of the FG, a growing body of evidence indicated that face processing was not its only function. Neuroimaging became more popular as a technique to study brain function, and as a result, specific brain regions could be much more easily implicated in a wide range of cognitive processes, including processing shape and surface properties such as color and texture (Barrett et al. 2001), number and word recognition (Allison et al. 1994), and within-category identification (Gauthier et al. 2000). Yet while these studies showed that the FG is involved in myriad visual process, individuals with ASD do not characteristically demonstrate deficits in the lower level visual processes (e.g., shape and surface properties); rather, they often demonstrate difficulty integrating more basic features into configural representations and processing certain higher level visual categories, specifically the socially relevant category of faces. Early research investigating face processing in ASD showed that, unlike typically developing peers, children with ASD fail to show both a “face inversion effect” – a disproportionate difficulty recognizing upside down faces – and a face “decomposition effect” – reduced recognition of face components or partially obscured faces (Langdell 1978). That is, children with ASD do not show more difficulty recognizing upside down faces than upright faces, nor do they show more difficulty recognizing face components or partially obscured faces than
Fusiform Gyrus (FG)
whole faces. These abnormalities in face processing suggest that, unlike typical counterparts, individuals with ASD do not process faces holistically (Freire et al. 2000), but instead, they may process faces using feature-based strategies similar to how typical people process objects. Given that ASD is at is core a social disorder and that face perception is among the most vital of social functions, it is especially noteworthy that individuals with ASD were observed to process faces atypically. But it remained uncertain whether these face-processing abnormalities stem from a primary brain abnormality, a primary behavioral difference (i.e., lowered attention to faces), or some combination and subsequent interaction thereof. Studies found that individuals with ASD already show lower attention to faces as young as 1 year of age (Osterling and Dawson 1994); by childhood, they show more difficulty than typically developing children discriminating and recognizing (Boucher and Lewis 1992) faces, and through adolescence and adulthood, they continue to show poorer recognition of faces than typically developing counterparts (McPartland et al. 2004). In order to disentangle the possible origins of these behavioral deficits in face processing – whether they arise from primary brain abnormalities or primary behavioral differences – later studies used neuroimaging techniques to examine the brain bases of face processing in ASD. Functional magnetic resonance imaging (fMRI) studies showed that, when viewing neutral faces, individuals with ASD exhibit lowered fusiform gyrus activity compared to typical counterparts (Schultz et al. 2000). Furthermore, Schultz et al. (2000) suggested that individuals with ASD process faces more similarly to how typical counterparts process objects, with decreased FG activity and increased inferior temporal gyrus activity. Event-related potential (ERP) studies showed that children with ASD, but not typically developing children, fail to exhibit differential brain activity for familiar versus unfamiliar faces (Dawson et al. 2002); similarly, adolescents and adults with ASD, but not typical counterparts, fail to exhibit differential brain activity for upright versus inverted faces (McPartland et al. 2004). Moreover, McPartland et al. (2004) found that
Fusiform Gyrus (FG)
individuals with ASD exhibit longer latencies to faces and that processing speed positively correlates with face recognition ability. These facesensitive ERPs have been localized to the FG. Finally, one recent study found that individuals with ASD have smaller and fewer neurons in the FG (van Kooten et al. 2008).
Current Knowledge Initial investigations of the FG revealed its involvement in face processing. While it has since been implicated in many additional roles, current investigations continue to focus on the role of the FG in face processing. There are presently three prevailing theories for the neural organization of face processing involving the FG. One is that the FG is specialized for face processing, which is modular and domain specific (Kanwisher et al. 1997). A second theory is that the FG is not specialized for faces per se but for objects with which one has experience or expertise; most humans have extensive experience with and are thus “experts” of faces, making the FG specialized for faces by default. This theory proposes that the FG is domain general, and it may also serve to differentiate between specific levels of categorization, particularly subordinate levels of identification (Gauthier et al. 2000). A third theory is that the FG and, more generally, inferotemporal cortex are neither specialized for face nor object processing but for face-like features. That is, the brain region containing and surrounding the FG is topographically arranged according to visual features of objects, and the FG “happens to be” where types of visual features associated with faces are processed (Haxby 1999). It still remains uncertain as to which, if any, of these theories most accurately describes the role of the FG as it relates to face processing. Moreover, these theories do not comprehensively reflect face-processing research. Yet other studies suggest that face processing and the role of the FG are affected by the manner in which one conceives a visual percept. Specifically, thinking about a visual percept in a social context or imagining faces may be sufficient for FG activation. Schultz
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et al. (2003) found that the FG activated for geometric shape stimuli behaving in social but not mechanistic ways, suggesting that FG activity is dependent upon a social framework. While this interpretation is inconsistent with any of the above three theories, it is consistent with findings from other studies. These unreconciled findings have important implications for the source of social dysfunction in ASD. A major question is whether the observed abnormalities in face processing in ASD are causes or effects of the diagnostic social deficits in ASD. While some studies argue the former (Trepagnier 1995), most evidence supports the latter; children with ASD may have experienced typical development with appropriate attention to faces early on followed by autistic regression, and individuals with ASD exhibit abnormalities in widely distributed brain systems, some of which are unrelated to face processing (Kemper and Bauman 1998). A larger body of research suggests that face-processing difficulties stem from developmental abnormalities in social interest and attention. Individuals with ASD may exhibit face-processing deficits because they do not acquire adequate experience with faces due to a lack of social motivation (Dawson et al. 2002). This theory is consistent with both the domainspecific and domain-general theories of face processing aforementioned. Another explanation of face-processing abnormalities in ASD that has not been empirically supported is that they stem from early developmental abnormalities in the FG or, more generally, inferotemporal cortex. Schultz and colleagues (2005) have proposed that face perception and social cognitive skills are part of a network supported by a system comprised of the amygdala and FG and that abnormalities in this brain system, which in turn cause deficits in the social perceptual network, may play a large role in the etiology of autism. They argue that the best neuroimaging evidence for brain-based differences in the ASDs thus far involves FG hypoactivation, and they describe three possible interdependent factors that might moderate the degree of FG activity in ASD. First, they suggest an attentional factor: lowered attention to a visual stimulus reduces FG activity. Second, they suggest a perceptual skill factor:
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chronically reduced attention to social stimuli, particularly faces, leads to reduced perceptual skill and reduced FG activity. Third, they suggest a social knowledge factor: the FG may encode social knowledge such that social judgment not necessarily involving faces may strongly activate the FG, and therefore, conversely, reduced social knowledge may reduce social judgments, which in turn may result in FG hypoactivation. Two recent studies have provided a confound to this model. Dalton et al. (2005) found that when individuals with ASD view faces, amygdala and FG activation are strongly and positively correlated with time spend fixating the eyes. In addition, eye fixation is strongly and positively associated with amygdala activation, suggesting a heightened emotion response associated with eye fixation. Similarly, Morris, Pelphrey, and McCarthy (2007) found that when typically developing individuals view faces, the scanpath one follows alters FG activation such that greater focus on the eyes (typical scanpath) as opposed to other face regions (atypical scanpath) elicits greater FG activation. These findings indicate that, in addition to degree of attention, the specific features attended to within social stimuli (i.e., faces) affect FG activation. These findings therefore imply that controlling gaze fixation for individuals with ASD when viewing social stimuli may serve to alter the neural mechanisms underlying their social deficits.
Future Directions Further research concerning FG function will continue to disentangle the various models of face processing and, concerning the etiology of ASD, whether FG abnormalities are primary or whether deficits in social motivation and ability are primary to deficits in social skills. In addition, future neuroimaging studies will seek to investigate the effects on FG activation of various scanpaths for social stimuli in individuals with ASD. These studies will contribute to a refining of the understanding of both the causes of and potential treatments for ASD.
Fusiform Gyrus (FG)
See Also ▶ Face Perception ▶ Face Recognition ▶ Occipital Lobe
References and Reading Allison, T., Mccarthy, G., Nobre, A., Puce, A., & Belger, A. (1994). Human extrastriate visual-cortex and the perception of faces, words, numbers, and colors. Cerebral Cortex, 4(5), 544–554. Barrett, N. A., Large, M. M., Smith, G. L., Michie, P. T., Karayanidis, F., Kavanagh, D. J., et al. (2001). Human cortical processing of colour and pattern. Human Brain Mapping, 13(4), 213–225. Boucher, J., & Lewis, V. (1992). Unfamiliar face recognition in relatively able autistic children. Journal of Child Psychology and Psychiatry, 33, 843–859. Dalton, K. M., Nacewicz, B. M., Johnstone, T., Schaefer, H. S., Gernsbacher, M. A., Goldsmith, H. H., et al. (2005). Gaze fixation and the neural circuitry of face processing in autism. Nature Neuroscience, 8(4), 519–526. Dawson, G., Carver, L., Meltzoff, A. N., Panagiotides, H., McPartland, J., & Webb, S. J. (2002). Neural correlates of face and object recognition in young children with autism spectrum disorder, developmental delay, and typical development. Child Development, 73(3), 700–717. DeRenzi, E., & Saetti, M. C. (1997). Associative agnosia and optic aphasia: Qualitative or quantitative difference? Cortex, 33(1), 115–130. Freire, A., Lee, K., & Symons, L. A. (2000). The faceinversion effect as a deficit in the encoding of configural information: Direct evidence. Perception, 29(2), 159–170. Gauthier, I., Tarr, M. J., Moylan, J., Skudlarski, P., Gore, J. C., & Anderson, A. W. (2000). The fusiform “face area” is part of a network that processes faces at the individual level. Journal of Cognitive Neuroscience, 12(3), 495–504. Gross, C. G., Rochamir, C. E., & Bender, D. B. (1972). Visual properties of neurons in inferotemporal cortex of macaque. Journal of Neurophysiology, 35(1), 96–111. Haxby, J. V. (1999). Human neural systems for face perception. Biological Psychiatry, 45(8S), 102S–102S. Kanwisher, N., McDermott, J., & Chun, M. M. (1997). The fusiform face area: A module in human extrastriate cortex specialized for face perception. Journal of Neuroscience, 17(11), 4302–4311. Kemper, T. L., & Bauman, M. (1998). Neuropathology of infantile autism. Journal of Neuropathology and Experimental Neurology, 57(7), 645–652.
Fusiform Gyrus (FG) Langdell, T. (1978). Recognition of faces – Approach to study of autism. Journal of Child Psychology and Psychiatry, 19(3), 255–268. McPartland, J., Dawson, G., Webb, S. J., Panagiotides, H., & Carver, L. J. (2004). Event-related brain potentials reveal anomalies in temporal processing of faces in autism spectrum disorder. Journal of Child Psychology and Psychiatry, 45(7), 1235–1245. Mesulam, M. M. (1994). Higher visual functions of the cerebral cortex and their disruption in clinical practice. In D. M. Albert & F. A. Jakobiec (Eds.), The principles and practice of ophthalmology: The Harvard system (pp. 2640–2653). Philadelphia: W.B. Saunders. Morris, J. P., Pelphrey, K. A., & McCarthy, G. (2007). Controlled scanpath variation alters fusiform face activation. Social Cognitive and Affective Neuroscience, 2(1), 31–38. Osterling, J., & Dawson, G. (1994). Early recognition of children with autism – A study of 1st birthday home videotapes. Journal of Autism and Developmental Disorders, 24(3), 247–257. Schultz, R. T. (2005). Developmental deficits in social perception in autism: the role of the amygdala and fusiform face area. International Journal of Developmental Neuroscience, 23(2–3), 125–141. Schultz, R. T., Gauthier, I., Klin, A., Fulbright, R. K., Anderson, A. W., Volkmar, F., et al. (2000). Abnormal ventral temporal cortical activity during face discrimination among individuals with autism and Asperger
2149 syndrome. Archives of General Psychiatry, 57(4), 331–340. Schultz, R. T., Grelotti, D. J., Klin, A., Kleinman, J., Van der Gaag, C., Marois, R., et al. (2003). The role of the fusiform face area in social cognition: Implications for the pathobiology of autism. Philosophical Transactions of the Royal Society, B: Biological Sciences, 358(1430), 415–427. Trepagnier, C. (1995). Infant social gaze in autism. In Proceedings of the 1995 National Conference on Autism (pp. 453–457)., 493. Trepagnier, C. (1996). A possible origin for the social and communicative deficits of autism. Focus on Autism and Other Developmental Disabilities Fall, 11, 170–182. Underleider, L. G., & Mishkin, M. (1982). In D. J. Ingle, M. A. Goodale, & R. J. W. Mansfield (Eds.), Analysis of visual behavior (pp. 549–586). Cambridge, MA: MIT Press. Van Essen, D. C., Anderson, C. H., & Felleman, D. J. (1992). Information processing in the primate visual system, an integrated systems perspective. Science, 255, 419–423. van Kooten, I. A. J., Palmen, S. J. M. C., von Cappeln, P., Steinbusch, H. W. M., Korr, H., Heinsen, H., et al. (2008). Neurons in the fusiform gyrus are fewer and smaller in autism. Brain, 131, 987–999. Yin, R. K. (1970). Face recognition by brain-injured patients: A dissociable ability? Neuropsychologia, 8(4), 395–402.
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Gabapentin Lawrence David Scahill Nursing and Child Psychiatry, Yale Child Study Center, Yale University School of Nursing, New Haven, CT, USA Marcus Autism Center, Children’s Healthcare of Atlanta, Atlanta, GA, USA Department of Pediatrics, Emory University, Atlanta, GA, USA
There are no studies of gabapentin in children with autism spectrum disorders. However, it is used as an anticonvulsant in children.
References and Reading Martin, A., Scahil, L., & Christopher, K. (2010). Pediatric psychopharmacology: Principles and practice (2nd ed.). New York: Oxford University Press.
Synonyms
Gabarone Fanatrex; Gabarone; Horizant; Neurontin ▶ Gabapentin
Definition Gabapentin is an anticonvulsant medication that is also used for the treatment of anxiety. It is also used for the treatment of bipolar illness. The mechanism of action of gabapentin is complicated and not completely understood. It appears to assist with the decreased excitability in the brain. However, it does not appear to directly affect the GABA system in the brain. Recent evidence suggests that it exerts its positive effects by turning down calcium channels. This would fit with its effect to decrease excitability in the brain.
GAD ▶ General Anxiety ▶ Generalized Anxiety Disorder
Games with Rules ▶ Play
© Springer Nature Switzerland AG 2021 F. R. Volkmar (ed.), Encyclopedia of Autism Spectrum Disorders, https://doi.org/10.1007/978-3-319-91280-6
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Gaming Technologies in Rehabilitation of Individuals with Autism
Gaming Technologies in Rehabilitation of Individuals with Autism Parisa Ghanouni Occupational Science and Occupational Therapy Department, University of British Columbia, Vancouver, Canada Occupational Science and Occupational Therapy Department, Dalhousie University, Halifax, NS, Canada
Children with autism spectrum disorder (ASD) have difficulties in social interactions, affecting their social participation and quality of life. There is a growing use of assistive technologies to support social participation of children with ASD. Despite potential improvements of social skills after using these programs, there are concerns on technology dependence and generalization of learned skills. It is still unclear to what extent virtual environments are effective and what populations on the autism spectrum are the suitable candidates to take advantage of using virtual gaming programs. This manuscript highlights trends in current literature and recommends ideas for future studies.
esteem in communicating with their peers (Lopata et al. 2008). This may act as a vicious cycle that precludes children from acquiring social skills to interact appropriately with peers. The delivery of early and effective therapeutic services is crucial to enhance the outcomes and functioning of clients with ASD (Eikeseth 2009; Matson and Smith 2008). Gaming programs have been widely used in the fields of education and rehabilitation to address a diverse range of clients’ needs. These programs provide an opportunity for practicing in a fun environment with gradual exposure to stimuli, providing consistency and stimulus control, giving real-time feedback, and augmenting attention to performance (Parsons and Cobb 2011). These programs can provide a simulated environment in which the intensity of training, level of difficulty, and amount of feedback can be adjusted. Virtual gaming programs can simulate real-life situations, which increases the probability of transferring learned skills to real-life contexts, and provide a safe and risk-free environment to practice skills that may be dangerous or risky to complete in real-life contexts (Bellani et al. 2011). This allows for the creation of an individualized program that keeps clients motivated to endure the program.
Current Knowledge Definition and Historical Background Autism spectrum disorder (ASD) is a childhood neurodevelopmental condition that is currently being diagnosed in about 1 in 59 children. Children with ASD may experience difficulties in communication and socio-emotional skills, which affect their daily functioning (American Psychiatric Association 2013; Baron-Cohen et al. 2001; Simonoff et al. 2008). Children with ASD have challenges with understanding and applying social rules and interpreting verbal and nonverbal social cues (Kelly et al. 2008; Ghanouni et al. 2019). All these factors can have repercussions for the child’s ability to socialize, develop relationships, and make friends. As children get older and gain insights about these significant deficits, they demonstrate decreased self-
Recently, there have been rapid advancements in developing low-cost technologies in rehabilitation, including computer-based programs, virtual reality games, motion-tracking games, and video games (Anderson-Hanley et al. 2011; Parsons and Cobb 2011). However, developing an appropriate program for children with ASD is a complex task due to the heterogeneity of the disorder and co-occurring conditions (Wilczynski et al. 2007). Not every program can fit the broad range of needs that may be observed in individuals on the spectrum. It is necessary to tailor each program to address the symptoms and deficits that a child with ASD demonstrates. In addition to the importance of individualized programs, the involvement of clients in making decisions about the program to suit their family style and preferences
Gaming Technologies in Rehabilitation of Individuals with Autism
would maximize outcomes (Elwyn et al. 2000; Guadagnoli and Ward 1998). Looking at the literature, there has been mixed findings on potential effects of using video game and virtual reality programs in individuals with ASD (Parsons and Cobb 2011). Some of the literature did not show significant results, whereas others demonstrated significant improvements in socio-emotional skills. Gaming programs were demonstrated to be effective for improving specific social skills, such as social initiation/interaction, verbal communication, and facial affect and emotion recognition for children diagnosed with ASD. Using humanoid or cartoon characters in computer-based intervention programs has been shown to potentially improve emotion recognition in individuals with ASD (Golan et al. 2010; Hopkins et al. 2011; Tanaka et al. 2010). Increased perspective-taking and a higher level of social interaction within short-term usage of the computer-based interventions, as complementary programs, hold great promise for individuals with ASD (Hughes 2014; Golan et al. 2010; Moore et al. 2000; Mesa-Gresa et al. 2018). The majority of the findings demonstrated positive trends for social skill development but lack the rigor and power to reach levels of significance. Although there was no reported negative experience of using these gaming programs, there are concerns on the dosage of the program and the extent to which users of video games can generalize the learned skills to real life. If the task can only be performed in a virtual setting and in a controlled environment, the benefits of the program should be justified. Using the virtual settings, which include real-life scenarios, helps individuals with ASD relate to these scenarios when they face them in real life. Adjusting the level of difficulty of the task, as individuals with ASD progress over time, keeps them engaged in the activities. Modification of sensory, visual, and auditory modalities should also be considered when working with individuals with sensory problems. Given the interest of individuals with ASD to use virtual settings, screen time and technology dependence are other concerns (Montes 2015). The optimal goal is helping users to be independent, and not mentally reliant on technology that may affect the quality of daily
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interactions. Although setting schedules might help monitor screen time, there is still a significant gap in the literature on how we can mitigate potential risks of using virtual settings and appropriate dosage of programs to facilitate learning new skills in individuals with ASD. In addition to the content of virtual settings, another presumption is that not everyone can be an ideal candidate to use it. Being able to navigate through virtual environments, relate scenarios, and transfer learned skills to daily life requires high levels of practice, environmental support, as well as individual mental and functional capacity. Severity of disorder, selective and sustained attention, executive function, and IQ level might be factors that play significant roles for transition of learned skills from virtual settings to daily activities.
Future Direction Identifying subgroups of individuals with ASD who have the potential to benefit from using these virtual settings is a recommendation for future research. Providing more comprehensive profiles, using brain imaging to examine areas that are activated in virtual settings as opposed to real settings, and seeking potential differences in brain functions provide additional insight in deciding which tool, when, and how they might be useful (Bohil et al. 2011; Weber et al. 2006). Given the heterogeneous nature of ASD and disperse behavioral studies, brain imaging techniques such as EEG can advance knowledge and provide additional understanding of the extent in which a simulated virtual environment is similar to in vivo settings. Following up with participants of the study after receiving gaming programs to examine the degree in which the learned skilled can be generalized to other settings and/or maintained after a period of time is also warranted. Additionally, future research will need to consider more rigorous studies using stronger study designs by increasing sample size, addressing co-intervention, comparing different types of gaming programs, and using outcome measures with strong psychometric properties. As a result, informed modifications can be made
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Gaming Technologies in Rehabilitation of Individuals with Autism
to potentially optimize learning in virtual settings for individuals with ASD.
See Also ▶ Augmentative and Assistive Technology ▶ Computer-Based Intervention Assistive Technology ▶ Video Games, Use of
References and Reading American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders: DSM-5 (5th ed.). Washington, DC: American Psychiatric Association. Anderson-Hanley, C., Tureck, K., & Schneiderman, R. L. (2011). Autism and exergaming: Effects on repetitive behaviors and cognition. Psychology Research and Behavior Management, 4, 129–137. Baron-Cohen, S., Wheelwright, S., Hill, J., Raste, Y., & Plumb, I. (2001). The “Reading the mind in the eyes” test revised version: A study with normal adults, and adults with Asperger syndrome or high-functioning autism. Journal of Child Psychology and Psychiatry, 42, 241–251. Bellani, M., Fornasari, L., Chittaro, L., & Brambilla, P. (2011). Virtual reality in autism: State of the art. Epidemiology and Psychiatric Sciences, 20, 235–238. Bohil, C. J., Alicea, B., & Biocca, F. A. (2011). Virtual reality in neuroscience research and therapy. Nature Reviews. Neuroscience, 12, 752–762. Eikeseth, S. (2009). Outcome of comprehensive psychoeducational interventions for young children with autism. Research in Developmental Disabilities, 30, 158–178. Elwyn, G., Edwards, A., Kinnersley, P., & Grol, R. (2000). Shared decision making and the concept of equipoise: The competences of involving patients in healthcare choices. British Journal of General Practice, 50, 892–899. Ghanouni, P., Jarus, T., Zwicker, J., Lucyshyn, G., Chauhan, J., & Moir, S. (2019). Perceived barriers and existing challenges in participation of children with autism Spectrum disorders: “He did not understand and no one Else seemed to understand him”. Journal of Autism and Developmental Disorders, 49, 3136–3145. Golan, O., Ashwin, E., Granader, Y., McClintock, S., Day, K., Leggett, V., & Baron-Cohen, S. (2010). Enhancing emotion recognition in children with autism spectrum conditions: An intervention using animated vehicles with real emotional faces. Journal of Autism and Developmental Disorders, 40, 269–279.
Guadagnoli, E., & Ward, P. (1998). Patient participation in decision-making. Social Science & Medicine, 47, 329–339. Hopkins, I. M., Gower, M. W., Perez, T. A., Smith, D. S., Amthor, F. R., Wimsatt, F. C., & Biasini, F. J. (2011). Avatar assistant: Improving social skills in students with an ASD through a computer-based intervention. Journal of Autism and Developmental Disorders, 41, 1543–1555. Hughes, D. E. (2014). The design and evaluation of a video game to help train perspective-taking and empathy in children with autism spectrum disorder. Orlando: University of Central Florida. Kelly, A., Garnett, M., Attwood, T., & Peterson, C. (2008). Autism spectrum symptomology in children: The impact of family and peer relationships. Journal of Abnormal Child Psychology, 36, 1069–1108. Lopata, C., Volker, M. A., Putnam, S. K., Thomeer, M. L., & Nida, R. E. (2008). Effect of social familiarity on salivary cortisol and self-reports of social anxiety and stress in children with high functioning autism spectrum disorders. Journal of Autism and Developmental Disorders, 38, 1866–1877. Matson, J. L., & Smith, K. R. (2008). Current status of intensive behavioral interventions for young children with autism and PDD-NOS. Research in Autism Spectrum Disorders, 2, 60–74. Mesa-Gresa, P., Gil-Gómez, H., Lozano-Quilis, J.-A., & Gil-Gómez, J.-A. (2018). Effectiveness of virtual reality for children and adolescents with autism Spectrum disorder: An evidence-based systematic review. Sensors, 18, 2486. Montes, G. (2015). Children with autism spectrum disorder and screen time: Results from a large, nationally representative US study. Academic Pediatrics, 16, 122–128. Moore, D., McGrath, P., & Thorpe, J. (2000). Computeraided learning for people with autism–a framework for research and development. Innovations in Education and Teaching International, 37, 218–228. Parsons, S., & Cobb, S. (2011). State-of-the-art of virtual reality technologies for children on the autism spectrum. European Journal of Special Needs Education, 26, 355–366. Simonoff, E., Pickles, A., Charman, T., Chandler, S., Loucas, T., & Baird, G. (2008). Psychiatric disorders in children with autism spectrum disorders: Prevalence, comorbidity, and associated factors in a population-derived sample. Journal of the American Academy of Child & Adolescent Psychiatry, 47, 921–929. Tanaka, J. W., Wolf, J. M., Klaiman, C., Koenig, K., Cockburn, J., Herlihy, L., . . . Schultz, R. T. (2010). Using computerized games to teach face recognition skills to children with autism spectrum disorder: The Let’s face it! Program. Journal of Child Psychology and Psychiatry, 51, 944–952. Wang, X., Laffey, J., Xing, W., Galyen, K., & Stitcher, J. (2017). Fostering verbal and non-verbal social interactions in a 3D collaborative virtual learning
Gangliosidoses environment: a case study of youth with autism spectrum disorders learning social competence in iSocial. Education Technology Research Development, 65, 1015–1039. Weber, R., Ritterfeld, U., & Mathiak, K. (2006). Does playing violent video games induce aggression? Empirical evidence of a functional magnetic resonance imaging study. Media Psychology, 8, 39–60. Wilczynski, S. M., Menousek, K., Hunter, M., & Mudgal, D. (2007). Individualized education programs for youth with autism spectrum disorders. Psychology in the Schools, 44, 653–666.
Gamma-Aminobutyric Agonist ▶ Zolpidem
Ganglioside Lipidosis ▶ Gangliosidoses
Gangliosidoses Emma Lecarie Yale Child Study Center, New Haven, CT, USA
Synonyms Autosomal recessive disorder; Ganglioside lipidosis; Lysosomal lipid storage disorder; Sphingolipidoses
Definition Gangliosidosis contains different types of lipid storage disorders caused by the accumulation of lipids called gangliosides. Gangliosides are made and biodegraded rapidly in early life as the brain develops. The two main types of gangliosidosis are GM1 gangliosidosis and GM2 gangliosidosis, and these have two distinct genetic causes. Both
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types are autosomal recessive, meaning that both copies of the gene in each cell have the mutation, and affect males and females equally. This disorder occurs in 1 in 100,000–200,000 newborns. GM1 gangliosidosis is an inherited disorder caused by mutations in the GLB1 gene, which is a gene that provides instructions for making an enzyme called beta-galactosidase and is located in the lysosomes. Beta-galactosidase helps break down several molecules including the GM1 ganglioside and is important for normal functioning of nerve cells in the brain. Buildup of GM1 gangliosides leads to destruction of nerve cells in the brain and spinal cord. GM1 gangliosides are inherited, autosomal recessive sphingolipidoses, resulting from marked deficiency of acid beta-galactosidase. Diagnosis can be made by either enzyme analysis of the betagalactosidase enzyme or by molecular genetic testing of the GLB1 gene. Enzyme activity may determine diagnosis, but it is not necessarily predictive of carrier status in the relatives of affected people. Prenatal diagnosis of GM1 gangliosidosis is possible by measurement of acid betagalactosidase in cultured amniotic cells. There is no effective medical treatment, other than symptomatic and supportive treatments. Symptomatic treatment for neurological signs and symptoms, such as anticonvulsants for seizures, does not alter progression of the condition. Supportive treatments include proper nutrition and hydration. Additionally, there is potential benefit from physical and occupational therapy. There is active research in areas of enzyme replacement and gene therapy, but this has not yet been advanced to human trials. The three types of GM1 from most to least severe are Early Infantile GM1, Late Infantile GM1, and Adult GM1. The onset of Early infantile GM1 occurs shortly after birth and usually becomes apparent by the age of 6 months. Symptoms include neurodegeneration, seizures, liver and spleen enlargement, coarsening of facial features, skeletal irregularities, joint stiffness, distended abdomen, muscle weakness, exaggerated startle response to sound, and problems with gait. Diagnosed children may be deaf and blind by age 1. Half of patients with
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GM1 develop cherry-red spots in the eye. These children often die by age 3 from either cardiac complications or pneumonia. The onset of Late Infantile GM1 is usually between 1 and 3 years old. Neurological symptoms include ataxia, seizures, dementia, and difficulties with speech. These children also experience developmental regression, but no cherry-red spots, distinctive facial features, or enlarged organs as seen in Early Infantile GM1. The progression is slower than Early Infantile GM1 but still causes a shortened life expectancy. The onset of Adult GM1 is between 3 and 30 years old. Typical symptoms are muscle atrophy, neurological complications, corneal clouding, and dystonia. This is the mildest form of GM1 and life expectancy is quite variable. GM2 gangliosidosis results from mutations in at least 1 of 3 recessive genes (HEXA, HEXB, GM2A). The products of these three genes are the alpha subunits of b-hexosaminidase A, the beta subunits of Hex A, and the GM2 activator protein. GM2 encompasses a group of three related genetic disorders that results from a deficiency of beta-hexosaminidase, an enzyme found in the lysosomes that catalyzes the biodegradation of gangliosides. In GM2, a harmful quantity of gangliosides accumulates in the brain’s nerve cells, leading to premature death of cells. The signs and symptoms become apparent in infancy. Infants appear normal until 3–6 months of age when development slows and muscles used for movement weaken. Infants often develop exaggerated startle reaction to loud noises, as well as seizures, vision and hearing loss, intellectual disability, paralysis, and cherry-red spots in the eye as the disease progresses. All three GM2 disorders are extremely rare in the general population. All are associated with failure of the same metabolic pathway and have the same outcome, but each represents a distinct molecular point of failure in a subunit that is required for activation of the enzyme. Tay-Sachs disease is the most common of the GM2 gangliosidoses. It is an autosomal recessive genetic disorder caused by a mutation in the HEXA gene and results in GM2 ganglioside storage in the central nervous system (CNS).
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Tay-Sachs usually develops around 6 months of age and results in death by age 4. Sandhoff disease is an autosomal recessive metabolic disorder caused by mutations on chromosome 5 in the HEXB gene. The most common form is infantile Sandhoff disease, which is fatal in early childhood. Sandhoff disease is clinically indistinguishable from Tay-Sachs disease. The third type of GM2, AB Variant, is an autosomal recessive metabolic disorder caused by a mutation in the GM2A gene. The GM2A gene provides instructions for making a protein called the GM2 activator, which is a cofactor that is required for the normal function of beta-hexosaminidase A. All three types of GM2 are extremely rare and normally fatal by early childhood. There does not appear to be a correlation between clinical disease progression and survival in GM2 gangliosidosis. The main reasoning for this lack of correlation is most likely due to the impact of improved supportive care over time. As gangliosidosis is a rare disorder, there is a deficiency of research on the relation between gangliosidosis and autism spectrum disorder (ASD). GM1 gangliosides in the cerebrospinal fluid (CSF) were found to be increased in individuals with autism compared to age-matched controls and those with non-progressive neurologic disorders. This increase of CSF gangliosides in the synaptic junctions indicates enhanced activity of the inhibitory synapses. Proper glycoprotein sialylation is necessary for synaptic function and a sialylation deficiency in the CNS is related to the occurrence of ASD.
References and Reading Barone, R., Sturiale, L., Fiumara, A., Palmigiano, A., Bua, R. O., Rizzo, R., . . . & Garozzo, D. (2016). CSF N-glycan profile reveals sialylation deficiency in a patient with GM2 gangliosidosis presenting as childhood disintegrative disorder. Autism Research, 9(4), 423–428. Bley, A. E., Giannikopoulos, O. A., Hayden, D., Kubilus, K., Tifft, C. J., & Eichler, F. S. (2011). Natural history of infantile GM2 gangliosidosis. Pediatrics, peds-2011.
Gastrointestinal Disorders and Autism Breiden, B., & Sandhoff, K. (2018). Ganglioside metabolism and its inherited diseases. In Gangliosides (pp. 97–141). New York: Humana Press. Skjeldal, O. H., Sponheim, E., Lekman, A., & Svennerholm, L. (1994). Gangliosides in CSF in autistic children. Pediatric Neurology, 11(2), 174. Volk, B. W., Adachi, M., & Schneck, L. (1975). The gangliosidoses. Human Pathology, 6(5), 555–569.
GARS ▶ Gilliam Autism Rating Scale (GARS)
GARS-2 ▶ Gilliam Autism Rating Scale (GARS)
Gastrointestinal Disorders and Autism Koorosh Kooros Pediatric Gastroenterology and Nutrition, Rady Children’s Hospital, San Diego, University of California San Diego, San Diego, CA, USA
Synonyms
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A meta-analysis representing the first rigorous evaluation of evidence regarding GI symptoms in children with ASD, quantifying the past 32 years of research, indicate greater risk of general GI symptoms among children with ASD than in those without ASD, indicating that this population may be more prone to specific symptoms of abdominal pain, constipation, and diarrhea by greater than twoto threefolds (McElhanon, 2014). In these children, a variety of gastrointestinal dysfunctions and associated symptoms have been reported frequently. Gastrointestinal problems in individuals with ASDs can be challenging to evaluate. Clinical practice guidelines exist for the evaluation and management of ASDs by primary care and other physicians responsible for the care of individuals with ASDs but do not include routine consideration of potential gastrointestinal and other medical problems (Volkmar et al. 1999). Many individuals with ASDs are nonverbal or minimally verbal and cannot express pain or discomfort through speech. As a result, they may not be able to communicate information about their symptoms. Even individuals with ASDs who acquire verbal communication skills may have difficulty describing subjective experiences or symptoms. In addition, the magnitude of the observed association combined with difficulties identifying and studying GI dysfunction in ASD warrants the adoption of a lower referral threshold by practitioners for evaluation and treatment by a gastroenterologist if an underlying problem is suspected.
GI issues; Nutritional issues
Historical Background Definition Autism spectrum disorders (ASDs) are a group of developmental disorders characterized by impairments in social interaction; varying degrees of verbal and nonverbal communication deficits; and restricted, repetitive, and stereotyped patterns of behavior and interests. The term includes autistic disorder, Asperger disorder, and pervasive developmental disorder, not otherwise specified (PDD-NOS) (Johnson et al. 2007).
Autism spectrum disorders (ASDs) are clinically heterogeneous neurodevelopmental disorders. Gastrointestinal (GI) dysfunction is frequently cited among children with ASD, and many causal and therapeutic hypotheses of ASD involve the GI system. This includes the idea that there is a specific GI pathology associated with ASD, triggered by abnormal immune function or elevated intestinal permeability. A great amount of controversy has surrounded this topic since publication and public awareness
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in 1998 naming a new pathologic entity, “autistic enterocolitis,” (Wakefiled et al. 1998) as responsible for developmental regression in 12 children after administration of the measles-mumpsrubella (MMR) vaccine. Ultimately, this research was retracted. Additional theories have posited that children with ASD are at greater risk for gluten sensitivity (Lau et al. 2013), lactase deficiencies (Williams et al. 2011), and gene variants (genetic evidence implicating genes in the METreceptor tyrosine kinase pathway in ASD) (Campbell et al. 2008). Although the presence of a unique GI pathophysiology specific to ASD’s has yet to be identified, elevated risk for GI symptoms in this population remains a critical issue in pediatric settings, because this population is significantly more likely to use GI agents and experience hospitalizations related to GI disturbance compared with peers (Croen et al. 2006). In addition, key issues such as the prevalence and best treatment of these conditions are incompletely understood. A fundamental difficulty in recognizing and characterizing gastrointestinal dysfunction with ASDs is the communication difficulties experienced by many affected individuals. A multidisciplinary panel reviewed the medical literature with the aim of generating evidence-based recommendations for diagnostic evaluation and management of gastrointestinal problems in this patient population. The panel concluded that evidence-based recommendations are not yet available (Buie et al. 2010a). The consensus expert opinion of the panel was that individuals with ASDs deserve the same thoroughness and standard of care in the diagnostic workup and treatment of gastrointestinal concerns as should occur for patients without ASDs. Problem behaviors may be the symptoms of underlying gastrointestinal disorders. Insufficient data were available to determine whether GI symptoms often linked with an organic pathology. Questions also remain about the relative contribution of behavioral factors, such as toileting and feeding problems, to the observed association between diarrhea, constipation, and abdominal pain in ASD. An approach that identifies and treats medical problems simultaneously with behavioral symptoms may be necessary. Further research is
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necessary to better characterize the nature of the gastrointestinal symptoms experienced by individuals with autism as well as the biological etiology.
Current Knowledge Chronic Abdominal Pain Chronic abdominal pain is defined as intermittent or constant abdominal pain that exceeds 1 or 2 months in duration. For children with ASDs, assessment may be challenging. Although underlying causes of chronic abdominal pain are frequently benign, parents are often worried that the child with an ASD is in gastrointestinal distress and that the clinician should be concerned about missing a serious disease. Generally, for children without ASDs between the ages of 4 and 18 years, functional abdominal pain can be diagnosed correctly by the primary care practitioner when alarm symptoms (e.g., involuntary weight loss, deceleration of linear growth, gastrointestinal blood loss, significant vomiting, chronic severe diarrhea, persistent right upper or right lower quadrant pain, unexplained fever, family history of inflammatory bowel disease, abnormal or unexplained physical findings) are absent, results of the physical examination are normal, and stool does not contain occult blood. There are no specific and reliable signs or symptoms that enable the health care provider to distinguish between organic and functional disorders. Children with ASD may be able to communicate distress with language, but many have atypical communication and may express abdominal pain with unusual behaviors (e.g., pressing on the abdomen and tapping on the areas of distress) or change in state of being (e.g., sleep disturbance, self-injurious behavior, and aggression) that may not be perceived as indicating the source of the discomfort (Horvath and Perman 2002). The presence of any alarm symptom should initiate an evaluation. Even in the absence of alarm symptoms, a diagnostic evaluation or empiric trial of a therapeutic intervention may be considered. Education is an important part of treatment. In the absence of alarm symptoms and after an unrevealing diagnostic evaluation and failure of empiric treatment to resolve the
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symptom or behavior, it may be helpful to review with the family the child’s symptoms and explain that although the pain is real, there is no evidence at present of a serious or chronic disease. The clinical picture can change over time, and individuals with ASDs should be reevaluated if their symptoms or signs change. Constipation Constipation is the occurrence for 2 weeks or so of a delay or difficulty in defecation. The causes of constipation are many and may be organic or nonorganic; medications can be a potential cause (e.g., opiates, phenobarbital, antacids, sucralfate, anticholinergics, antidepressants, sympathomimetics, antihypertensives) (Constipation Guidelines Committee of the North American Society for Pediatric Gastroenterology, Hepatology and Nutrition 2006). Children with ASDs can have sensory processing abnormalities and develop stool-withholding behaviors or constipation related to altered pain responses. Even children with ASDs who have daily bowel movements may have retention of stool that is not evident to parents, teachers, or health care providers. The evaluation of all children who present with constipation should include a thorough medical history and physical examination. Information from the history and physical examination usually enables the physician to determine if further evaluation is warranted or if functional constipation is present. Determination of what the family or child means when they use the term “constipation” is important. The history should delineate the frequency of bowel movements, the consistency and size of stools, and whether the child experiences abdominal pain. A history of stool-withholding behavior reduces the likelihood of a causative organic condition. For children with ASDs, the physical examination may not identify palpable stool in the left lower quadrant, and a careful rectal examination might not be feasible. Every attempt should be made to examine the rectum, although at times this cannot be accomplished. The rectal examination enables assessment of stool retention, anal tone, and occult mass, as well as the presence or absence of blood and helps to reassure the family that the child’s anatomy is normal. It
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need not be repeated on subsequent visits unless there is a change in the history or physical examination. A plain radiograph of the abdomen may reveal a rectal fecal mass not palpable on the abdominal examination. If large amounts of stool are found on rectal examination, an abdominal radiograph is not needed to establish the presence of fecal impaction (Constipation Guidelines Committee of the North American Society for Pediatric Gastroenterology, Hepatology and Nutrition 2006). Diagnostic clues can help to identify some organic causes of constipation. Hirschsprung disease is not less common in children with ASDs, and a history of delayed passage of stool after birth should raise suspicion of aganglionosis. Anatomic abnormalities such as an anteriorly displaced anus, which is more common in girls than boys, can be diagnosed by careful inspection of the rectum. Children with altered intestinal motility may have underlying mitochondrial disease (Chitkara et al. 2003). Recent studies have suggested a potential association of ASDs and mitochondrial dysfunction (Oliveira et al. 2005). Treatment of constipation includes mineral oil, magnesium hydroxide, lactulose, sorbitol, and polyethylene glycol (PEG), or a combination of lubricant (mineral oil) and laxative is recommended for the daily management of constipation in children. Chronic Diarrhea Chronic diarrhea occurs when loose stools persist for 2 weeks or longer, with or without an increase in stool frequency. Most episodes of acute diarrhea resolve within a week and are frequently caused by self-limited infections. In contrast, the causes of chronic diarrhea more frequently include noninfectious causes than for acute diarrhea. In the US general pediatric population, the most common causes of chronic diarrhea are functional disorders, malabsorption syndromes, inflammatory bowel disease (Crohn disease or ulcerative colitis), and chronic infections. The causes of diarrhea in children with ASD are likely the same as in children without ASDs, and the differential diagnosis should be approached with similar rigor. Chronic nonspecific diarrhea of
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childhood can first present between 6 and 36 months of age and often resolves by 60 months of age. It is characterized by loose and sometimes frequent stools and, importantly, the absence of other abnormalities such as growth failure, abdominal pain, and difficulty in passing stool. If the latter signs or symptoms are present, other causes of chronic diarrhea should be considered. Guidelines for the diagnostic evaluation of chronic diarrhea have not yet been developed, but recommended approaches are available in standard pediatric gastroenterology texts (Guarino and De Marco 2008). These same approaches are relevant for the child with an ASD. A careful history and physical examination are important and include definition of the age at symptom onset and whether symptoms develop abruptly or gradually. Causes of chronic diarrhea in children include enteric infections, immunodeficiency, abnormal immune response, inflammatory bowel disease, congenital persistent diarrhea, hereditary lactase deficiency, chronic nonspecific diarrhea of childhood, and syndromic persistent diarrhea and malnutrition (in developing countries and worldwide). Family history of allergy or atopic disease may increase the likelihood of cow’s milk allergy. It is important to assess a child’s growth and nutrition. Poor weight gain may be a result of malabsorption or inadequate or inappropriate feeding; delayed growth may then be the result of a child being fed a diluted, hypocaloric formula or clear liquids in an effort to reduce diarrhea. In contrast, for infants or children who appear to be thriving or overweight while suffering from chronic diarrhea, a careful dietary record for 1 week may determine if a patient is being overfed or drinking excessive amounts of fruit juices that are known to induce diarrhea. A functional cause of chronic diarrhea is suggested by protracted symptoms (12 months) or lack of significant weight loss, nocturnal diarrhea, and straining with defecation. Loose stool in children with ASDs may be misdiagnosed as diarrhea. Constipation is a common, albeit somewhat paradoxical, cause of loose stool and may be difficult to confirm by history or physical examination. Stool might not be palpable in the left lower quadrant, and a rectal examination may be challenging and traumatic for the child. A plain abdominal radiograph may be useful. Frequently, an empiric trial with a
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stool softener, such as PEG 3350 (MiraLAX) for 2–4 weeks, supports the diagnosis by causing a change in behavior. Lactose intolerance can be difficult to diagnose. A diagnostic trial of a strict lactose-free diet may be difficult to maintain, especially for a patient for whom food choices are limited. A lactose breath test (LBT), which requires an overnight fast and repeated collection of breath samples over 3 h, can be difficult to perform on some children. Similar information can be obtained at the time of an upper endoscopy via tissue analysis for disaccharidase-specific activity. Food allergy, another but less frequent cause of diarrhea, is often challenging to diagnose because most instances of intestinal food allergy are cell mediated rather than mediated by immunoglobulin E (IgE). IgE-based tests may not identify individuals who do not have atopic or immediate reactions, and IgG-based tests are of no value in assessing intestinal food allergy. Referral to an allergist for skin testing may be appropriate. Assessment for celiac disease should be performed for any child with an ASD and gastrointestinal symptoms. Testing at a minimum should include a total IgA level, tissue transglutaminase IgA antibodies with or without endomysial IgA antibodies. Antigliadin antibodies are less reliable. Stool samples to test for enteric pathogens, ova/parasites, or occult blood can be obtained easily at the time of a lower endoscopy but otherwise may be difficult to collect. Biopsies of the colon and ileum are routinely obtained on endoscopy and determine whether there is acute or chronic mucosal inflammation. Therapeutic interventions vary depending on the cause of chronic diarrhea. Gastroesophageal Reflux Disease GER, the term for passage of gastric contents into the esophagus, can produce diverse symptoms and complications, called gastroesophageal reflux disease (GERD). During infancy, in contrast to nonpathologic reflux, GERD presents most frequently as an adverse effect of recurrent vomiting but also as apparent life-threatening events (ALTE). As a child matures, other symptoms and signs of GERD include chest pain or heartburn, esophagitis, food refusal, and extraesophageal manifestations of the airway. As in
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children without ASDs, the expression of disease related to GER can vary widely in children with ASDs. Certain children may express discomfort through problem behaviors, restriction of foods of specific texture, or simply pointing to their chest. Objective measures become important to elicit from the patient and family, such as the frequency and timing of regurgitation, the character and amount of material regurgitated, and the kinds of foods that are tolerated by the child. The presenting symptom or sign is a clue to the underlying cause. Children with ASDs who have obstruction caused by, for example, malrotation or antral web may regurgitate many times an hour, whereas those individuals who have typical GERD may experience symptoms when they lie down at night or after meals. The vomiting of undigested foods suggests a delay in gastric emptying; hematemesis suggests the presence of inflammation or ulceration. In young children, a preference for liquids or a refusal to eat textured foods or foods that require chewing should raise suspicion of esophagitis. Diagnostic evaluation begins with a careful history and physical examination. In some children more than 2 years of age, recurrent regurgitation or vomiting disrupts their participation in childhood activities. As for children without ASDs, an empiric trial of acid suppression may be of diagnostic value, but then the clinician may want to order an upper gastrointestinal series to exclude an anatomic abnormality, as well as upper endoscopy with biopsy to look for inflammation associated with GERD, allergy, or eosinophilic esophagitis. Other warning signals for additional diagnostic testing include gastrointestinal bleeding, abdominal tenderness, and fever. A diagnostic trial of acid suppression, with an appropriate dose of a proton pump inhibitor (PPI), should be considered before invasive studies. Diagnosis of GERD may be delayed in individuals with ASD who do not localize pain or provide the classic history. PPI medications should be given 30 min before the first meal of the day, and if two doses are prescribed, 30 min before the evening meal. Assessment of response to a PPI trial is somewhat subjective in children with ASDs and might depend on changes in
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behavior as perceived by care providers (parents, teachers, or health care providers). If further diagnostic testing is pursued, upper gastrointestinal radiographs can be challenging for many children who are unable to cooperate with drinking barium and lying down quietly for the procedure. Because of these potential barriers, it can be difficult to identify anatomic abnormalities, such as a malrotation, annular pancreas, or antral inflammation or narrowing, that may be the cause of GERD-like symptoms. Most children with ASDs are unlikely to tolerate placement of a transnasal pH probe for a prolonged period. A Bravo pH monitoring system (Given Imaging Ltd., Yoqneam, Israel), which is endoscopically attached to the distal esophagus, may be better tolerated. For such children, upper endoscopy under general anesthesia is often the diagnostic test initially used and might provide information about other gastrointestinal conditions such as carbohydrate malabsorption or food allergy or intolerance. Endoscopy is usually reserved for children who are unresponsive to a diagnostic trial of gastric acid suppression or when other clinical factors, such as hematemesis or food refusal, support the scheduling of invasive tests. Treatment of GERD depends on the cause. Surgical therapy should be considered with a diagnosis of anatomic abnormality such as malrotation, whereas esophagitis may be treated best by prolonged acid suppression. Subsequent evaluation depends on the resolution and recurrence of an individual’s symptoms. Upper endoscopy should be considered to follow children with long-standing GERD who may be at risk for developing complications such as esophageal stricture, Barrett esophagus, and esophageal cancer. As in children without ASDs, GERD in children with ASDs can be a chronic problem, with waxing and waning of symptoms. The management of such children often requires continuity with advancing age and an appreciation by the health care team of the natural history of disorders that underlie GERD (Vandenplas et al. 2009). Sleep and Gastrointestinal Disturbance Sleep disorders are among the many comorbid conditions present in ASDs (Lai et al. 2014). An
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important point to consider is that sleep problems may be exacerbated by or even be the result of, other comorbid conditions. Other comorbid disorders in children with ASD are quite prevalent. In a population of over 160 children with ASD, Liu et al. observed attention deficit hyperactivity disorder (31%), epilepsy (11%), asthma (17%), allergies (50%), and gastrointestinal (GI) symptoms (21% with vomiting, reflux, spit-ups, 17% with chronic diarrhea, 18% with chronic constipation, 18% with abdominal pain, and 14% with intestinal bloating) (Liu et al. 2006). Although the mechanism of these sleep disturbances is difficult to ascertain, nighttime awakening with pain or abdominal discomfort is common with gastroesophageal reflux and reflux esophagitis in children. Autistic children with GI symptoms have been found to have a higher prevalence of sleep disturbances, compared with those in the general population who do not have GI symptoms and with their siblings. Furthermore, GI symptoms have been significantly correlated with insomnia and parasomnias. Taking into account that GI problems may be responsible for a great deal of sleep problems, one should consider that treatment focused on these conditions may bring relief or at least improvement in sleep problems symptoms. Lifestyle modifications, including smaller but more frequent meals or elevated head position during sleep should be considered as possible first-line treatments. Other approaches that should be taken into account whenever a child with ASD presents with sleep problems include, but are not limited to, proton pump inhibitors for gastroesophageal reflux or diet elimination/substitution therapies in food allergy such as cow milk protein allergy, lactose intolerance, or celiac disease. Naturally, of paramount importance is adhering to medical indications for introducing these treatments by way of proper diagnostic tests (Klukowski et al. 2015).
Future Directions Medical disorders, including gastrointestinal problems, occur commonly in individuals with ASDs. Because the symptoms may be atypical,
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these medical conditions may be undiagnosed. Whether the prevalence is higher than in the general population is not known with certainty. Prospective, well-controlled studies are necessary. Many parents and care providers have observed and reported improvements in problem behaviors with nutritional or medical interventions to address gastrointestinal symptoms and disorders. Some of these therapies are based on purely observational reports. The care of individuals who are nonverbal or have difficulties in communication or who display self-injurious or other problem behaviors presents special challenges. Nevertheless, the approach to evaluation and diagnosis of possible underlying medical conditions, in particular gastrointestinal disorders, should be no different from the standard of care for persons without ASDs. Management of gastrointestinal problems in individuals with ASDs usually begins with the primary care provider and may warrant consultation with a gastroenterologist and other specialists. Anecdotal reports that restricted diets may ameliorate symptoms of ASDs in some children have not been supported or refuted in the scientific literature, but these data do not address the possibility that there exists a subgroup of individuals who may respond to such diets. Professional supervision of restricted diets is recommended to prevent nutritional inadequacies (Buie et al. 2010a). Such reports help propagate interest in the use of dietary manipulation (e.g., gluten and casein-free diets) as an ASD-focused treatment. Dietary interventions, including the gluten- and casein-free diets, nutritional supplements, enzymes, and antimicrobial agents, have not been substantiated by empirical investigation, presenting a clear need to investigate how possible deviations in the GI tract in ASD relate to current dietary recommendations being promoted and implemented in the ASD community (McELhanon et al., 2014). One should also consider possible deviations in the establishment and maintenance of the gut microbiome in ASD and the relative contribution of early feeding practices to overall gut health and acceptance of new feeding tastes and textures. Studies of fecal DNA extracts have found certain bacterial clusters overrepresented in children with
Gastrointestinal Disorders and Autism
ASD and gastrointestinal complaints compared with children with similar GI complaints but typical neurobehavioral development (Mulle et al. 2013). Provisional evidence also suggests that children with ASD are at greater risk for suboptimal breastfeeding, including late initiation and shorter duration of exclusive breastfeeding (Al-Farsi et al. 2012). In addition to promoting optimal nutrition, breast milk assists in the development of the gastrointestinal tract, pancreas and endocrine system, and related mucosal defenses, and it is therefore possible that suboptimal breastfeeding may result in atypical colonization of the gut microbiome in ASD. In turn, changes in the gut microbiome may help explain anecdotal reports of improvement in behavioral functioning in response to dietary changes if such changes serve a probiotic function and improve symptoms of irritable bowel syndrome (e.g., bloating, abdominal pain, flatulence) among certain children with ASD (McELhanon et al., 2014). Microbiota is an emerging topic that has attracted several researchers to look for the possible connection between the GI microflora and behavioral abnormalities. Earlier repot of deficient disaccharidase enzymatic activity in ASD children and GI symptom prompted investigations looking for intestinal mucosal microbiota involved in carbohydrate metabolism (Horvath et al. 1999). Abnormal carbohydrate digestion and transport and mucosal dysbiosis (imbalance in the intestinal microbial ecosystem) was reported in the ASD children (Williams et al. 2011). Reduced level of fermenters has been found in the intestinal microflora of the ASD patients. The microbiota-gut-brain axis refers to the ability of gut microbiota to communicate with brain and regulate behavior. Fecal microbiota transplantation has been used in treating several GI disorders but increase knowledge and control trials are needed before it can be used broadly in clinic. Imbalance in gut microbiota population may render the intestinal mucosa susceptible to injuries, infections, inflammation, abnormal digestion, immune imbalance, immune reaction and cross reaction in other tissues including the brain. More research is emerging in this area (Samsam et al. 2014).
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Future research is expected to clarify the role of metabolic disorders, allergic/toxic reactions, immune dysregulation, and inflammatory changes in the etiology of gastrointestinal disturbances in individuals with ASDs. Whether unique genetic, metabolic, or physiologic conditions exist and are specific to ASDs remains to be determined. New knowledge in these areas will advance our approach to the management of ASDs. Recognition that problem behaviors might indicate an underlying medical condition will facilitate diagnosis and treatment and ultimately improve the quality of life for many persons with ASDs. Welldesigned trials are needed to develop an evidence base for optimal diagnostic and treatment strategies to manage gastrointestinal disorders in children with ASDs. Until then, application and, where necessary, adaptation of conventional recommendations for the general pediatric population are relevant to children with ASDs (Buie et al. 2010b).
See Also ▶ Allergies ▶ Constipation ▶ Gluten-Free Diet ▶ Intestinal Permeability Studies ▶ Leaky Gut Syndrome ▶ Medical Conditions Associated with Autism ▶ Metabolic Testing
References and Reading Buie, T., Campbell, D., Fuchs, G. J., III, Furuta, G. T., Levy, J., VandeWater, J., et al. (2010a). Evaluation, diagnosis, and treatment of gastrointestinal disorders in individuals with ASDs: A consensus report. Pediatrics, 125, S1–S18. McElhanon, B. O., McCracken, C., Karpen, S., & Sharp, W. G. (2014a). Gastrointestinal symptoms in autism spectrum disorder: A meta-analysis. Pediatrics, 133(5), 872–883. Buie, T., Fuchs, G. J., 3rd, Furuta, G. T., Kooros, K., Levy, J., Lewis, J. D., et al. (2010b). Recommendations for evaluation and treatment of common gastrointestinal problems in children with ASDs. Pediatrics, 125 (Suppl. 1), S19–S29. Chitkara, D. K., Nurko, S., Shoffner, J. M., Buie, T., & Flores, A. (2003). Abnormalities in gastrointestinal
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2164 motility are associated with diseases of oxidative phosphorylation in children. American Journal of Gastroenterology, 98(4), 871–877. Constipation Guidelines Committee of the North American Society for Pediatric Gastroenterology, Hepatology and Nutrition. (2006). Evaluation and treatment of constipation in infants and children: Recommendations of the North American society for pediatric gastroenterology, hepatology and nutrition. Journal of Pediatric Gastroenterology Nutrition, 43(3), e1–e13. Guarino, A., & De Marco, G. (2008). Persistent diarrhea. In R. E. Kleinman, I. R. Sanderson, O. Goulet, P. M. Sherman, G. Mieli-Vergani, & B. L. Shneider (Eds.), Walker’s pediatric gastrointestinal disease: Pathology, diagnosis, management (5th ed., pp. 265–274). Hamilton: BC Decker. Horvath, K., & Perman, J. A. (2002). Autism and gastrointestinal symptoms. Current Gastroenterology Reports, 4(3), 251–258. Johnson, C. P., Myers, S. M., & American Academy of Pediatrics, Council on Children with Disabilities. (2007). Identification and evaluation of children with autism spectrum disorders. Pediatrics, 120(5), 1183–1215. Oliveira, G., Diogo, L., Grazina, M., Garcia, P., Ataide, A., Marques, C., et al. (2005). Mitochondrial dysfunction in autism spectrum disorders: A population-based study. Developmental Medicine and Child Neurology, 47(3), 185–189. Vandenplas, Y., Rudolph, C. D., Di Lorenzo, C., Hassall, E., Liptak, G., Mazur, L., et al. (2009). Pediatric gastroesophageal reflux clinical practice guidelines: Joint recommendations of the North American Society of Pediatric Gastroenterology, Hepatology, and Nutrition and the European Society of Pediatric Gastroenterology, Hepatology, and Nutrition. Journal of Pediatric Gastroenterology Nutrition, 49(4), 498–547. Volkmar, F., Cook, E. H., Jr., Pomeroy, J., Realmuto, G., & Tanguay, P. (1999). Practice parameters for the assessment and treatment of children, adolescents, and adults with autism and other pervasive developmental disorders. Journal of the American Academy of Child & Adolescent Psychiatry, 38(Suppl. 12), 32S–54S. American Academy of Child and Adolescent Psychiatry Working Group on Quality Issues (published correction appears in J Am Acad Child Adolesc Psychiatry. 2000; 39(7):938. Lai, M. C., Lombardo, M. V., & Baron-Cohen, S. (2014). Autism. Lancet, 383, 896–910. Liu, X., Hubbard, J. A., Fabes, R. A., et al. (2006). Child Psychiatry and Human Development, 37, 179. Klukowski, M., Wasilewska, J., & Lebensztejn, D. (2015). Dev Period Med., 19(2), 157–161. McElhanon, B. O., McCracken, C., Karpen, S., et al. (2014b). Gastrointestinal symptoms in autism spectrum disorder: A meta-analysis. Pediatrics, 133, 872–883. Mulle, J. G., Sharp, W. G., & Cubells, J. F. (2013). The gut microbiome: A new frontier in autism research. Current Psychiatry Reports, 15(2), 337.
GAUTENA Autism Society Al-Farsi, Y. M., Al-Sharbati, M. M., Waly, M. I., et al. (2012). Effect of suboptimal breast-feeding on occurrence of autism: A case–control study. Nutrition, 28 (7–8), e27–e32. Horvath, K., Papadimitriou, J. C., Rabsztyn, A., Drachenberg, C., & Tildon, J. T. (1999). Gastrointestinal abnormalities in children with autistic disorder. The Journal of Pediatrics, 135, 559–563. Williams, B. L., Hornig, M., Buie, T., Bauman, M. L., Cho Paik, M., Wick, I., Bennett, A., Jabado, O., Hirschberg, D. L., & Lipkin, W. I. (2011). Impaired carbohydrate digestion and transport and mucosal dysbiosis in the intestines of children with autism and gastrointestinal disturbances. PLoS One, 6, e24585. Samsam, M., Ahangari, R., & Naser, S. A. (2014). Pathophysiology of autism spectrum disorders: Revisiting gastrointestinal involvement and immune imbalance. World Journal of Gastroenterology : WJG, 20(29), 9942–9951. RETRACTED: Wakefield AJ, Murch SH, Anthony A, et al. (1998). Ileal-lymphoid-nodular hyperplasia, non-specific colitis, and pervasive developmental disorder in children. Lancet; 351(9103):637–641. Lau, N. M., Green, P. H., Taylor, A. K., et al. (2013). Markers of celiac disease and gluten sensitivity in children with autism. PLoS One, 8(6), e66155. Campbell, D. B., Li, C., Sutcliffe, J. S., Persico, A. M., & Levitt, P. (2008). Genetic evidence implicating multiple genes in the MET receptor tyrosine kinase pathway in autism spectrum disorder. Autism Research, 1(3), 159–168. Croen, L. A., Najjar, D. V., Ray, G. T., Lotspeich, L., & Bernal, P. (2006). A comparison of health care utilization and costs of children with and without autism spectrum disorders in a large group-model health plan. Pediatrics, 118(4), e1203-e1211.
GAUTENA Autism Society Amaia Lopetegui GAUTENA, Donostia, Gipuzkoa, Spain
Background and Context GAUTENA is the acronym in Basque language of Gipuzkoa Autism Society www.gautena.org. This society of families of people with autism spectrum disorder (from now on referred to as ASD) was founded in 1978, in Gipuzkoa, one of the three provinces that constitute the Basque Country (Spain). In compliance with its Statute of Autonomy from 1979, the Basque Country has full
GAUTENA Autism Society
capacity and responsibility in areas such as Health, Education, and Social Services. This circumstance, together with the financial autonomy that the mentioned political regime confers to the Basque region, helps to understand GAUTENA’s development in favor of people with ASD in Gipuzkoa, a region of 1909 km2 with a population of about 710,000 inhabitants. The Basque Country, traditionally an industrial region and most recently focusing on the services sector, enjoyed in 2016 a per capita income of 31,805€ (10% higher than the average of the EU 28). The social protection expenditure per person, measured in purchasing power parity (PPP), was 8.341€, while the average of the UE 28 amounted to 7.903€. In terms of the UN Human Development Index that takes into account life expectancy, education, and wealth, the Basque Country is placed in the eight position in the world (0.91)
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just next to the USA (0.92), although it is not fair to compare specific regions with full countries.
Landmark Contributions Based on the previous work of other associations of families involved with disabilities and keeping a good relationship with them, GAUTENA began to work in 1978 with the purpose of achieving a community-based network of services for people with ASD within a society that pursues to be more inclusive every day. It currently provides support to 800 families and has a staff of about 230 members. Although the families pay some fees for the provided services, financing is mainly public (92%). In the last years, several laws at state and community level have reinforced the role of social services in the society.
Population: 710.000 Families: 800 Staff: 230 (68% full time) Public funding: 92% - Health: 3% - Education: 33% - Social Services: 56% Families: 8%
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Family support HQ GAUTENA HQ
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Through different formal agreements with the Public Administration in the areas of Education, Health, and Social Services, GAUTENA encourages and manages a network of services that offer comprehensive support throughout the whole life cycle to people with ASD, such as diagnosis and treatment services, education, day services, sheltered housing, temporary stays, leisure, and support to families. The key characteristics at the time of identifying GAUTENA’s reality are the application of a suitable community-based model, the significant level of public support, the social sensitivity in our territory encouraging this kind of initiatives, and a nonprofit management. The organization is based on the pillars of quality-oriented practice, an ethical framework with empowering for human rights and personalization, and research and social innovation, in order to adjust the model to the changing needs of people with ASD. Based on these pillars, the main goal is the continuous fostering of a good quality of life for the users and their families and a more comprehensive and inclusive community for all.
GAUTENA Autism Society
Education
The Public Administration policy in the Basque educational system strongly encourages and supports inclusion of students with special educational needs. For this reason, a good number of people with ASD – over 400 – supported by GAUTENA usually attend regular schools where they receive different levels of individualized support. Some of these students attend lessons in classrooms managed directly by GAUTENA, 19 classes at present, located in different districts of Gipuzkoa. In these classrooms, called “stable classrooms,” an average number of five students receive the support of two teachers. In such classrooms, the personalized mainstreaming of the students with ASD into the school is encouraged. At present, there are 98 students who attend these GAUTENA classrooms, located in regular schools (a special school we had was voluntarily closed years ago). The educational period extends from the age of 3 to the age of 20 with the possibility of an extraordinary extension up to 21. Day Service
Major Activities Services Network Through formal agreements with the Public Administration in areas of Education, Health, and Social Services, GAUTENA manages a service network that includes diagnosis and treatment, education, day services, housing, temporary stays, leisure and free-time activities, as well as support to families. More detailed information is given below. Diagnosis and Treatment
The clinical team, which includes professionals of psychiatry and psychology, is in charge of the diagnosis and treatment of the people with ASD referred to GAUTENA from the Public Administration, mostly from the National Health System. In collaboration with the families, the GAUTENA team gives support through individual or group treatment sessions to more than 500 children and also offers specialized support for their schooling.
At the end of their school period, after they have reached the age of 20, people with ASD can access a well-developed sheltered employment organization, where GAUTENA sits on its Board, or try a supported job in the ordinary market. Up to the present time, it has been found that most of the people with ASD with from medium to severe affectation reaching adulthood turn toward the Day Service Centers managed by GAUTENA for day services. GAUTENA’s four Day Centers offer a suitable combination of work on personal and vocational skills, together with a wide use of community-based resources that make possible to enjoy a good day program for about 112 adults with ASD in Gipuzkoa. Housing Service
GAUTENA offers different types of housing to those who request this service, ranging from flats in blocks (Sheltered Flats) to small dwelling units (Group Homes) for people who need more structured support. This housing network includes one house for temporary stays extending the coverage
GAUTENA Autism Society
through different ways – weekends, short stays, critical periods, medium-period stays, etc. – to a significant group of families, children, teenagers, and adults. At the present time, 14 housing units provide permanent support to 78 people, and another house is used for temporary stays for around other 50 families.
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www.thecouncil.org (USA) or that of Plena Inclusión/FEAPS www.feaps.org (Spanish Confederation in favor of People with Intellectual and Developmental Disabilities). In 2016 GAUTENA was granted by the European Parliament one of the European Citizen Awards for “having transformed the European values into support programs for the people with ASD.”
Family Support Service
This service focuses directly on the families of people with ASD and offers them a wide range of support services, from family welcoming programs to the organization of support groups, training conferences and seminars, and leisure activities for families with their children with ASD. Also navigating the necessary processes to obtain economic subsidies, among other administrative benefits, is supported. This service involves and trains volunteers, usually university students, who may become staff persons later on. The Family Support Service organizes the leisure and free-time activities carried out by GAUTENA throughout the year and especially during the holiday periods, an initiative that involves more than 500 users. Approach to Quality GAUTENA is an organization inspired in community-based models, having paid since it’s beginning a great deal of attention to the quality of services provided to the person with ASD. Based on technical criteria inspired in the most reputable international programs, since the middle of the 1990s, it has been working in compliance with the Quality Assurance criteria of ISO 9001 Standard, having received the pertinent accreditation from the Spanish Organization for Standardization (AENOR). In 2000, GAUTENA received a pilot accreditation from the National Autistic Society (UK) www.nas.org.uk. In 2008, GAUTENA received the Silver Q to Management from EUSKALIT (Basque Foundation for Excellence www.euskalit.net) for having obtained more than 400 points in the application of the EFQM model (European Foundation for Quality Management www.efqm.eu). Besides, GAUTENA enhances its work in favor of quality with other approaches such as that of “The Council”
Looking at the Future The Quality approach taken by GAUTENA starts from the response to the rights of people with ASD by guaranteeing a coherent ethical approach in a personalized plan, with the purpose of fostering quality of life for people with ASD and their families. For that purpose, GAUTENA is developing instruments for the practical promotion of the rights of the people to whom support is given and especially for those that, because of their significant needs, almost whole responsibility is taken up by the staff. At the same time, an Ethical Adequacy Plan prepared in collaboration with the University of Deusto www.deusto.es (Bilbao and San Sebastian) is being applied, in order to embed ethics into the philosophy and practice of professionals and families in GAUTENA. While developing rights, ethics, and quality of life, GAUTENA is giving as much support as possible to initiatives in research of ASD and has collaborated with important international entities. An agreement with the Research Foundation Policlinica Gipuzkoa Fundazioa, in San Sebastián, has been established, and it is taking part in research projects on genetics, phenotyping, immunology, epidemiology, and the use of augmentative communication devices www.policlini cagipuzkoa.com. In the frame of this agreement, GAUTENA is currently part of the ASDEU project, sponsored by the European Commission, involving over 10,500 children in its county. In addition, social innovation, understood as the advance of innovative responses to the needs of people with disabilities, guides GAUTENA’s steps and makes it to take part in general activities such as the one of the Basque Innovation Agency (Innobasque) www.innobasque.com. GAUTENA applies a policy of encouragement of
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benchmarking and alliances, both in its most immediate area in Gipuzkoa and Basque Country, as well as in Spain and Europe, and it is an active member of several organizations, being a founding society of Autism Europe www. autismeurope.org.
See Also ▶ Community Services ▶ Ethics
References and Reading Fuentes, J., Barinaga, R., & Gallano, I. (2000). Applying TEACCH in developing autism services in Spain: The GAUTENA project. International Journal of Mental Health, 29(2), 78–88.
Gaze Rechele Brooks Institute for Learning and Brain Sciences, University of Washington, Seattle, WA, USA
Definition Following the eye gaze of other people is a crucial component of face-to-face social interactions from infancy to adulthood. The ability to follow where a person looks is usually called “gaze following” or “responding to joint attention.” This and other gaze behaviors are important because they are powerful learning mechanisms. Specifically, research has shown that gaze following and other gaze behaviors predict language development for children with and without autism (Brooks and Meltzoff 2008; Mundy et al. 2007; Toth et al. 2006; Wetherby et al. 2007). In addition, the patterns of use and perception of gaze may help identify which at-risk children develop autism spectrum disorders (Jones et al. 2014).
Gaze
There are several eye gaze behaviors that are widely studied, including mutual gazing, cued gaze shifting, gaze alternating, and gaze following. Each of these gaze behaviors has potential functions in early development. Special attention is paid to relevant findings for young children with autism spectrum disorder (ASD), as well as for infants at risk for developing ASD because their sibling has ASD. This entry uses gaze following to illustrate developmental changes in infants’ understanding of eye gaze. Gaze following is an interesting developmental phenomenon because it provides a window into infants’ social cognition. Adults typically understand a person’s gaze in a way that puts them in touch with the other person’s mind, informing them of the other’s desires, emotions, intentions, and perceptions. When adults notice where someone is looking, they do not simply track the movement of the head and eyes; rather, adults understand that people use their eyes to see things of interest in their surroundings. In that sense, developmental scientists consider gaze following an important front-end ability that feeds into the development of understanding other minds (see entry on “▶ Theory of Mind”). For example, a mother could look with a disgusted facial expression at an object while saying, “I don’t like that.” For adults viewing this act, they can follow her gaze to find out what has caught her attention and what she is talking about, as well as what disgusted her. Individuals who do not follow gaze would miss this important social and referential information. This is of particular concern for children with ASD because they have developmental deficits in following the gaze of others, and this may contribute to other downstream problems in social understanding (Mundy et al. 2009; Jones et al. 2014; Toth et al. 2006).
Current Knowledge As soon as infants are born, they are introduced to social interactions. At first, parents and infants share face-to-face eye contact in the dyadic context. Later, the social interactions become triadic when the dyad includes a third entity,
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such as when an infant shifts her gaze between a parent and an object. The questions for developmental psychologists are when do infants begin to follow the gaze of other people and how do infants interpret others’ eye gaze. Gaze behaviors could indicate that both adult and child are aware that they are sharing attention. Indeed, triadic social interactions are often referred to as joint attention because the adult and the infant attend to the same topic. However, it is unclear whether young infants and children with ASD are aware that they are sharing attention and intentions. This has led to an ongoing debate in the field about how to interpret early gaze behaviors, especially gaze following (Flom et al. 2007; Mundy et al. 2009). On one side of the debate, infants gaze follow because they already understand that people can make psychological contact with external objects, that people see something interesting. On the other side of the debate, gaze following occurs for lower-order physical reasons, simply because of the signal value of the other’s head movement. According to this latter perspective, infants do not understand anything about the adult as a perceiver of objects, but simply process the movement shown by the head. This ongoing debate has fueled extensive research on the emergence and meaning of gaze following. Another important issue is the developmental outcome for children who have little or no interest in attending to the gaze of other people. This is a common concern of parents of children with ASD. A significant question is whether children with ASD notice the gaze of others and whether they interpret it as having psychological meaning. In order to answer this question, it is important to make conceptual distinctions among different types of closely related behaviors that are sometimes confused in the literature about gaze processing. As described below, there are crucial logical, developmental, and clinical distinctions among them. Interest in Eyes and Mutual Gaze. Mutual gaze – staring directly into the eyes of another person – is one of the earliest experiences people have. It is a context for sharing emotions and communicating. Social partners recognize that
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they are looking at each other. The developmental question is whether infants recognize that others are looking at them. It is known that young infants have preferences for face-like patterns containing eyes or eyespots (Johnson et al. 2008). For example, newborns look longer at faces with eyes facing toward them than eyes averted to the side. By the age of 2 months, infants begin scanning the eye region more than the edges of the face. These early patterns could stem from perceptual biases and preferences, but they are not clear evidence for noticing the eyes or recognizing eyes as having social and communicative intent. Detecting cues of mutual gaze may be difficult for individuals with ASD. As an interesting test, Elsabbagh and colleagues (2012) presented gaze cues that contrasted eyes shifting to the side versus looking straight ahead to simulate looking away versus making eye contact. When infants (6–10 months old) viewed such cues, the infants with a subsequent ASD diagnosis were less sensitive on neural measures to the gaze shifts than infants without a later ASD diagnosis. In addition to difficulties with detecting mutual gaze, children with ASD often scan faces and eyes differently than children with typical development (Nation and Penny 2008). Decreased scanning of eyes may emerge as early as 6 months of age (Jones and Klin 2013). Across ages, children and adults with ASD are often interested in the mouth, yet they do not completely ignore the eyes (Webb 2008). An overall concern would be that reduced attention to eyes could lead to less experience with the socially meaningful information that eyes convey. Peripheral Cue with Gaze Shifts. Another line of eye-gaze research investigates whether gaze shifts influence the attention of observers. To address this issue, gaze shifts are used to cue the location of peripheral targets, such as a rectangle or an asterisk (Johnson et al. 2008; Nation and Penny 2008). The classic stimulus with this task is a digitized face with eyes that shift to one side before a target appears on the right or left. The face usually vanishes before the peripheral targets appear in this paradigm. Adults and infants typically look with shorter latencies to the target that has been cued. If the cue shifts to
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the right side, they attend to a target that appears on the right faster than on the left. These are fascinating effects, but they do not provide evidence about following gaze in biologically plausible settings. In the real world, when a mother looks at and labels an object (“There’s a ball!”), her face does not vanish to free up attentional resources for the child. Moreover, the peripheral targets do not pop into existence to attract attention as they do in the cueing paradigms. Rather, in everyday settings, the world stays stable and the parental gaze spotlights a preexisting object. Nonetheless, infants’ responses to directional shifts in cueing studies may be a precursor to actual gaze following. For individuals with ASD, the key question is whether they respond to gaze cues. In many but not all studies, the direction of the gaze shifts cued the reactions of children and adults with ASD (Nation and Penny 2008). For example, at 2 years old, toddlers with ASD responded faster to locations cued by gaze shifts than non-cued locations, which was similar to typically developing toddlers (Chawarska et al. 2003). Of interest with this study, the 2-year-olds with ASD on average “passed” this test of cueing, but they failed to follow a person’s gaze in a real room. Thus, the cueing phenomenon is not necessarily correlated with what individuals do in ecologically valid settings, which suggests that calling this “gaze following” would be a misnomer. Gaze Cues in Habituation Studies. A third body of work that addresses infants’ reactions to gaze cues is habituation studies. In the first phase of a prototypical habituation study, a person looks at one of two objects. The trials are repeated until there is a decline in infants’ looking at the presentation (i.e., habituation). In the second phase, infants’ looking times are measured to examine which of two types of presentations attracts renewed interest (i.e., dishabituation). For example, Woodward (2003) found that infants, who habituated to a person looking at a ball on the left, dishabituated more strongly to that person looking at a bear on the left (new object, same location) than to that person looking at the ball on the right (same object, new location). This suggests that infants encoded the relationship
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between the looker and the object, and that they dishabituated when the link was broken. The findings also showed that there is a developmental change for encoding this looker-object link, as seen by 12-month-olds succeeding in the task and younger infants failing. A consideration is that these habituation-dishabituation studies chiefly document infants’ recognition abilities. Infants recognize (and encode) that the other person is looking, but they are not required to produce an action to follow that person’s gaze. To date, there are no parallel studies of gaze with habituation for children with ASD. It would be interesting to examine whether children with ASD encode the looker-object link. Further, does a child’s ability to spontaneously follow a person’s looking behavior in a real situation influence the habituation test (or vice versa)? Gaze Alternation and Joint Engagement. Joint engagement refers to when infants and caretakers attend to and play with the same toy. This also is called a triadic interaction because the child and the parent communicate about a third object, such as a toy. Adults are aware of shared or joint attention in the triad, but an interesting question is whether infants are aware of it. An argument has been that infants show their awareness of joint attention by initiating eye contact with the adult and shifting their gaze to the toy. This type of gaze alternation is called “coordinated joint engagement” (Adamson et al. 2009). From observations of parents and infants at play, in the majority of joint play, infants did not alternate their gaze between the parent and a shared toy. Nonetheless, there were some instances of gaze alternation by the infants. The general pattern is that coordinated joint engagement emerges at about 9 months when it is relatively infrequent and brief, but gradually becomes more frequent and lasts longer, by 15–18 months, for typically developing infants (Carpenter et al. 1998). Because of the importance seen in these gaze behaviors, researchers have designed tasks to prompt and assess the capacity for alternating gaze (Mundy et al. 2007; Wetherby et al. 2007). In a typical version of the gaze-prompting tasks, an experimenter activates a mechanical toy that is not within reach of the child. In a longitudinal
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study, Mundy et al. (2007) found that 9-month-old infants readily made eye contact with the experimenter and alternated gaze to toys in the context of gaze-prompting tasks. These gaze behaviors were key components of their measure of “initiation of joint attention” and did not significantly increase by 18 months of age. The findings with the gaze-prompting tasks suggest that infants are capable of alternating their own gaze in structured situations before they consistently do so during unstructured play. Of note, these tasks do not directly measure how well infants understand the gaze of others. Studies show that children with ASD are less likely to alternate gaze in a triadic context than children with typical development. At 20 months, toddlers with ASD were less likely to shift their gaze from the adult to a toy (or vice versa) than comparison groups in gaze-prompting tasks (Wetherby et al. 2007). As seen in play observations, 30-month-old children with ASD had many opportunities for joint play with the support of their parents, yet they rarely had episodes of coordinated joint engagement (Adamson et al. 2009). Even at older ages, gaze alternation remains less frequent among children with ASD than their peers with typical development; this has been viewed as a fundamental problem for children with ASD (Mundy et al. 2009; Toth et al. 2006). Following the Gaze of Others. A simple head turn takes on referential meaning when a person looks at something in the environment. Adults recognize this and turn to see what the other has seen. However, it is unclear whether young infants with typical development and children with ASD turn for that reason. In the typical gaze-following test, an adult looks at an object by turning and aligning his (or her) head and eyes with an object in the room, and in studies of “responding to joint attention,” the adult also points during the look (Mundy et al. 2007). Research has shown that infants turn in the direction of the adult’s head (and arm) movement, as early as 3–6 months of age under certain conditions, and that this following becomes more frequent between 12 and 18 months for typically developing infants (Flom et al. 2007; Jones et al. 2014; Mundy et al. 2007). The difficulty in
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interpreting these findings is that as the adult turns toward a target, the adult’s large and salient head motion may draw infants in the correct direction without them processing the adult’s gaze at all. Infants may simply be following the head motion that they see in their visual field and find the target object by chance. To test whether infants follow head motion or eye gaze, Brooks and Meltzoff (2002) designed a special test using a gaze-following paradigm. The key manipulation was that the adult turned toward an object with open eyes for one group and closed eyes for the other group. The test controlled for head motion because the adult made the same head motion for both groups – what differed was whether the adult’s eyes were open or shut. If infants relied on simple head motion, they would turn in the same direction as the adult in both cases. If, however, infants recognize that eyes are part of the referential act, they would turn to look at the target object in one situation but not the other. The results showed that 12- to 18-month-old infants were more likely to look at the adult’s target when she turned with open eyes than closed eyes. Infants did not simply follow head motion because they differentiated the head turns. Instead, infants followed the gaze of the adult to the target objects; infants from 12 to 18 months genuinely followed gaze. The question remained as to when gaze following develops. To test for the emergence of gaze following, Brooks and Meltzoff (2005) used the same test of open versus closed eyes with 9-, 10-, and 11-month-olds. The findings suggested that genuine gaze following emerges at 10–11 months of age. At the two older ages, infants followed the adult’s head turns with open eyes more often than turns with closed eyes. In contrast, 9-month-olds looked at the target object in both cases, for eyes open and closed. The 9-month-olds “failed” to follow gaze – they followed the adult’s head turns even when her eyes were closed. These findings suggested that young infants were following head motion rather than eye gaze. With their lack of interest in eyes, will children with ASD follow a person’s gaze? It is unknown whether children with ASD follow eyes versus head motion because this particular empirical
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test has not yet been done. However, in other gaze-following protocols, infants at risk for ASD have deficits that are detectable after 12 months of age (Jones et al. 2014). By 20 months, toddlers with ASD were unlikely to follow the line of regard of an adult, even though the adult looked and pointed at a target; their scores were lower than those of toddlers with other types of developmental delay or with typical development (Wetherby et al. 2007). By about 4 years of age, children with ASD are more likely to follow the adult’s line of regard than younger children with ASD, but many children with ASD are still less likely to follow an adult’s actions than peers with typical development (Mundy et al. 2009; Nation and Penny 2008).
Future Directions There are several provocative issues left to resolve for gaze behaviors. How do gaze behaviors relate to one another (e.g., detecting mutual gaze and gaze following)? Does improving one type of gaze behavior lead to global changes in other gaze behaviors? What are the developmental steps that help infants and children understand the referential nature of looking and seeing? Does the difficulty that children with ASD have with an elementary behavior such as gaze following contribute to and predict their later deficits in understanding others’ minds (i.e., theory of mind)? Researchers have shown that interventions can prompt children with ASD to follow others’ line of regard, but they have not examined whether children with ASD are specifically following eye gaze (e.g., Kasari et al. 2006; Schreibman et al. 2015). Future research can continue to explore best practices to help children with ASD develop an interest in eyes and follow the eye gaze of others. One suggestion is that infants’ own experiences may help them learn. Infants and children may use their own visual experiences as a framework for understanding that behavior in others. When infants open and close their own eyes, they immediately change whether they can (or cannot) see their surroundings. This
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experience could help them recognize the same effect of eye closure for others. In an experimental study for infants with typical development, infants’ own experiences with visual obstructions changed how they interpreted another person’s line of sight (Meltzoff and Brooks 2008). The findings suggest that infants’ visual experiences change what they understand about looking. Will interventions designed to vary the visual experiences of toddlers and children with ASD help them learn to follow the gaze of others? This and other interventions that actively involve the infant or child may greatly advance developmental progress for children with ASD (Schreibman et al. 2015). The overall goal to improve children’s gaze following is important because gaze following predicts language development, which in turn can help children understand the minds of other people (Brooks and Meltzoff 2015; Mundy et al. 2009).
See Also ▶ Eye Gaze ▶ Joint Attention ▶ RJA/IJA (Initiating/Responding to Joint Attention) ▶ Theory of Mind
References and Reading Adamson, L. B., Bakeman, R., Deckner, D. F., & Romski, M. A. (2009). Joint engagement and the emergence of language in children with autism and Down syndrome. Journal of Autism and Developmental Disorders, 39, 84–96. Brooks, R., & Meltzoff, A. N. (2002). The importance of eyes: How infants interpret adult looking behavior. Developmental Psychology, 38, 958–966. Brooks, R., & Meltzoff, A. N. (2005). The development of gaze following and its relation to language. Developmental Science, 8, 535–543. Brooks, R., & Meltzoff, A. N. (2008). Infant gaze following and pointing predict accelerated vocabulary growth through two years of age: A longitudinal, growth curve modeling study. Journal of Child Language, 35, 207–220. Brooks, R., & Meltzoff, A. N. (2015). Connecting the dots from infancy to childhood: A longitudinal study
Gelotophobia and Autism connecting gaze following, language, and explicit theory of mind. Journal of Experimental Child Psychology, 130, 67–78. Carpenter, M., Nagell, K., & Tomasello, M. (1998). Social cognition, joint attention, and communicative competence from 9 to 15 months of age. Monographs of the Society for Research in Child Development, 63(4, Serial No. 255), 1–174. Chawarska, K., Klin, A., & Volkmar, F. (2003). Automatic attention cueing through eye movement in 2-year-old children with autism. Child Development, 74, 1108–1122. Elsabbagh, M., Mercure, E., Hudry, K., Chandler, S., Pasco, G., Charman, T., . . . The BASIS Team. (2012). Infant neural sensitivity to dynamic eye gaze is associated with later emerging autism. Current Biology, 22, 338–342. Flom, R., Lee, K., & Muir, D. (Eds.). (2007). Gazefollowing: Its development and significance. Mahwah: Lawrence Erlbaum. Johnson, M. H., Grossmann, T., & Farroni, T. (2008). The social cognitive neuroscience of infancy: Illuminating the early development of social brain functions. In R. V. Kail (Ed.), Advances in child development and behavior (Vol. 36, pp. 331–372). San Diego: Elsevier. Jones, W., & Klin, A. (2013). Attention to eyes is present but in decline in 2–6-month-old infants later diagnosed with autism. Nature, 504, 427–431. Jones, E. J. H., Gliga, T., Bedford, R., Charman, T., & Johnson, M. H. (2014). Developmental pathways to autism: A review of prospective studies of infants at risk. Neuroscience and Biobehavioral Reviews, 39, 1–33. Kasari, C., Freeman, S., & Paparella, T. (2006). Joint attention and symbolic play in young children with autism: A randomized controlled intervention study. Journal of Child Psychology and Psychiatry, 47, 611–620. Meltzoff, A. N., & Brooks, R. (2008). Self-experience as a mechanism for learning about others: A training study in social cognition. Developmental Psychology, 44, 1257–1265. Mundy, P., Block, J., Delgado, C., Pomares, Y., Van Hecke, A. V., & Parlade, M. V. (2007). Individual differences and the development of joint attention in infancy. Child Development, 78, 938–954. Mundy, P., Sullivan, L., & Mastergeorge, A. M. (2009). A parallel and distributed-processing model of joint attention, social cognition and autism. Autism Research, 2, 2–21. Nation, K., & Penny, S. (2008). Sensitivity to eye gaze in autism: Is it normal? Is it automatic? Is it social? Development and Psychopathology, 20, 79–97. Schreibman, L., Dawson, G., Stahmer, A. C., Landa, R., Rogers, S. J., McGee, G. G., . . . Halladay, A. (2015). Naturalistic developmental behavioral interventions: Empirically validated treatments for autism spectrum disorder. Journal of Autism and Developmental Disorders, 45, 2411–2428.
2173 Toth, K., Munson, J., Meltzoff, A. N., & Dawson, G. (2006). Early predictors of communication development in young children with autism spectrum disorder: Joint attention, imitation, and toy play. Journal of Autism and Developmental Disorders, 36, 993–1005. Webb, S. J. (2008). Impairments in social memory in autism? Evidence from behaviour and neuroimaging. In J. Boucher & D. Bowler (Eds.), Memory in autism (pp. 188–209). New York: Cambridge University Press. Wetherby, A. M., Watt, N., Morgan, L., & Shumway, S. (2007). Social communication profiles of children with autism spectrum disorders late in the second year of life. Journal of Autism and Developmental Disorders, 37, 960–975. Woodward, A. L. (2003). Infants’ developing understanding of the link between looker and object. Developmental Science, 6, 297–311.
GEC (Global Executive Composite) ▶ BRIEF (Behavior Rating Inventory of Executive Functions)
Gelotophobia and Autism Geraldine Leader and Arlene Mannion Irish Centre for Autism and Neurodevelopmental Research (ICAN), National University of Ireland, Galway (NUI Galway), Galway, Ireland
Definition Gelotophobia can be defined as the fear of being laughed at. It comes from the Greek word “gelos,” which means laughter, and the word “phobia” meaning fear (Ruch and Proyer 2008a; Titze 2009).
Historical Background Gelotophobia was originally derived from clinical case studies and introduced as a clinical
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condition by Titze (1996, 2009). However, gelotophobia is not a pathology but is experienced in the typically developing population also (Ruch et al. 2014). Gelotophobia is an interindividual differences variable. At its extreme end, it may lead to a pathological fear, but only in rare cases of gelotophobia (Ruch and Proyer 2008a, b). Given the implications and consequences associated with gelotophobia, such as social anxiety and social withdrawal (Titze 1996, 2009), this is an important area of study. The phenomenon of gelotophobia has not only been investigated in the typically developing population. Previous research has found that 40% of individuals with eating disorders exceeded the threshold for a slight form of gelotophobia, 35.7% of individuals with personality disorders experienced gelotophobia (Forabosco et al. 2009), and 24.6% of shamebound neurotics showed gelotophobic tendencies (Ruch and Proyer 2008b). Gelotophobia is of importance to study in individuals with autism spectrum disorder (ASD). Due to the deficits in social communication and social cognition, individuals with ASD may experience difficulties with empathy. This is a result of their reduced theory of mind capacity which hinders their ability to distinguish between one’s own mental state and that of others (Woods et al. 2013). Individuals with ASD may have greater difficulty understanding and compartmentalizing their own emotions and conveying these emotions to others (Woods et al. 2013). Individuals with ASD may have difficulty in understanding nonverbal cues, reciprocal interaction, and initiating appropriate nonverbal communication (Frith 2004; Wing 1981). Individuals with ASD may also have difficulty understanding humor. Weiss et al. (2012) compared whether children with ASD could appreciate slapstick comedy in comparison to children who were typically developing. While children with ASD could appreciate the material, they had greater difficulties in discriminating humous from non-humous material. Given these reasons, gelotophobia is of relevance to study in individuals with ASD.
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Current Knowledge Samson et al. (2011) conducted the first study that investigated gelotophobia in individuals with Asperger’s syndrome. Samson et al. (2011) investigated gelotophobia and its relationship with recalled experiences of having been laughed at in the past in 40 individuals with Asperger’s syndrome and in 83 neurotypically developing control group participants. It was found that in individuals with Asperger’s syndrome, 45% exceeded the cut-off of having at least a slight fear of being laughed at. This was in comparison to only 6% of control participants who exceeded this cut-off. There were statistically significant higher levels of gelotophobia found in the Asperger’s syndrome group, compared to the control group. It was found that 17.5% of individuals with Asperger’s syndrome had a marked fear of being laughed at, with a further 7.5% endorsing a severe fear of being laughed at. High levels of gelotophobia were found in individuals with Asperger’s syndrome in comparison to control group participants, when both groups had a high frequency of recalled experiences of being laughed at. Therefore, recalled experiences of being laughed at does not explain why individuals with Asperger’s syndrome demonstrated high levels of gelotophobia. In control group participants, who were measured on the Autism-Spectrum Quotient, gelotophobia was positively associated with autism spectrum level, which demonstrates a relationship between gelotophobia and Asperger’s syndrome. The percentage of those with Asperger’s syndrome who presented with gelotophobia (45%) was the highest percentage reported in the literature at that time. Samson et al. (2011) outlined a number of reasons as to why gelotophobia may be more common in individuals with Asperger’s syndrome. First, individuals with Asperger’s syndrome may have a higher frequency of situations where they were laughed at in the past. Second, individuals with Asperger’s syndrome often lack social awareness and therefore may have difficulties understanding how to deal with someone laughing at them. Third, individuals with
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Asperger’s syndrome may have difficulty reading social cues and may not be able to differentiate between good-natured teasing and bullying. Samson et al. (2011) provided a novel area of research for autism researchers to address. Following Samson et al. (2011), there has been only a small number of studies that have investigated gelotophobia in individuals with ASD. Samson (2013) conducted a literature review on sense of humor in individuals with autism spectrum disorders (ASD). While focused on humor, the literature review provided a valuable summary of the literature on humor in ASD and briefly discussed gelotophobia. Wu et al. (2015) investigated the role of parental attachment in relation to gelotophobia in adolescents with ASD. Participants were 101 Taiwanese high school students with ASD with average intelligence and in 163 typically developing students. Adolescents with ASD showed significantly higher levels of gelotophobia than those without ASD. A negative relationship was found between attachment to fathers and gelotophobia in the ASD group. Therefore, as the quality of attachment to their fathers increased, gelotophobia decreased. However, no relationship was found between maternal attachment and gelotophobia. Therefore, by increasing the quality of father-child attachment in ASD, this may have implications for the occurrence of gelotophobia (Wu et al. 2015). The authors suggested that future research should investigate the relationship between attachment to peers and gelotophobia. Tsai et al. (2018) investigated the relationship between gelotophobia and personality traits in 123 Taiwanese high school students with ASD and in 156 typically developing students. The ASD group demonstrated higher rates of gelotophobia than the control group. It was found that 42.3% of individuals with ASD had some form of gelotophobia, with 4.9% showing extreme gelotophobia, 4.9% with marked gelotophobia, and 35.8% with slight gelotophobia. The personality traits examined were the Big Five: Extraversion, Conscientiousness, Agreeableness, Openness, and Emotional Stability. Extraversion was found to be a mediator of gelotophobia. Therefore, individuals who
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showed lower levels of extraversion experienced more gelotophobia, as these individuals were less able to engage in humor with others. The authors suggested that these results may be applicable to participants from an Asian cultural background, and therefore, the results of this research need to be replicated across different cultures. Grennan et al. (2018) conducted a literature review on the link between gelotophobia and highfunctioning ASD (hfASD). In this review, the characteristics of gelotophobia were discussed, including the conceptualization and measurement implications of gelotophobia, the etiology and consequences of gelotophobia, and the social competence of gelotophobes. The importance of studying gelotophobia in hfASD was discussed, as well as the relationships between hfASD and other variables relevant to gelotophobia, including comorbid psychopathology, quality of life, social functioning, perceived social support, past experiences of bullying, and shame-bound emotions. This review demonstrated the need for further research to be conducted in the area and discussed the importance of better understanding the relationship between gelotophobia and comorbid psychopathology in individuals with ASD. Following this, Leader et al. (2018) conducted an empirical investigation specifically on individuals with hfASD, where gelotophobia was examined in 103 adults with hfASD and in 137 typically developing control participants. Moreover, it was the first study of its kind to explore the relationship between gelotophobia and other psychosocial constructs and the presence of psychopathological disorders in individuals with hfASD. The study examined gelotophobia in relation to a number of other variables, including social functioning, perceived social support, life satisfaction, quality of life, past experiences of bullying, and comorbid psychopathology. Individuals with hfASD presented with high rates of gelotophobia (87.4%) compared to 22.6% of control participants. This was a higher rate of gelotophobia, almost double, than what was found in the results of the Samson et al. (2011) study. Significantly higher levels of gelotophobia and higher past experiences of bullying were found in those with hfASD, in
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comparison to typically developing control participants. The hfASD group showed lower quality of life, life satisfaction, social functioning, and perceived social support, in comparison to the control group. Of the 84.7% of participants with hfASD who presented with gelotophobia, 34% demonstrated a marked form of gelotophobia, 29.1% presented with an extreme form, and 24.3% presented with a slight form of gelotophobia. Leader et al. (2018) found significant negative correlations between gelotophobia and quality of life, life satisfaction, perceived social support, and depression and anxiety. A significant positive correlation was found between gelotophobia and personal involvement in bullying and social functioning. The predictors of gelotophobia were examined in Leader et al. (2018). Social functioning, past experiences of bullying, anxiety, and life satisfaction were identified as predictors of gelotophobia. It was found that past experiences of verbal bullying were a stronger predictor of gelotophobia than physical and indirect forms of bullying.
Future Directions Given the small number of studies that have focused on gelotophobia in individuals with ASD, there is a need for future research to focus on a number of specific directions. First, other methodologies should be used other than selfreport. Self-report may lead to incidences of social desirability among participants. Where research requires individuals to report remembered situations of past experiences of bullying, self-report can be compromised by age-related memory loss. Future research could also include interviewing instead of questionnaires, field observation, and the use of peer and parental reports (Wu et al. 2015). Samson et al. (2011) outlined that peerreports by teachers or parents could be used to investigate the influence of prior experiences of being laughed at, and whether this affects the development of gelotophobia later in life. Other peer report could come from other individuals in
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a person’s life, such as siblings or employers, in order to encapsulate the influence of bullying exposure on gelotophobia. Second, more research is needed to determine the relationship between gelotophobia and other fears and phobias. Samson et al. (2011) suggested that future research should focus on gelotophobia in relation to the development of social phobia. Leader et al. (2018) found that the presence of an anxiety disorder diagnosis was a predictor of gelotophobia. This possible link between gelotophobia and anxiety needs to be examined in future research. Third, research is needed on ASD populations other than individuals with hfASD. Gelotophobia needs to be investigated across the autism spectrum, from individuals with more severe symptoms of ASD to those with milder symptoms. Fourth, the relationship between gelotophobia and ASD severity needs to be better understood. Samson (2013) outlined that if the relationship between humor and ASD severity is investigated in future research studies, a model could be designed to explain the connections between symptom severity and the domains of humor in individuals with ASD. Fifth, given the negative consequences of gelotophobia, research is needed to determine how gelotophobia can be treated. Gelotophobia must first receive further empirical investigative work in order to support its prevalence and further determine its consequences, symptomatology, and consequences in individuals with ASD. This will in the future lead to the development of specific intervention and preventative strategies for its treatment. While literature exists on strategies to reduce bullying in ASD, empirical evidence is needed on the intervention of and prevention of gelotophobia in individuals in ASD. This treatment may include learning to differentiate between teasing and mocking, and strategies which may help individuals with hfASD to disentangle harmless teasing from hostile bullying (Attwood 2004). Sixth, research is needed on the use of the Picture-Geloph in individuals with hfASD. The Picture-Geloph is a semi-projective tool, piloted by Ruch et al. (2009) which involves 20 cartoons depicting social situations which
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involve laughter or potential laughter. Whether these cartoons are interpreted in the same way by individuals with hfASD as they are by individuals in the typically developing population is a question for future research to determine. Seventh, gelotophobia needs to be investigated with children with ASD. A child version of the PhoPhiKat (Ruch and Proyer 2009) has been developed, which measures gelotophobia, gelotophilia (i.e., the joy of being laughed at), and katagelasticism (i.e., the joy of laughing at others). The child version has been used in previous research with typically developing children (Proyer et al. 2012). Research is needed to determine the validity of this measure in children with ASD. Finally, research is needed to determine the relationship between gelotophobia and alexitymania in individuals with hfASD. Alexitymania is the inability to express emotions and has been identified as being an issue in individuals with ASD (Costa et al. 2017; Fitzgerald and Bellgrove 2006). A link between gelotophobia and alexitymania has been discovered (Boda-Ujlaki and Séra 2013), yet future research is needed to empirically explore these concepts in individuals with hfASD. In conclusion, the area of gelotophobia and ASD is a novel field of autism research, where much more research is needed to expand our understanding of the concept of gelotophobia in individuals with ASD.
See Also ▶ Mental Health and ASD ▶ Social Behaviors and Social Impairment
References and Reading Attwood, T. (2004). Strategies to reduce the bullying of young children with Asperger syndrome. Australian Journal of Early Childhood, 29(3), 15. Boda-Ujlaki, J. E., & Séra, L. (2013). Gelotophobia, alexithymia and emotional intelligence. Mentálhigiéné és Pszichoszomatika, 14, 297–321.
2177 Costa, A., Steffgen, G., & Samson, A. C. (2017). Expressive incoherence and alexithymia in autism spectrum disorder. Journal of Autism and Developmental Disorders, 47(6), 1659–1672. Fitzgerald, M., & Bellgrove, M. A. (2006). The overlap between alexithymia and Asperger’s syndrome. Journal of Autism and Developmental Disorders, 36(4), 573. Forabosco, G., Ruch, W., & Nucera, P. (2009). The fear of being laughed at among psychiatric patients. Humor: International Journal of Humor Research, 22(1), 233–251. Frith, U. (2004). Emanuel Miller lecture: Confusions and controversies about Asperger syndrome. Journal of Child Psychology and Psychiatry, 45, 672–686. Grennan, S., Mannion, A., & Leader, G. (2018). Gelotophobia and high-functioning autism spectrum disorder. Review Journal of Autism and Developmental Disorders, 5(4), 349–359. Leader, G., Grennan, S., Chen, J. L., & Mannion, A. (2018). An investigation of gelotophobia in individuals with a diagnosis of high-functioning autism spectrum disorder. Journal of Autism and Developmental Disorders, 48(12), 4155–4166. Proyer, R. T., Neukom, M., Platt, T., & Ruch, W. (2012). Assessing gelotophobia, gelotophilia, and katagelasticism in children: An initial study on how six to nine-year olds deal with laughter and ridicule and how this relates to bullying and victimization. Child Indicators Research, 5(2), 297–316. Ruch, W., & Proyer, R. T. (2008a). The fear of being laughed at: Individual and group differences in gelotophobia. Humor: International Journal of Humor Research, 21(1), 47–67. Ruch, W., & Proyer, R. T. (2008b). Who is gelotophobic? Assessment criteria for the fear of being laughed at. Swiss Journal of Psychology, 67, 19–27. Ruch, W., & Proyer, R. T. (2009). Extending the study of gelotophobia: On gelotophiles and katagelasticists. Humor: International Journal of Humor Research, 22(1–2), 183–212. Ruch, W., Altfreder, O., & Proyer, R. T. (2009). How do gelotophobes interpret laughter in ambiguous situations? An experimental validation of the concept. Humor: International Journal of Humor Research, 22(1–2), 63–89. Ruch, W., Hofmann, J., Platt, T., & Proyer, R. T. (2014). The state-of-the-art in gelotophobia research: A review and some theoretical extensions. Humor: International Journal of Humor Research, 27, 23–45. Samson, A. C. (2013). Humor(lessness) elucidated – Sense of humor in individuals with autism spectrum disorders: Review and introduction. Humor: International Journal of Humor Research, 26(3), 393–409. Samson, A. C., Huber, O., & Ruch, W. (2011). Teasing, ridiculing and the relation to the fear of being laughed at in individuals with Asperger’s syndrome. Journal of Autism and Developmental Disorders, 41, 475–483.
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2178 Titze, M. (1996). The Pinocchio Complex: Overcoming the fear of laughter. Humor & Health Journal, 5, 1–11. Titze, M. (2009). Gelotophobia: The fear of being laughed at. Humor: International Journal of Humor Research, 22(1), 27–48. Tsai, M. N., Wu, C. L., Tseng, L. P., An, C. P., & Chen, H. C. (2018). Extraversion is a mediator of gelotophobia: A study of autism spectrum disorder and the Big Five. Frontiers in Psychology, 9, 150. Weiss, E. M., Schulter, G., Freudenthaler, H. H., Hofer, E., Pichler, N., & Papousek, I. (2012). Potential markers of aggressive behavior: The fear of other person’s laughter and its overlaps with mental disorders. PLoS One, 7, e38088. Wing, L. (1981). Asperger syndrome: A clinical account. Psychological Medicine, 11, 115–129. Woods, A. G., Mahdavi, E., & Ryan, J. P. (2013). Treating clients with Asperger’s syndrome and autism. Child and Adolescent Psychiatry and Mental Health, 7, 32–40. Wu, C. L., An, C. P., Tseng, L. P., Chen, H. C., Chan, Y. C., Cho, S. L., & Tsai, M. L. (2015). Fear of being laughed at with relation to parent attachment in individuals with autism. Research in Autism Spectrum Disorders, 10, 116–123.
Genahist™ [OTC]
referred to as “sex differences”) between the sexes.
Historical Background Kanner’s earliest writings about autism highlighted the now frequently documented gender difference in the prevalence of the disorder (1943). These early case studies, of eight boys and three girls, described few other gender differences within the sample; the male and female children were described similarly with respect to their limited social reciprocity and communication functioning and atypical interests and repetitive patterns of behavior. Nevertheless, since Kanner’s time, there has been much speculation that gender differences exist in the clinical presentation of ASD. These concerns have been raised in no small part because much of the research on ASD has used clinic and research samples with a predominantly or exclusively male composition (Lai et al. 2014).
Genahist™ [OTC] ▶ Diphenhydramine
Gender Differences Marisela Huerta Center for Autism and the Developing Brain, Weill Cornell Medicine, New York, NY, USA
Definition Gender differences in autism spectrum disorders (ASD) primarily refer to the behavioral phenotypic differences between affected males and females. Gender differences have also been examined with respect to the diagnosis and prevalence of ASD. Of note, the term “gender differences” is used here to describe socially defined as well as biologically defined differences (elsewhere
Current Knowledge Researchers have proposed models of ASD that explain and predict gender differences such as Simon Baron-Cohen’s “extreme male brain” theory which proposes that testosterone-driven differences in cognition are at the core of ASD (2005). Others propose a “female protective model”; this model states that females require larger genetic insults for ASD symptom expression and has found some support in genetic studies (Jacquemont et al. 2014). More recently, the concept of “camouflaging” has been used to explain the gender differences in ASD; the term is currently used in an interchangeable manner to describe a number of behaviors the use of compensatory strategies (Dean et al. 2017; Lai et al. 2017; Livingston and Happé 2017), the presence of an atypical cluster of symptoms (Kirkovski et al. 2013), as well as self-reported efforts to minimize or “hide” impairments (Bargiela et al. 2016;
Gender Differences
Lai et al. 2017). However, despite the fact that there are very clear sex differences in the prevalence of ASD, the existing literature describes few differences in the behavioral and clinical presentation of ASD in males and females. The following are highlights of the work completed to date. Current prevalence estimates place the overall male-to-female ratio of ASD at 3:1 (Loomes et al. 2017). In samples with moderate to severe intellectual disability, the male-to-female ratio decreases to 1.95:1, while in samples without cognitive impairment, the ratio is closer to 5.5:1 (see Fombonne 2005 for a review). There is also evidence that the male-to-female ratio in ASD is higher in “essential autism” (ASD with no significant congenital anomalies) than in “complex autism” or ASD associated with significant dysmorphology or microcephaly (Miles et al. 2005). As with ASD prevalence, research studies have consistently found gender differences in cognitive functioning. Affected females, on average, have lower nonverbal and adaptive scores than males (Van Wijngaarden-Cremers et al. 2014; Frazier et al. 2014; Volkmar et al. 1993; Lord et al. 1982). Gender differences in language development merit further study. Overall, when compared to affected males, females demonstrate lower verbal ability and greater difficulties in the areas of language processing and comprehension (e.g.; Frazier et al. 2014; Konstantareas et al. 1989). In the area of pragmatic language skills, toddler girls demonstrate greater impairments than boys (Carter et al. 2007; Hartley and Sikora 2009); this gender difference is reversed in school-age samples (Conlon et al. 2019). With respect to the core features of ASD, largescale studies have been able to account for the effects of developmental factors (such as IQ and language level) and have found no gender differences in autism severity (Kaat et al. 2020; Tillmann et al. 2018). Similarly, gender differences have not been found in the social communication behavior of toddlers, children, and adults with autism (van Wijngaarden-Cremers et al. 2014). In contrast, studies that examine social
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behaviors more generally find that female children demonstrate compensatory strategies (e.g., Dean et al. 2017) and present with relative strengths compared to males (Jamison et al. 2017; Head et al. 2014). Consistent with these findings, female children demonstrate fewer impairments than males on “higher-level” social communication items of the ADOS, behaviors that as a group are not specific to a diagnosis of ASD (Bishop et al. 2016). In this same study, no gender differences were found on items uniquely associated with ASD, those that assess basic social communication. Various studies have identified gender differences in some aspects of restricted and repetitive behavior (RRBs). In samples of children, adolescents, and adults, but not toddlers, females with ASD demonstrate fewer RRB symptoms than males (Van Wijngaarden-Cremers et al. 2014). Recent studies reveal that this gender difference is found across all levels of functioning in RRB total scores of the ADI-R and RBS-R restricted interest scores (Frazier et al. 2014). Notably, RRBs described as “insistence on sameness” behaviors on the ADI-R do not appear to be predicted by gender (Hus et al. 2007; Richler et al. 2010). Qualitative examination of repetitive behavior in school-age children reveals further differences. Girls, as compared to boys, are less likely to engage in repetitive use of objects and more likely to demonstrate fixations, often on ageappropriate topics (Hiller et al. 2014). In regard to the broader behavioral and psychological functioning of children and adults with ASD, results vary by developmental level and age. In a sample composed primarily of children and young adults with ASD and intellectual disability, males and females demonstrate similar levels of disruptive behavior, hyperactivity, and attentional difficulties (see, e.g., Brereton et al. 2006). In the Simons Simplex Study, comprised primarily of cognitively able children and adolescents with ASD, females demonstrate greater externalizing problems relative to males (Frazier et al. 2014) girls also show greater sleep problems, anxiety/mood, and emotional problems (Hartley
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and Sikora 2009; Holtmann et al. 2007, Mandy et al. 2012). The findings with respect to attention problem and overactivity in children are mixed (Mandy et al. 2012; Frazier et al. 2014). In adolescence and adulthood, females diagnosed with ASD demonstrate greater increases in anxiety and depression (Gotham et al. 2015; Solomon et al. 2012). Self-report data also suggests such gender differences in mood disruptions, but not in anxiety (Lever and Geurts 2016). Few studies have longitudinally examined gender differences in the core symptoms of ASD, and fewer still have included sufficient samples of females. Emerging research suggests that in early childhood, female children may demonstrate better trajectories than their male counterparts (Waizbard-Bartov et al. 2020). In adulthood, males and females present with similar clinical profiles (Howlin et al. 2004; Shattuck et al. 2007; Taylor and Seltzer 2010). However, when broader outcomes are examined (e.g., employment), there is evidence that adult females fare worse than males (Howlin et al. 2004; Taylor and Mailick 2014). Self-report from adult women with ASD further suggests that unique challenges arise when impairments associated with ASD intersect with cultural norms and expectations of women’s behavior (Bargiela et al. 2016). In sum, the existing body of research points to a clear gender difference in the prevalence ASD; the male-to-female ratio is estimated at 3:1, with a greater frequency of females at the lower end of cognitive ability. Taking together all of the currently available research, gender differences in the behavioral phenotype of ASD are subtle (e.g., qualitative differences in repetitive behavior) and reflect interactions among multiple factors (e.g., developmental level). At present, sex differences in social functioning are limited to broader areas of functioning that are nonspecific to ASD. Importantly, the current research on gender differences in ASD should be considered in light of the following: female children with ASD are less likely to have a formal diagnosis of ASD (Giarelli et al. 2010) and more likely to be diagnosed later than
Gender Differences
their male peers (Begeer et al. 2013; McCormick et al. 2020).
Future Directions While significant strides have been made in recent years to utilize large samples to examine differences between the male and female autism phenotype (see Kaat et al. 2020; Tillmann et al. 2018), further quantitative and qualitative examination of the gender differences in ASD in large and wellcharacterized samples of children and adults is needed. Important to this work will be our ability to identify other within-group differences in ASD (see Joseph et al. 2002; Miles et al. 2005; Lord et al. 2015; Stelios et al. 2017) and further refine our ability to assess the core symptoms of ASD across development and contexts (Anagnostou et al. 2015).
See Also ▶ Subtyping Autism
References and Reading Anagnostou, E., Jones, N., Huerta, M., Halladay, A. K., Wang, P., et al. (2015). Measuring social communication behaviors as a treatment endpoint in individuals with autism spectrum disorder. Autism, 19(5), 622–636. Banach, R., Thompson, A., Goldberg, J., Tuff, L., Zwaigenbaum, L., & Mahoney, W. (2009). Brief report: Relationship between non-verbal IQ and gender in autism. Journal of Autism and Developmental Disorders, 39(1), 188–193. Bargiela, S., Steward, R., & Mandy, W. (2016). The experiences of late-diagnosed women with autism spectrum conditions: An investigation of the female autism phenotype. Journal of Autism and Developmental Disorders, 46, 3281–3294. Baron-Cohen, S., Richler, J., Bisarya, D., Gurunathan, N., & Wheelwright, S. (2003). The systemizing quotient: An investigation of adults with Asperger syndrome or high functioning autism, and normal sex differences. Philosophical Transactions of the Royal Society of London, 358, 361–374.
Gender Differences Begeer, S., Mandell, D., & Wijnker-Holmes, B. (2013). Sex differences in the timing of identification among children and adults with autism spectrum disorders. Journal of Autism and Developmental Disorders, 43, 1151–1156. Bishop, S. L., Havdahl, K. A., Huerta. M., Lord. C. (2016) Subdimensions of social-communication impairment in autism spectrum disorder. Journal of Child Psychology and Psychiatry, 57(8), 909–916. https://doi.org/10. 1111/jcpp.12510 Brereton, A. V., Tonge, B. J., & Einfeld, S. L. (2006). Psychopathology in children and adolescents with autism compared to young people with intellectual disability. Journal of Autism and Developmental Disorders, 36(7), 863–870. Carter, A. S., Black, D. O., Tewani, S., Connolly, C. E., Kadlec, M. B., & Tager-Flusberg, H. (2007). Sex differences in toddlers with autism spectrum disorders. Journal of Autism and Developmental Disorders, 37(1), 86–97. Conlon, O., Volden, J., Smith, I. M., et al. (2019). Gender differences in pragmatic communication in schoolaged children with autism spectrum disorder (ASD). Journal of Autism and Developmental Disorders, 49(5), 1937–1948. https://doi.org/10.1007/s10803018-03873-2 Dean, M., Harwood, R., & Kasari, C. (2017). The art of camouflage: Gender differences in the social behaviors of girls and boys with autism spectrum disorder. Autism, 21(6), 678–689. https://doi.org/10.1177/ 1362361316671845 Fombonne, E. (2005). Epidemiology of autistic disorder and other pervasive developmental disorders. The Journal of Clinical Psychiatry, 66, 3–8. Frazier, T. W., Georgiades, S., Bishop, S. L., & Hardan, A. Y. (2014). Behavioral and cognitive characteristics of females and males with autism in the Simons Simplex Collection. Journal of the American Academy of Child and Adolescent Psychiatry, 53(3), 329–340. Giarelli, E., Wiggins, L. D., Rice, C. E., Levy, S. E., Kirby, R. S., Pinto-Martin, J., et al. (2010). Sex differences in the evaluation and diagnosis of autism spectrum disorders among children. Disability and Health Journal, 3(2), 107–116. Gotham, K, Brunwasser, S. M., Lord, C. (2015). Depressive and anxiety symptom trajectories from school age through young adulthood in samples with autism spectrum disorder and developmental delay. Journal of the American Academy of Child and Adolescent Psychiatry, 54(5), 369–76.e3. https://doi.org/10.1016/j.jaac. 2015.02.005 Hartley, S. L., & Sikora, D. M. (2009). Sex differences in autism spectrum disorder: An examination of developmental functioning, autistic symptoms, and coexisting behavior problems in toddlers. Journal of Autism and Developmental Disorders, 39, 1715–1722. Head, A. M., McGillivray, J. A., & Stokes, M. A. (2014). Gender differences in emotionality and sociability in
2181 children with autism spectrum disorders. Molecular Autism, 5, 19. Hiller, R. M., Young, R. L., & Weber, N. (2014). Sex differences in autism spectrum disorder based on DSM-5 criteria: Evidence from clinician and teacher reporting. Journal of Abnormal Child Psychology, 42, 1381–1393. Holtmann, M., Bolte, S., & Poustka, F. (2007). Autism spectrum disorders: Sex differences in autistic behaviour domains and coexisting psychopathology. Developmental Medicine and Child Neurology, 49(5), 361–366. Howlin, P., Goode, S., Hutton, J., & Rutter, M. (2004). Adult outcome for children with autism. Journal of Child Psychology and Psychiatry, 45, 212–229. Hus, V., Pickles, A., Cook, E. H., Risi, S., & Lord, C. (2007). Using the autism diagnostic interviewrevised to increase phenotypic homogeneity in genetic studies of autism. Biological Psychiatry, 61(4), 438–448. Jacquemont, S., Coe, B. P., Hersch, M., et al. (2014). A higher mutational burden in females supports a “female protective model” in neurodevelopmental disorders. American Journal of Human Genetics, 94(3), 415– 425. https://doi.org/10.1016/j.ajhg.2014.02.001 Jamison, R., Bishop, S. L., Huerta, M., Halladay, A. K. (2017). The clinician perspective on sex differences in autism spectrum disorders. Autism, 21(6), 772–784. https://doi.org/10.1177/1362361316681481 Joseph, R., Tager-Flusberg, H., & Lord, C. (2002). Cognitive profiles and social-communicative functioning in children with autism spectrum disorder. Journal of Child Psychology and Psychiatry, 43(6), 807–821. Kanner, L. (1943). Autistic disturbances of affective content. The Nervous Child, 2, 217–250. Kaat, A. J., Shui, A. M.,, Ghods, S. S., et al. (2020). Sex differences in scores on standardized measures of autism symptoms: a multisite integrative data analysis [published online ahead of print, 2020 Apr 20]. Journal of Child Psychology and Psychiatry. https://doi.org/10. 1111/jcpp.13242 Kim, S., & Lord, C. (2010). Restricted and repetitive behaviors in toddlers and preschoolers with autism spectrum disorders based on the autism diagnostic observation scheduled (ADOS). Autism Research, 3(4), 162–173. Kirkovski, M., Enticott, P. G., & Fitzgerald, P. B. (2013). A review of the role of female gender in autism spectrum disorders. Journal of Autism and Developmental Disorders, 43(11), 2584–2603. https://doi.org/10.1007/ s10803-013-1811-1 Klin, A., Saunier, C. A., Sparrow, S. S., Ciccheti, D. V., Volkmar, F. R., & Lord, C. (2007). Social and communication abilities and disabilities in higher functioning individuals with autism: The Vineland and the ADOS. Journal of Autism and Developmental Disorders, 37, 748–759.
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2182 Konstantareas, M. M., Homatidis, S., & Busch, J. (1989). Cognitive, communication, and social differences between autistic boys and girls. Journal of Applied Developmental Psychology, 10(4), 411–424. Lai, M. C., Lombardo, M. V., & Auyeung, B. (2014). Sex/gender differences and autism: Setting the scene for future research. Journal of the American Academy of Child and Adolescent Psychiatry, 54, 11–24. Lai M. C., Lombardo M. V., Ruigrok A. N., et al. (2017). Quantifying and exploring camouflaging in men and women with autism. Autism, 21(6), 690–702. https:// doi.org/10.1177/1362361316671012 Lever, A. G., Geurts, H. M. (2016). Psychiatric co-occurring symptoms and disorders in young, middle-aged, and older adults with autism spectrum disorder. Journal of Autism and Developmental Disorders, 46, 1916– 1930. https://doi.org/10.1007/s10803-016-2722-8 Little, L. M., Wallisch, A., Salley, B., & Jamison, R. (2017). Do early caregiver concerns differ for girls with autism spectrum disorders? Autism, 21(6), 728–732. Livingston, L. A. & Happé, F. (2017). Conceptualising compensation in neurodevelopmental cisorders: Reflections from autism spectrum disorder. Neuroscience and Biobehavioral Reviews 80: 729–42. Loomes, R., Hull, L., & Mandy, W. (2017). What is the male-to-female ratio in autism spectrum disorder? A systematic review and meta-analysis. Journal of the American Academy of Child & Adolescent Psychiatry, 56(6), 466–474. Lord, C., Schopler, E., & Revicki, D. (1982). Sex differences in autism. Journal of Autism and Developmental Disorders, 12(4), 317–330. Lord, C., Bishop, S. L., & Anderson, D. (2015). Developmental trajectories as autism phenotypes. American Journal of Medical Genetics Part C: Seminars in Medical Genetics, 169(2), 198–208. Luyster, R., Richler, J., Rise, S., Hsu, W., Dawson, G., Bernier, R., et al. (2005). Early regression in social communication in autism spectrum disorders: A CPEA study. Developmental Neuropsychology, 27(3), 311–336. Mandy, W., Chilvers, R., Chowdhury, U., Salter, G., Seigal, A., & Skuse, D. (2012). Sex differences in autism spectrum disorder: evidence from a large sample of children and adolescents. Journal of Autism and Developmental Disorders, 42(7), 1304–1313. https:// doi.org/10.1007/s10803-011-1356-0 McCormick, C. E. B., Kavanaugh, B. C., Sipsock, D., et al. (2020). Autism heterogeneity in a densely sampled U.S. population: Results from the first 1,000 participants in the RI-CART Study. Autism Research, 13(3):474–488. https://doi.org/10.1002/aur.2261 McLennan, J. D., Lord, C., & Schopler, E. (1993). Sex differences in higher functioning people with autism. Journal of Autism and Developmental Disorders, 23(2), 217–227. Miles, J. H., Takahashi, T. N., Bagby, S., Sahota, P. K., Vaslow, D. F., Wang, C. H., et al. (2005). Essential
Gender Differences versus complex autism: Definition of fundamental prognostic subtypes. American Journal of Medical Genetics, 135(A), 171–180. Richler, J., Huerta, M., Bishop, S., & Lord, C. (2010). Developmental trajectories of restrictive and repetitive behaviors and interests in children with autism spectrum disorders. Development and Psychopathology, 22(1), 55–69. Shattuck, P. T., Seltzer, M. M., Greenberg, J. S., Orsmond, G., Bolt, D., Kring, S., et al. (2007). Change in autism symptoms and maladaptive behaviors in adolescents and adults with an autism spectrum disorder. Journal of Autism and Developmental Disorders, 37(9), 1735–1747. Solomon, M., Miller, M., Taylor, S. L., Hinshaw, S. P., & Carter, C. (2012). Autism symptoms and internalizing psychopathology in girls and boys with autism spectrum disorders. Journal of Autism and Developmental Disorders, 42(1), 48–59. Stelios, G., Bishop, S. L., & Frazier, T. (2017). Editorial perspective: Longitudinal research in autism- introducing the concept of ‘chronogeneity’. Journal of Child Psychology and Psychiatry, 58(5), 634–636. Taylor J. L., Mailick M. R. (2014). A longitudinal examination of 10-year change in vocational and educational activities for adults with autism spectrum disorders. Developmental Psychology, 50(3), 699–708. https:// doi.org/10.1037/a0034297 Taylor, J. L., & Seltzer, M. M. (2010). Changes in the autism behavioral phenotype during the transition to adulthood. Journal of Autism and Developmental Disorders, 40, 1431–1446. Tillmann, J., Ashwood, K., Absoud, M., Bolte, S., BonnetBrillhault, F., Buitelaar, J. K., et al. (2018). Evaluating sex and age differences in ADI-R and ADOS scores in a large European multi-site sample of individuals with autism spectrum disorder. Journal of Autism and Developmental Disorders, 48(7), 2490–2505. Advance online publication. Van Wijngaarden-Cremers, P. J., van Eeten, E., Groen, W. B., Van Deurzen, P. A., Oosterling, I. J., & Van der Gaag, R. J. (2014). Gender and age differences in the core triad of impairments in autism spectrum disorders: A systematic review and meta-analysis. Journal of Autism and Developmental Disorders, 44(3), 627–635. Volkmar, F. R., Szatmari, P., & Sparrow, S. S. (1993). Sex differences in pervasive developmental disorders. Journal of Autism and Developmental Disorders, 23(4), 579–591. Waizbard-Bartov, E., Ferrer, E., Young, G. S., et al. (2020). Trajectories of autism symptom severity change during early childhood [published online ahead of print, 2020 May 14]. Journal of Autism and Developmental Disorders. https://doi.org/10.1007/ s10803-020-04526-z Wing, L., & Gould, J. (1979). Severe impairments of social interaction and associated abnormalities in children: Epidemiology and classification. Journal of Autism and Developmental Disorders, 9(1), 11–29.
Gender Dysphoria and Autism Spectrum Disorders
Gender Dysphoria and Autism Spectrum Disorders Roald A. Øien1,2, Ella Maja Viktoria Bergman3 and Anders Nordahl-Hansen4,5 1 Department of Psychology/Department of Education, UIT – The Arctic University of Norway, University of Tromsø, Tromsø, Norway 2 Yale Child Study Center, Yale Autism Program, Yale University School of Medicine, New Haven, CT, USA 3 Department of Education, UiT – The Arctic University of Norway, Tromsø, Norway 4 Department of Special Needs Education, UiO University of Oslo, Oslo, Norway 5 Faculty of Education, Østfold University College, Halden, Norway
Definition The term Gender Dysphoria (GD) is defined as a mismatch between the phenotypic sex of an individual and that person’s perception of their biological gender (American Psychiatric Association 2013), and was previously defined as Gender Identity Disorder (American Psychiatric Association 2000; World Health Organization 1992). GD hereby used to describe Gender Identity Disorder (GID), and Gender Dysphoria (GD) is increasingly reported in ASD research.
Background The term gender refers not to the sex one is born with but to the psychological identification of oneself as the traditional dichotomy female or male, but also as both, neither, or something else regardless of the physical attributes. The awareness of one’s gender usually emerges around 18 months to 3 years of age. However, sometimes an individual’s natal sex is not the same as their gender (George and Stokes 2017a). In DSM-5, the term “gender dysphoria” (GD) is used and van Schalkwyk et al. (2015) points out the difference in terminology from the DSM-IV,
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where the diagnosis previously was named “gender identity disorder,” which gave the impression that being transgender was a form of mental illness. There are still some concerns regarding the criteria in DSM-5 that the individual must experience a certain presence of distress to be diagnosed, which might result in individuals experiencing perhaps confusion, but not distress, not receiving proper help and guidance (van Schalkwyk et al. 2015). This process towards an acceptance of a more diverse view of gender identity and gender roles can be compared to the one of homosexuality, which was classified as a mental illness by the American psychiatric association until 1987 (Burton 2015). It is estimated that between 0.005–0.014% of all natal males and between 0.002–0.003% of all natal females would have been diagnosed with GD following current diagnostic criteria (George and Stokes 2017b). GD can, for instance, be expressed by a wish to dress in clothes typically worn by the opposite sex (defined by that individual’s cultural norms), adapting to cross-gender norms, and typical thought patterns related to the opposite sex.
Current Knowledge There has been increased attention towards individuals with ASD who also express genderrelated issues in the past years, and both case reports and systematic studies have been published showing that it is apparent that there is some overlap between ASD and GD (George and Stokes 2017b). A Dutch study (De Vries et al. 2010) looked at the rates of ASD in 204 children and adolescents referred to a gender identity clinic, finding that 7.8% (n ¼ 16) of them were thought to qualify as having ASD. Of these individuals, one in seven children had persistent GID (they used DSM-IV, which is more inclusive concerning criteria compared to ICD-10). Five of nine adolescents met criteria for GID, and the remaining met the criteria for Gender Identity Disorder Not Otherwise Specified (GID-NOS) or transvestic fetishism, leading the authors to conclude that ASD is more common in GID individuals (De Vries et al. 2010). van Schalkwyk et al.
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(2015) points out that this conclusion might be more correctly rephrased as “ASD is more common in individuals with a broad range of genderrelated concerns or questions” (van Schalkwyk et al. 2015). They also suggest that genderformation in children can be seen as a process in development, where children reflect upon their identity through and by their gender and eventually forming an idea of this. However, due to large variations in cognitive and social development in children, this might be difficult to quantify by age-groups in research. This concept of gender-formation in childhood as a process with many possible outcomes is supported by research, where children with GD were followed up 10 years later, showing that only 27% of the individuals still qualified for a GD diagnosis. The majority of the non-GD individuals had instead developed a non-heterosexual identity. This indicates that gender identity and sexuality in some children are fluid concepts that later in puberty tend to crystallize into a more “final” form. ASD children display different patterns in social development, so this development of gender- and sexual identity might take on other forms or time perspectives, compared to TD children (van Schalkwyk et al. 2015). It is clear that co-occurrence of ASD and GD can lead to significant confusion, distress, and difficulties for the affected individual, primarily if the individual is not supported and guided. Therefore, it is essential for clinicians, health personnel, school teachers, and relatives to be informed and prepared to help and guide ASD individuals and to be aware of the increased possibility of confusion and uncertainty regarding their gender identity and sexuality already from an early age. Pasterski et al. (2014) examined the co-occurrence of GD and autistic traits in an adult population with a confirmed diagnosis of transsexualism. To measure autistic traits, the Autism Spectrum Quotient (AQ) was administered. It is a selfadministered questionnaire designed to measure where an individual lies on the spectrum, from normal to autistic. It uses a Likert scale comprised of five scales: social skills, attention switching, attention to detail, communication, and imagination. A person who scores high on the AQ scale are often not very skilled at social interaction, have an
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unusual ability to focus, but struggles with switching attention, have an eye for details but lacks imagination, traits prevalent among individuals on the spectrum. Their results showed that 7.1% in Female to Male (FtM) and 4.8% in Male to Female (MtF) individuals scored above the estimated threshold for a potential diagnosis of autism, a clear average above the prevalence in the general population. The result that there were no significant differences in the co-occurrence of ASD and GD between natal females and males differs somewhat from other reports. Other studies have found that there’s a higher ratio of natal males in childhood who present with ASD and GD diagnoses, separately (Pasterski et al. 2014), and in the similar study by Jones et al. (2012) where they compared FtM and MtF transsexuals and found that the FtM group displayed a higher mean AQ than control groups of both sexes and the MtF group. They suggested that the Extreme Male Brain (EMB) theory might explain autistic traits as a part of a masculinized phenotype in FtM transsexuals. A recent study examined the relationship between GD, ASD, and sexual orientation by looking for GD traits, and not the diagnosis, in a population of individuals diagnosed with ASD. They further hypothesized that GD traits would have a mediating effect between sexual orientation and ASD traits (George and Stokes 2017b). Their results concluded that the earlier seen association between GD traits and autistic traits also is true the reversed direction. Autistic individuals score higher on the GD scale than the typically developing control group, indicating that there is a bigger gender-variance in an autistic population. Autistic persons born as women only differed from those borne as men only on one of the subscales. Levels of GD traits were significantly higher among all other sexuality groups (homosexuality, bisexuality, asexuality, and other) than heterosexuality, and by heterosexuality, the authors refer to being attracted to the opposite of one’s natal sex. The EMB theory is also discussed here, with similar conclusions as mentioned earlier that high levels of fetal testosterone (fT) to some degree might explain the heightened rate of GD in ASD females, by shaping the individual in a masculine way. However, this reasoning does not explain the same heightened
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rates of GD in males with ASD (George and Stokes 2017b). The internet has made explicit sexual material available for the public, and there are countless of other sources that we are daily exposed to, providing different messages of relations, sexuality, norms, and ideals (George and Stokes 2017b). These changes have also sparked new life into the research on sexuality, and also on the topic of autism and sexuality. Flirting, courting, and relationships are activities that many typically developing (TD) individuals find challenging at times, and with an ASD where one of the most prominent traits concerns difficulties in the social domain, this can prove especially challenging. There are not a plethora of studies on autism and sexuality, and the few have been conducted on institutionalized, autistic male individuals with varying cognitive abilities living in group homes. These individuals were often reported engaging in more or less problematic sexual behavior, such as masturbating in public and inappropriate removal of clothes in front of others. Important to note though is that the studied subjects might have influenced the results from these studies and may not be representable for so-called “high functioning” autistic individuals living successfully as a part of society (George and Stokes 2017b). It is worth noting though that these most studies have had small- and gender-atypical male samples might have obscured more correct tendencies (Gilmour et al. 2012). The study by George and Stokes (2017b) found higher rates of asexuality in ASD individuals compared to TD but speculate that there might be several reasons for this. It is not uncommon for ASD individuals to experience sensory issues, hypersensitivity, tactile defensiveness, and other physical obstacles and might also find social interaction challenging. These factors might make lead to a decision of celibacy, even though it necessarily doesn’t mean that the individual lacks feelings of sexual desire. Males and females with ASD also reported higher rates of bisexuality compared to their sex-matched TD group, supporting the finding by Gilmour et al. (2012). Several participants expressed that either heterosexual or homosexual sexual identity did not describe their identity correct but suggested others such as asexual, intersexual,
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transsexual, and “other.” George and Stokes (2017b) discuss the EMB theory and points out that higher than usual levels of fT among males could be expected to lead to a hyper-masculinized individual resulting in heterosexuality. There is no convincing evidence for this connection. Some studies find this to be the case, and others have found opposite results where male ASD individuals have more homosexual tendencies, some found no relationship, and other studies again have found a relationship between lower fT and higher homosexuality. It is clear that there is no consensus on how fT affects the sexuality of males.
Future Directions The field of autism spectrum disorders (ASD) and gender dysphoria (GD, here also includes GID) have seen a massive increase in research on this somewhat rare co-occurrence over the past decades. Notably, a considerable rise can be found in studies published in scientific journals over the past 3–4 years (Øien et al. 2018). This increased interest in gender dysphoria, trends, and patterns could explain sexuality and ASD we see elsewhere in the society, where sexual attitudes that previously were taboo is receiving increasing acceptance, and the empowering of the LGBT movement has also been seen. Furthermore, it is likely that the technologies such as social media and easier access to relevant information and internet communities have affected and inspired individuals to be open about their sexuality and provided them with a way of interacting and discussing their difficulties with someone in the same situation. While we do see a significant improvement in attitudes and acceptance for sexual minorities, there is still a long way to go before we meet a global improvement for all of these sexual minorities. Most studies are conducted in liberal Western countries, and little is currently known about the co-occurrence in more conservative countries where, e.g., homosexuality is still highly taboo. Besides from the demand for more studies from other cultures, the most prominent difficulty for the field of GD and ASD research is
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the fact that the co-occurrence is rare, and thus very few individuals are to be recruited to studies.
See Also
Gene Regulatory Networks in Autism
Gene Regulatory Networks in Autism Melody Oliphant and Thomas Fernandez Yale Child Study Center, Yale University School of Medicine, New Haven, CT, USA
▶ DSM-5
Structure References and Reading American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders (Vol. 1, 4th ed.). Arlington: American Psychiatric Publishing. https:// doi.org/10.1176/appi.books.9780890423349. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (DSM-5 ®). Washington, DC: American Psychiatric Publishing. Burton, N. (2015, 09.18.2015). When homosexuality stopped being a mental disorder. Retrieved 27 June 2018 from https://www.psychologytoday.com/blog/ hide-and-seek/201509/when-homosexuality-stoppedbeing-mental-disorder. De Vries, A. L., Noens, I. L., Cohen-Kettenis, P. T., van Berckelaer-Onnes, I. A., & Doreleijers, T. A. (2010). Autism spectrum disorders in gender dysphoric children and adolescents. Journal of Autism and Developmental Disorders, 40(8), 930–936. George, R., & Stokes, M. (2017a). Sexual orientation in autism spectrum disorder. Autism Research, 11, 133–141. George, R., & Stokes, M. A. (2017b). Gender identity and sexual orientation in autism spectrum disorder. Autism. https://doi.org/10.1177/1362361317714587. Gilmour, L., Schalomon, P. M., & Smith, V. (2012). Sexuality in a community based sample of adults with autism spectrum disorder. Research in Autism Spectrum Disorders, 6(1), 313–318. Jones, R. M., Wheelwright, S., Farrell, K., Martin, E., Green, R., Di Ceglie, D., & Baron-Cohen, S. (2012). Brief report: Female-to-male transsexual people and autistic traits. Journal of Autism and Developmental Disorders, 42(2), 301–306. Øien, R. A., Cicchetti, D. V., & Nordahl-Hansen, A. (2018). Gender dysphoria, sexuality and autism spectrum disorders: a systematic map review. Journal of Autism and Developmental Disorders, 1–10. Pasterski, V., Gilligan, L., & Curtis, R. (2014). Traits of autism spectrum disorders in adults with gender dysphoria. Archives of Sexual Behavior, 43(2), 387–393. van Schalkwyk, G. I., Klingensmith, K., & Volkmar, F. R. (2015). Gender identity and autism spectrum disorders. The Yale Journal of Biology and Medicine, 88(1), 81. World Health Organization. (1992). The ICD-10 classification of mental and behavioural disorders. Geneva: World Health Organization.
Findings from genetic studies of autism spectrum disorder (ASD) reflect a central divergence as to whether the majority of genetic risk arises from common variation that is frequent but with low penetrance, or from rare variation with a high degree of penetrance and large effect. ASD is characterized by a high degree of both genetic and phenotypic heterogeneity. Despite long-standing evidence that ASD is a highly heritable, polygenic disorder, there still remains some uncertainty about the full allelic spectrum that underlies ASD. With the advent of next-generation sequencing technologies, researchers have focused on the contribution of de novo variation through whole exome sequencing (WES) studies and genomewide genotyping arrays to identify rare single nucleotide variants (SNVs), insertion-deletions (indels), and copy number variants (CNVs) associated with ASD. The identification of such variation in ASD-diagnosed individuals has resulted in the discovery of dozens of definitive ASD risk loci and genes over the past decade (De Rubeis et al. 2014; Dong et al. 2014; Iossifov et al. 2014; Iossifov et al. 2012; Neale et al. 2012; O’Roak et al. 2012; Pinto et al. 2014; Sanders et al. 2012), and confirmed the critical role of de novo germline mutation in the phenotypic presentation of ASD (Sanders et al. 2015). Until recently, studies that attempted to investigate the role of common variation in ASD often proved insufficiently powered to reliably identify statistically significant findings. A populationbased study resolved such limitations by analyzing the complex interplay of common, rare, inherited, and de novo variation across a Swedish epidemiological sample (Gaugler et al. 2014). The study concluded that while high-impact rare variation,
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especially de novo mutations, may largely determine the individual liability for developing ASD, common variants account for the most substantial contribution to the narrow-sense heritability. In the investigation of de novo variation implicated in ASD, the most common study design involves the study of simplex families, either classified as trios (affected proband and two unaffected parents) or quartets that also include at least one unaffected sibling. Across these studies, most estimates suggest that as many as 1000 genomic loci underlie risk for ASD. To assess the contribution of risk-conferring de novo mutations, researchers have assessed the recurrence of disruptive genetic events within the same gene or genomic loci as well as the significance of such recurrence, while burden analyses evaluated the presence or absence of significant enrichment among classes of mutations in cases compared to controls. Across multiple studies of genetic variation in ASD, burden analyses consistently demonstrate a significant excess of de novo loss-of-function (LoF) or likely-gene-disrupting (LGD) mutations in probands that result in a premature truncation of the gene, shift in the protein reading frame, or canonical splice site variant across large cohorts (De Rubeis et al. 2014; Iossifov et al. 2012; O’Roak et al. 2011; Sanders et al. 2015). While statistical evidence for the contribution of de novo missense mutations in ASD appears less robustly replicated in the literature, some studies have demonstrated the importance of such variation in the development of ASD (Sanders et al. 2012), especially those missense variants predicted to be damaging by tools such as PolyPhen-2 (De Rubeis et al. 2014). Multiple studies have confirmed the importance of rare CNVs as a source of risk in the genomic architecture of ASD (Bucan et al. 2009; Kumar et al. 2008; Marshall et al. 2008; Mefford et al. 2008; Moreno-De-Luca et al. 2013; Pinto et al. 2010, 2014; Sanders et al. 2011; Sanders et al. 2015; Sebat et al. 2007; Weiss et al. 2008). Indeed, two studies have further shown that de novo CNVs in individuals diagnosed with ASD demonstrated greater frequency, length, and intersection of disrupted genes when compared to
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controls (Pinto et al. 2014; Sanders et al. 2015). Beyond such findings, one study hypothesized that separate classes of mutations may converge on a common set of genes associated with ASD.
Function Bolstered by the development of advanced statistical modeling methods and software packages, researchers have sought to translate genetic findings of de novo variation associated with ASD into a broad understanding of the pathophysiological mechanisms that underlie ASD neurobiology. The central methodology of such translational efforts remains guided by two central principles: (1) recurrent de novo LGD or damaging missense mutations that occur within the same gene in unrelated individuals point toward genes that are likely to be associated with ASD, given the infrequency of such occurrences; and (2) risk loci and risk genes that share the same associated phenotype will converge in functional clusters that correlate to shared biological pathways, regulatory functions, developmental context, or coexpression networks. Several seminal papers that investigated genetic regulatory networks implicated in ASD with a statistically robust framework led to a finding widely replicated throughout the literature: a convergence on genes that encode for critical components of networks involved in proper synaptic function, chromatin modification, and the regulation of transcription in ASD (De Rubeis et al. 2014; Pinto et al. 2014; Sanders et al. 2015). Among these genes, both de novo CNVs and de novo LGD mutations have been shown to play a role in establishing such convergence, further emphasizing the possibility that different classes of variation may target a common biological set of ASD risk genes. These pathways, however, include a diverse variety of biological components, from voltagegated ion channels to histone-modifying enzymes. Specifically, one report identified significant enrichment for histone-modifier genes, which encode histone-modifying enzymes (De Rubeis et al. 2014). These histone-modifying enzymes work in tandem with chromatin remodelers to
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read and recognize posttranslational modifications and ultimately activate or repress transcription. Another report further hypothesized that genes involved in chromatin modification may in fact regulate the expression of synaptic genes (Sanders et al. 2015). Such a theory draws support from findings that show both direct and indirect regulatory targets of CHD8 are strongly enriched for genes both associated with ASD and implicated in neuronal and developmental pathways that control synapse formation (Cotney et al. 2015; Sugathan et al. 2014). Indeed, while clear evidence exists for the role of synaptic proteins and chromatin remodelers in the pathophysiology of ASD, a polygenic, complex disorder such as ASD likely results from disruptions in multiple distinct biological pathways. Studies of genetic regulatory networks in ASD have demonstrated the potential for future diagnostic and therapeutic targets through the convergence of ASD-risk genes in biological pathways related to neuronal development, kinase signaling cascades, as well as brain-expressed genes (Geschwind and State 2015). Rare genedisruptive events in ASD have further been shown to occur in genes found to interact with the neuronal RNA-binding protein known as fragile X mental retardation protein, or FMRPassociated genes (Darnell et al. 2011; Iossifov et al. 2012; Pinto et al. 2014). The significant enrichment of FMRP targets among ASD risk genes further emphasizes the role of proper human adaptation, neuroplasticity, and synaptic function in protecting against the development of ASD. In keeping with the theory that ASDassociated genes may form functional networks or clusters, convergence may exist across diverse biological functions, but in a highly specific developmental context. Indeed, a large number of genes that gave rise to the significant signal for enrichment among chromatin and transcription regulators involved genes with a prenatal expression profile (Pinto et al. 2014). Using data from the BrainSpan atlas, one study demonstrated highly significant findings for the convergence of coexpression networks harboring ASD-associated genes underlying the proper function of deeplayer glutamtergic cortical projection neurons in
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two overlapping periods of midfetal human development corresponding to 10–24 post-conception weeks (Willsey et al. 2013). Such a result highlights the promise that clues about the pathophysiological mechanisms implicated in ASD may exist not just in shared biological functions but also in shared cell types or developmental timeframes.
Path of Physiology Narrow-sense heritability estimates for ASD range from 17% to 50%, with such wide discrepancies likely derived from differences in study design, cohort composition (e.g., simplex vs. multiplex families), sample size, and ascertainment bias. Though as previously discussed, a population-based study suggested that narrowsense heritability is roughly 52% for ASD, with most of the total heritability explained by common variation and only a small fraction attributable to rare de novo mutations (Gaugler et al. 2014; Klei et al. 2012). However, though these de novo events contribute modestly to the variance in liability for ASD, de novo carrier status determined affected status for ASD in 80% of de novo CNV carriers and 57% of de novo LGD carriers (Gaugler et al. 2014). A separate study of the genomic risk loci underlying ASD found that roughly 70% of de novo CNVs and 46% of de novo LGD mutations carried ASD risk, with both estimates higher among females than males (Sanders et al. 2015). Of individuals affected with ASD, multiple studies estimate the contribution of de novo CNVs and/or de novo SNVs in mediating ASD risk at roughly 10% (Iossifov et al. 2012; Sanders et al. 2015). Phenotypically, deviations in head circumference in either direction, a history of seizures, and the presence of an unaffected sibling in the family all increased the mutational burden of a de novo event. While several studies have demonstrated a significant increase in ASD risk with increased paternal age (De Rubeis et al. 2014; Dong et al. 2014; Iossifov et al. 2012; O’Roak et al. 2012; Saha et al. 2009; Sanders et al. 2012), other studies did not observe such a difference with either a
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higher paternal or maternal age (Sanders et al. 2015). Additionally, investigations of the genetic risk architecture of ASD, particularly the demonstrated convergence of genomic risk loci in regulatory networks associated with synaptic function, have shown that common genetic mechanisms may underlie frequently comorbid disorders such as intellectual disability (ID), schizophrenia, developmental delay, and epilepsy (De Rubeis et al. 2014; Sanders et al. 2015; Zoghbi and Bear 2012).
See Also ▶ Genetics
References and Reading Bucan, M., Abrahams, B. S., Wang, K., Glessner, J. T., Herman, E. I., Sonnenblick, L. I., et al. (2009). Genome-wide analyses of exonic copy number variants in a family-based study point to novel autism susceptibility genes. PLoS Genetics, 5(6), e1000536. Cotney, J., Muhle, R. A., Sanders, S. J., Liu, L., Willsey, A. J., Niu, W., et al. (2015). The autismassociated chromatin modifier CHD8 regulates other autism risk genes during human neurodevelopment. Nature Communications, 6, 6404. Darnell, J. C., Van Driesche, S. J., Zhang, C., Hung, K. Y., Mele, A., Fraser, C. E., et al. (2011). FMRP stalls ribosomal translocation on mRNAs linked to synaptic function and autism. Cell, 146(2), 247–261. De Rubeis, S., He, X., Goldberg, A. P., Poultney, C. S., Samocha, K., Cicek, A. E., et al. (2014). Synaptic, transcriptional and chromatin genes disrupted in autism. Nature, 515(7526), 209–215. Dong, S., Walker, M. F., Carriero, N. J., DiCola, M., Willsey, A. J., Ye, A. Y., et al. (2014). De novo insertions and deletions of predominantly paternal origin are associated with autism spectrum disorder. Cell Reports, 9(1), 16–23. Gaugler, T., Klei, L., Sanders, S. J., Bodea, C. A., Goldberg, A. P., Lee, A. B., et al. (2014). Most genetic risk for autism resides with common variation. Nature Genetics, 46(8), 881–885. Geschwind, D. H., & State, M. W. (2015). Gene hunting in autism spectrum disorder: on the path to precision medicine. Lancet Neurology, 14(11), 1109–1120. Iossifov, I., O’Roak, B. J., Sanders, S. J., Ronemus, M., Krumm, N., Levy, D., et al. (2014). The contribution of de novo coding mutations to autism spectrum disorder. Nature, 515(7526), 216–221. Iossifov, I., Ronemus, M., Levy, D., Wang, Z., Hakker, I., Rosenbaum, J., et al. (2012). De novo gene disruptions
2189 in children on the autistic spectrum. Neuron, 74(2), 285–299. Klei, L., Sanders, S. J., Murtha, M. T., Hus, V., Lowe, J. K., Willsey, A. J., et al. (2012). Common genetic variants, acting additively, are a major source of risk for autism. Mol Autism, 3(1), 9. Kumar, R. A., KaraMohamed, S., Sudi, J., Conrad, D. F., Brune, C., Badner, J. A., et al. (2008). Recurrent 16p11.2 microdeletions in autism. Human Molecular Genetics, 17(4), 628–638. Marshall, C. R., Noor, A., Vincent, J. B., Lionel, A. C., Feuk, L., Skaug, J., et al. (2008). Structural variation of chromosomes in autism spectrum disorder. American Journal of Human Genetics, 82(2), 477–488. Mefford, H. C., Sharp, A. J., Baker, C., Itsara, A., Jiang, Z., Buysse, K., et al. (2008). Recurrent rearrangements of chromosome 1q21.1 and variable pediatric phenotypes. The New England Journal of Medicine, 359(16), 1685–1699. Moreno-De-Luca, D., Sanders, S. J., Willsey, A. J., Mulle, J. G., Lowe, J. K., Geschwind, D. H., et al. (2013). Using large clinical data sets to infer pathogenicity for rare copy number variants in autism cohorts. Molecular Psychiatry, 18(10), 1090–1095. Neale, B. M., Kou, Y., Liu, L., Ma’ayan, A., Samocha, K. E., Sabo, A., et al. (2012). Patterns and rates of exonic de novo mutations in autism spectrum disorders. Nature, 485(7397), 242–245. O’Roak, B. J., Deriziotis, P., Lee, C., Vives, L., Schwartz, J. J., Girirajan, S., et al. (2011). Exome sequencing in sporadic autism spectrum disorders identifies severe de novo mutations. Nature Genetics, 43(6), 585–589. O’Roak, B. J., Vives, L., Girirajan, S., Karakoc, E., Krumm, N., Coe, B. P., et al. (2012). Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations. Nature, 485(7397), 246–250. Pinto, D., Delaby, E., Merico, D., Barbosa, M., Merikangas, A., Klei, L., et al. (2014). Convergence of genes and cellular pathways dysregulated in autism spectrum disorders. American Journal of Human Genetics, 94(5), 677–694. Pinto, D., Pagnamenta, A. T., Klei, L., Anney, R., Merico, D., Regan, R., et al. (2010). Functional impact of global rare copy number variation in autism spectrum disorders. Nature, 466(7304), 368–372. Saha, S., Barnett, A. G., Foldi, C., Burne, T. H., Eyles, D. W., Buka, S. L., et al. (2009). Advanced paternal age is associated with impaired neurocognitive outcomes during infancy and childhood. PLoS Medicine, 6(3), e40. Sanders, S. J., Ercan-Sencicek, A. G., Hus, V., Luo, R., Murtha, M. T., Moreno-De-Luca, D., et al. (2011). Multiple recurrent de novo CNVs, including duplications of the 7q11.23 Williams syndrome region, are strongly associated with autism. Neuron, 70(5), 863–885. Sanders, S. J., He, X., Willsey, A. J., Ercan-Sencicek, A. G., Samocha, K. E., Cicek, A. E., et al. (2015). Insights into Autism Spectrum Disorder Genomic Architecture and Biology from 71 Risk Loci. Neuron, 87(6), 1215–1233.
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2190 Sanders, S. J., Murtha, M. T., Gupta, A. R., Murdoch, J. D., Raubeson, M. J., Willsey, A. J., et al. (2012). De novo mutations revealed by whole-exome sequencing are strongly associated with autism. Nature, 485(7397), 237–241. Sebat, J., Lakshmi, B., Malhotra, D., Troge, J., LeseMartin, C., Walsh, T., et al. (2007). Strong association of de novo copy number mutations with autism. Science, 316(5823), 445–449. Sugathan, A., Biagioli, M., Golzio, C., Erdin, S., Blumenthal, I., Manavalan, P., et al. (2014). CHD8 regulates neurodevelopmental pathways associated with autism spectrum disorder in neural progenitors. Proceedings of the National Academy of Sciences of the United States of America, 111(42), E4468–E4477. Weiss, L. A., Shen, Y., Korn, J. M., Arking, D. E., Miller, D. T., Fossdal, R., et al. (2008). Association between microdeletion and microduplication at 16p11.2 and autism. The New England Journal of Medicine, 358(7), 667–675. Willsey, A. J., Sanders, S. J., Li, M., Dong, S., Tebbenkamp, A. T., Muhle, R. A., et al. (2013). Coexpression networks implicate human midfetal deep cortical projection neurons in the pathogenesis of autism. Cell, 155(5), 997–1007. Zoghbi, H. Y., & Bear, M. F. (2012). Synaptic dysfunction in neurodevelopmental disorders associated with autism and intellectual disabilities. Cold Spring Harbor Perspectives in Biology, 4(3), a009886.
General Anxiety Lindsey Sterling1 and Jeffrey J. Wood2 1 Department of Psychiatry, Jane and Terry Semel Institute for Neuroscience and Human Behavior UCLA, Los Angeles, CA, USA 2 Departments of Psychiatry and Education, UCLA/Geffen School of Medicine, UCLA Center for Autism Research and Treatment, University of California, Los Angeles, CA, USA
Synonyms GAD
General Anxiety
TR; American Psychiatric Association [APA] 2000). Individuals suffering from general anxiety commonly experience distress with accompanying disturbances of sleep, concentration, and social or occupational/academic functioning.
Categorization Diagnostic criteria for GAD as defined by the DSM-IV-TR (APA 2000) are as follows: (a) Excessive anxiety and worry that occur more days than not for at least 6 months, about a variety of events, circumstances, or activities (such as work or school performance). (b) The worry is difficult for the person to control. (c) The anxiety and worry are associated with three or more (in children, one or more) of these six symptoms (with at least some symptoms present for more days than not for the past 6 months): restlessness or feeling keyed up or on edge, being easily fatigued, difficulty concentrating or mind going blank, irritability, muscle tension, and sleep disturbance (difficulty falling or staying asleep, or restless unsatisfying sleep). The diagnostic criteria for GAD apply to individuals with ASD; however, it is important to consider that symptoms may be qualitatively different with variable behavioral manifestations within the context of an ASD (cf. Wood and Gadow 2010). For example, anxiety may be more associated with acting out behaviors in children with ASD than in typically developing children (White et al. 2009). Individuals with ASD often have limited insight or ability to express emotion (e.g., Hill et al. 2004), which may lead to frustration and consequent acting out behaviors. Careful assessment is needed to determine whether observed behaviors reflect ASD, anxiety, disruptive behavior symptoms, or a combination thereof.
Short Description or Definition General anxiety, or generalized anxiety disorder (GAD), is marked by excessive anxiety and worry occurring more days than not for at least 6 months, about a number of events or activities (DSM-IV-
Epidemiology Anxiety disorders are prevalent among youth and adults with ASD. Specific phobias, social anxiety
General Anxiety
(social phobia), and GAD are common subtypes. Epidemiological reports of large and/or national samples indicate that anxiety disorders in typical youth range from 10% to 20% (Achenbach et al. 1998; Shaffer et al. 1996). An early study indicated that approximately 4.9% of typical adolescents met criteria for GAD at some point in their lives (Whitaker et al. 1990). Symptoms of anxiety may be the most common associated psychiatric concern among individuals with ASD (Skokauskas and Gallagher 2010) and occur so frequently that they are sometimes assumed to be a component of the disorder. In fact, in many cases, it is difficult to distinguish between symptoms of anxiety and ASD (see below: Part H. Evaluation and Differential Diagnosis). Comorbid anxiety disorders are reported in 11–84% of youth with ASD (e.g., Sukhodolsky et al. 2007), which is more common than in typically developing youth, youth with conduct disorder, and even some otherwise typical clinically anxious youth.
Natural History, Prognostic Factors, and Outcomes Individuals with general anxiety tend to report a slow, gradual onset of chronic worry rather than a sudden manifestation of symptoms. Children who have general anxiety have reported that greater impairment is associated with intensity of worries, number of associated symptoms (e.g., restlessness, trouble concentrating), and number of worry domains (e.g., school, family); according to clinicians, greater impairment is associated with intensity of worries, number of associated symptoms, and number of comorbid DSM-IV disorders (Layne et al. 2009). If not treated properly, anxiety disorders in youth predict adult anxiety disorders; more than half of adults with anxiety and mood disorders had a history of a childhood anxiety disorder (Ferdinand and Verhulst 1995; Kessler et al. 1994). Some youth with anxiety disorders may experience fluctuation or a remission of symptoms, but more often, childhood anxiety disorders are chronic and precede anxiety or mood disorders later in life (Keeton et al. 2009). In
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terms of vulnerability factors for developing anxiety, evidence suggests that being female, having high trait anxiety, and significant life events predict presence of chronic anxiety in adulthood (de Beurs et al. 2000). Family liability, temperament, and environmental factors may also contribute to the development of GAD. In a large community sample, GAD in adulthood was associated with parental GAD, high behavioral inhibition, exposure to childhood separation events, and parental overprotection (Beesdo et al. 2010). High emotionality and shy temperament are prominent features in teens with anxiety as well as their parents and siblings (Masi et al. 2003). As well, offspring of anxious parents have a significantly greater chance of developing an anxiety disorder (Micco et al. 2009). Common traits in parents and siblings suggest a shared genetic liability for the development of anxiety disorders. In fact, a meta-analysis of available twin studies estimated a heritability rate of 0.32 for GAD (Hettema et al. 2001). Similarly, it has been suggested that maternal mood symptoms, such as interpersonal sensitivity, hostility, phobic anxiety, depression, and anxiety, may be related to depression and anxiety in youth with ASD (Mazefsky et al. 2010). High rates of psychopathology in first-degree relatives of individuals with ASD may suggest a common genetic liability to develop ASD and disorders such as anxiety. In fact, it has been proposed that a multigenic form of inheritance (i.e., involving multiple genes) results in a “lesser variant” of ASD in some individuals, manifesting with anxious and obsessive traits rather than the fullblown disorder (Wilcox et al. 2003). The lesser variant has also been referred to as the “broad autism phenotype,” which comprises a constellation of subtle social and communication impairments, in addition to increased rates of psychopathology, in some relatives of individuals with ASD (e.g., Bolte et al. 2007). Higher rates of depression and anxiety in nonautistic relatives of individuals with ASD do not appear to be accounted for by the stress of raising a child (or children) with autism, providing further support for common genetic substrates (Piven and Palmer 1999).
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Clinical Expression and Pathophysiology Clinical features. Individuals with GAD are characterized by excessive fears and worries about a range of issues, such as natural disasters, illness, or the future. Common domains of worry among children include school performance, the health of significant others, minor daily events (e.g., saying the wrong thing), social status, family concerns, and natural phenomena (Layne et al. 2009; Masi et al. 2004). In addition to persistent worry, youth with GAD experience other difficulties including restlessness and trouble relaxing, trouble concentrating and attending, and trouble sleeping (Layne et al. 2009). It is common for youth to seek excessive reassurance, respond negatively to routine criticism and evaluation, have a negative selfimage, experience irritability, and have physical complaints (DSM-IV-TR; Masi et al. 2004). Such difficulties can cause significant distress and may interfere with school performance, the development and maintenance of peer relationships, and family functioning (e.g., by leading to conflict). Relation to gender. In a large prospective community cohort of youth followed from late childhood to late adolescence, girls were more likely to have all types of anxiety than boys (Van Oort et al. 2009). This is consistent with previous findings indicating that GAD is more commonly reported in girls, and the gender difference is present by late childhood. Despite the gender disparity, boys and girls with GAD appear to have similar numbers and types of symptoms (Masi et al. 2004). Relation to age. Due to the gradual onset of chronic worry, it can sometimes be difficult to determine the exact age when symptoms develop. However, available evidence suggests GAD has an average age of onset of 8.5 years (Masi et al. 2004) and symptoms may increase in middle adolescence (Van Oort et al. 2009). It can be diagnosed as early as symptoms of worry can be verbalized by children or inferred with confidence by parents. Though core ASD symptoms may improve as youth with ASD enter adolescence (Seltzer et al. 2004), some individuals with ASD experience greater risk for the development of psychopathology at this time. Compared to their typically
General Anxiety
developing peers, increased rates of psychopathology, including anxiety and depression, have been reported by adolescents with ASD, as well as their teachers and parents (Hurtig et al. 2009). It may be that increased awareness of their own challenges and the complex social milieu of adolescence exacerbate anxiety in ASD (White et al. 2009). Relation to cognitive functioning and verbal ability. Although anxiety symptoms are present among individuals with ASD who have a range of cognitive functioning, the type of anxiety may vary depending on cognitive and verbal ability. For example, in higher functioning youth, anxieties can be better articulated and may consist of persistent worrying about performance or that something bad might happen; in youth with less verbal capacity, anxiety may manifest more behaviorally, such as reluctance to change a routine in order to avoid a novel situation. Evidence does suggest that anxiety is more common in higher functioning youth, such as those with Asperger syndrome (e.g., Gadow et al. 2005); however, such findings may reflect better ability to verbally report and express psychiatric symptoms. Relation to other problems. Pediatric anxiety disorders in typically developing youth predict other childhood sequelae such as substance abuse problems, suicide attempts, and hospitalization (e.g., Kendall et al. 2004). Anxiety disorders have also been associated with significant functional impairment, behavioral problems, and heightened symptomatology in youth with ASD. For example, higher parent-rated anxiety in youth with ASD was associated with greater impairment in social responsiveness (Sukhodolsky et al. 2007) and social skills deficits (Bellini 2004). Several large studies of children with ASD have found strong linkages between high anxiety and increased severity of ASD symptoms such as repetitive behaviors (e.g., Sukhodolsky et al. 2007), sensory symptoms (Ben-Sasson et al. 2008), and total ASD symptoms even when controlling for intellectual impairment, social maladjustment, and degree of speech impairment in a causal model (Kelly et al. 2008). Heightened fears and anxieties in youth with ASD have also been
General Anxiety
associated with more externalizing behavioral problems, including conduct, impulsivity, and hyperactivity (Evans et al. 2005). In another study of the pediatric ASD population, high levels of parent-rated anxiety were strongly associated with more loose associations in a performancebased assessment of formal thought disorder (Solomon et al. 2008). Pathophysiology. In terms of neuropathology, research indicates that the amygdala plays a primary function in the development and expression of anxiety, given its role in processing emotional cues (e.g., Phelps and LeDoux 2005), particularly fearful stimuli. Youth with general anxiety tend to exhibit increased activation in neural fear circuitry, including the amygdala, in response to fearful stimuli (e.g., McClure et al. 2007). Interestingly, abnormalities of the amygdala have also been implicated in the development and maintenance of social impairments (e.g., Baron-Cohen et al. 2000) and abnormal fears and high rates of anxiety in ASD (Amaral and Corbett 2003). In terms of neurobiological vulnerability, the serotonin transporter gene has been implicated in a number of anxiety disorders as well as in ASD.
Evaluation and Differential Diagnosis Evaluation. As summarized by Keeton et al. (2009), three primary aspects of anxiety are evaluated during diagnostic assessment: anxious behaviors, thoughts, and physical symptoms (Keeton et al. 2009). In terms of behavior, clinicians observe and probe for avoidant behavior and its functional impact on daily activities. It is also important to identify thought patterns related to the child’s anxiety. Youth with general anxiety may report more worries, ask more questions (e.g., reassurance seeking), and have more thoughts reflecting concerns about performance. Older children and adolescence may be able to articulate their thoughts and worries, whereas age-appropriate approaches (e.g., use of cartoons/pictures, thought bubbles) may be required to facilitate description of thoughts and feelings in younger children. Physical symptoms, including headaches, gastrointestinal problems, dizziness,
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and restlessness, are common among youth with anxiety disorders. It can be helpful for a physician to rule out medical problems before assessing for an anxiety disorder. During the assessment for anxiety, clinicians should review ways in which physical symptoms may interfere with daily functioning, such as sleep, appetite, and school performance. A thorough clinical evaluation would include assessment of other psychiatric concerns as well as family mental health history, and discussion (and perhaps observation) of the child’s functioning in different contexts (e.g., school, social, and home). Anxiety is commonly assessed through clinical interview and rating scales from parents/ caregivers, children, and teachers, following a multi-informant, multimethod approach. Semistructured diagnostic interviews, such as the Kiddie Schedule for Affective Disorders and Schizophrenia (KSADS; Ambrosini 2000) and the Anxiety Disorder Interview Schedule (ADIS; Silverman and Albano 1996), have parent and child versions to allow for consensus scoring. Rating scales, such as the Multidimensional Anxiety Scale for Children (MASC), Screen for Child Anxiety Related Emotional Disorders (SCARED), the Revised Child Anxiety and Depression Scale (RCADS), and the Pediatric Anxiety Rating Scale (PARS), also have parent and child versions and correspond to DSM-IV criteria for anxiety disorders. Because few measures to assess anxiety have been developed and validated for use with individuals with ASD (one exception: the parentreport Childhood Anxiety Sensitivity Index; CASI), clinicians and researchers tend to utilize the same measures as those used in the typical population. Despite the utility of these measures in documenting the presence of anxiety in youth with ASD, it is important to note that symptoms of anxiety may manifest differently in youth with ASD; moreover, dependence on child and parent report to evaluate anxiety can be problematic in ASD. Individuals with ASD are often characterized by diminished ability to think abstractly or communicate effectively through speech or facial expression and often lack awareness of internal states or motivation to report symptoms (Lainhart
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and Folstein 1994). Research has shown that children with ASD provide less coherent representations of emotional experiences than their typical peers (Losh and Capps 2006). Children may not verbally express their feelings and worries to parents, and internalizing symptoms are not always easily observed by caretakers (Kuusikko et al. 2008). When evaluating possible anxiety symptoms in youth with ASD, it is important that a skilled clinician, familiar with both ASD and general anxiety, interpret child report, parent report, and observed behaviors with these considerations in mind. Using a modified version of the Kiddie Schedule for Affective Disorders and Schizophrenia (Geller et al. 1996), Leyfer and colleagues developed and piloted the Autism Comorbidity Interview-Present and Lifetime Version (ACI-PL) (Leyfer et al. 2006). Based on the ACI-PL, 44% of children in the study had specific phobia, which is much higher than rates found in typically developing children and adolescents. Interestingly, generalized anxiety disorder occurred at low rates in the ASD group (2%; Leyfer et al. 2006). Higher rates of anxiety disorders have been reported in other studies (e.g., 84% using the Diagnostic Interview Schedule for Children; Muris et al. 1998), suggesting that different assessment approaches may yield variable rates. Differential diagnosis. General anxiety rarely occurs in isolation; in a sample of 157 children and adolescents, another psychological disorder was present in 93% of the patients (Masi et al. 2004). GAD can have a similar clinical presentation as other anxiety disorders, including panic disorder, social phobia, separation anxiety disorder (SAD), and obsessive-compulsive disorder (OCD). Some symptoms also overlap with depression (as well as the basic pattern of ruminative thought), and the two disorders are highly comorbid in many samples. Symptoms of anxiety can also appear similar to symptoms and behaviors associated with autism spectrum disorders (e.g., perseverative thought and speech). Other anxiety disorders: In both typical and ASD populations, it is important to consider how symptoms of general anxiety might overlap with other anxiety disorders. Masi et al. (2004)
General Anxiety
reported that comorbid anxiety disorders were present in 75% of the youth in their sample of youth with GAD (Masi et al. 2004). It can be difficult to make the distinction between panic disorder and GAD, especially when individuals with general anxiety experience acute situational or anticipatory anxiety (i.e., heightened anxiety in certain situations); this can be particularly challenging in youth with ASD, who reportedly have more fears related to “specific situations” (Evans et al. 2005). One distinguishing factor is that panic attacks do not generally occur with preceding generalized anxiety (Rickels and Rynn 2001). General anxiety commonly overlaps with social phobia as well. Individuals with social phobia exhibit phobic avoidant behavior that is specific to a social situation, whereas someone diagnosed with GAD may have “free-floating” anxiety (i.e., not restricted to particular circumstances) that contributes to feelings of apprehension, causing a person to be shy or uncomfortable in public (Rickels and Rynn 2001). Among youth with GAD, SAD has a high rate of overlap (e.g., 31.8%; Masi et al. 2004) and tends to precede the onset of GAD. Youth who have both SAD and GAD tend to be younger, report more physical symptoms, and have a higher incidence of panic disorder than youth with GAD alone. It can also be difficult to discriminate between OCD and general anxiety, and the two disorders frequently co-occur (see Comer et al. 2004 for review). OCD is characterized by excessive recurrent thoughts (i.e., obsessions) and sometimes accompanied by repetitive and purposeful behavioral attempts to reduce the anxiety (i.e., compulsions; DSM-IV-TR). Such symptoms can be easily confused with GAD when it is unclear whether a child’s recurrent thoughts reflect true, irrational obsessions or more general worrying. Depression: Anxiety and depression are highly comorbid, and depressive disorders may occur in over half of typical youth with GAD (Masi et al. 2004). The presence of depressive symptoms may cause increased interference with daily activities and more functional impairment and predict worse outcome in individuals with GAD. Evidence also suggests that anxiety disorders may precede and be a risk factor for the
General Anxiety
development of depression later in life (Beesdo et al. 2010). ASD: Symptoms of anxiety and ASD may have similar overt manifestations in some cases, making it difficult to interpret behaviors and determine whether they reflect symptoms of anxiety or ASD. For example, avoidance of unfamiliar situations is a commonly observed feature of ASD and general anxiety even though underlying symptom composition and motivating factors may differ (e.g., lack of interest vs. anxious apprehension). As a second example, many individuals with ASD are sensitive to sensory stimuli such as loud noises; similarly, individuals with general anxiety may have a heightened “startle response” or reaction to sudden loud sounds. Third, persistent, repetitive thought (and associated speech) characterizes both ASD and GAD for many individuals: in the former case, often taking the form of perseverative “special” interests, and in the latter case, worry and catastrophizing. Although the affective content often differs, the inability to shift attention away from specific thoughts is common to both ASD and GAD (Wood and Gadow 2010). It is important to consider the possibility that anxiety symptoms are present and are not accounted for by symptoms expected to occur as part of an ASD. “Diagnostic overshadowing” of psychiatric problems by developmental disabilities (Davis et al. 2008; Reiss 1982) may tend to obscure anxiety disorders in youth with ASD such that clinicians may conceptualize anxiety symptoms as falling under the umbrella of ASD criteria when in fact they represent a coexisting anxiety disorder. In recent years, mounting evidence has suggested that many individuals with ASD do have a coexisting anxiety disorder (e.g., Leyfer et al. 2006); however, more work needs to be done to develop accurate ways of assessing for anxiety in this population.
Treatment Treatment for general anxiety in children should ideally involve parents as well as the child. Depending on the individual characteristics of the child and the treatments available to families,
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children with general anxiety may benefit from behavioral intervention, medication, or a combination of both. Behavioral treatments (CBT). There is an abundance of evidence indicating that cognitivebehavioral therapy (CBT) is effective (e.g., Walkup et al. 2008); it is considered the treatment of choice for pediatric anxiety. Numerous randomized controlled trials conducted with children have demonstrated that CBT is efficacious and significantly decreases remission rates (e.g., Cartwright-Hatton et al. 2004). Manualized CBT programs, such as the Coping Cat program (Kendall 2000), have been used extensively with anxious youth. The CBT approach assumes that pathological anxiety reflects the interaction between cognitive distortions, somatic or physiological arousal, and avoidance of fearful stimuli. As such, therapists help children identify beliefs and thought patterns that may be irrational and unrealistic and replace them with rational or realistic perspectives (i.e., cognitive restructuring). Other components of CBT usually include psychoeducation with children and parents, problem solving, relaxation training and techniques to manage physiological arousal, modeling, and imaginary or in vivo exposure to increasingly anxiety-provoking stimuli. Intervention should also include strategies to maintain treatment gains and prevent relapse. Cognitive-behavioral therapy must be tailored to the developmental level of the child, including cognitive capacity (Sauter et al. 2009). This is especially relevant when treating symptoms of anxiety in youth with ASD. Recent evidence suggests modified CBT can effectively target symptoms of anxiety in children with ASD, particularly when a parent component is included (Chalfant et al. 2007; Reaven 2009; White et al. 2009; Wood et al. 2009). For example, Wood’s manualized CBT intervention for youth with comorbid ASD and anxiety (Wood et al. 2009) incorporates parents into each of the weekly treatment sessions; parents then implement strategies at home to facilitate generalization. Symptoms of anxiety are addressed using core components of CBT, such as hierarchical exposure therapy techniques. However, to address developmental level, social
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challenges, and other unique characteristics associated with having an ASD, modifications are made to the intervention. For instance, therapeutic concepts are taught using multimodal stimuli (e.g., discussion scaffolded by drawing or writing), and concrete examples are provided rather than abstract descriptions of symptoms and cognitive processes. Children’s special interests may be used as metaphors to maintain enthusiasm and motivation. In Wood’s manualized intervention, youth and parents are also taught friendship skills, which are practiced at school, in the community (e.g., playgrounds), and on playdates and sleepovers. In addition to the amelioration of anxiety symptoms, improvement in parent-reported core autism symptoms has been reported in children receiving CBT for anxiety compared to children in a waitlist group (Wood et al. 2009). This suggests identification and appropriate treatment of associated symptoms of anxiety can impact core symptoms of autism, thereby improving global functioning. Pharmacological treatments. Selective serotonin reuptake inhibitors (SSRIs) have been established as the most effective pharmacological approach for children with general anxiety. For example, in a randomized, double-blind comparison of an SSRI (fluvoxamine) and placebo in youth between the ages of 6–17, the SSRI was significantly more effective in reducing anxiety symptoms (Walkup et al. 2001). Nevertheless, there is limited information on the long-term safety of medication use in youth with anxiety, and this remains a continued focus of research. Available evidence does point toward the efficacy of both CBT and treatment with SSRI, with recent findings suggesting that combination of the two approaches provides maximum benefit (Walkup et al. 2008). Although pharmacological treatments have not demonstrated consistent efficacy in treating core symptoms of ASD, it is possible that medications, including SSRIs, can ameliorate associated psychiatric symptoms (e.g., Kolevzon et al. 2006). The appropriate use of medications that help regulate emotion in ASD might theoretically facilitate a child’s capacity to benefit from educational
General Anxiety
and behavioral modification interventions. However, clinical trials need to be conducted to determine whether pharmacological approaches effectively target anxiety in this population.
See Also ▶ Cognitive Behavioral Therapy (CBT) ▶ Separation Anxiety Disorder
References and Reading Achenbach, T. M., Howell, C. T., McConaughty, S. H., & Stranger, C. (1998). Six-year predictors of problems in a national sample: IV. Young adult signs of disturbance. Journal of the American Academy of Child and Adolescent Psychiatry, 37, 718–727. Amaral, D. G., & Corbett, B. A. (2003). The amygdala, autism and anxiety. Novartis Foundation Symposium, 251, 177–187. Ambrosini, P. J. (2000). Historical development and present status of the schedule for affective disorders and schizophrenia for school-age children (K-SADS). Journal of the American Academy of Child and Adolescent Psychiatry, 39, 49–58. American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders (4th ed.). Washington, DC: Author. Text Revision. Baron-Cohen, S., Ring, H. A., Bullmore, E. T., Wheelwright, S., Ashwin, C., & Williams, S. C. R. (2000). The amygdala theory of autism. Neuroscience and Biobehavioral Reviews, 24, 355–364. Beesdo, K., Pine, D. S., Lieb, R., & Wittchen, H. U. (2010). Incidence and risk patterns of anxiety and depressive disorders and categorization of generalized anxiety disorder. Archives of General Psychiatry, 67, 47–57. Bellini, S. (2004). Social skills deficits and anxiety in highfunctioning adolescents with autism spectrum disorders. Focus on Autism and Other Developmental Disabilities, 19, 78–86. Ben Shalom, D., Mostofsky, S. H., Hazlett, R. L., Goldberg, M. C., Landa, R. J., Faran, Y., et al. (2006). Normal physiological emotions but differences in expression of conscious feelings in children with high-functioning autism. Journal of Autism and Developmental Disorders, 36, 395–400. Bolte, S., Knecht, S., & Poustka, F. (2007). A case-control study of personality style and psychopathology in parents of subjects with autism. Journal of Autism and Developmental Disorders, 37, 243–250. Cartwright-Hatton, S., Roberts, C., Chitsabesan, P., Fothergill, C., & Harrington, R. (2004). Systematic review of the efficacy of cognitive behaviour therapies
General Anxiety for childhood and adolescent anxiety disorders. The British Journal of Clinical Psychology, 43, 421–436. Chalfant, A. M., Rapee, R., & Carroll, L. (2007). Treating anxiety disorders in children with high functioning autism spectrum disorders: A controlled trial. Journal of Autism and Developmental Disabilities, 37, 1842–1857. Comer, J. S., Kendall, P. C., Franklin, M. E., Hudson, J. L., & Pimental, S. S. (2004). Obsessing/worrying about the overlap between obsessive-compulsive disorder and generalized anxiety disorder in youth. Clinical Psychology Review, 24, 663–683. Davis, E., Saeed, S. A., & Antonacci, D. J. (2008). Anxiety disorders in persons with developmental disabilities: Empirically informed diagnosis and treatment. Reviews literature on anxiety disorders in DD population with practical take-home messages for the clinician. The Psychiatric Quarterly, 79, 249–263. de Beurs, E., Beekman, A. T., Deeg, G. J., Van Dyck, R., & van Tiurg, W. (2000). Predictors of change in anxiety symptoms of older persons: Results from the Longitudinal Study Amsterdam. Psychological Medicine, 30, 515–527. Evans, D. W., Canavera, K., Kleinpeter, F. L., Maccubbin, E., & Taga, K. (2005). The fears, phobias, and anxieties of children with autism spectrum disorder and down syndrome: comparisons with developmentally and chronologically age matched children. Child Psychiatry and Human Development, 36(1), 3–27. Ferdinand, R. F., & Verhulst, F. C. (1995). Psychopathology from adolescence into young adulthood: An 8-year follow-up study. The American Journal of Psychiatry, 152, 1586–1594. Gadow, K. D., DeVincent, C. J., Pomeroy, J., & Azizian, A. (2005). Comparison of DSM-IV symptoms in elementary school-age children with PDD versus clinic and community samples. Autism, 9, 392–415. Geller, B., Williams, M., Zimerman, B., & Frazier, J. (1996). Washington University in St. Louis Kiddie Schedule for Affective Disorders and Schizophrenia (WASH-U-KSADS). St. Louis: Washington University. Ghaziuddin, M. (2002). Asperger syndrome: Associated psychiatric and medical conditions. Focus on Autism and Other Developmental Disabilities, 17, 138–144. Hettema, J. M., Neale, M. C., & Kendler, K. S. (2001). A review and meta-analysis of the genetic epidemiology of anxiety disorders. The American Journal of Psychiatry, 158, 1568–1578. Hill, E., Berthoz, S., & Frith, U. (2004). Brief report: Cognitive processing of own emotions in individuals with autistic spectrum disorder and in their relatives. Journal of Autism and Developmental Disorders, 34, 229–235. Hurtig, T., Kuusikko, S., Mattila, M. L., Haapsamo, H., Ebeling, H., & Jussila, K. (2009). Multi-informant reports of psychiatric symptoms among highfunctioning adolescents with Asperger syndrome or autism. Autism, 13, 583–598.
2197 Keeton, C. P., Kolos, A. C., & Walkup, J. T. (2009). Pediatric generalized anxiety disorder. Paediatric Drugs, 11, 171–183. Kelly, A. B., Garnett, M. S., Atwood, T., & Peterson, C. (2008). Autism spectrum symptomatology in children: The impact of family and peer relationships. Journal of Abnormal Child Psychology, 36, 1069–1081. Kendall, P. C. (1994). Treating anxiety disorders in children: Results of a randomized clinical trial. Journal of Consulting and Clinical Psychology, 62, 100–110. Kendall, P. C. (2000). Cognitive-behavioral therapy for anxious children: Therapist manual (2nd ed.). Ardmore: Workbook Publishing. Kendall, P. C., Safford, S., Flannery-Schroeder, E., & Webb, A. (2004). Child anxiety treatment: Outcomes in adolescence and impact on substance use and depression at 7.4-year follow-up. Journal of Consulting and Clinical Psychology, 72, 276–287. Kessler, R. C., McGonagle, K. A., Zhao, S., Nelson, C. B., Hughes, M., Eshleman, S., et al. (1994). Lifetime and 12-month prevalence of DSM-III psychiatric disorders in the United States. Results from the National Comorbidity Study. Archives of General Psychiatry, 51, 8–19. Kolevzon, A., Mathewson, K. A., & Hollander, E. (2006). Selective serotonin reuptake inhibitors in autism: A review of efficacy and tolerability. The Journal of Clinical Psychiatry, 67, 407–414. Kuusikko, S., Pollock-Wurman, R., Jussila, K., Carter, A. S., Mattila, M. L., Ebeling, H., et al. (2008). Social anxiety in high-functioning children and adolescents with autism and Asperger syndrome. Journal of Autism and Developmental Disorders, 38, 1697–1709. Lainhart, J. E., & Folstein, S. E. (1994). Affective disorders in people with autism: A review of published cases. Journal of Autism and Developmental Disorders, 24, 587–601. Layne, A. E., Bernat, D. H., Victor, A. M., & Bernstein, G. A. (2009). Generalized anxiety disorder in a nonclinical sample of children: Symptom presentation and predictors of impairment. Journal of Anxiety Disorders, 23, 283–289. Leyfer, O. T., Folstein, S. E., Bacalman, S., Davis, N. O., Dinh, E., Morgan, J., Tager-Flusberg, H., & Lainhart, J. E. (2006). Comorbid psychiatric disorders in children with autism: Interview development and rates of disorders. Journal of Autism and Developmental Disorders, 36, 849–861. Losh, M., & Capps, L. (2006). Understanding of emotional experience in autism: Insights from the personal accounts of high-functioning children with autism. Developmental Psychology, 42, 809–818. Masi, G., Mucci, M., Favilla, L., Brovedani, P., Millepiedi, S., & Perugi, G. (2003). Temperament in adolescents with anxiety and depressive disorders and in their families. Child Psychiatry and Human Development, 33, 245–259.
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2198 Masi, G., Millepiedi, S., Mucci, M., Poli, P., Bertini, N., & Milantoni, L. (2004). Generalized anxiety disorder in referred children and adolescents. Journal of the American Academy of Child and Adolescent Psychiatry, 43, 752–760. Mazefsky, C. A., Conner, C. M., & Oswald, D. P. (2010). Association between depression and anxiety in highfunctioning children with autism spectrum disorders and maternal mood symptoms. Autism Research, 3, 120–127. McClure, E. B., Monk, C. S., Nelson, E. E., Parrish, J. M., Adler, A., Blair, R. J., et al. (2007). Abnormal attention modulation of fear circuit function in pediatric generalized anxiety disorder. Archives of General Psychiatry, 64, 97–106. Micco, J. A., Henin, A., Mick, E., Kim, S., Hopkins, C. A., Biederman, J., et al. (2009). Anxiety and depressive disorders in offspring at high risk for anxiety: A metaanalysis. Journal of Anxiety Disorders, 23, 1158–1164. Muris, P., Steerneman, P., Merckelbach, H., Holdrinet, I., & Meesters, C. (1998). Comorbid anxiety symptoms in children with pervasive developmental disorders. Journal of Anxiety Disorders, 12, 397–393. Phelps, E. A., & LeDoux, J. E. (2005). Contributions of the amygdala to emotion processing: From animal models to human behavior. Neuron, 48, 175–187. Piven, J., & Palmer, P. (1999). Psychiatric disorder and the broad autism phenotype: Evidence from a family study of multiple incidence autism families. American Journal of Psychiatry, 156, 557–563. Reaven, J. (2009). Children with high-functioning autism spectrum disorders and co-occurring anxiety symptoms: Implications for assessment and treatment. Journal for Specialists in Pediatric Nursing, 14, 192–199. Reiss, S. (1982). Psychopathology and mental retardation: A survey of developmental disabilities mental health problems. Mental Retardation, 20, 128–132. Rickels, K., & Rynn, M. (2001). Overview and clinical presentation of generalized anxiety disorder. The Psychiatric Clinics of North America, 24, 1–17. Sauter, F. M., Heyne, D., & Westenberg, P. (2009). Cognitive behavior therapy for anxious adolescents: Developmental influences on treatment design and delivery. Clinical Child and Family Psychology Review, 12, 310–335. Seltzer, M. M., Shattuck, P., Abbeduto, L., & Greenberg, J. S. (2004). Trajectory of development in adolescents and adults with autism. Mental Retardation and Developmental Disabilities Research, 10, 234–247. Shaffer, D., Fisher, P., Dulcan, M. K., Davies, M., Piacentini, J., Schwab-Stone, et al. (1996). The NIMH Diagnostic Interview Schedule for Children Version 2.3 (DISC-2.3): Description, acceptability, prevalence rates, and performance on the MECA study. Methods for the Epidemiology of Child and Adolescent Disorders Study. Journal of the American Academy of Child and Adolescent Psychiatry, 35, 865–877. Silverman, W. K., & Albano, A. M. (1996). The anxiety disorders interview schedule for DSM-IV – Child and parent versions. San Antonio: Graywind.
General Anxiety Skokauskas, N., & Gallagher, L. (2010). Psychosis, affective disorders and anxiety in autistic spectrum disorder: Prevalence and nosological considerations. Psychopathology, 43, 8–16. Solomon, M., Ozonoff, S., Carter, C., & Caplan, R. (2008). Formal thought disorder and the autism spectrum: Relationship with symptoms, executive control, and anxiety. Journal of Autism and Developmental Disorders, 38, 1474–1484. Sukhodolsky, D. G., Scahill, L., Gadow, K. D., Arnold, L. E., Aman, M. G., McDougle, C. J., et al. (2007). Parent-rated anxiety symptoms in children with pervasive developmental disorders: Frequency and association with core autism symptoms and cognitive functioning. Journal of Abnormal Child Psychology, 36, 117–128. Van Oort, F. V., Greaves-Lord, K., Verhulst, F. C., Ormel, J., & Huizink, A. C. (2009). The developmental course of anxiety symptoms during adolescence: The TRAILS study. Journal of Child Psychology and Psychiatry, 50, 1209–1217. Walkup, J. T., Labellarte, M. J., Riddle, M. A., Pine, D. S., Greenhill, L., Klein, R., et al. (2001). Fluvoxamine for the treatment of anxiety disorders in children and adolescents. The New England Journal of Medicine, 344, 1279–1285. Walkup, J. T., Albano, A. M., Piacentini, J., Birmaher, B., Compton, S. N., Sherrill, J. T., et al. (2008). Cognitive behavioral therapy, sertraline, or a combination in childhood anxiety. The New England Journal of Medicine, 359, 2753–2766. Whitaker, A., Johnson, J., Shaffer, D., Rapoport, J. L., Kalikow, K., Walsh, B. T., et al. (1990). Uncommon troubles in young people: Prevalence estimates of selected psychiatric disorders in a nonreferred adolescent population. Archives of General Psychiatry, 47, 487–496. White, S. W., Oswald, D., Ollendick, T., & Scahill, L. (2009). Anxiety in children and adolescents with autism spectrum disorders. Clinical Psychology Review, 29, 216–229. Wilcox, J. A., Tsuang, M. T., Schnurr, T., & BaidaFragoso, N. (2003). Case-control family study of lesser variant traits in autism. Biological Psychiatry, 47, 171–177. Wood, W. J. J., & Gadow, K. D. (2010). Exploring the nature and function of anxiety in youth with autism spectrum disorders. Clinical Psychology: Research and Practice, 17, 281–292. Wood, J. J., & McLeod, B. D. (2007). Child anxiety disorders: A family-based treatment manual for practitioners. New York: Norton. Wood, J. J., Drahota, A., Sze, K., Har, K., Chiu, A., & Langer, D. A. (2009). Cognitive behavioral therapy for anxiety in children with autism spectrum disorders: A randomized, controlled trial. Journal of Child Psychology and Psychiatry, 50(3), 224–234.
General Case Programming (GCP)
General Case Analysis ▶ General Case Programming (GCP)
General Case Model ▶ General Case Programming (GCP)
General Case Programming (GCP) Mark Groskreutz Special Education and Reading Department, The Center of Excellence on Autism Spectrum Disorders, Southern Connecticut State University, New Haven, CT, USA
Synonyms General case analysis; General case model; Training the general case
Definition General case programming is an instructional process with the ultimate goal of increasing the likelihood that a learner will generalize a skill set (i.e., target behavior) learned during structured teaching situations to relevant real-world settings and situations in which the target behavior should be demonstrated. General case programming can be broken into three phases: design, implementation, and assessment. The design phase is key to general case programming and begins with analysis of the target skill to be taught. The instructor must identify the essential behaviors and all variations of those behaviors that are necessary for successful performance of that skill in relevant situations. The instructor must also consider and identify the full range of stimuli that may be encountered when the
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learner engages in the target skill in real-world settings. After analysis of the relevant behaviors and stimuli, the instructor can select teaching materials that encompass as many of the behavioral and stimulus variations as possible. To reduce the likelihood of overgeneralization (engaging in the target behavior in inappropriate situations), the instructor must also identify materials that share some, but not all, characteristics with the target skill materials and ensure the learner does not engage in the target skill inappropriately. During the implementation phase, the instructor exposes the learner to the selected materials and teaches the relevant behaviors using teaching strategies appropriate for the learner (see Horner et al. 1982, 1986; Horner and Albin 1988 for recommendations on presentation order of examples). Implementation continues until the learner demonstrates success with at least one set of materials. In the assessment phase, the learner is exposed to untaught examples to test for generalization. If necessary, additional sets of materials can be used during subsequent teaching sessions until the learner shows generalization or is taught the full variety of relevant responses including the full variety of materials. In sum, general case programming is an instructional process that is designed to increase the efficiency of teaching and subsequent likelihood of generalization of new skills. General case programming is based on thorough analysis of target skills sets and selection of materials that include the full variations in behaviors and materials likely to be encountered in real-world situations.
See Also ▶ Generalization and Maintenance
References and Reading Horner, R. H., & Albin, R. W. (1988). Research on generalcase procedures for learners with severe disabilities. Education and Treatment of Children, 11, 375–388. Horner, R. H., Sprague, J. R., & Wilcox, B. (1982). Constructing general case programs for community
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2200 activities. In B. Wilcox & G. T. Bellamy (Eds.), Design of high school programs for severely handicapped students (pp. 61–98). Baltimore: Paul H. Brookes. Horner, R. H., McDonnell, J. J., & Bellamy, G. T. (1986). Teaching generalized behaviors: General case instruction in simulation and community settings. In R. H. Horner, L. H. Meyer, & H. D. B. Fredericks (Eds.), Education of learners with severe handicaps: Exemplary service strategies (pp. 289–315). Baltimore: Paul H. Brookes.
General Case Teaching, General Case Instruction ▶ Multiple Exemplar Training
General Health Questionnaire (GHQ-12) ▶ Autism Family Experience Questionnaire (AFEQ), The
General Health Questionnaire-12 (GHQ-12) ▶ Autism Family Experience Questionnaire (AFEQ)
Generalization and Maintenance Brooke Ingersoll1 and Allison Wainer2 1 Department of Psychology, Michigan State University, East Lansing, MI, USA 2 Department of Psychiatry, Rush Medical College, Chicago, IL, USA
Definition The term generalization, defined most broadly (Stokes and Baer 1977), is used to describe
General Case Teaching, General Case Instruction
when skills learned in a training environment transfer to the natural environment after training has ended. Generalization, in its more narrow definition, is a behavioral term that is used to describe the spread of effect of a training procedure to untrained stimuli and responses, as well as the durability of treatment effects over time. Generalization includes three specific forms: Stimulus generalization, response generalization, and maintenance. Stimulus generalization involves the occurrence of a behavior in response to another similar stimulus. For example, a child who learns to say “ball” (behavior) in response to a picture of a ball (trained stimulus) shows stimulus generalization if he says “ball” in response to an actual ball (new stimulus). Response generalization involves the spread of an effect of a trained response to other similar responses. For example, a child who is taught to greet another person by saying “Hi,” also begins to greet people by waving. Maintenance involves the continued use of a trained behavior over time, after the direct intervention period has ended. An important outcome of any intervention is that its effects generalize to similar stimuli outside of the treatment setting. Without generalization, the ability to use a given skill may be confined to a highly specific environment or stimulus, and thus is not particularly valuable for use in real-world contexts. For individuals with ASD, generalizing skills to new settings, new people, or in response to new events can be especially challenging. In addition, it is important that the effects of intervention lead to generalized responding, such that training in one behavior leads to the development of a variety of related behaviors without direct training; otherwise, training is inefficient and leads to an inflexible pattern of behavioral responses. Further, it is critical that gains made in treatment be maintained over time, after the intervention is complete. Individuals with ASD may show deficits in skill maintenance, in part, because the types and patterns of reinforcement in the real world are often divergent from those employed in treatment settings. As such, for individuals with ASD, newly acquired skills may extinguish rather quickly in extra-therapy contexts.
Generalization and Maintenance
It has been suggested that without targeted intervention, generalization and maintenance of skill does not occur automatically for individuals with ASD (Koegel and Rincover 1977). Some have suggested that these difficulties may be related to an insistence of sameness, stimulus over-selectivity, and/or lack of motivation in individuals with ASD (Arnold-Saritepe et al. 2009). While the etiology of these deficits remain unclear, it is obvious that without the ability to translate knowledge to various contexts, an individual will continue to experience impairment in important areas of functioning. As such, it is critical that intervention programs incorporate specific components to help encourage behavioral change outside of the direct therapy environment.
Historical Background Difficulty with treatment generalization and maintenance for children with autism was identified as early as 1973 by Lovaas and colleagues, who noted that children in their study of intensive behavioral intervention who returned to an institutional setting lost the majority of their treatment gains; however, children who returned to live with their parents who had been trained to carry out the intervention at home continued to make gains. Historically, generalization was conceptualized as a passive process that was a natural consequence of the inability to effectively utilize stimulus discrimination. It was thought that if generalization did not occur, an individual had been successfully taught to discriminate among specific stimuli and responses. Stokes and Baer (1977) were some of the first to argue that generalization is an active process and that it should be directly targeted as a teaching outcome. In this way, “generalization may be claimed when no extratraining manipulations are needed for extratraining changes; or may be claimed when some extra manipulations are necessary, but their cost or extent is clearly less than that of the direct intervention” (Stokes and Baer 1977). In sum, an intervention can be considered effective when relevant targeted behaviors are observed in untrained environments (e.g., across
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subjects, settings, people, behaviors, and/or time) without the same schedule of prompting and reinforcement utilized in training conditions (Stokes and Baer 1977). Building off this idea of generalization as an active process, Stokes and Baer (1977) suggested that treatments should directly target generalization and maintenance by incorporating “generalization-promotion” techniques into intervention protocols. In an initial review of the generalization literature, these authors described several “generalization-promotion” techniques that were being utilized to track and target generalization. These techniques were categorized into nine groups: 1. Train and Hope: These procedures document the presence or absence of generalization but do not explicitly target the generalization process. 2. Sequential Modification: Such procedures involve the implementation of intervention techniques and the probing for generalization. If generalization is not observed, the procedures are systematically altered to determine and then use the techniques that are most effective for promoting generalization. 3. Introduce to Natural Maintaining Contingencies: This process relies on incorporating into the treatment program, circumstances and reinforcement that are natural to the behaviors being taught. In this way, the reinforcement and consequences that are used in treatment are the same, or very similar to, those which are available in real-world contexts. 4. Train Sufficient Exemplars: These procedures involve teaching with several different examples of the same lesson until an individual is able to generalize sufficiently across related conditions and responses. 5. Train Loosely: This process relies on incorporating variety into the teaching conditions, settings, stimuli, and responses in order to mirror real-world situations. 6. Use Indiscriminable Contingencies: This process depends on the use of intermittent reinforcement, such that situations in which reinforcement would be expected are
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undistinguishable from those in which reinforcement would not be expected. In this way, target behaviors should eventually occur across contexts, regardless of whether or not reinforcement is available. 7. Program Common Stimuli: Such procedures involve making the treatment context similar to the generalization context. Thus, the settings and stimuli utilized in training are the same or similar to those available to an individual in real-world contexts. 8. Mediate Generalization: This process relies on the incorporation of a particular stimulus (e.g., an object or a person) that is present in the training conditions and generalization conditions. In this way, access to the mediating stimulus during generalization will prompt the individual to use the behaviors learned in the training condition. 9. Train to Generalize: These procedures involve giving an individual direct instruction to generalize, or reinforcing generalization as if it were a specific behavior.
Current Knowledge Although considerable individual variation exists with regards to the development of generalization and maintenance of skill, there is a significant body of research to indicate that certain factors can be particularly beneficial in supporting these processes. Many of these factors are based on the incorporation of the techniques described by Stokes and Baer (1977) into intervention programs. Currently, the literature indicates that implementing intervention in natural settings, using parents as intervention providers, utilizing peer-mediated intervention, and self-management are especially effective in supporting the development of skill generalization and maintenance. Naturalistic Interventions: Some of the first effective intervention programs developed for individuals with ASD utilized a traditional behavioral approach, discrete trial training, which relies on a highly structured and controlled learning environment. In such programs,
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the therapist and child usually sit face-to-face at a table with the therapist prompting the child for a series of discrete skills presented in successive trials. Child behavior is typically reinforced with food or access to a preferred toy. Discrete trial training has been shown to be effective for teaching a range of skills (e.g., Lovaas et al. 1973). However, results of these studies suggested limited generalization of skill, especially in nontreating settings, with nontreatment therapists, and in the spontaneous use of the new skill. In fact, subsequent research has demonstrated that highly structured intervention environments and artificial reinforcers can limit the generalization and maintenance of newly acquired skill (Lovaas et al. 1973; Spradlin and Siegel 1982). As such, programs utilizing behavioral techniques directly in natural contexts have emerged in order to actively promote generalization during intervention. Naturalistic behavioral interventions use prompting and reinforcement but are implemented in natural settings (e.g., during play interactions or other daily routines) and utilize more natural reinforcement (e.g., praise or the natural consequence of behavior). Examples of such programs include pivotal response training (PRT), incidental teaching, and milieu teaching. A significant amount of research has demonstrated that naturalistic behavioral techniques are effective at increasing verbal and nonverbal communication, play, imitation, and joint attention (Schreibman et al. 2015). Importantly, research has also demonstrated that the use of these types of intervention techniques promote generalization of skill to untrained stimuli, settings, and interaction partners (e.g., Kaiser and Hester 1994). Several studies have compared naturalistic and traditional behavioral approaches for teaching specific language skills (e.g., preposition use, color adjectives) as well as broader language functioning. Across studies, naturalistic behavioral approaches have been found to be superior to traditional behavioral approaches in terms of producing skill generalization (Delprato 2001; Mohammadzaheri et al. 2014). As such, the literature indicates that the implementation of intervention in natural settings with natural reinforcement seems to be an
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especially effective way to increase the generalization and maintenance of skill for individuals with ASD. Parents as Intervention Providers: A variation of implementing intervention in natural contexts is to train parents as intervention providers. By training parents in intervention techniques, children are provided with increased access to intervention in more of their natural settings with their natural interaction partners. A growing body of literature suggests that parents can be successfully trained in a variety of intervention approaches to improve the quality of parent-child interactions, to produce gains in language skills, imitation skills, joint attention/ joint engagement, and appropriate play skills, and to decrease problem behavior in children with ASD (e.g., Bearss et al. 2015; Green et al. 2010; Kasari et al. 2010; Wetherby et al. 2014). Moreover, generalization and maintenance of skill was observed in the majority of these studies; the children were able to utilize variations of newly acquired skill in untrained environments, with untrained interaction partners, and in response to untrained stimuli. These results indicate that using parents as agents of change can lead to better generalization and maintenance of skill for individuals with ASD. Peer-Mediated Intervention: Another way to promote generalization and maintenance in individuals with ASD is to utilize peers in intervention programs. In this way, individuals with ASD can receive training in the classroom, the playground, and during daily routine interactions with sameage individuals. A number of studies have shown that typically developing children can be successfully trained to directly implement, or support the implementation of, naturalistic intervention techniques to elicit changes in behavior from peers with ASD (e.g., Kasari et al. 2012; Kamps et al. 2015). Research has demonstrated that the utilization of multiple peer trainers can help promote generalization of skill, particularly socialcommunication skills, to untrained peers (Pierce and Schreibman 1997). Moreover, additional research has suggested that peer-mediated naturalistic interventions can enhance generalization of skill to untrained environments, untrained
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stimuli, and untrained interaction partners, including siblings (e.g., Belchic and Harris 1994; Chang and Locke 2016). Self-Management: Self-management, in which the individual is taught to monitor his own behavior and to provide self-reinforcement, has also been found to be successful for producing and maintaining improvements in appropriate social interaction and play skills in individuals with autism (Carr et al. 2014). With self-management, the individual’s behavior becomes under their own control, and thus the presence of a treatment provider is not necessary. This helps individuals generalize skills to new environments and maintain behavioral improvements over time in the absence of treatment providers. In order to utilize the techniques described above in an effective and ecologically valid way, one must first consider the individual receiving instruction. Different stimuli, responses, settings, and consequences are going to be relevant and reinforcing for a given individual. In order to implement intervention to promote generalization and maintenance of skill, a thorough understanding of the individual and his or her needs is imperative.
Future Directions Although it has been identified that individuals with ASD experience difficulties with generalization and maintenance and that some teaching techniques are more effective than others for promoting these processes, the research on the application of these techniques continues to be limited. In a 2009 review of the intervention literature, Arnold-Saritepe and colleagues found that more than 40% of entries did not evaluate or discuss generalization and maintenance. Thus, as a field, our understanding of the most effective ways to encourage generalization and maintenance is still somewhat restricted. It is important for researchers and clinicians to continue to utilize, explore, and expand upon the techniques described above in order to actively target generalization and maintenance of skills in individuals with ASD.
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See Also ▶ Parent Training ▶ Peer-Mediated Intervention
References and Reading Arnold-Saritepe, A. M., Phillips, K. J., Mudford, O. C., De Rozario, K. A., & Taylor, S. A. (2009). Generalization and maintenance. In J. L. Matson (Ed.), Applied behavior analysis for children with autism spectrum disorders. New York: Springer. Bearss, K., Johnson, C., Smith, T., Lecavalier, L., Swiezy, N., Aman, M., . . . & Sukhodolsky, D. G. (2015). Effect of parent training vs parent education on behavioral problems in children with autism spectrum disorder: A randomized clinical trial. JAMA, 313, 1524–1533. Belchic, J. K., & Harris, S. L. (1994). The use of multiple peer exemplars to enhance the generalization of play skills to the siblings of children with autism. Child & Family Behavior Therapy, 16, 1–25. Carr, M. E., Moore, D. W., & Anderson, A. (2014). Selfmanagement interventions on students with autism: A meta-analysis of single-subject research. Exceptional Children, 81, 28–44. Chang, Y. C., & Locke, J. (2016). A systematic review of peer-mediated interventions for children with autism spectrum disorder. Research in Autism Spectrum Disorders, 27, 1–10. Delprato, D. J. (2001). Comparisons of discrete-trial and normalized behavioral intervention for young children with autism. Journal of Autism and Developmental Disorders, 31, 315–325. Green, J., Charman, T., McConachie, H., Aldred, C., Slonims, V., Howlin, P., . . . & Barrett, B. (2010). Parent-mediated communication-focused treatment in children with autism (PACT): A randomised controlled trial. The Lancet, 375, 2152–2160. Kaiser, A. P., & Hester, P. P. (1994). Generalized effects of enhanced milieu teaching. Journal of Speech and Hearing Research, 37, 1320–1340. Kamps, D., Thiemann-Bourque, K., Heitzman-Powell, L., Schwartz, I., Rosenberg, N., Mason, R., & Cox, S. (2015). A comprehensive peer network intervention to improve social communication of children with autism spectrum disorders: A randomized trial in kindergarten and first grade. Journal of Autism and Developmental Disorders, 45, 1809–1824. Kasari, C., Gulsrud, A. C., Wong, C., Kwon, S., & Locke, J. (2010). Randomized controlled caregiver mediated joint engagement intervention for toddlers with autism. Journal of Autism and Developmental Disorders, 40, 1045–1056. Kasari, C., Rotheram-Fuller, E., Locke, J., & Gulsrud, A. (2012). Making the connection: Randomized controlled trial of social skills at school for children with autism spectrum disorders. Journal of Child Psychology and Psychiatry, 53, 431–439.
Generalized Anxiety Disorder Koegel, R. L., & Rincover, A. (1977). Research on the difference between generalization and maintenance in extra-therapy responding. Journal of Applied Behavior Analysis, 10, 1–12. Lovaas, O. I., Koegel, R., Simmons, J. Q., & Long, J. S. (1973). Some generalization and follow up measures on autistic children in behavior therapy. Journal of Applied Behavior Analysis, 6, 131–166. Mohammadzaheri, F., Koegel, L. K., Rezaee, M., & Rafiee, S. M. (2014). A randomized clinical trial comparison between pivotal response treatment (PRT) and structured applied behavior analysis (ABA) intervention for children with autism. Journal of Autism and Developmental Disorders, 44, 2769–2777. Pierce, K., & Schreibman, L. (1997). Multiple peer use of pivotal response training to increase social behaviors of classmates with autism: Results from trained and untrained peers. Journal of Applied Behavior Analysis, 30, 157–160. Schreibman, L., Dawson, G., Stahmer, A. C., Landa, R., Rogers, S. J., McGee, G. G., . . . & Halliday, A. (2015). Naturalistic developmental behavioral interventions: Empirically validated treatments for autism spectrum disorder. Journal of Autism and Developmental Disorders, 45, 2411–2428. Spradlin, J. E., & Siegel, G. M. (1982). Language training in natural and clinical environments. Journal of Speech and Hearing Disorders, 47, 2–6. Stokes, T. F., & Baer, D. M. (1977). An implicit technology of generalization. Journal of Applied Behavior Analysis, 10, 349–367. Wetherby, A. M., Guthrie, W., Woods, J., Schatschneider, C., Holland, R. D., Morgan, L., & Lord, C. (2014). Parent-implemented social intervention for toddlers with autism: An RCT. Pediatrics, 134, 1084–1093.
Generalized Anxiety Disorder Ana Figueroa Quintana Child and Adolescent Psychiatry Unit, Hospital Perpetuo Socorro, Las Palmas, Spain
Synonyms GAD
Short Description or Definition Generalized Anxiety Disorder does not go under any other denomination. The DSM-IV-TR includes Overanxious Disorder of Childhood (DSM-III-R) in this category.
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Generalized Anxiety Disorder (GAD) is characterized by the presence of excessive and uncontrollable worries about a number of life events such as weather, illnesses, natural disasters, risky situations, failure, or the future. These worries are accompanied by at least three of the six possible associated symptoms of negative affect/tension: restlessness, feeling keyed up, or on edge; easily fatigued; difficulty concentrating, mind going blank; irritability; muscle tension; and sleep disturbance. Because usually one or more situations provoke these symptoms, the patient tends to avoid them. Avoidance behavior is a cardinal symptom of all anxiety disorders, including GAD (Table 1).
Generalized Anxiety Disorder, Table 1 Diagnostic criteria for DSM-IV generalized anxiety disorder (A) Excessive anxiety and worry (apprehensive expectation), occurring more days than not, for at least 6 months, about a number of events or activities (such as work or school performance) (B) The person finds it difficult to control the worry (C) The anxiety and worry are associated with three (or more) of the following six symptoms (with at least some symptoms present for more days than not, for the past 6 months). Note: Only one item is required in children (1) Restlessness, or feeling keyed up or on edge (2) Being easily fatigued (3) Difficulty concentrating or mind going blank (4) Irritability (5) Muscle tension (6) Sleep disturbance (difficulty falling or staying asleep, or restless unsatisfying sleep) (D) The focus of the anxiety and worry is not confined to features of an Axis I disorder, for example, the anxiety or worry is not about: having a panic attack (as in panic disorder), being embarrassed in public (as in social phobia), being contaminated (as in obsessive-compulsive disorder), being away from home or close relatives (as in separation anxiety disorder), gaining weight (as in anorexia nervosa), having multiple physical complaints (as in somatization disorder), or having a serious illness (as in hypochondriasis), and the anxiety and worry do not occur exclusively during posttraumatic stress disorder (E) The anxiety, worry, or physical symptoms cause clinically significant distress or impairment in social, occupational, or other important areas of functioning (F) The disturbance is not due to the direct physiological effects of a substance (e.g., a drug of abuse, a medication) or a general medical condition (e.g., hyperthyroidism), and does not occur exclusively during a mood disorder, psychotic disorder, or a pervasive developmental disorder
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Due to their multiple and uncontrollable worries, the patient has a constant level of arousal and a sense of impending doom but does not develop panic attacks. Patients with GAD frequently emphasize the negative aspects, have unrealistic expectancies about the future, ask too many questions, feel insecure about their competencies, seek constant reassurance, and respond negatively to ordinary criticism and evaluation (Keeton et al. 2009). Physical symptoms (or somatic complaints) are common in patients with GAD: nausea, abdominal pain, dizziness, headache, tachycardia, sweating, etc. Many patients have previously visited other medical specialties to rule out medical problems. All these symptoms often interfere, sometimes severely, in the patient’s sleep, concentration, and eating habits; and they can alter his/her capacity for normal relationships (with peers and family). To meet diagnostic criteria, symptoms must provoke marked distress and interfere with social, emotional, or educational functioning (American Psychiatric Association [APA] 2000). GAD and Autism Currently, anxiety is not considered a phenomenological characteristic of Autism spectrum disorders (ASD; White et al. 2009). It is logical to think that the social disability associated with ASD could provoke anxiety – especially in higher functioning youth who are conscious of their deficits (White et al. 2009). In children with ASD the rate of anxiety disorders range from 18% (Gadow et al. 2004) to 87% (Muris et al. 1998). deBruin et al. (2006) found that approximately 55% of the sample of ASD had at least one anxiety disorder. Simonoff et al. (2008) reported an overall anxiety disorder diagnosis rate of almost 42%. Gjevik et al. (2011) found that 41% of the 71 children and adolescents with ASD had an anxiety disorder. These studies suggest that anxiety disorders are more frequent in patients with ASD than in the general population. However frequent, these are rarely diagnosed with ASD due to a clinical consensus that they are better explained by the ASD itself (White et al. 2009). Given the substantial symptom overlap and the lack of clarity in differential diagnosis, this topic remains controversial.
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Some of the most frequently reported anxiety disorders and symptoms seen in children with ASD are simple phobias, generalized anxiety disorder, separation anxiety disorder, obsessivecompulsive disorder (OCD), and social phobia. There is some evidence that the prevalence of anxiety may differ across the specific diagnoses. Children with Asperger Syndrome appear most likely to experience anxiety, followed by PDDNOS, and then Autism Disorder. These group differences must be interpreted cautiously given that diagnosis is often related to other factors such as level of cognitive functioning, which may also impact psychiatric comorbidity (White et al. 2009). Nonetheless, identification is difficult due to the patient’s concurrent difficulties with communication, the presence of other behavior problems, the lack of standardized assessments specific to these patients, and the need for greater collateral sources of assessment information (Davis et al. 2008). This explains why there is limited systematic study of the diagnosis and treatment of anxiety disorders in patients with ASD.
Categorization GAD is not classified into categories.
Epidemiology Lifetime prevalence of GAD in the general population ranges from 2% to 15%. GAD occurs in up to 10% of the pediatric population, most of all adolescents. Childhood-onset GAD has an average age of onset of 8.5 years. There is a 2:1 female-to-male preponderance, at all ages. Boys and girls of all ages with GAD have similar number and types of symptoms (Keeton et al. 2009).
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the patient did not meet criteria for another Axis I disorder. The diagnostic criteria for GAD were revised substantially in DSM-III-R (APA 1987) and then again in DSM-IV (APA 1994). Although GAD can start in childhood or adolescence, most frequently GAD begins in early adulthood. A large proportion of patients with GAD cannot report a specific moment of onset. Typically a person starts with subclinical symptoms, and these worsen progressively. There are no consistent differences in the clinical presentation between early onset and late onset GAD. Once it is established, GAD tends to have a chronic and persistent course. Exceptionally patients can have periods of full remission, but usually only have periods of improvement and others of worsening. Fluctuations are common, mainly related to life stressors. If left untreated GAD may persist, and even lead to another psychiatric disorder such as another type of anxiety disorder, a depressive disorder or a substance use disorder, in adolescence or adulthood. Treatment reduces the probability of persistence and of comorbidity, thus improving prognosis. The initial severity of symptoms is a negative prognostic factor. Another prognostic factor is the parents’ reaction to the child’s fears. Overprotective parents usually try to diminish the child’s distress by letting him avoid a stress-provoking situation, but this can, in the long term, increase the child’s fear and avoidance, and prevent the development of coping skills (Keeton et al. 2009). Impairment/Outcome GAD is associated with significant physical and psychological symptoms and impacts health, family relationships, academic performance, and employment.
Clinical Expression and Pathophysiology Natural History, Prognostic Factors, and Outcomes DSM-III was the first to include the category of GAD (APA 1980), and it did so as a residual category. The diagnosis was permitted only if
The clinical features of GAD were already reviewed. Approximately 90% of patients with GAD have at least one other psychiatric disorder at some point of time in their lives (Wittchen et al. 1994).
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The National Comorbidity Survey (NCS) estimated that 65% of people with current GAD had at least one other psychiatric disorder at the time of assessment, mainly another anxiety disorder or mood disorder (Brawman-Mintzer et al. 1993). In children, GAD is often diagnosed with another anxiety disorder (social phobia or separation anxiety disorder), depression or ADHD. In children with GAD, co-occurrence with social phobia or separation anxiety disorder is the rule, rather than the exception. Up to 60% of anxious children have at least two of the three, and 30% have the three of them (Keeton et al. 2009). Pathophysiology The etiology of anxiety is still under study, but biological (including genetic) and psychosocial (environmental) factors are involved. These factors cause anxiety by modulating the following: 1. The neural circuits underlying emotion process 2. Psychological processes 3. Behavioral tendencies, including temperament In relation to genetics, heritability of GAD is around 20–30%. Genes that increase the risk for anxiety disorders, also increase the risk for depressive disorders. Environmental factors, such as the death of a loved one, lack of family attachment, or conflicts with peers, exert the greatest influence in the emergence of anxiety disorders. An anxiety disorder can start with external cues, but also with how the person processes them. Moreover, genetic and environmental factors can interact to cause an anxiety disorder in a child. For example, a child can inherit an anxiety disorder from a parent. On top of that, an anxious parent may also have distinctive parenting practices, and encourage a child to maladaptive patterns of responding to ambiguous situations, such as avoiding them (Keeley and Storch 2009). In relation to neural circuits, fear is regulated mainly by the prefrontal cortex (PFC) and the amygdala. Within the PFC there are two areas involved in anxiety: The orbitofrontal cortex makes a representation of negative and positive reinforcers. The
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anterior cingulated cortex regulates the emotional response. The amygdala registers the emotional significance of environmental stimuli and stores emotional memories. When circuits connecting the PFC and the amygdala are altered, the amygdala is often activated by neutral harmless stimuli. When activated, the person perceives a stimulus as dangerous; and the amygdala starts a cascade reaction, activating other brain regions, leading to the following anxiety symptoms: • The HPA axis, resulting in CNS activation. The hypothalamus secretes corticotrophin releasing factor (CRF) that induces the secretion of ACTH, which induces the secretion of cortisol and adrenaline from the adrenal gland. This causes hyperglycemia and tachycardia, so that the brain and muscles respond to danger. • The dorsomedial nucleus of the vagus nerve and nucleus ambiguous, activating the parasympathetic nervous system. • The parabrachial nuclei that increases respiratory frequency, resulting in dyspnea and hyperventilation. • The locus ceruleus that also releases adrenaline, which raises blood pressure and heart rate, activates sweating, and induces tremor (Revised in Soutullo and Figueroa-Quintana 2010). Among temperamental traits, behavioral inhibition is the most studied. The neurotransmitters most implicated in anxiety are serotonin and noradrenaline. Experts believe that anxiety appears when there is an underactivation of the serotoninergic system and an overactivation of the noradrenergic system. Disruption of the gamma-aminobutyric acid (GABA) system has also been implicated. The role of corticosteroid regulation in anxiety is presently under study.
Evaluation and Differential Diagnosis Diagnostic Evaluation Assessment requires a multi-informant, multimethod approach involving the child, the parents,
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and schoolteachers. The clinician should perform a structured or a semi-structured clinical interview. Several semi-structured diagnostic interviews are available. For children, the Anxiety Disorder Interview Schedule for DSM-IV (ADIS-IV) is designed for youth aged 6–17. It assesses DSM-IV anxiety, mood, externalizing, tic, substance use, and pervasive developmental disorders. The Kiddie Schedule of Affective Disorders and Schizophrenia for School age children, Present and Lifetime version (K-SADS-PL) is used for children 6–18 years old, to assess all Axis I diagnosis, except pervasive developmental disorders. Using these interviews, the clinician should assess the three primary groups of anxiety symptoms: behaviors, thoughts, and physical symptoms. The clinician should explicitly ask about their presence, their frequency and how they interfere with their daily functioning. Anxious children tend to report more physical symptoms, while their parents usually emphasize the avoidant behaviors. The evaluation should also include past psychiatric history, family psychiatric history, and medical history. If possible, it is helpful that the clinician actually sees the patient in an anxietyprovoking natural situation (i.e., school). Rating scales completed by the patient, caregivers, and teachers should never be used as diagnostic instruments. However, they provide valuable information for the diagnosis and the monitoring of symptoms. The clinician can use several scales, none of which are specific for GAD (Keeton et al. 2009). The Multidimensional Anxiety Scale for Children (MASC) is a standardized validated 39-item self-report measure for youth aged 8–19 years. It yields four factors that assemble DSM-IV anxiety diagnoses: social anxiety, separation anxiety/ panic, physical symptoms, and harm avoidance. It effectively discriminates between controls and youth with anxiety and depression. However, it does not discriminate among the different types of anxiety disorders. The SCARED is a 41-item measure for youth aged 9–18 years, with factors matching DSM-IV
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anxiety disorders, plus school phobia, such as somatic/panic, generalized anxiety, separation anxiety, and social phobia. It effectively differentiates between anxiety and non-anxiety, anxiety and depression, and anxiety and disruptive disorders. Brief versions of the SCARED are available for screening and can be helpful in primary care settings. The SCARED-R is a revised 66-item version that includes items for separation anxiety and specific phobias, obsessive-compulsive disorder and posttraumatic stress disorder. Other scales that can be used in children are the Spence Children’s Anxiety Scale (SCAS) and the Pediatric Anxiety Rating Scale (PARS). The Hamilton Anxiety Scale can be used in adults.
Differential Diagnosis • Normal anxiety: in a stressful situation • Organic cause: hyperthyroidism, Cushing syndrome, etc. • Drug use: symptoms appear only when under the effect of the drug GAD should also be differentiated from other psychiatric disorders as follows: • Other anxiety disorders • Obsessive-compulsive disorder • Depression
Treatment There are multiple treatment options for GAD. Treatment selection depends on factors related to the disorder (severity, duration, dysfunction), the patient and his/her family (developmental age, insight, treatment preferences, family motivation and availability, and financial resources), and the clinician (availability and experience). The therapeutic alliance is an essential element, and is best developed in the context of psychoeducation, which is also fundamental in the treatment process. Educating the family and
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the child (according to his or her developmental age) increases the insight and motivation for treatment. Psychoeducation should always include the concept of anxiety as a normal emotion, etiological and maintenance factors, the natural course of the disorder, treatment alternatives, and prognosis. It is also helpful to inform them on how to manage some symptoms such as avoidance behaviors or cognitive biases. Two treatments, cognitive-behavioral therapy (CBT) and selective serotonin reuptake inhibitors (SSRIs), have demonstrated efficacy for the treatment of pediatric anxiety disorders including GAD (Keeton et al. 2009). CBT Many randomized clinical trials have demonstrated short-term and long-term effectiveness of CBT. Thus, CBT has become the initial treatment of choice, except when anxiety symptoms are too severe to work with the child (medication would be indicated in this case). CBT can be given in monotherapy if symptoms are only mild. Moreover, parents often perceive CBT as a more acceptable initial treatment, compared to medication. The targets of CBT are gaining consciousness about the symptoms, controlling worries, changing the persistent over-arousal state, and confronting fears without permitting avoidance. There are several CBT-manualized programs, which are based on classical and operant principles, and social learning. CBT is composed of psychoeducation, cognitive restructuring (like reducing negative self-talk, addressing negative thoughts), problem solving, relaxation training (addressing physical symptoms), modeling, contingency management, exposure, and relapse prevention. The duration of the treatment depends on the disorder severity and treatment response. Because of the nature of the worries that characterize GAD, patients may need greater use of imaginary than in vivo exposures. Medications There are a number of placebo-controlled studies assessing short-term effectiveness of different
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medications in children and adolescents with GAD. There is none studying long-term effectiveness. SSRIs SSRIs are the first-line option. In fact, SSRIs tend to be more effective in anxiety disorders than in OCD or depression (Bridge et al. 2007). Sertraline, fluvoxamine, and fluoxetine have US FDA pediatric indications for obsessive-compulsive disorder (OCD), and fluoxetine for pediatric major depression. However, there is sufficient evidence suggesting that these and other SSRIs (such as citalopram and escitalopram) are effective for GAD and other anxiety disorders, in children and adults. There is not a universal algorithm for choosing among the different SSRI, neither in adults nor children. The clinician should start with a low dose of an approved SSRI and titrate it weekly, assessing clinical response and side effects. Dosing can begin with 25 mg/day of fluvoxamine, 10 mg/day of fluoxetine, or 25 mg/day of sertraline, or even lower in young children. Most SSRIs can be dosed daily in the morning. Evening dosing is possible if treatment does not disrupt sleep. However, even when a patient significantly improves by weeks 8–12, he or she frequently remains symptomatic and dysfunctional. If the patient presents only partial response, the dose should be increased. To achieve maximum benefit children may need the same dose as adults. Clinical benefit can be observed during the first week, but it is usually in the second to fourth week that the patient refers improvement. Maximum benefit can occur over 12–16 weeks, with additional benefit accruing over 6–12 months (additive long-term effect). This, along with the fact that anxiety disorders tend to be chronic, suggest that the treatment should be maintained for an extended period. That way, medication can help consolidate initial gains (continuing exposure to anxiety-provoking situations in an anxiety-free state); help the patient to fully benefit from CBT and work on selfconcept, avoidance, social relations, etc.; and prevent relapse. In the absence of evidence, experts
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recommend continuing treatment at least 1 year after achieving full remission. Deciding when to stop treatment may be difficult. It is not advisable to discontinue medication at the start of school, while at camp, during final exams, or even during vacations. A discontinuation trial is more appropriate when the patient can be monitored closely (to immediately assess relapse), and is in a stable environment at school and at home. As with all medications, the dose of the SSRI should be tapered progressively, avoiding abrupt discontinuation to prevent withdrawal effects. If symptoms return, medication should be restarted. During the treatment with an SSRI, if the patient does not show clear benefit by weeks 6–8, another SSRI should be tried. Patients usually tolerate SSRIs well. Common side effects include drowsiness, stomach aches, headaches, restlessness, and behavioral activation. These are generally mild and usually dissipate after the first weeks of treatment. If these are marked or persistent the clinician should lower the dose. If they persist, the clinician should discontinue the treatment and try another SSRI. Sometimes, improvement could be difficult to differentiate from behavioral activation, for example, when the child becomes less fearful and starts confronting previously feared situations, and even testing the limits of adults. Apathy is a less-known, late-onset side effect; clinicians often miss it as a side effect, and classify it as a symptom, which has treatment implications. Clinicians have informed anecdotal cases of newonset easy bleeding or bruising. In most cases, coagulation tests and bleeding times are either delayed or normal. Rarely, a patient may suffer a manic switch or episode, characterized by changes in mood (elevated, euphoric, or irritable) or behavior (grandiosity, fast speech, higher level of energy and activity), and possible psychotic symptoms. Should it appear, this usually starts later in the course of treatment, and is less likely to remit when lowering the dose. It may diminish when discontinuing the treatment, or may require starting a mood stabilizer or an
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antipsychotic. Children 10–14 years of age are considered at greatest risk for this severe side effect. Potential risks should always be considered in the context of potential benefits. With SSRIs for the treatment of anxiety disorders, the benefits clearly outweigh the risks, but systematic tracking of side effects is mandatory. Monitoring height, weight, and physical status is also prudent. There is no need to routinely run laboratory testing or ECG at baseline or during follow-up, if the patient is asymptomatic. Also, ongoing medical symptoms should be monitored by the pediatrician or the general practitioner. A recently published comparative treatment trial examined the effectiveness of sertraline, CBT, combination therapy with sertraline and CBT, and placebo for 488 youth aged 7–17 years, who presented GAD, Separation anxiety disorder, and/or social phobia. Response rates were based on the Clinical Global ImpressionsImprovement (CGI-I) following 12 weeks of treatment. All therapies were statistically superior to placebo, and combination therapy was superior to both monotherapies. Response rates were up to 80.7% for combination therapy, 59.7% for CBT, and 54.9% for sertraline. This was the first study to examine the combined effects of CBT and SSRI for children and adolescents with anxiety disorders, and confirmed that CBT alone and sertraline alone are effective short-term treatments, and that there is a clear clinical advantage in combining both (Walkup et al. 2008).
Second-Line Medications Venlafaxine and Duloxetine There are some studies suggesting that noradrenaline and serotonin inhibitors, such as venlafaxine and duloxetine, may be effective in GAD and other anxiety disorders. Tricyclic Antidepressants Tricyclic antidepressants have demonstrated efficacy in anxiety disorders. However, they are second-line treatments because they require
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pre- and post-ECG controls, have more side effects than SSRIs, and may be lethal in overdose. Benzodiacepines Benzodiacepines (BDZ) are frequently used in adults with GAD. Their use is more limited in children and adolescents as they have not been extensively studied in this population. Their main disadvantage is their potential for physical dependence. Atypical Antipsychotics Atypical antipsychotics have been studied as adjunct therapy to standard GAD pharmacotherapy in refractory patients (Bloch et al. 2006). Other Medications There is little evidence supporting the use of antihistamines, alpha-adrenoceptor agonists, or melatonin, for the treatment of anxiety disorders. However, in practice, clinicians may prescribe these if the patient presents insomnia. Buspirone has shown controversial results in adults, and one study could not demonstrate its effectiveness in children.
See Also ▶ Anxiety ▶ Cognitive Behavioral Therapy (CBT) ▶ Selective Serotonin Reuptake Inhibitors (SSRIs)
References and Reading Brawman-Mintzer, O., Lydiard, R. B., Emmanuel, N., Payeur, R., Johnson, M., Roberts, J., Jarrell, M. P., & Ballenger, J. C. (1993). Psychiatric comorbidity in patients with generalized anxiety disorder. American Journal of Psychiatry, 150(8), 1216–1218. Brown, T. A., O’Leary, T. A., & Barlow, D. H. (2001). Generalized anxiety disorder. In D. H. Barlow (Ed.), Clinical handbook of psychological disorders: A stepby-step treatment manual (3rd ed.). New York: Guilford. Davis, E., Saeed, S. A., & Antonacci, D. J. (2008). Anxiety disorders in persons with developmental disabilities:
2211 Empirically informed diagnosis and treatment. Reviews literature on anxiety disorders in DD population with practical take-home messages for the clinician. Psychiatric Quarterly, 79(3), 249–263. Egger, H. L., & Angold, A. (2006). Common emotional and behavioral disorders in preschool children: Presentation, nosology, and epidemiology. Journal of Child Psychology and Psychiatry, 47(3–4), 313–337. Gadow, K. D., DeVincent, C. J., Pomeroy, J., & Azizian, A. (2004). Psychiatric symptoms in preschool children with PDD and clinic and comparison samples. Journal of Autism and Developmental Disorders, 34(4), 379–393. Gjevik, E., Eldevik, S., Fjæran-Granum, T., & Sponheim, E. (2011). Kiddie-SADS reveals high rates of DSM-IV disorders in children and adolescents with autism spectrum disorders. Journal of Autism and Developmental Disorders, 41(6), 761–769. Keeley, M. L., & Storch, E. A. (2009). Anxiety disorders in youth. Journal of Pediatric Nursing, 24(1), 26–40. Keeton, C. P., Kolos, A. C., & Walkup, J. T. (2009). Pediatric generalized anxiety disorder: Epidemiology, diagnosis, and management. Pediatric Drugs, 11(3), 171–183. McGee, R., Feehan, M., Williams, S., Partridge, F., Silva, P. A., & Kelly, J. (1990). DSM-III disorders in a large sample of adolescents. Journal of the American Academy of Child & Adolescent Psychiatry, 29(4), 611–619. Muris, P., Steerneman, P., Merckelbach, H., Holdrinet, I., & Meesters, C. (1998). Comorbid anxiety symptoms in children with pervasive developmental disorders. Journal of Anxiety Disorders, 12(4), 387–393. Simonoff, E., Pickles, A., Charman, T., Chandler, S., Loucas, T., & Baird, G. (2008). Psychiatric disorders in children with autism spectrum disorders: Prevalence, comorbidity, and associated factors in a populationderived sample. Journal of the American Academy of Child & Adolescent Psychiatry, 47(8), 921–929. Soutullo, C., & Figueroa-Quintana, A. (2010). Convivir con niños y adolescentes con ansiedad [Living with children and adolescents with anxiety]. Madrid: Editorial Médica Panamericana. Walkup, J. T., Albano, A. M., Piacentini, J., Birmaher, B., Compton, S. N., Sherrill, J. T., Ginsburg, G. S., Rynn, M. A., McCracken, J., Waslick, B., Iyengar, S., March, J. S., & Kendall, P. C. (2008). Cognitive behavioral therapy, sertraline, or a combination in childhood anxiety. New England Journal of Medicine, 359(26), 2753–2766. White, S. W., Oswald, D., Ollendick, T., & Scahill, L. (2009). Anxiety in children and adolescents with autism spectrum disorders. Clinical Psychology Review, 29(3), 216–229. Wittchen, H. U., Zhao, S., Kessler, R. C., & Eaton, W. W. (1994). DSM-III-R generalized anxiety disorder in the National Comorbidity Survey. Archives of General Psychiatry, 51(5), 355–364.
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Generative Complexity Trina D. Spencer Rightpath Research and Innovation Center, University of South Florida, Tampa, FL, USA Institute for Human Development, Northern Arizona University, Flagstaff, AZ, USA
Generative Complexity Waterhouse, L., Fein, D., & Nichols, E. G. W. (2007). Autism, social neuroscience, and endophenotypes. In F. R. Volkmar (Ed.), Autism and pervasive developmental disorders (2nd ed., pp. 307–335). New York: Cambridge University Press. Retrieved from http://ebookee. o r g / F r e d - R - Vo l k m a r - A u t i s m - a n d - P e r v a s i v e Developmental-Disorders_311848.html.
Generativity Definition In his 1993 book, Origins of Order: SelfOrganization and Selection in Evolution, Stuart Kauffman proposed generative complexity as a mechanism responsible for adaptive human skills. His model suggests that organisms and their neurobiological systems will self-generate with greater complexity. Generative complexity is thought to be a result of spontaneous variation driven by genetics. This unrestrained complexity is then shaped into an organized system of adaptive variations through a Darwinian selection process. Kauffman’s generative complexity extends evolutionary theories to explain how human social behavior and other complex traits have evolved. Social behaviors such as bonding, imitation, and emotional expression may be related to distinct brain circuits. Impairment in these areas may be at the level of intermediate traits (those that are between genes and social behaviors). Applying the model of generative complexity, geneticists and other researchers are working to identify intermediate traits associated with the social behavioral deficits used to diagnose children with autism.
See Also ▶ Candidate Genes in Autism ▶ Epigenetic Mechanisms ▶ Functional Connectivity
References and Reading Kauffman, S. (1993). The origins of order: Selforganization and selection in evolution. New York: Oxford University Press.
▶ Imagination
Genetic Disorders ▶ Medical Conditions Associated with Autism
Genetics Paul El-Fishawy State Laboratory, Child Study Center, Yale University, New Haven, CT, USA
Definition Genetics is a branch of biological science that focuses on the study of how the biological instructions for the development and functioning of living organisms are passed from generation to generation and how changes in these instructions lead to variation in the traits of organisms. In addition, genetics seeks to understand how these biological instructions operate within cells, the building blocks of living organisms. Deoxyribonucleic acid (DNA) is the molecule that carries the instructions for the development and functioning of living organisms and that is passed down from generation to generation. The instructions are spelled out in a sequence or code of four chemical units called nucleotide bases. Certain segments of the DNA molecule called genes contain the code for creating the components of cells, most importantly, molecules called
Genome-wide Association
proteins. On average, all humans possess genomes that are about 99% identical to each other. However, it is the genetic variation between individuals that is of particular interest to physicians and scientists searching for the underpinnings of human disease. It is presumed that if genetic factors increase the liability for certain conditions in a subset of the human population, the risks will be found among the small portion of the genome that varies from one person to the other. Variations of all types, including rare mutations and common polymorphisms and sequence and structural variations, all contribute to the development of human disease. Some variations are passed from one generation to the next and give rise to inherited diseases. Mutations can also arise anew in germ cells during the creation of the zygote. These are called de novo mutations. Mutations can also arise in adult cell types. These are called somatic mutations. The measure of the extent to which genetic factors contribute to a particular condition, syndrome, or disease is called heritability. Studies that compare how frequently pairs of monozygotic twins (identical twins) share a phenotype compared to how frequently pairs of dizygotic twins (nonidentical twins) share that phenotype are called twin studies. These types of studies show that autism has a high degree of heritability. The identification of genetic factors contributing to ASD can have a number of benefits. Such knowledge can improve early diagnosis, help better predict the risk of an offspring developing autism, identify subgroups of individuals with differential responses to treatments, and contribute to a better understanding of the underlying biology of autism, which, in turn, can point to new strategies for treatment and prevention.
See Also ▶ Dizygotic (DZ) Twins ▶ Monozygotic (MZ) Twins ▶ Recessive Genes ▶ Twin Studies in Autism ▶ Variable Expressivity of Genes
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References and Reading Alberts, B., Bray, D., et al. (2002). The cell. New York: Garland Science. Bailey, A., Le Couteur, A., Gottesman, I., Bolton, P., Simonoff, E., Yuzda, E., et al. (1995). Autism as a strongly genetic disorder: Evidence from a British twin study. Psychological Medicine, 25(1), 63. El-Fishawy, P., & State, M. W. (2010). The genetics of autism: Key issues, recent findings, and clinical implications. The Psychiatric Clinics of North America, 33(1), 83–105. Strachan, T., & Read, A. P. (2004). Human molecular genetics. New York: Garland Press. Watson, J. D., & Crick, F. H. C. (1953). Molecular structure of nucleic acids. Nature, 171(4356), 737–738.
Genome-wide Association Thomas Fernandez Yale Child Study Center, Yale University School of Medicine, New Haven, CT, USA
Definition Genome-wide association (GWA) is an approach to studying a disease or trait whereby markers (usually common genetic variants, or polymorphisms) across the genomes of many individuals with (cases) and without (controls) the phenotype are scanned in order to find genetic variations associated with the disease or trait. The associated genetic variants, with allele frequencies that differ significantly between cases and controls, can serve as powerful indicators of regions of the human genome where the phenotype-causing events reside. The effect size of a variant associated with the phenotype in a GWA study is usually reported as an odds ratio (ratio of proportion of individuals in the case group carrying the risk allele to proportion of control individuals carrying the same allele). Statistical significance of the odds ratio (whether it is significantly different from 1) is typically calculated using a chi-squared test. In order to protect against false positive associations that may occur with the large number of statistical comparisons required for a GWA study,
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Genome-wide Association, Fig. 1 Manhattan plot. A scatter plot typically used to summarize results of a GWA study. Genomic coordinates are displayed along the x-axis, and the negative logarithm of association p-value
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the commonly accepted genome-wide significance threshold is p < 5 108. GWA study results are commonly summarized by a “Manhattan plot” (Fig. 1), which shows the negative logarithm of the p-value for each marker polymorphism across the genome.
provided the rationale for the use of GWA studies to map loci contributing to common disease. In order to test this hypothesis, several tools and resources were developed and converged to allow identification of common variants throughout the genome. The completion of the Human Genome Project in 2003 provided a human reference genome sequence, and the International HapMap Project in 2005 identified a majority of the common single nucleotide polymorphisms (SNPs) investigated in GWA studies and also identified haplotypes which indicated those SNPs describing most of the genetic variation. The first GWA study, published in 2005, identified two SNPs within the complement factor H gene with significantly different allele frequencies between 96 cases with age-related macular degeneration and 50 controls (Klein et al. 2005). In 2007, the Wellcome Trust Case Control Consortium reported a landmark GWA study, including 14,000 cases of seven common diseases (including types 1 and 2 diabetes, coronary heart disease, hypertension, rheumatoid arthritis, Crohn’s disease, and bipolar disorder) and 3,000
Historical Background Prior to the introduction of GWA studies (around 2000), the main approach to investigating genetic causes of human disease was genetic linkage studies in families. While linkage is useful for single gene Mendelian disorders, using this approach for common and complex diseases generally yields inconsistent results. Genetic association arose as an alternative to linkage for detecting weaker genetic effects and was initially performed by interrogating polymorphisms in individual candidate genes between cases and controls. The common disease-common variant hypothesis, that common polymorphisms (>1% allele frequency) contribute to susceptibility of common diseases,
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controls (Welcome Trust Case Control Consortium 2007). This study suggested many unexpected genes underlying risk for the included diseases.
Current Knowledge Current testing for GWA typically involves testing hundreds of thousands to millions of common SNP markers for association with disease in cases versus controls. Recent advances in microarray technologies, in which millions of SNP probes can be arranged on a single microscope slide, have allowed researchers to query these genetic markers, evenly spaced throughout the genome, in a single reaction and at relatively low cost. This allows for testing of association throughout the entire genome, without having to choose candidate loci in advance. Many early GWA studies were plagued with difficulties which resulted in inconsistent findings. For example, overestimation of allelic effect sizes led to initial cohort sizes that were underpowered, and inadequate corrections for cryptic confounds such as relatedness and ancestry could lead to false positive findings. As these difficulties have become more appreciated and sufficiently addressed, GWA studies in several areas of medicine, including diabetes, intracranial aneurysms, and inflammatory bowel disease, have yielded reproducible results (Hindorff et al. 2011; Manolio 2010). In autism spectrum disorders (ASD), three associated alleles, one each from three independent GWA studies, have been identified that meet accepted discovery criteria, as of December 2011: (1) an intragenic region on chromosome 5p14 between the genes CDH9 and CDH10 (Wang et al. 2009), (2) a region on 5p15.3 within 80 kb of SEMA5A (Weiss et al. 2009), and (3) an intragenic region on 20p12 within MACROD2 (Anney et al. 2010). While each study demonstrated genome-wide significance and internal replication, none of the three studies replicate one another, and a combined analysis of data from all three studies appears to decrease evidence for association for all three alleles (Devlin et al.
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2011). Despite this lack of convergence so far, it is believed that multiple common variants of very small effect remain to be identified in ASD, and increasing sample sizes may be required (State and Levitt 2011). The current state of GWA studies in ASD illustrates an important lesson learned over the last decade for GWA studies across all of medicine: most of the variants found are associated with only a small increased risk of the disease (e.g., odds ratios of 1.1–1.5 per associated allele) and explain only a small amount of the heritability, as determined from twin studies. These modest effect sizes have little clinically useful predictive value and suggest that most GWA studies have low power to detect associations at typical sample sizes (Altshuler et al. 2008; Manolio 2010). Furthermore, the question of why so much heritability is unexplained by GWA studies has led to an increased focus on the contribution of rare and structural variants (poorly detected by currently available genotyping arrays), interactions between genes, interactions between genes and environmental factors, and studies of ancestrally diverse populations (Manolio 2010; Manolio et al. 2009).
Future Directions With the application of rigorous methodology and large sample sizes to GWA studies in ASD, there is hope that definitive replication of common ASD alleles will emerge. This will lead to the additional challenge of translating such findings into potentially targetable neurobiological mechanisms. Even with replication, there remains the challenge of narrowing an associated locus to a single variant that is directly implicated in disease etiology, an achievement which has so far remained elusive for GWA studies throughout medicine. Next-generation sequencing technologies are now allowing a genome-wide view of all nucleotides in the genome. As the 1,000 Genomes Project (1000 Genomes Project Consortium 2010) and other large-scale efforts aim to create an accurate map of all single nucleotide and structural variants in the genome, GWA studies may be extended to rare variants which could explain
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some of the unmapped heritability in common polymorphism GWA studies.
See Also
Geodon association scan reveals novel loci for autism. Nature, 461(7265), 802–808. Welcome Trust Case Control Consortium. (2007). Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature, 447(7145), 661–678.
▶ Common Disease-Common Variant Hypothesis
Geodon References and Reading 1000 Genomes Project Consortium. (2010). A map of human genome variation from population-scale sequencing. Nature, 467(7319), 1061–1073. Altshuler, D., Daly, M. J., & Lander, E. S. (2008). Genetic mapping in human disease. Science, 322(5903), 881–888. Anney, R., Klei, L., Pinto, D., Regan, R., Conroy, J., Magalhaes, T. R., et al. (2010). A genome-wide scan for common alleles affecting risk for autism. Human Molecular Genetics, 19(20), 4072–4082. Devlin, B., Melhem, N., & Roeder, K. (2011). Do common variants play a role in risk for autism? Evidence and theoretical musings. Brain Research, 1380, 78–84. Ge, D., Fellay, J., Thompson, A. J., Simon, J. S., Shianna, K. V., Urban, T. J., et al. (2009). Genetic variation in IL28B predicts hepatitis C treatment-induced viral clearance. Nature, 461(7262), 399–401. Hindorff, L., MacArthur, J., Wise, A., Hall, P., Klemm, A., & Manolio, T. A. (2011). Catalog of published genomewide association studies. Retrieved December 15, 2011 from www.genome.gov/gwastudies Klein, R. J., Zeiss, C., Chew, E. Y., Tsai, J. Y., Sackler, R. S., Haynes, C., et al. (2005). Complement factor H polymorphism in age-related macular degeneration. Science, 308(5720), 385–389. Manolio, T. A. (2010). Genomewide association studies and assessment of the risk of disease. The New England Journal of Medicine, 363(2), 166–176. Manolio, T. A., Collins, F. S., Cox, N. J., Goldstein, D. B., Hindorff, L. A., Hunter, D. J., et al. (2009). Finding the missing heritability of complex diseases. Nature, 461(7265), 747–753. Pearson, T. A., & Manolio, T. A. (2008). How to interpret a genome-wide association study. Journal of the American Medical Association, 299(11), 1335–1344. State, M. W., & Levitt, P. (2011). The conundrums of understanding genetic risks for autism spectrum disorders. Nature Neuroscience, 14(12), 1499–1506. Wang, K., Zhang, H., Ma, D., Bucan, M., Glessner, J. T., Abrahams, B. S., et al. (2009). Common genetic variants on 5p14.1 associate with autism spectrum disorders. Nature, 459(7246), 528–533. Weiss, L. A., Arking, D. E., Gene Discovery Project of Johns Hopkins & the Autism Consortium, Daly, M. J., & Chakravarti, A. (2009). A genome-wide linkage and
▶ Ziprasidone
Geodon (Ziprasidone) Wouter Staal Neuroscience, Radboud University Nijmegen Medical Centre Karakter, Nijmegen, The Netherlands
Synonyms Marketed as Geodon; Zeldox
Definition Ziprasidone is an atypical antipsychotic agent with a high affinity for dopamine, serotonin, and alpha-adrenergic receptors and a moderate affinity for histamine receptors. The mechanism of action of ziprasidone is unknown. Ziprasidone is a complex molecule with the chemical name 5-[2[4-(1,2-benzisothiazol-3-yl)-1-piperazinyl]ethyl]6-chloro-1,3-dihydro-2Hindol-2-one. Its action is thought to be caused by antagonizing dopamine receptors, especially D2. Antagonism of histaminic and alpha-adrenergic receptors may be related to adverse effects of ziprasidone, such as sedation and orthostasis. Ziprasidone can be administered intramuscularly or orally with food. Steady-state plasma concentrations are achieved within 1–3 days with a mean half-life ranging from 2 to 5 h. Ziprasidone is largely metabolized by aldehyde oxidase and for a smaller portion by cytochrome P450 3A4
Geri Dawson
(CYP3A4). Ziprasidone has a number of adverse effects most notably including sedation, insomnia, orthostasis, life-threatening neuroleptic malignant syndrome, akathisia, tardive dyskinesia, chest pains, and impaired erectile function. In the United States, ziprasidone is Food and Drug Administration (FDA) approved for the treatment of schizophrenia and for acute treatment of mania and mixed states associated with bipolar disorder.
See Also ▶ Antipsychotics: Drugs ▶ Pyramidal System
References and Reading De Hert, M., Dobbelaere, M., Sheridan, E. M., Cohen, D., & Correll, C. U. (2011). Metabolic and endocrine adverse effects of second-generation antipsychotics in children and adolescents: A systematic review of randomized, placebo controlled trials and guidelines for clinical practice. European Psychiatry, 26(3), 144–158. Komossa, K., Rummel-Kluge, C., Hunger, H., Schwarz, S., Bhoopathi, P. S., Kissling, W., et al. (2009). Ziprasidone versus other atypical antipsychotics for schizophrenia [Review]. Cochrane Database of Systematic Reviews, 4, CD006627. Newcomer, J. W. (2005). Second-generation (atypical) antipsychotics and metabolic effects: A comprehensive literature review. CNS Drugs, 19 (Suppl. 1), 1–93.
Geri Dawson Sara Jane Webb Psychiatry and Behavioral Sciences and UW Autism, Seattle Children’s Research Institute, University of Washington, Seattle, WA, USA
Name and Degrees Geraldine Dawson Ph.D. Duke University, Durham NC, USA
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B.S. University of Washington, Seattle WA, 1974 Ph.D. University of Washington, Seattle WA, 1979 Postdoctoral Fellow & Clinical Intern. UCLA, Los Angeles CA, 1979–1980
Major Appointments (Institution, Location, Dates) Assistant Professor of Psychology, University of North Carolina, Chapel Hill NC Director, Child Clinical Psychology Program University of Washington, Seattle WA Associate Professor, Psychology University of Washington, Seattle WA Professor with tenure, Psychology University of Washington, Seattle WA Founding Director, UW Autism Center, University of Washington, Seattle WA Chief Science Officer, Autism Speaks Research Professor, Psychiatry University of North Carolina at Chapel Hill, NC Professor Emeritus, Psychology University of Washington, Seattle WA Public Member, Interagency Autism Coordinating Committee, DHHS Director, Duke Center for Autism and Brain Development Duke University, Durham NC Faculty Affiliate, Duke Institute for Brain Sciences, Durham NC Professor with tenure, Psychiatry & Behavioural Science Duke University, Durham NC Professor, Psychology and Neuroscience, Duke University, Durham NC Professor, Pediatrics Duke University, Durham NC President, International Society for Autism Research Faculty Affiliate, Center for Child and Family Policy Duke University, Durham NC
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Major Honors and Awards Fellow, American Psychological Association Fellow, American Psychological Society Since 2000 Autism Society of America Honoree for Research Contributions to the Autism Community (2004) Autism Society of Washington Medical Professional of the Year (2004) Autism Hero Award Cure Autism Now Foundation (2006) NIH Top Science Advances in Autism Research Award - (2007, 2008, 2009, 2010, 2012, 2013, 2016) Top Ten Science Advances of the Year Award Autism Speaks (2008, 2009, 2010, 2012, 2013) Geoffrey Beene Rock Star of Science Award (2010) Univ. of Wash College of Arts & Science Timeless Outstanding Service & Achievement Award (2012) Association for Psychological Science James McKeen Cattell Lifetime Achievement Award (2012) The News and Observer Triangle and NC Tarheel of the Week (2014) President, International Society for Autism Research (2015) Duke School of Medicine Noteworthy Faculty Recognition Award (2016)
Landmark Clinical, Scientific, and Professional Contributions Dr. Geraldine Dawson’s research on autism spectrum disorders has focused on early detection and intervention and elucidating the nature of brain dysfunction and development. A former Professor at University of Washington and Chief Scientific Officer at Autism Speaks, Dr. Dawson is currently the Director of the Duke Center for Autism and Brain Development at Duke University, President of the International Society for Autism Research, and a Professor of Psychiatry and Behavioral
Geri Dawson
Sciences, Pediatrics, and Psychology and Neuroscience at Duke University. Dr. Dawson’s work demonstrated how maternal depression influences early brain activity and stress responses in infants and children (Dawson et al. 1992). Dawson used home videotapes to demonstrate that autism symptoms emerge during the first year of postnatal life (Osterling and Dawson 1994) and empirically validate the phenomenon of autistic regression (Werner and Dawson 2005). She published the first case study of an infant with autism (Dawson et al. 2000). Dawson demonstrated that autism is associated with early deficits in social attention (Dawson et al. 1998; Dawson et al. 2004) and proposed that such attention deficits are related to reduced sensitivity to the reward value of social stimuli (“social motivation hypothesis”) (Dawson et al. 2002). Dawson’s laboratory pioneered the use of electrophysiological techniques to study brain function in autism, including during early childhood (Dawson et al. 1986; Dawson et al. 1988; Dawson et al. 2002). Research in her laboratory identified autism neural signatures (event-related brain potentials in response to faces) (Dawson et al. 2002) that were later shown to be associated with early risk identification (Jones et al. 2016) and endophenotypes in relatives (Dawson et al. 2005), and used for biomarkers in autism clinical trials (Dawson et al. 2012a, b). As part of the NIMH STAART (Studies To Advance Autism Research and Treatment), and in collaboration with Dr. Sally Rogers, Dr. Dawson co-developed and empirically validated the Early Start Denver Model (Rogers and Dawson 2010; Rogers et al. 2012), the first comprehensive early intervention program for very young children with autism (Dawson et al. 2010; Estes et al. 2015). ESDM has been translated into 14 languages and is used worldwide. Her research demonstrating that early intervention based on ESDM can normalize brain activity in children with autism was recognized by TIME magazine as one of the top 10 medical breakthroughs of 2012 (Dawson et al. 2012b). Dawson has also been a contributor to several autism studies on using structural and functional MRI (e.g., Sparks et al. 2002; Friedman et al. 2007) and genetic risk factors.
Geri Dawson
Short Biography Dr. Dawson is a licensed, practicing Child Clinical Psychologist, obtaining her PhD from University of Washington and completing her postdoctoral training and internship at the UCLA Neuropsychiatric Institute. As a faculty at the University of North Carolina and University of Washington, Dr. Dawson has trained 23 doctoral students and 5 postdoctoral fellows who have gone on to careers as clinical psychologists and university faculty. She continues mentoring students at Duke, where from 2013 to 2016, she trained 1 doctoral student, 9 postdoctoral fellows, 1 clinical psychology intern, and 2 medical students. As faculty at UW, she received continuous funding for her research from 1980 to 2008 including serving as Director for multidisciplinary center grants from the NIH Collaborative Programs for Excellence in Autism, STAART, and Autism Center of Excellence scientific programs. These multidisciplinary autism research programs focused on genetics, neuroimaging, early diagnosis, and treatment. At UW, Dr. Dawson also founded and oversaw the UW Autism Center endowed treatment center for children with ASD. At Autism Speaks, she directed $20–30 million in annual research funding, including funding for the Autism Treatment Network, the Autism Genetic Resource Exchange, the Autism Genome Project, and the Autism Tissue Program. Under her leadership at Autism Speaks, several new initiatives were launched, including the Translational Medicine Research Initiative, Postdoctoral Research Fellowship, the Trailblazer Award, the Autism 10K Genome Project, the Early Access to Care Initiative, the Global Autism Public Health Initiative, and DELSIA (Delivering Scientific Innovation for Autism). At Duke University, Dr. Dawson is the Founding Director of the Duke Center for Autism and Brain Development at Duke University, North Carolina, which includes an integrated interdisciplinary clinical care, research, and training facility. Her current research at Duke focuses on early detection and early behavioral intervention and clinical trials testing novel molecular and cellular therapies for
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autism using eye-tracking, EEG, and MRI as biomarkers and outcome measures. Dr. Dawson has published over >270 articles and 10 books on early detection and treatment of autism and brain development. She has testified before the US Senate in support of legislation for autism research funding and insurance coverage for autism services.
See Also ▶ Broader Autism Phenotype ▶ Early Intervention ▶ Early Start Denver Model ▶ Face Perception ▶ Retrospective Infant Video Analysis ▶ USA and Autism
References and Reading Dawson, G., Finley, C., Phillips, S., & Galpert, L. (1986). Hemispheric specialization and the language abilities of autistic children. Child Development, 57, 1440–1453. PMID: 3802970. Dawson, G., Finley, C., Phillips, S., & Galpert, L. (1988). Reduced P3 amplitude of the event-related brain potential: Its relationship to language ability in autism. Journal of Autism and Developmental Disorders, 18, 493–504. PMID: 3215878. Dawson, G., Grofer, L., Hill, D., Panagiotides, H., & Speiker, S. (1992). Frontal lobe activity and affective behavior of infants of mothers with depressive symptoms. Child Development, 63, 725–737. PMID: 1600832. Dawson, G., Meltzoff, A., Osterling, J., & Brown, E. (1998). Children with autism fail to orient to social stimuli. Journal of Autism and Developmental Disorders, 28, 479–485. PMID: 9932234. Dawson, G., Osterling, J., Meltzoff, A. N., & Kuhl, P. (2000). Case study of the development of an infant with autism from birth to 2 years of age. Journal of Applied Developmental Psychology, 21, 299–313. PMID: 23667283. Dawson, G., Carver, L., Meltzoff, A. N., Panagiotides, H., & McPartland, J. (2002). Neural correlates of face recognition in young children with autism spectrum disorder, developmental delay, and typical development. Child Development, 73, 700–717. PMID: 12038546. Dawson, G., Toth, K., Abbott, R., Osterling, J., Munson, J., Estes, A., & Liaw, J. (2004). Defining the early social attention impairments in autism: Social orienting, joint
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2220 attention, and responses to emotions. Developmental Psychology, 40(2), 271–283. PMID: 14979766. Dawson, G., Webb, S. J., Wijsman, E., Schellenberg, G., Estes, A., Munson, J., & Faja, S. (2005). Neurocognitive and electrophysiological evidence of altered face processing in parents of children with autism: Implications for a model of abnormal development of social brain circuitry in autism. Development and Psychopathology, 17, 679–697. PMID: 16262987. Dawson, G., Rogers, S., Munson, J., Smith, M., Winter, J., Greenson, J., Donaldson, A., & Varley, J. (2010). Randomized, controlled trial of an intervention for toddlers with autism: The early start Denver model. Pediatrics, 125(1), e17–e23. PMCID: PMC4951085. Noted in the IACC summary of advances 2010. Dawson, G., Bernier, R., & Ring, R. H. (2012a). Social attention: A possible early indicator of efficacy in autism clinical trials. Journal of Neurodevelopmental Disorders, 4, 11. PMCID: PMC3436672. Dawson, G., Jones, E. J., Merkle, K., Venema, K., Lowy, R., Faja, S., Kamara, D., Murias, M., Greenson, J., Winter, J., Smith, M., Rogers, S., & Webb, S. J. (2012b). Early behavioral intervention is associated with normalized brain activity in young children with autism. Journal of the American Academy of Child and Adolescent Psychiatry, 51(11), 1150–1159. PMID: 23101741. Noted in the IACC Summary of Advances 2012; TIME Magazine’s Top Ten Medical Breakthrough 2012. Estes, A., Munson, J., Rogers, S., Greenson, J., Winter, J., & Dawson, G. (2015). Long-term outcomes of early intervention in 6 year old children with autism spectrum disorder. Journal of the American Academy of Child and Adolescent Psychiatry, 54(7), 580–587. https://doi.org/10.1016/j.jaac.2015.04.005. Friedman, S. D., Shaw, D. W., Artru, A. A., Dawson, G., Petropoulos, H., & Dager, S. R. (2007). Gray and white matter brain chemistry alterations in young children with autism. Archives of General Psychiatry, 63, 786–794. PMID: 16818868. Jones, E. J. H., Venema, K., Earl, R., Lowy, R., Barnes, K., Estes, A., Dawson, G., & Webb, S. J. (2016). Reduced engagement with social stimuli in 6-month-old infants with later autism spectrum disorder: A longitudinal prospective study of infants at high familial risk. Journal of Neurodevelopmental Disorders, 18(8), 7. PMID: 26981158. Osterling, J., & Dawson, G. (1994). Early recognition of children with autism: A study of first birthday home videotapes. Journal of Autism and Developmental Disorders, 24, 247–257. PMID: 8050980. Pinto, D., et al. (2010). Functional impact of global rare copy number variation in autism spectrum disorders. Nature, 466, 368–372. PMCID: PMC3021798. Rogers, S. J., & Dawson, G. (2010). The early start Denver model for young children with autism: Promoting language, learning, and engagement. New York: The
German Measles Guilford Press. Translated into Japanese, Italian, Dutch, French, Spanish, Portugese, Chinese, Arabic, Romanian, Korean, Polish, German, Russian, and Turkish. Rogers, S. J., Dawson, G., & Vismara, L. (2012). An Early Start for your Child with Autism. New York: Guilford Press. Awarded a 2012 American Journal of Nursing book of the year award – 1st place. Translated into Spanish, Dutch, Japanese, Portuguese, French, Chinese, Lithuanian, and Russian. Sparks, B. F., Friedman, S. D., Shaw, D. W., Aylward, E. H., Echelard, D., Artru, A. A., Maravilla, K. R., Giedd, J. N., Munson, J., Dawson, G., & Dager, S. R. (2002). Brain structural abnormalities in young children with autism Spectrum disorder. Neurology, 59, 184–192. PMID: 12136055. Werner, E., & Dawson, G. (2005). Validation of the phenomenon of autistic regression using home videotapes. Archives of General Psychiatry, 62, 889–895. PMID: 16061766.
German Measles ▶ Rubella
Germany and Autism Sven Bölte Center of Neurodevelopmental Disorders (KIND), Department of Women’s and Children’s Health and Child and Adolescent Psychiatry, Center for Psychiatry Research, Karolinska Institutet and Stockholm County Council, Stockholm, Sweden
Historical Background Leo Kanner had studied medicine in Berlin between 1913 and 1919, got Prussian citizenship in 1920, worked at the Charité, and as a private practitioner in Berlin until 1924 (Neumärker 2003) before immigrating to the USA. His concept of autism (Kanner 1943) was introduced to Germany shortly after World War II by Hermann Stutte, a child and adolescent psychiatrist from Marburg, who had exchange with Arn Van
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Krevelen, a Dutch child and adolescent psychiatrist from Leiden who was among the first Europeans to confirm the condition in the early 1950s (Van Krevelen 1952a, b). During the same time psychiatrist Gerhard Bosch described the first cases of autism in Frankfurt, particularly focusing on language development issues and the differentiation of autism and schizophrenia. His book “Infantile autism: A philosophical study of the languages of autism” was published in 1962 (in English 1970) and includes thoughtful and empathetic case descriptions together with many valuable novel insights such as a discussion of the differences and similarities between Kanner’s and Hans Asperger’s (1944) descriptions and a need to broaden the concept of autism, long before the term Asperger syndrome and autism spectrum were coined. Bölte and Bosch (2004, 2005) followed up on Bosch’s cases 40 years after initial diagnosis. In the 1970s, Doris Weber from Marburg studies autism in relation to Heller’s dementia infantilis (1908) [childhood disintegrative disorder], a diagnosis often given to children with autism at that time in Germany, while Gerhardt Nissen in Würzburg studied Kanner’s and Asperger’s description versus psychogenic and somatic forms of autism. The concept of Asperger syndrome was not used frequently in Germany before the early 1970s. While until the later 1970s, autism was still widely considered an infantile psychosis and psychoanalytic views were also still influential, it is today widely accepted among many professions that autism is a brainbased developmental disorder of multiple genetic and environmental origin. The understanding of autism being a spectrum disorder was well established before the release of DSM-5 (American Psychiatric Association 2013). Autism awareness and services in many parts of the society and among clinical professionals working with adults have gradually developed in recent years. National clinical guidelines for the assessment of autism spectrum disorder have recently been updated by the German Society of Child and Adolescent Psychiatry and Psychotherapy and the German Association for Psychiatry, Psychotherapy, and Psychosomatics (http://www.awmf.org/ leitlinien/detail/ll/028-018.html).
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Legal Situation The Social Security State Book came into effect in 2001, entailing rules for rehabilitation and participation for individuals with disabilities. The Federal Act of Equal Opportunities was released in 2002 aiming to achieve equal opportunities for people in public space (“accessibility”). The General Equal Treatment Act came into effect in 2006 and comprises among other things the equalization of people with disabilities in the area of the civil law. The United Nations convention on the rights of people with disabilities was ratified in 2006. For the state, counties and municipalities, administrations and courts, these guidelines are now applicable law, and all national laws need to be interpreted in the light of the UN convention. The degree of disablement and the entitlement are connected to the International Classification of Diseases (ICD-10, WHO 1992) codes for different forms of autism spectrum disorder (F84.0, F84.1, F84.5). Thus, an ICD-10 diagnosis is required before an assessment of the degree of disability which is a prerequisite for access to additional clinical and other services. The assessment of the degree of disability is complex and often lengthy and is reassessed with varying intervals. The Social Security State Book and The General Equal Treatment Act include several support means for people classified as having different degrees of disability, for instance, additional autism-specific individual intervention in addition to medical treatment; speech, language, and occupational therapy; integration aids for kindergarten and school; and further education and employment. It also comprises costs for traveling, a study assistant for structuring and orientation, and assistive technologies. In case of a high degree of disability there are several options for sheltered or supported/integrated employment. In long-term full support residential care, the complete basic needs are covered, plus pocket money. In partly supported homes, people with autism cover the costs for food, clothing, and rent themselves by their salary or different forms of relief payments. Caregivers can apply for care insurance payments for supporting their child if they
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compensate for the individual with autism’s disabilities, instead of using public services. In this case, the medical service of the statutory nursing care insurance decides on the degree of an individual’s disability based and physician and nurse assessments. Another option is the so-called personal budget that was released in 2008, where a social service may not only be given as a traditional benefit in kind, but a payment or voucher to a caregiver. This gives the possibility to caregivers to decide themselves which help they want to provide to their child. For parents of children with autism it is also recommended that they do a “disabled testament” in order to ensure that after their deaths the complete heritage is not transferred to the welfare agency, so that their offspring can still receive care beyond livelihood using the heritage. Currently, a new regulation, the Federal law of intregration and participation, is under negotiation, planned to come into effect January 1, 2017. While it is aiming to improve the inclusion of individuals with functional disabilities, its draft has caused considerable concerns about weakened access to services for individuals with autism.
Development of Services In the beginning, individuals diagnosed with autism were treated at university hospitals and then referred to institutions for long-term residential care, mostly designed for intellectual disability care. As autism was rarely diagnosed until the later 1960s, there was little apparent need or interest for specialized care for autism. The Max-Planck Institute of Psychiatry Research in Munich introduced behavior therapy to autism influenced by US American developments. Like in other countries, a major breakthrough for autism was achieved in Germany by an emerging engagement of parents trying to get adequate diagnostic assessment and treatment for their children with mostly severe forms of autism. In 1970, “Hilfe für das autistische Kind” (today renamed in “Autismus Deutschland”; www.autismus.de) was founded. During this time, many parents sought advice from Hans Kehrer, child and adolescent
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psychiatrist in Münster, who also had published the first articles on behavior therapy for social and languages difficulties in autism (Kehrer and Körber 1971; Kehrer and Budde 1972) and the first comprehensive text book on autism (Kehrer 1978). The parent society developed rapidly and started to establish autism therapy centers favoring family work, the upcoming TEACCH approach, and behavior modification, but also a variety of alternative forms of intervention. Today, more and more special school classes and even occasionally regular schools provide day care and inclusive teaching for children with autism. A range of academic and municipality hospitals have established autism centers, specialized day care units, or outpatient department for autism assessment and intervention aiming to follow evidence-based best practice principles. There is also a clear tendency to accomplish early diagnosis of autism and treatment in child and adolescent psychiatry. Even many private practitioners offer autism-specific services, some center-like organizations offering particular forms of intervention only, such as Applied Behavior Analysis, TEACCH, PECS, or social skills training. In the last decade, services for adults with autism spectrum disorder have started to improve in clinical settings, but also in other fields of society (e.g., employment, living). For instance, the psychiatry departments at the universities in Freiburg and Cologne run specialized departments for higher functioning autism. Most of this development is driven by increasing diagnoses rates of autism among adults and a previous lack of autism expertise among professionals in the adult mental health field, but also by the increasing voice of the neurodiversity and empowerment movement, for instance organized within the “Aspies e.V., Menschen im Autismusspektrum” (www.aspies.de).
Research Autism science has developed considerably in Germany in the last decade. For instance, while in the year 2005 only 19 original articles (cited in PubMed) on autism where published with a
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German academic affiliation, the number was 171 in the year 2015. Many German clinics and labs are today involved in European and international autism research collaborations and projects, such as the European Collaboration in Science and Technology (COST) activity “Enhancing the Scientific Study of Early Autism” (www.costessea.com), EU-AIMS (www.eu-aims.eu), European Autism Interventions – A Multicentre Study for Developing New Medications – the largest single grant for autism in the world, and the largest for the study of any mental health disorder in Europe, or The Autism Genome Project (www. autismspeaks.org/science/initiatives/autism-genomeproject). Universities that are particularly active in autism research are Aachen, Berlin, Cologne, Frankfurt, Freiburg, Mannheim, and Marburg. In 2008, the Scientific Society Autism Spectrum (WGAS) (www. wgas-autismus.org) was established. The WGAS is devoted to supporting basic and applied autism research primarily in Germany, Switzerland, and Austria. Among other things, the WGAS meets this goal through producing scientific publications, facilitating annual events such as the Scientific Meeting for Autism Spectrum Conditions (WTAS) (http:// wgas-autismus.org/meeting-wtas), promoting networking among colleagues active within the autism field, and the formation of professional groups. WGAS also provides training and continuing education for researchers, support for junior scientists, and sponsorship of awards for outstanding research contributions.
Training The majority of autism specific training in Germany is still conveyed via postgraduate courses, predominantly by autism therapy centers or private providers. Such education often focuses on applied intervention techniques, such as ABA, TEACCH, PECS, and social skills training but also and autism assessment and other preclinical and applied areas. Autism often only plays a minor role in the comprehensive postgraduate curricula to accomplish approbation for being a certified psychotherapist. Clinical ADOS training is offered by several
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academic child and adolescent psychiatry departments. Some higher education departments have established autism-specific modules or curricula for students, such as the Department of Psychology and Pedagogics at Ludwig Maximilian University Munich, The Jacobs University in Bremen, and the University of Applied Science in Münster.
References and Reading American Psychiatric Association (APA). (2013). Diagnostic and statistical manual of mental disorders, 5th edition text-revision (DSM-5). Washington, DC: APA Press. Asperger, H. (1944). Die autistischen Psychopathen im Kindesalter. Archiv für Psychiatrie und Nervenkrankheiten, 117, 76–136. Bölte, S., & Bosch, G. (2004). Bosch’s cases: A 40 years follow-up. German Journal of Psychiatry, 7, 10–13. Bölte, S., & Bosch, G. (2005). The long-term outcome in two females with autism spectrum disorder. Psychopathology, 38, 151–154. Bosch, G. (1962). Der frühkindliche Autismus – eine klinische und phänomenologisch-anthropologische Untersuchung am Leitfaden der Sprache. Berlin: Springer. Bosch, G. (1970). Infantile autism: A clinical and phenomenological-anthropological approach taking language as the guide. New York: Springer. Heller, T. (1908). Dementia infantilis. Zeitschrift für die Erforschung und Behandlung des jugendlichen Schwachsinns, 2, 141–165. Kanner, L. (1943). Autistic disturbances of affective contact. Nervous Child, 2, 217–253. Kehrer, H. E. (1978). Kindlicher Autismus (Bibliotheca psychiatrica, Vol. 157). Basel: S. Karger. Kehrer, H. E., & Budde, R. (1972). Verhaltenstherapie von Kontaktstörungen bei Autismus Infantum. Acta Paedopsychiatrica, 39, 253–263. Kehrer, H. E., & Körber, H. P. (1971). Sprachbehandlung durch Verhaltenstherapie bei autistischen-mutistischen Kindern. Acta Paedopsychiatrica, 38, 2–17. Neumärker, K. J. (2003). Leo Kanner: His years in Berlin, 1906–1924: The roots of autistic disorder. Historical of Psychiatry, 14, 205–218. Van Krevelen, A. (1952a). Early infantile autism. Zeitschrift für Kinderpsychiatrie. Revue de psychiatrie infantile, 19, 81–97. Van Krevelen, A. (1952b). A case of early infantile autism. Nederlands Tijdschrift voor Geneeskunde, 96, 202–206. World Health Organization (WHO). (1992). The ICD-10 classification of mental and behavioural disorders. Clinical descriptions and guidelines. Geneva: WHO.
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Gesell, Arnold W. Thornton N. Deegan Yale Child Study Center, New Haven, CT, USA
Name and Degrees • Gesell, Arnold Lucius (June 1, 1880–May 21, 1961). • Arnold Gesell received his B.Ph. in 1903 from the University of Wisconsin and his Ph.D. in Education in 1906 from Clark University. In 1911, Gesell accepted an assistant professorship at Yale University, teaching graduate courses in education while working toward his M.D., which he received in 1915.
Major Appointments (Institution, Location, Dates) • Stevens Point High School, Stevens Point, WI, 1899–1901, Teacher of US history, ancient history, German, accounting, and commercial geography. • Chippewa Falls High School, Chippewa Falls, WI, 1903–1904, Principal. • Los Angeles State Normal School, Los Angeles, CA, 1908–1910, Professor of Psychology. • Yale University, New Haven, CT, 1911–1948, Assistant Professor of Education, Professor of Education, Professor of Child Hygiene, Founder and Director of the Yale Psycho-Clinic (later renamed Yale Clinic of Child Development, later renamed Yale Child Study Center), Professor Emeritus of Child Hygiene. • Connecticut State Board of Education, Hartford, CT, 1915–1919, School Psychologist. • Connecticut Commission on Child Welfare, Hartford, CT, 1919–1921, Member. • American Psychological Association, Washington, D.C., 1922–1936, Director. • American Child Health Association, 1928–1940, Director.
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• New Haven Hospital, New Haven, CT, 1928–1948, Attending pediatrician.
Major Honors and Awards Dr. Gesell received numerous honors and awards during his lifetime. Among them are honorary Doctor of Science degrees from Clark University (1930) and the University of Wisconsin (1953). The Gesell Institute of Child Development is named in his honor.
Landmark Clinical, Scientific, and Professional Contributions Dr. Gesell is most renowned for his Developmental Schedules, which track the behavioral development of typical children through the first few years of life. Dr. Gesell was also known for his groundbreaking observational methods, pioneering the use of cinematography, still photography, and one-way vision screens to observe subjects without disrupting them. Cinematography especially provided a way to reproduce nuances of behavior faithfully and completely for later analysis. Through his professional contributions, Dr. Gesell helped establish child development as a scientific field, effectively bridging the gap between psychology and pediatrics.
Short Biography Arnold Gesell was born on June 21, 1880, in the small town of Alma, located on the western edge of Wisconsin, on the eastern bank of the Mississippi River. Gesell’s father, Gerhard, was a photographer with a studio in Alma and had a great interest in education, and Arnold’s mother, Christine, was an elementary school teacher. Arnold was also the first of five children, with two brothers and two sisters. This family upbringing had a tremendous effect upon Arnold. Experiencing childhood with a family
Gesell, Arnold
background in education, in the presence of other children, led Arnold to be fascinated with childhood, education, and growing up, and more specifically with the experiences of those who deviated from what was considered normal. This in turn led him to study education and psychology, that he might better understand the mechanics of human development. Gesell attended the University of Wisconsin, receiving his B.Ph. in 1903, thereafter serving as principal at Chippewa Falls High School in Chippewa Falls, Wisconsin, for 1 year before attending Clark University in Worcester, Massachusetts. Here he met G. Stanley Hall, who advocated the investigation of the mental traits and abilities of children, which left a distinct impression on Gesell long after he received his Ph.D. in Education from Clark in 1906 and left to further his career. It was G. Stanley Hall’s influence, perhaps more than any other, that ignited Gesell’s interest in child development and which would last him the rest of his career. In 1911, Gesell accepted an assistant professorship at Yale University, teaching courses in education at the graduate school while simultaneously enrolled at Yale Medical School. In the same year, he founded the Yale Psycho-Clinic, which is now the Yale Child Study Center. He received his M.D. in 1915, and in the years that followed began expanding his work at the Psycho-Clinic. Dr. Gesell’s primary interest was intellectually disabled children; however, he realized that an understanding of what constituted “normal” was essential to understanding “abnormal.” Dr. Gesell hypothesized that normal human behavior develops in a systematic, patterned way and can therefore be charted. Dr. Gesell, drawing on inspiration from his father, introduced cinematography as one of the main observational tools used for research at the Clinic. Using this and other groundbreaking tools and methods, Dr. Gesell produced the Gesell Developmental Schedules, which illustrated general patterns and stages of normal behavioral development. Arnold Gesell was also noteworthy for his extensive studies with twins and also for being vocal on issues related to education and family welfare. A prolific
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writer, Dr. Gesell published numerous books and scientific articles on a variety of subjects, from social and family issues to developmental diagnosis. Dr. Gesell served as Director of the Yale Child Study Center until 1948, when he retired and became professor emeritus. Milton J.E. Senn, M. D, superseded him in his role as Director of the YCSC. He died in 1961.
See Also ▶ Object Permanence
References and Reading Ames, L. B. (1989a). Arnold Gesell: Themes of his work. New York: Human Sciences Press. Ames, L. B. (1989b). The Arnold Gesell papers. Washington, DC: Library of Congress. Gesell, A. (1930). The guidance of mental growth in infant and child. New York: Macmillan. Gesell, A. (1932). How science studies the child. The Scientific Monthly, 34(3), 265–267. http://www.jstor. org/stable/15125 Gesell, A. (1934). An atlas of infant behavior: A systematic delineation of the forms and early growth of human behavior patterns. New Haven: Yale University Press. Gesell, A. (1940). The first five years of life: A guide to the study of the preschool child. New York: Harper & Brothers. Gesell, A. (1946). Cinematography and the study of child development. The American Naturalist, 80(793), 470–475. Accessed 22 Mar 2010. http://www.jstor. org/stable/2458189 Gesell, A. (1952). In E. G. Boring et al. (Eds.), A history of psychology in autobiography (Vol. 4, pp. 123–142). Worcester: Clark University Press. Gesell, A., Thompson, H., & Amatruda, C. S. (1934). Infant behavior: It’s genesis and growth. New York: McGraw-Hill. Gesell, A., Thompson, H., & Amatruda, C. S. (1938). The psychology of early growth, including norms of infant behavior and a method of genetic analysis. New York: Macmillan. Gesell, A., Ilg, F., & Ames, L. B. (1946). The child from five to ten. New York: Harper & Brothers. Herman, E. (2001). Families made by science: Arnold Gesell and the technologies of modern child adoption. Isis, 92, 684–715. Miles, W. R. (1964). Arnold Lucius Gesell 1880–1961: A biographical memoir. Washington, DC: National Academy of Sciences.
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Gestural Imitation ▶ Body Movements, Imitation of
Gesture Form and Function in ASD Ashley B. de Marchena Department of Behavioral and Social Sciences, University of the Sciences, Philadelphia, PA, USA
Definition Communicative co-speech gestures are spontaneously produced body movements (primarily hand movements) that occur during fluent speech and add both semantic and pragmatic content to communication.
Historical Background Differences in co-speech gesture production have been clinically observed since Kanner first described autism in 1943 (▶ Kanner, Leo).
Current Knowledge Impairments in nonverbal communication (including gesture) are required for a diagnosis of autism spectrum disorder (ASD), and the absence of early gestures is considered a red flag for ASD in toddlers. Despite their clear relevance to social-communication functioning, gestures produced during the context of developed, fluent speech have received limited empirical attention. This entry reviews what is known about both the production and comprehension of these co-speech gestures in verbally fluent children, adolescents, and adults with ASD.
Gestural Imitation
Gesture Versus Speech: How Does Gesture Communicate? Speech and gesture both convey meaning but in different, complementary formats. Speech conveys informational in discrete/digital units (i.e., words) that are culturally defined and consistently used to mean the same thing across contexts (i.e., consistent semantics). Speech presents this information in a linear, sequential format (i.e., consistent syntax). In contrast, gestures are “global-synthetic: a representation that is not divided into parts but rather captures the whole of the referent” (Goldin-Meadow and Alibali 2013). Further, whereas the meaning of words is almost always arbitrarily related to their sound, the meaning of gesture is often directly related to its form (i.e., iconicity). As an example, imagine a child describing a roller coaster: “first it went up up up, then it slowed down, then we shot back down to the ground and went around and around!” In speech, this information must be conveyed one unit at a time and combined using the rules of syntax. Now picture the gestures that might accompany this description – they are likely portrayed continuously across the entire utterance, showing the roller coaster as a whole, and incorporating multiple features at once, including shape, speed, and direction. Note also that the child’s gestures are likely to portray information that may be absent or difficult to describe in speech. For example, in what way did the roller coaster go around and around? Was it a loop-deloop, or was it a flat circle parallel to the ground? This information, while clunky and complicated to describe in speech, is likely to be automatically enacted in gesture as the child’s visual representation of the roller coaster spills out into their hands (i.e., Hostetter and Alibali’s “gesture as simulated action” framework). For an excellent review on the role of gesture in speaking and learning in typical development, see GoldinMeadow and Alibali (2013). Gesture benefits communication both by supporting and elaborating information conveyed in speech and also by engaging listeners’ attention and interest during discourse; thus, gestures alter and enhance the communicative signal, both by adding content to the discourse and by adding
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social structure to the discourse. Although debated, most gesture theorists believe that gesture and speech are fundamentally part of the same system; thus, to truly understand gesture production and comprehension, it must be studied within the context of its integration with speech. Accordingly, the current entry reviews only research that includes both modalities, across both production and comprehension. Gesture Production in ASD There are at least three reasons why the field should care about understanding gesture production in ASD during developed, fluent speech: (1) co-speech gestures enhance the communicative signal, (2) co-speech gestures benefit a speakers’ own cognitive processes, and (3) co-speech gestures may provide a means for elucidating qualitative differences in behavior in ASD that have been challenging to describe and quantify. These are reviewed below. Co-speech Gestures Enhance the Communicative Signal
From the perspective of production, one way to look at how much content gesture adds to speech is simply to look at the rate of gestures produced over a discourse (i.e., gestures per minute/word/ utterance). Gesture rate is often assumed to be reduced in ASD throughout the lifetime, because reductions in gesture are such a prominent sign of ASD in toddlers. In fact, gesture rate does not appear to be consistently reduced in verbally fluent children, adolescents, and adults on the autism spectrum. While a number of studies have demonstrated small reductions in gesture rate across different kinds of tasks (de Marchena and Eigsti 2014; Medeiros and Winsler 2014; Silverman et al. 2017; So et al. 2014), many studies have failed to demonstrate group differences in case-control studies using similar tasks (Attwood et al. 1988; Capps et al. 1998; de Marchena and Eigsti 2010; de Marchena et al. 2018; Hobson et al. 2010; Morett et al. 2016; So and Kit-Yi Wong 2016). Taken as a whole, the literature suggests that gesture rate may be slightly reduced in ASD, but not enough to account for the very noticeable differences in gesture production
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that have long been reported by clinicians. So if rate does not adequately portray the picture of atypical gesture in ASD, let us now turn to what gesture communicates. Gestures add content to verbal interactions in multiple ways. Gestures can add concrete visuospatial information to speech (e.g., imagine someone describing a tiny egg they found on a hike while holding their finger and thumb a half-inch apart). They can add emphasis or signal uncertainty. They also serve critical pragmatic functions that are often overlooked, such as linking the context of the physical environment to the contents of speech, thereby allowing listeners to make inferences about a speaker’s intended meaning (e.g., imagine a speaker who says “wow, it’s getting stuffy in here” while pointing at a window near you; Kelly et al. 1999). The functions and meanings of gestures are typically quantified by categorizing gestures into distinct “types”; there are multiple systems for this. Categorizing gesture presents certain challenges, as gesture is a holistic, analog system that does not categorize easily. The first study to look at gesture production in ASD across different types of co-speech gestures (Attwood et al. 1988) found differences in the types of gestures produced; specifically, adolescents with ASD did not use expressive gestures (i.e., gestures making reference to emotions or other internal states), but were comparable to controls in their use of pointing and instrumental gestures. People with ASD may use more iconic gestures (i.e., gestures depicting physical aspects of a referent, such as shape, size, or motion; “descriptive” gestures on the ADOS) than controls (Morett et al. 2016; So and Kit-Yi Wong 2016), perhaps because enacting experiences is more accessible than verbalizing them (Capps et al. 1998). However, not all studies find an increased rate of iconic gesture production in ASD, and some studies find no difference in gesture types at all (de Marchena and Eigsti 2010; Silverman et al. 2017). In addition to conveying specific content, gestures also add social structure to the discourse. For example, gestures may be used to cite another person’s contribution to the conversation (e.g., an open palm moving toward an interlocutor could
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be translated as, “like you said earlier”); to signal agreement, disagreement, hesitation, or emphasis; and to regulate turn-taking dynamics (e.g., “you go ahead,” or “I’m still talking”). Thus, gesture helps structure the interaction itself. Autistic adults have been found to use these “interactive gestures” (Bavelas et al. 1992) more often than controls, particularly to help regulate conversational turn-taking, suggesting again that gesture may be a particularly accessible communicative modality (de Marchena et al. 2018). Gesture can also structure a conversational exchange through the use of anaphor, in which a physical area within the gesture space is used to consistently refer to a specific referent within a discourse (e.g., when talking about two friends, whenever I talk about Sadie, I always gesture toward my upper right, but when talking about Oliver, I always gesture toward my upper left). During an explanation task, children with ASD were found to be internally consistent in their own use of anaphor; however, when an examiner suggested specific locations for referents by using anaphor herself (e.g., the toothbrush is on the right, the toothpaste is on the left), children with ASD were less likely to adopt the spatial locations indicated by the examiner (So and KitYi Wong 2016) relative to controls. This suggests weaknesses in attending to others’ gestures and subsequently incorporating that information into one’s own communication, which could easily lead to communication breakdowns. On the other hand, there is some evidence that individuals with ASD intend for at least some of their own gestures to be communicative. Like controls, adolescents with ASD produce more gestures when in the presence of a visible listener (Morett et al. 2016), suggesting that they recognize the communicative benefits of gesture and do not simply gesture to facilitate their own cognitive processes. Co-speech Gestures Benefit a Speakers’ Own Cognitive Processes
In addition to its social-communicative power, gesture also confers a range of benefits for speakers themselves. Some of these benefits relate directly to communication: for example, gesturing
Gesture Form and Function in ASD
can help speakers plan and organize the contents of a story. The act of gesturing can also help facilitate word finding; indeed, when speakers are prevented from gesturing, they are less likely to recover from tip-of-the-tongue experiences. In addition to these communication-relevant benefits, gesture also facilitates cognitive processes that are relatively independent of communication, such as working memory and spatial reasoning. Strikingly, gesture can cause knowledge change – either directly (the act of gesturing can cause cognitive change, such as learning a new mathematical concept) or socially (gesture can convey information about a speaker’s readiness to learn and current knowledge state, allowing teachers or therapists to tailor their instruction in the moment). In the only study to probe gesture’s benefits on cognition in ASD, gestures produced by adolescents with ASD were measured during both a storytelling task and an executive function task. Teens with ASD produced fewer gestures than controls during the storytelling task (in which gestures serve a range of socialcommunication functions) but more gestures during the executive function task, in which gestures are more likely to support cognitive processes such as visual attention and concentration (de Marchena and Eigsti 2014). These findings suggest that gesture does convey cognitive benefits for speakers with ASD. Co-speech Gestures May Elucidate Qualitative Differences in Behavior in ASD
Finally, the way in which co-speech hand gestures are produced by speakers may provide an assay for measuring qualitative features of behavior that have thus far proved difficult to quantify in ASD. Per DSM-5 (American Psychiatric Association 2013) criteria, nonverbal communication in ASD is defined both in terms of quantity (i.e., “total lack of. . .”) and quality (i.e., “abnormalities in. . .”). As reviewed above, by many concrete/ objective measures, gesture production in ASD is quite similar to gesture production by controls: gestures are produced at a similar rate and for roughly the same purposes. Yet this similarity is quite inconsistent from clinical impressions, which often report large differences in how people
Gesture Form and Function in ASD
with ASD gesture, and, accordingly, with clinical measures. Quantity of nonverbal communication is relatively well-characterized on ASD questionnaires. The Autism Diagnostic Observation Schedule (▶ Autism Diagnostic Observation Schedule), a clinical observational tool, prompts clinicians to attend to both quality and quantity of different gesture types. However, the specific ways in which gestures might differ qualitatively in ASD have yet to be adequately characterized empirically. Two studies have looked at how gesture production affects overall communication quality. de Marchena and Eigsti (2010) recorded adolescents with ASD telling the “Monkey” cartoon from the ADOS, in which participants were prompted to tell a story based on six black-and-white line drawings. They then played recordings of these adolescents to diagnosis-naïve college students either with or without video (i.e., audio only). Controls were rated as telling higher quality stories in the video condition (i.e., when raters could see them); in contrast, teens with ASD were rated as telling higher-quality stories in the audio-only condition. This suggests that something about the gestures, facial expressions, or general movements produced by autistic teens may have been distracting to observers, whereas these behaviors benefited communication in controls. Similarly, Morett and colleagues (2016) found that quality ratings for stories produced by teens with and without ASD differed when participants were speaking to a visible interlocutor, but did not differ when told to a hidden interlocutor (and participants were therefore using fewer nonverbals). Morett and colleagues further found that features of speech, gesture, and speech-gesture combinations predicted communication quality in controls, whereas only speech predicted communication quality in teens with ASD, suggesting once again that gesture does not benefit communication to the same extent in ASD. As described above, gestures are often used at the same rate, and for similar communicative functions, in ASD. So why are they not benefiting communication to the same extent? Several studies have now looked at the quality of each individual gesture produced within a discourse and
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consistently found large group differences in children, adolescents, and adults on the spectrum (de Marchena et al. 2018; Hobson et al. 2010; Hobson and Lee 1998; Silverman et al. 2017). Group differences have been identified in terms of (1) the physical form of the gesture (Hobson et al. 2010; Hobson and Lee 1998), (2) the clarity of the gesture as a communicative act (i.e., whether a movement was a gesture or not; Silverman et al. 2017), and (3) the intended meaning or purpose of the gesture (de Marchena et al. 2018; Silverman et al. 2017). Further supporting the claim that qualitative features of gesture may better describe ASD, Silverman and colleagues (2017) found that qualitative features of gesture predicted clinical measures (e.g., ADOS scores), whereas gesture rate did not. Further research is needed to understand how these differences may be related within individuals with ASD (e.g., are atypically formed gestures harder to identify as acts of communication)? So what about gesture in ASD leads to reduced quality and, in turn, less communicative power? More research is needed to answer this question and to point to new possible targets for intervention. One possibility is that gestures are less wellintegrated into speech, making it less clear that gesture and speech represent a unified package of meaning. In fact, gesture and speech are less temporally synchronous in ASD (de Marchena and Eigsti 2010; Morett et al. 2016), and the degree of asynchrony between speech and gesture predicts observers’ ratings of communication quality (de Marchena and Eigsti 2010). Observed differences in speech-gesture synchrony are consistent with the growing literature on difficulties with multimodal integration in ASD. Second, atypicalities in gesture may be secondary to motor skills differences in ASD that are now widely understood to be part of the behavioral phenotype. Specifically, difficulties with praxis (▶ Praxis), or the ability to perform skilled movements, are strongly theoretically linked to co-speech gesture production, particularly when considering gestures that involve motor enactment of visuospatial features, such as shape or motion. The relationship between gesture and praxis, or motor behavior in general, has not yet been directly tested.
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Hobson and Lee (1998), who reported a decreased rate and quality of spontaneous greeting/farewell waves in ASD, informally described the waving as “ill-directed, perfunctory, or clumsy and limp” (Hobson and Lee 1998, p. 124), consistent with a wide range of possible social-motor differences and consistent with stakeholder reports that gestures in ASD are often produced in surprising or odd ways (e.g., pointing with the middle finger, which can run the risk of the occasional faux pas!). Gesture Comprehension in ASD For neurotypical individuals, gestures facilitate comprehension both by enhancing information already conveyed in speech and by communicating information that goes beyond speech. The small literature on gesture comprehension in ASD supports the conclusion that bimodal processing (i.e., comprehension of speech-gesture combinations) is slowed and inefficient in ASD, despite relatively intact accuracy on structured tasks. People with ASD thus may benefit less from gestures during spontaneous conversation, even if they are able to parse gestures themselves when given enough time. For example, when given instructions presented in both speech and gesture, both adolescents (Silverman et al. 2010) and adults (Canfield et al. under review) with ASD are slower to process these speech-gesture combinations compared to controls. This effect is particularly strong when the information presented in gesture is incongruent with the information in speech (Canfield et al. under review), suggesting that autistic adults may prioritize the verbal modality over the gestural modality. However, slowed processing is found even when speech and gesture present the same information, for example, saying “find the loop,” while tracing a loop with a finger. In fact, controls tend to be faster to respond when presented with semantically congruent information across speech and gesture, whereas teens with ASD respond more quickly when information is presented in the audio channel only. These findings suggest that individuals with ASD are slowed by the additional processing demands of incorporating gestural information (Silverman et al. 2010), even when
Gesture Form and Function in ASD
this information is semantically identical to speech. Neural processing of communicative co-speech gestures also appears to be atypical in ASD. In the only fMRI study of co-speech gesture in ASD (Hubbard et al. 2009), participants passively viewed videos of people talking either with or without accompanying beat gestures (i.e., gestures conveying emphasis, with little to no specific semantic content). Typical children showed increased activation of the superior temporal sulcus while viewing beat gestures, suggesting that they interpreted these movements as communicative; children with ASD, on the other hand, increased visual cortex activation, suggesting that while they viewed the movements as visually salient, they did not automatically interpret them as a signal relevant to communication. These differences in gesture processing, which have been observed across measures of reaction time, eye tracking, and fMRI signal, are in sharp contrast to gesture comprehension accuracy in ASD on behavioral tasks, which is generally strong. Children with ASD are able to understand deictic, iconic/descriptive (Dimitrova et al. 2017), and emblems/conventional gestures (Attwood et al. 1988) on structured tasks. Likewise, adults with ASD are as accurate as controls at identifying whether an iconic/descriptive gesture matches a target action (e.g., chopping; Canfield et al. under review). Comprehension accuracy may be reduced when gestures are unexpected or atypical (e.g., nodding the head toward an object instead of pointing at it; Hobson et al. 2010). A major limitation of the comprehension literature thus far is that it has been conducted exclusively with relatively simple, trial-by-trial, highly structured tasks. To date, just one study provides a hint at what gesture comprehension in ASD looks like in a more naturalistic setting. Using a problemsolving task that parents and children completed together, Medeiros and Winsler (2014) found that in controls, parents gestured more when their kids weren’t as good at the task, but this relationship not observed in ASD. This suggests that parents of neurotypical children perceive that their kids benefit more from nonverbal communication support, which is not assumed by parents of children with ASD.
Gesture Form and Function in ASD
Future Directions As reviewed above, by many concrete/objective measures, gesture production in ASD is quite similar to gesture production by controls: gestures are produced at a similar rate, and for roughly the same purposes. This is quite inconsistent from clinical impressions and established clinical instruments, which often report large differences in how people with ASD gesture. In contrast, qualitative differences in how gesture is performed or integrated into conversation are robust; understanding the nuance of these differences will lead to a richer understanding of communication in ASD. One potential starting point is testing the relationship between gesture production and motor skills in ASD. With respect to comprehension, much more information is needed to truly understand how people with ASD process and understand gestures in the real world. Accuracy may be relatively high in ASD, when there is enough time – and when gestures are tagged by an experimenter as semantically relevant. However, the real world moves fast, and observed inefficiencies in processing suggest that gesture may often be left behind. Ultimately, we must understand and intervene at the level of the real world; thus, naturalistic research is needed to push the field forward and pave the way for interventions to improve nonverbal communication in verbally fluent individuals on the autism spectrum.
See Also ▶ Autism Diagnostic Observation Schedule ▶ Kanner, Leo ▶ Nonverbal Communication ▶ Praxis
References and Reading American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (DSM-5 ®). Arlington: American Psychiatric Publishing. Attwood, A., Frith, U., & Hermelin, B. (1988). The understanding and use of interpersonal gestures by autistic
2231 and Down’s syndrome children. Journal of Autism and Developmental Disorders, 18, 241–257. Bavelas, J. B., Chovil, N., Lawrie, D. A., & Wade, A. (1992). Interactive gestures. Discourse Processes, 15(4), 469–489. Canfield, A., Castelluccio, B., & Eigsti, I. M. (under review). Comprehension of gestures in adults with autism spectrum disorder: Gesture-speech incongruence. Capps, L., Kehres, J., & Sigman, M. (1998). Conversational abilities among children with autism and children with developmental delays. Autism, 2, 325–344. de Marchena, A., & Eigsti, I. M. (2010). Conversational gestures in autism spectrum disorders: Asynchrony but not decreased frequency. Autism Research, 3(6), 311–322. de Marchena, A., & Eigsti, I. M. (2014). Context counts: The impact of social context on gesture rate in verbally fluent adolescents with autism spectrum disorder. Gesture, 14(3), 375–393. de Marchena, A., Kim, E. S., Bagdasarov, A., ParishMorris, J., Maddox, B. B., Brodkin, E. S., & Schultz, R. T. (2018). Atypicalities of gesture form and function in autistic adults. Journal of Autism and Developmental Disorders. https://doi.org/10.1007/ s10803-018-3829-x. Dimitrova, N., Özçalışkan, Ş., & Adamson, L. B. (2017). Do verbal children with autism comprehend gesture as readily as typically developing children? Journal of Autism and Developmental Disorders, 47(10), 3267–3280. https://doi.org/10.1007/s10803017-3243-9. Goldin-Meadow, S., & Alibali, M. W. (2013). Gesture’s role in speaking, learning, and creating language. Annual Review of Psychology, 64(1), 257–283. https://doi.org/10.1146/annurev-psych-113011143802. Hobson, R. P., & Lee, A. (1998). Hello and goodbye: A study of social engagement in autism. Journal of Autism and Developmental Disorders, 28, 117–127. Hobson, R. P., García-Pérez, R. M., & Lee, A. (2010). Person-centred (deictic) expressions and autism. Journal of Autism and Developmental Disorders, 40(4), 403–415. https://doi.org/10.1007/s10803-0090882-5. Hubbard, A. L., Wilson, S. M., Callan, D. E., & Dapretto, M. (2009). Giving speech a hand: Gesture modulates activity in auditory cortex during speech perception. Human Brain Mapping, 30(3), 1028–1037. Kelly, S. D., Barr, D. J., Church, R. B., & Lynch, K. (1999). Offering a hand to pragmatic understanding: The role of speech and gesture in comprehension and memory. Journal of Memory and Language, 40(4), 577–592. https://doi.org/10.1006/jmla.1999.2634. Medeiros, K., & Winsler, A. (2014). Parent–child gesture use during problem solving in autistic spectrum disorder. Journal of Autism and Developmental Disorders, 44(8), 1946–1958. https://doi.org/10.1007/s10803014-2069-y.
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2232 Morett, L. M., O’Hearn, K., Luna, B., & Ghuman, A. S. (2016). Altered gesture and speech production in ASD detract from in-person communicative quality. Journal of Autism and Developmental Disorders, 46(3), 998–1012. https://doi.org/10.1007/s10803-015-2645-9. Silverman, L. B., Bennetto, L., Campana, E., & Tanenhaus, M. K. (2010). Speech-and-gesture integration in high functioning autism. Cognition, 115(3), 380–393. https://doi.org/10.1016/j.cognition. 2010.01.002. Silverman, L. B., Eigsti, I.-M., & Bennetto, L. (2017). I tawt i taw a puddy tat: Gestures in canary row narrations by high-functioning youth with autism spectrum disorder: Gesture production in ASD. Autism Research. https://doi.org/10.1002/aur.1785. So, W.-C., & Kit-Yi Wong, M. (2016). I use my space not yours: Use of gesture space for referential identification among children with autism spectrum disorders. Research in Autism Spectrum Disorders, 26, 33–47. https://doi.org/10.1016/j.rasd.2016.03.005. So, W.-C., Wong, M. K.-Y., Lui, M., & Yip, V. (2014). The development of co-speech gesture and its semantic integration with speech in 6- to 12-year-old children with autism spectrum disorders. Autism. https://doi.org/ 10.1177/1362361314556783.
Gestures Maureen Nevers Center on Disability and Community Inclusion, University of Vermont, Burlington, VT, USA Augmentative Communication Consultant, Center on Disability and Community Inclusion, Burlington, VT, USA
Synonyms Nonsymbolic communication
Definition Gestures are actions produced with the intent to communicate. Gestures can involve fingers, hands, arms, facial features, or the entire body (Iverson and Thal 1998). When these actions are performed with the purpose of affecting another person’s behavior, they are considered to be intentional communication acts (Bates 1976). Intentional use of gestures develops before speech and continues to develop as the child’s speech emerges (Iverson and Thal 1998).
Gestures
Gestures can be organized according to their purpose or communicative function. Bruner (1981) classified gestures according to three broad categories based on functions: social interaction, behavior regulation, and joint attention. Differences have been found in the gesture development of children later diagnosed with autism spectrum disorders (ASD). Social interaction gestures are used to initiate or maintain the attention of another person in a social experience. These gestures might include waving “hi” or “bye,” requesting games or routines, and shaking the head for “yes” or “no.” In their sample of 9–12-month-old infants, Colgan et al. (2006) examined the emergent use of social interaction gestures in infants later diagnosed with autism. They found that types of gestures used were strongly associated with autism status, suggesting that the examination of the number of different gestures in a child’s repertoire may be useful for detecting impairments in their language development. Behavior regulation gestures manage the behavior of others, such as requesting an object, getting another person to do something, or getting another person to stop doing something. In studies of the communicative functions of gestures, children with autism show relative strengths in the use of gestures serving the function of behavior regulation at preschool age (Mundy et al. 1990) and at school age (Wetherby and Prutting 1984). Gestures used to establish joint attention direct another person’s attention to an object or event and usually include manual pointing or making eye contact with an object. Children with ASD demonstrate significant weaknesses in the use of gestures for the purpose of joint attention (directing another’s attention or sharing interest with another person) both at preschool age (Mundy et al. 1990) and school age (Loveland and Landry 1986).
See Also ▶ Augmentative and Alternative Communication (AAC) Device ▶ Nonverbal Communication
Giftedness
References and Reading Acredolo, L. P., & Goodwyn, S. W. (1985). Symbolic gesturing in language development. Human Development, 28, 40–49. American Psychiatric Association. (2000). Pervasive developmental disorders. In Diagnostic and statistical manual of mental disorders (4th ed., pp. 69–70). Washington, DC: American Psychiatric Association. Text revision (DSM-IV-TR). Bates, E. (1976). Language and context. The acquisition of pragmatics. New York: Academic. Bruner, J. (1981). The social context of language acquisition. Language and Communication, 1(2/3), 155–178. Colgan, S. E., Lanter, E., McComish, C., Watson, L. R., Crais, E. R., & Baranek, G. T. (2006). Analysis of social interaction gestures in infants with autism. Child Neuropsychology, 12, 307–319. Crais, E. (2008). Using the best available evidence to identify infants and toddlers with (or at risk for) communication deficits (presentation). Retrieved on December 18, 2010 from Telability’s website: http:// www.telability.org/search/search.pl Crais, E., Douglas, D. D., & Campbell, C. C. (2004). The intersection of the development of gestures and intentionality. Journal of Speech, Language, and Hearing Research, 47, 678–694. Iverson, J. M., & Thal, D. J. (1998). Communicative transitions: There’s more to the hand than meets the eye. In A. M. Wetherby, S. F. Warren, & J. Reichle (Eds.), Transitions in prelinguistic communication (pp. 59–86). Baltimore: Brookes. Landa, R. J., Holman, K. C., & Garrett-Mayer, E. (2007). Social and communication development in toddlers with early and later diagnosis of autism spectrum disorders. Archives of General Psychiatry, 64, 853–864. Loveland, K., & Landry, S. H. (1986). Joint attention and language in autism and developmental language delay. Journal of Autism and Developmental Disorders, 16, 335–349. McDuffie, A., Yoder, P., & Stone, W. (2005). Prelinguistic predictors of vocabulary in young children with autism spectrum disorders. Journal of Speech, Language, and Hearing Research, 48, 1080–1097. Mundy, P., Sigman, M., & Kasari, C. (1990). A longitudinal study of joint attention and language development in autistic children. Journal of Autism and Developmental Disorders, 20, 115–128. Sigman, M., & Casari, C. (1995). Joint attention across contexts in normal and autistic children. In C. Moore & P. J. Dunham (Eds.), Joint attention: Its origins and role in development (pp. 189–204). Hillsdale: Laurence Erlbaum. Thal, D., & Tobias, S. (1992). Communicative gestures in children with delayed onset of oral expressive vocabulary. Journal of Speech and Hearing Research, 35, 1281–1289. Thal, D., & Tobias, S. (1994). Relationships between language and gesture in normal and late-talking toddlers. Journal of Speech and Hearing Research, 37, 157–171.
2233 Tomasello, M. (1995). Joint attention as social cognition. In C. Moore & P. J. Dunham (Eds.), Joint attention: Its origins and role in development (pp. 103–130). Hillsdale: Laurence Erlbaum. Werner, E., & Dawson, G. (2005). Validation of the phenomenon of autistic regression using home videotapes. Archives of General Psychiatry, 62, 889–895. Wetherby, A., & Prutting, C. (1984). Profiles of communicative and cognitive-social abilities in autistic children. Journal of Speech and Hearing Research, 27, 364–377. Wetherby, A. M., Watt, N., Morgan, L., & Shumway, S. (2007). Social communication profiles of children with autism spectrum disorders late in the second year of life. Focus on Autism and Other Developmental Disorders, 37, 960–975. Williams, J., Whiten, A., & Singh, T. (2004). A systematic review of action imitation in autism spectrum disorder. Journal of Autism and Developmental Disorders, 34, 285–299.
GI Issues ▶ Gastrointestinal Disorders and Autism
Giftedness Laurent Mottron1,2 and Michelle Dawson3 1 Center of Excellence in Pervasive Developmental Disorders of University of Montreal, Montreal, QC, Canada 2 Department of Psychiatry, Riviere-des-Prairies Hospital, University of Montreal, Montreal, QC, Canada 3 Hôpital Riviére des Prairies, Centre de recherche du CIUSS du Nord de l’île de Montréal et département de psychiatrie de l’Université de Montréal, Montréal, QC, Canada
Definition Giftedness refers to high values in the distribution of skills fundamental to human accomplishments. Intelligence, talent, and creativity are neighboring but distinct notions. Whereas domain-general giftedness partially overlaps with the notion of intelligence, domain-specific giftedness calls its nature into question, and there is no single
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intelligence-test-based threshold which consensually defines giftedness. Talent describes the outcome of giftedness in actual accomplishments, which depends on a combination of giftedness, training, and more or less optimal conditions for the acquisition of expertise. Creativity designates the capacity for innovation, which may or may not be associated with giftedness, and does not overlap with intelligence. Giftedness includes a genetic and/or innate component in the form of an overrepresentation of giftedness within some families or in the form of genetic variation favoring atypical information-processing abilities. The association between giftedness and autism is multifaceted. First, giftedness has been associated with a certain number of psychological particularities, including an overattention and emotional reactivity to the surrounding world, a tendency to interact with older peers, indifference toward social conventions, etc. Some of these particularities overlap with the autistic phenotype and may be difficult to separate. Second, the distribution curve of autistic intelligence features higher variance than that of nonautistic individuals, such that some autistics present extreme values of general intelligence, or “double exceptionality.” Third, autistic peaks of ability, or domains where autistics as a group perform at a superior level than their IQ (as measured with certain instruments) would have predicted, represent a “group giftedness” in the form of an innate advantage for certain cognitive operations. Peaks of ability represent the main element which makes the giftedness versus autism distinction difficult in clinical settings. For example, an advance of 2 years in word-decoding skills may announce an autism diagnosis, giftedness without autism, or a combination of both. Finally, savant syndrome is strongly associated with autism and is defined by the presence of exceptional performance in neurodevelopmental variants. All possible combinations of giftedness, intelligence, talent, and autism may be manifested in savant syndrome. The range of tasks for which savants display giftedness is variable, with some savants possessing multiple areas of excellence, combined or not with deficits in other areas. The degree, adaptive value, recognition by others, eventual outcomes, etc., of talent arising
Gilles de la Tourette Syndrome
from giftedness in autism all vary to the extreme.
See Also ▶ Autistic Savants ▶ Enhanced Perceptual Functioning ▶ Psychological Assessment
References and Reading Mackintosh, N. J. (2011). IQ and human intelligence (2nd ed.). New York: Oxford University Press. Miller, L. K. (1999). The savant syndrome: Intellectual impairment and exceptional skill. Psychological Bulletin, 125, 31–46. Mottron, L., Dawson, M., & Soulières, I. (2009). Enhanced perception in savant syndrome: Patterns, structure and creativity. Philosophical Transactions of the Royal Society B: Biological Sciences, 364, 1385–1391. Nicpon, M. F., Allmon, A., Sieck, B., & Stinson, R. D. (2011). Empirical investigation of twiceexceptionality: Where have we been and where are we going? Gifted Child Quarterly, 55, 3–17. Obler, L. K., & Fein, D. (Eds.). (1988). The exceptional brain: Neuropsychology of talent and special abilities. New York: Guilford.
Gilles de la Tourette Syndrome ▶ Tourette Syndrome
Gilliam Autism Rating Scale (GARS) Janine Robinson CLASS, Cambridgeshire and Peterborough NHS Foundation Trust, Fulbourn, Cambridgeshire, UK NHS England, London, UK
Synonyms GARS; GARS-2; Gilliam Autism Rating Scale, Second Edition; Gilliam Autism Rating Scale, Third Edition
Gilliam Autism Rating Scale (GARS)
Description The Gilliam Autism Rating Scale is a standardized instrument for the assessment and diagnosis of autism and other behavioral conditions (GARS). It relies on parental or teacher reports regarding the child’s presentation and behavior and is a quick measure to administer. Furthermore, no significant training is necessary. Nonetheless, some studies suggest a tendency to “miss” children who would otherwise meet the diagnostic criteria for an autism spectrum disorder (Lecavalier 2005; South et al. 2002). The scale was revised (GARS-2) in 2006 and based on the DSM-IV-TR (American Psychiatric Association 2000) and Autism Society of America definitions of autism. The core items (42) on the three subscales were retained in the revised version, although some minor changes in wording were made. The GARS-2 relies on parental rating of directly observed behaviors and developmental history of delay or abnormality. The instrument is comprised of three subscales, namely, Stereotyped Behaviors, Communication, and Social Interaction. Instructions are straightforward and require the clinician/parent to rate a number of items according to the frequency with which the behaviors occur. Ratings are presented in a Likert style, from 0 (never observed), 1 (seldom observed), 2 (sometimes observed), to 3 (frequently observed). Additional guidance/ example is provided for each rating, i.e., sometimes observed – individual behaves in this manner 3–4 times per 6-h period. Behavior is rated under typical circumstances, for instance, in the presence of familiar people, in usual places, and during day-to-day activities. Scores are summed to yield a total raw score for Stereotyped Behaviors. The Communication Subscale and Social Interaction Subscale are completed in the same manner, i.e., on the basis of behaviors observed by the parent/caregiver. However, if the individual does not have speech and does not sign or use other forms of communication, the former is omitted. A total raw score is derived by summing the ratings on each subscale (14 items each). Raw scores on individual subscales are converted to
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standard scores and represented as percentiles. Total standard scores for all 42 items yield a sum of standard scores. This is represented as an Autism Index and corresponds to a given percentile. The response booklet allows the plotting of the individual’s profile. Furthermore, interpretation of standard scores and the Autism Index yields three possible outcomes with respect to a diagnosis of autism: very likely, possibly, and unlikely. Information from parental interviews relates specifically to delays and abnormal functioning. This data may be gathered at interview or be completed by a parent directly. Responses take the form of yes or no. The questions relate to delays in social interaction and language used in social communication, specifically during the first 3 years of life. Abnormal functioning within the first 3 years of life in social interaction, language used in social communication, and symbolic or pretend play is assessed in the subsequent section. A no response to any of these questions indicates that the individual meets the DSM-IV-TR criteria for delays or abnormal functioning. Additional questions are presented in the response form for the benefit of the clinician when reflecting on the GARS-2 results and considering the diagnostic outcome. The measure was revised again in 2013 (GARS-3) with items reflecting the updated autism diagnostic criteria (DSM-5) (American Psychiatric Association 2013). A further four items have been included, and the GARS-3 is comprised of six subscales (total of 58 items), viz., Restricted/Repetitive Behaviors (13), Social Interaction (14), Social Communication (9), Emotional Responses (8), Cognitive Style (7), and Maladaptive Speech (7). The examiner/practitioner can use the interpretation guide to score and conclude probability and severity of ASD. DSM-5 criteria are included in a diagnostic validation form to confirm that criteria are met.
Historical Background A number of screening and diagnostic measures for autism spectrum disorders have been developed in the past two decades. The Gilliam Autism
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Rating Scale (GARS, Gilliam 1995) was initially developed as a screening measure to identify individuals with autism in research and clinical settings. Although it was widely used, there was little empirical evaluation of the measure and its usefulness (Lecavalier 2005; Mazefsky and Oswald 2006; Pandolfi et al. 2010; Norris and Lecavalier 2010). Gilliam 2006; Eaves et al. 2006; Schopler et al. 1980; and Ehlers and Gillberg 1993 revised the original instrument retaining most of the items on the three subscales but adding a section (25 items) relating to delays or abnormalities during the first 3 years of life. The scoring system was changed, and the total score, namely, the Autism Quotient (AQ), was replaced by an Autism Index (AI). The purpose of the GARS-3 is to assist teachers, parents, and clinicians in identifying and diagnosing autism in individuals from 3 to 22 years of age. Furthermore, consistent with GARS-2, the revised tool aims to provide information regarding severity of the condition. The GARS-3 may also be used to rate change, and the manual provides examples of discreet target behaviors for all items which may be useful for research.
Psychometric Data Normative data for the GARS-2 were presented by Gilliam (2006). The scale was tested on a sample of 1,107 autistic children and adolescents and compared with scores of children with intellectual disabilities and those without any disabilities. The author reported that the measure successfully screened for autism with the children in the autism group having higher Autism Index (AI) scores than the children in the other groups. A cutoff score of 85 was selected for the AI, and the author reports sensitivity, specificity, and positive predictive values of 0.85 in the intellectual disability group. Reported values were 0.84 for the multiple disability group. The sensitivity of 1.0, specificity of 0.87, and positive
Gilliam Autism Rating Scale (GARS)
predictive value of 0.85 were reported in the control group. The author reported internal consistency for the subscales (Cronbach’s alpha): Stereotyped Behaviors (0.84), Communication (0.86), Social Interaction (0.88), and 0.94 for the Autism Index. The author reported that concurrent and positive predictive validity had been established (Gilliam 2006). However, independent studies of the GARS/ GARS-2 have not replicated these findings and have expressed concern about the nature of the normative sample and the poor sensitivity of the measure, hence missing a number of children on the spectrum (Lecavalier 2005; Mazefsky and Oswald 2006; Sikora et al. 2008; South et al. 2002). While Eaves et al. (2006) established more positive results for the sensitivity (0.83) and specificity (0.68), these were still significantly lower than the results published by the author. Pandolfi et al. (2010) examined the validity of the GARS-2 and were unable to match the psychometric properties previously described by the GARS’ authors. The authors report good psychometric properties for the GARS-3 (2014) with the measure accurately discriminating autistic children from non-autistic children (sensitivity ¼ 0.97, specificity ¼ 0.97, ROC/AUC ¼ 0.93). Subscale internal consistency reliability coefficients are reported to exceed 0.85 (content sampling), and test-retest reliability coefficients (time sampling) are reported to exceed 0.80 for the subscales and 0.90 for the Autism Indexes (AI). Furthermore, the authors report interrater reliability intraclass coefficients above 0.80 and 0.84 for the AI. Karren (2017) reviewed the GARS-3 and described improved properties, including the DSM-5-based items and an instructional goals booklet to assist in planning for intervention. The author expresses concern around interpretation of results for the older group (20–22 years) and those from ethnic minority groups. Hence, further data are needed regarding its reliability and validity in broader clinical practice.
Gilliam Autism Rating Scale (GARS)
Clinical Uses The GARS-2 is a revision of the GARS and aims to assist in the screening and diagnosis of autism in individuals from the age of 3 years to 22 years. It is a quick and inexpensive means of gathering information from parents, teachers, or caregivers taking between 5 and 10 min to complete. The revised version also aims to provide an indication of severity of the condition and provides the possibility of detecting change over time with respect to discreet behaviors. There are currently a number of screening measures and rating scales for autism spectrum disorders. These may differ with respect to their focus, strengths, and weaknesses but with the common goal of quantifying autistic features and assisting in the diagnostic process (Lecavalier 2005; Thabtah and Peebles 2019). While some instruments are designed to be administered by a clinician or trained rater, such as the ADI-R (Lord et al. 1994), the ADOS-2 (Lord et al. 2000), and the Childhood Autism Rating Scale (CARS, Schopler et al. 1980), others rely on parental and teacher ratings of behaviors. The ADOS-2 and CARS-2 ratings are also based on direct observations. While widely used in clinics and research, these measures are time intensive for professionals. Standardized, informant-rated scales, such as the Childhood Autism Spectrum Test (CAST, Scott et al. 2002), the Social Communication Questionnaire (SCQ, Rutter et al. 2003), and the Autism Spectrum Screening Questionnaire (ASSQ, Ehlers and Gillberg 1993), can provide an alternative method of gathering information in an easy and efficient manner. Screening measures also differ with respect to their respective levels. The SCQ, GARS, and GARS-2 are presented as level 2 instruments, i.e., they are used with individuals who are already identified as being at risk for some developmental disabilities and aim to differentiate those with a possible autism spectrum disorder and those with other kinds of
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conditions. However, a number of studies have demonstrated concerns about the GARS and GARS-2 with respect to clinical utility owing to psychometric properties. The GARS and GARS-2 may be useful as a general screening tool rather than a primary tool in the screening and assessment of autism spectrum disorder. The GARS-3 offers more robust information to assist in assessment of ASD but may need further examination regarding its utility in particular groups.
See Also ▶ ADI-R ▶ Childhood Autism Rating Scale ▶ Clinical Assessment ▶ Diagnostic Process ▶ Screening Measures
References and Reading Akshoomoff, N., Corsello, C., & Schmidt, H. (2006). The role of the autism diagnostic observation schedule in the assessment of autism spectrum conditions in school and community. California School Psychologist, 11, 7–19. American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders (4th ed., text rev.). Washington, DC: American Psychiatric Association. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: Author. Eaves, R.C., Woods-Groves, S., Williams, Jr, T.O., & Fall, A. (2006). Reliability and validity of the Pervasive Developmental Disorders Ratings Scale and the Gilliam Autism Rating Scale. Education and training in developmental disabilities, 41, 300–309. Ehlers, S., & Gillberg, C. (1993). The epidemiology of Asperger Syndrome. A total population study. Journal of child psychology and psychiatry, 34, 1327–1350. Gilliam, J. E. (1995). Gilliam autism rating scale. Austin: PRO-ED. Gilliam, J. E. (2006). Gilliam autism rating scale-2. Austin: PRO-ED. Gilliam, J. E. (2014). Gilliam autism rating scale (3rd ed.). Austin: Pro-Ed.
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2238 Howlin, P. (2000). Autism and intellectual disability: Diagnostic and treatment issues. Journal of the Royal Society of Medicine, 93, 351–355. Karren, B. C. (2017). A test review: Gilliam, J. E. (2014). Gilliam Autism Rating Scale–Third Edition (GARS-3). Journal of Psychoeducational Assessment, 35(3), 342–346. https://doi.org/10.1177/0734282916635465. Lecavalier, L. (2005). An evaluation of the Gilliam Autism Rating Scale. Journal of Autism and Developmental Disorders, 35, 795–805. Lord, C., & Rutter, M. (2012). Autism diagnostic observation schedule (2nd ed.). Los Angeles: Western Psychological Services. Lord, C., Rutter, M., & Le Couteur, A. (1994). Autism diagnostic interview-revised: A revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. Journal of Autism and Developmental Disorders, 24, 659–685. Lord, C., Rutter, M., DiLavore, P. C., & Risi, S. (1999). Autism diagnostic observation schedule. Los Angeles: Western Psychological Services. Lord, C., Risi, S., Lambrecht, L., Cook, E. H., Jr., Leventhal, B. L., DiLavore, P. C., et al. (2000). Autism diagnostic observation schedule – Generic: A standard measure of social and communication deficits associated with the spectrum of autism. Journal of Autism and Developmental Disorders, 30, 205–223. Mazefsky, C. A., & Oswald, D. P. (2006). The discriminative ability and diagnostic utility of the ADOS-G, ADI-R, and GARS for children in a clinical setting. Autism, 10(6), 533–549. Norris, M., & Lecavalier, L. (2010). Screening accuracy of level 2 autism spectrum disorder rating scales. A review of selected instruments. Autism, 14(4), 263–284. Pandolfi, V., Magyar, C. I., & Dill, C. A. (2010). Constructs assessed by the GARS-2: Factor analysis of data from the standardization sample. Journal of Autism and Developmental Disorders, 40(9), 1118–1130. Rutter, M., Bailey, A., & Lord, C. (2003). SCQ: Social communication questionnaire. Los Angeles: Western Psychological Services. Schopler E., Reichler R. J., DeVellis R. F., & Daly K. (1980). Toward objective classification of childhood autism: Childhood Autism Rating Scale (CARS). Journal of autism and developmental disorders, 10, 91–103. Scott, F., Baron-Cohen, S., Bolton, P., & Brayne, C. (2002). The CAST (Childhood Asperger Syndrome Test): Preliminary development of a UK screen for mainstream primary-school age children. Autism, 6(1), 9–31. Sikora, D. M., Hall, T. A., Hartley, S. L., Gerrard-Morris, A. E., & Cagle, S. (2008). Does parent report of behavior differ across ADOS-G classifications: Analysis of scores from the CBCL and GARS. Journal of Autism and Developmental Disorders, 38(3), 440–448. South, M., Williams, B. J., McMahon, W. M., Owley, T., Filipek, P. A., et al. (2002). Utility of the Gilliam autism
Gilliam Autism Rating Scale, Second Edition rating scale in research and clinical populations. Journal of Autism and Developmental Disorders, 32(6), 593–599. Thabtah, F., & Peebles, D. (2019). Early autism screening: A comprehensive review. International Journal of Environmental Research and Public Health, 16, 3502.
Gilliam Autism Rating Scale, Second Edition ▶ Gilliam Autism Rating Scale (GARS)
Gilliam Autism Rating Scale, Third Edition ▶ Gilliam Autism Rating Scale (GARS)
Girls Night Out Model, The T. Rene Jamison and Jessica Oeth Schuttler Center for Child Health and Development, University of Kansas Medical Center, Kansas City, KS, USA
Definition Girls Night Out (herein referred to as GNO) is a social skills and self-care program designed to address the unique needs of adolescent females with ASD/DD with goals to improve their socialemotional health. GNO is a manualized intervention program that incorporates empirically based strategies and gathers data on program outcomes resulting in a meaningful, socially valid intervention for participants. The program is also unique in that it incorporates typically developing peers as mediators of the strategies, providing socially valid cues and reinforcement for use of skills. Sessions take place in community settings where social and self-care opportunities would naturally occur.
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Historical Background Females with ASD are currently a minority (approximately 25%) of those diagnosed with ASD, and comprise only about 15% of the subjects in published research literature (Watkins et al. 2014). This discrepancy in prevalence is likely related to multiple factors, including an increased genetic risk for males, females with ASD being more likely to be missed, or diagnosed later (Begeer et al. 2013) perhaps because of milder symptom expression (Lai et al. 2015), or because of compensatory skills developed by girls with ASD (Mandy et al. 2012). Researchers and practitioners are beginning to study more closely the unique symptomatology, experiences, and support needs of females with ASD (Lai et al. 2015). For example, a study examining diagnostic criteria for higher functioning males and females (Hiller et al. 2014) found that not meeting criteria for “restricted or fixated interests. . .” was predictive of being female. Females were more likely to demonstrate partial impairment in nonverbal skills and no impairment within the “sharing of interests” category as compared to males. If sex differences within the general population suggest females use more nonverbal communication (i.e., eye gaze, gestures), this may explain findings as in Hiller’s study suggesting less or only partial impairment in nonverbal communication. It is important to note that there are key differences in social interaction and friendship styles between males and females. For example, male friendships tend to be more activity-based, while female relationships include more emotionfocused, empathic connections and relationships (Hannah and Murachver 1999). As research continues on the unique presentation or symptomatology for females with ASD, relatively little is known about potential differences regarding intervention approaches to promote social-emotional health in females with ASD. The GNO program, developed with a basis in empirical approaches, is the first published intervention study specifically targeted to females with ASD (Jamison and Schuttler 2017). The first
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author developed GNO in 2008 in response to needs identified from clinical experience.
Rationale or Underlying Theory Rationale Although females with ASD, particularly high functioning, may demonstrate less impaired social-communication skills at some points in development (Van Wijngaarden-Cremers et al. 2014), they often experience increased social and communication impairments during adolescence as compared to males with ASD (Kirkovski et al. 2013; Koenig and Tsatsanis 2005). Some individuals with ASD demonstrate variable adaptive skills or skills well below the expected performance given their cognitive functioning (Ditterline and Oakland 2009). Thus, for adolescent girls with ASD who have self-care skills below expected norms and limited social interactions, the potential impact on self-perception is significant. Compared to males, typically developing adolescent females demonstrate an increased risk for developing internalizing symptoms such as anxiety and depression (Galambos et al. 2003). The elevated risk for internalizing symptoms and increased complexity in social interaction coupled with social impairment as a core feature in ASD result in adolescent girls with ASD experiencing a “double hit” (Solomon et al. 2012) or “double whammy” (Jamison and Schuttler 2015). In fact, adolescent girls with ASD exhibit significantly more internalizing symptoms (e.g., anxiety, depression) compared to typically developing girls (Jamison and Schuttler 2015) and to boys with ASD (Solomon, et al. 2012), suggesting this is a critical area for intervention. These potential sex differences in social behavior and coexisting socialemotional challenges reflect unique targets within interventions and programs aimed at improving social competence. Underlying Theory GNO aims to improve self-perception and selfconfidence, anticipating that greater confidence
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will result in the increased likelihood of engaging in social “entry” skills and social activities and, in turn, that improved social skills and increased competence will result in improved selfperception and positive social-emotional health. The curriculum targets socially valid behaviors (e.g., reciprocal conversation, shared interests, independence) related to social norms of adolescent females and the unique needs of females with ASD (i.e., improved social emotional competence and emotional health, independence). Instruction and practice occur within age-expected activities, providing a meaningful social experience to individuals who often report limited social opportunities or friendships (Bauminger and Kasari 2000). GNO’s framework is based on social learning theory, behavioral and cognitive-behavioral theories, while embracing concepts related to selfdetermination, developing social competence, empowering young women, and building inclusive communities. The theory of change is rooted in Social Learning Theory (SLT) and related behavioral or cognitive-behavioral theories. SLT suggests that people learn new behaviors and/or improve skills through observation (Bandura 1971) and that behaviors are the result of reciprocal interactions between cognitive, behavioral, and environmental determinants. Behavioral theory describes learning skills or behaviors through positive reinforcement and environmental contingencies (Skinner 1988) while in cognitive-behavioral theory, skills are learned by understanding emotional and cognitive interpretations of events, with cognitive and behavioral rehearsal of appropriate behaviors (Kendall 2006). Our model postulates that behavior changes or skills develop based on some combination of and interaction between observation, practice, and cognitive restructuring that occurs under specific conditions. These conditions include: planned observations for discrete behaviors or skills (live models, video models); repeated exposure and practice across multiple exemplars (skills, people, settings, activities); cognitive restructuring as a result of specific feedback (in vivo coaching and specific praise) and reinforcement of target skills
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(differential attention, verbal feedback, token economy); and planned generalization through use of the desired skill within settings future performance is needed. GNO includes similar age girls without ASD/NDD that model skills and use of supports and help to craft opportunities for genuine practice across exemplars, creating frequent, yet natural opportunities to practice target skills. The premise is that experiencing success demonstrating these skills should increase the likelihood of engaging in the behavior in the future, which could be the result of reinforcement principles or cognitive appraisal (e.g., confidence, schema of skill, awareness, etc.), or likely some combination of both. Increased competence or self-perception of competence (confidence) leads to increased engagement in social experiences, which increase social competence and selfperception. While skill increase or enhancement is the immediate goal, a broader intention is to promote independence and positive socialemotional health by improving self-perception and confidence through individually identified goals rooted in the concept of self-determination. Conducting sessions in natural settings and providing training to community-based providers promote communities that are more inclusive.
Goals and Objectives The goals of GNO are primarily twofold: (1) Promote social-emotional health, primarily related to improved social competence, self-perception, self-determination, and perceived quality of life and reduced internalizing symptoms and (2) greater independence and competency in self-care and social skills. Target skills reflect a developmental framework considering expected skills for adolescent females, the unique needs of females with ASD, and individual goals and areas of growth. Improving Social-Emotional Health GNO is designed specifically to address common symptoms or challenges females with ASD often encounter, including fewer peer interactions and
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social experiences, variable social skills, limited self-confidence, and negative self-perception. By increasing competence and confidence in these social skill areas, the program aims to mitigate symptoms of social anxiety or depression. Specific objectives related to this goal vary based on individual needs of participants, with core areas related to these broad skillsets: increased frequency and improved quality of conversation entry and reciprocity, more social experiences (e.g., group activities, getting together, phone/text, etc.), setting, monitoring, and achieving goals, and improved self- and caregiverratings of social emotional health and competence (self-confidence, self-perception, quality of life, social competence). Greater Independence and Competency in Self-Care and Social Skills GNO addresses challenges related to independence completing adaptive behaviors, particularly related to self-care and social skills. Program objectives include fostering greater independence and reducing overreliance on family or other caregivers for self-care and social skills, potentially leading to increases in social supports from peers. The goal is that improved competence within this arena will influence confidence and selfperception, promoting overall social-emotional health. Specific skillsets targeted and measured include: frequency and type of self-care and social activities, and level of independence navigating these activities and tasks.
Treatment Participants Population Developed for girls with ASD and other related neurodevelopmental disorders (NDD). Due to the high prevalence of internalizing symptoms in adolescent females with ASD, this intervention targets a broader concept of social-emotional health, and thus appropriate for girls with developmental disability and co-existing mental health disorders or symptoms related to anxiety, depression, and poor self-perception.
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Age and Developmental Considerations Designed for girls about 14–19 years old, with positive outcomes found for girls ranging from 8th grade, high school, and a few years past high school graduation (e.g., 18–21 special education program). Girls of a variety of abilities can participate in GNO and benefit from the intervention. Previous inclusion criteria for research participation required participants to read words at or above a 4th grade level in order to reference printed materials and supports and to demonstrate some conversational speech. However, curriculum and materials are adaptable for individuals of a variety of abilities. Previous adaptations include modified materials (e.g., lower reading level, large print for visual impairments) and inclusion of girls with limited expressive language but capabilities to make some comments contributing to individual or group conversations and receptive abilities necessary to follow directions and comprehend conversation topics and lesson content. Because participants include girls with and without ASD/NDD, groups regularly include girls with a range of cognitive, academic, and communication abilities and various social experiences and interests. The current program description focuses on the adolescent age range, with adaptations currently in progress to modify the curriculum for school-age girls and young adults. Other Considerations Independence and choice are valued and thus consent for participation is required from participants as well as caregivers. However, it is not uncommon for participants to express a hesitancy to begin the program and associated social activities. Parents or caregivers are coached at enrolment on how to present details of the program, and acknowledge feelings and concerns of their participant, while also maintaining focus on potential positives of the experience. Our experience implementing the program over time suggests that frequently, while individuals may not express a desire for friendships or an initial interest in GNO, by committing to attending and attempting activities, they are often surprised by the positive effects they experience. These
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impacts include increased social interests, desire for ongoing relationships, and positive experiences with the program and fellow participants/ peers. Initial enthusiasm of the participant is not essential or predictive of outcomes or engagement in GNO activities. Post session survey data indicate high satisfaction regarding program participation for all participants and peer volunteers. A primary aim in the delivery of this intervention is to tailor supports and strategies to meet the individual goals and needs of each participant, provide social interactions within familiar and unfamiliar activities or settings, promote independence in social and self-care skills, and to facilitate connections (or desire for connections) between girls with similar interests. Group characteristics might also influence participant outcomes. Enrolment requires parents, peers, and participants to demonstrate availability for nearly all sessions and reliable transportation. Participants are most likely to benefit from this intervention when they attend all (or nearly all) sessions, practice skills, and utilize supports within and outside of weekly sessions.
Treatment Procedures Program Structure GNO entails weekly, 2-h sessions over the course of 12–15 weeks. Each session meeting targets one or more of the core curriculum areas and take place during age-typical self-care or leisure activities within community-based settings. Groups include 4–6 participants with a disability (ASD/NDD) and at least an equal number of trained peer volunteers. Peer training includes information about peer expectations and activities prior to beginning GNO as well as immediately prior to about half or more of subsequent sessions. Each session follows a similar format (see table for GNO Session Format), including multiple opportunities to practice targeted self-care or social skills with subsequent sessions designed to build on and reinforce skills from previous sessions. The program utilizes carefully selected empirically based strategies to teach and reinforce skills related to core curriculum concepts.
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Targeted social conversation skills are practiced and reinforced in each session and supported by cue cards with tips for conversation (known as “conversation keyrings”), and video modeling clips depicting conversation skills based on the relating to others concept. Facilitators utilize differential reinforcement, including specific feedback paired with “GNO bucks” (token economy), to reinforce targeted curriculum skills and behaviors tailored to the individual. Participants access support materials housed in personalized GNO Planners with homework designed to promote practice and use of skills outside of sessions, including self-monitoring. Participants earn GNO bucks for bringing required materials, selfmonitoring, and completing homework (Table 1). GNO Curriculum The curriculum is organized into three core areas: relationship building skills, promoting independence in self-care skills, and self-determination in social competence and self-perception. The Girls Night Out Model, The, Table 1 GNO Session Format • Facilitator meeting, preparation, community partner training • Peer training • GNO opening activities - GNO “business” (distribute conversation keyring topics, pay people with GNO bucks for WIDTW sheets, planners, HW) - Social time (review conversation topics, facilitators provide specific feedback paired with GNO bucks) • Follow up on homework • Planned activity or lesson (teach, practice, community partner consult) • Practice during social or self-care activity with in vivo coaching, specific feedback, and GNO bucks to reinforce target skills • Data collection • Closing activities - Shop at GNO store (token economy) - Group picture and community partner thank you (if applicable) - Homework: Assign my GNO friend, review new homework • Facilitator debriefing - Integrity checklist, participant notes, next session plans
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table summarizes targeted skills within each of these three core areas, with strategies utilized to teach and reinforce these concepts throughout the intervention. (1) Relationship building skills address the heightened focus on conversation, relationship building, and shared interests that occur in adolescent female relationships (Hannah and Murachver 1999) and include conversation and friendship skills organized around relating to the person, the activity, and the relationship. Specific social conversation skills serve as “entry” tools to initiate conversation with other girls across contexts and the lifespan, with a focus on both conversation skills (i.e., asking questions/ making comments, reciprocal exchanges, complimenting, etc.) and conversation content (i.e., exchange of person-related information, activity or setting related topics, etc.). Concepts to promote “relating to the relationship” include establishing common ground, making plans, and offering encouragement or emotional support. Establishing common ground is obtaining and organizing information about people in your life (personal and shared interests) to create social opportunities and establish relationships. Participants utilize the information about friends or potential friends to guide if, when, and how to initiate making plans and how to identify activities of interest or mechanisms to find social opportunities. Emotional support includes giving and receiving compliments and using verbal and nonverbal encouragers, with ample opportunities to practice and demonstrate these skills during a variety of activities and settings (e.g., fitness, hair salon, ropes course). (2) Building independence in completing self-care skills acknowledges difficulties some individuals with ASD experience with daily living skills and addresses the changes in required self-care resulting from biological changes during adolescence. Building independence in self-care skills contributes to improved self-perception and confidence, leading to increased competence in social relationships. The focus within the curriculum is on promoting independence in determining relevant self-care needs and activities, improving skills necessary to complete these tasks, and utilizing supports designed to promote independent use of skills.
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Specific areas of focus within the curriculum include selection of clothing consistent with context (setting, weather, activity, etc.); body care (skin, hair); and health (fitness, exercise), tailoring these self-care priorities and activities to align with individual participant values, needs, and interests. Goal development, reinforcement methods, and practice opportunities support skill development while empowering participants to recognize their individual preferences. (3) GNO facilitators promote self-determination in social competence and self-perception through specific activities designed to engage participants in guiding their own social-emotional health outcomes. Activities include soliciting information from participants and their families about areas of interest and growth, facilitating individual goal setting and monitoring, and providing opportunities for a shift towards independence and positive peer support. Support targets relate to goal setting, use of visual supports to promote self-sufficiency, and accountability to themselves (selfmonitoring, goal revision) and others within their support network (GNO facilitators and participants, caregivers, community partners). Program components include designing strategic opportunities to support the participant’s experience of autonomy, mastery, and control (Ward and Meyer 1999; Wehmeyer 1998) by crafting situations to practice and reinforce specific skills or actions. Community partnerships leverage shared expertise creating rich practice experiences based on knowledge and skills of partner (e.g., hair care and style expertise by salon) and strategies or supports provided by the program (e.g., visual supports for participants and facilitator feedback to community partners). Peer training includes techniques to elicit desired skills from participants or to set the occasion for target behaviors. For example, peers make “bids” in conversation to elicit questions (e.g., “I’m so excited I can hardly wait for this weekend”) and extend pauses before continuing in conversation to allow participants ample time to form questions. Participants receive specific feedback and reinforcement (GNO bucks) for use of targeted skills and experience successes within a safe, supportive, and socially valid context. Homework is designed to promote skill use
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outside of session, including generalization of skills and goal attainment. Participants earn GNO bucks for self-monitoring and bringing materials to sessions and support fellow GNO participants in goal-related activities or skill practice outside of session as well. Relating to others Conversation “entry” skills (person and activity topics, giving compliments, asking questions) Finding common ground Making plans Emotional support Promoting independence in self-care skills Selecting appropriate clothing Body care (hygiene) Skin care Hair care Health (fitness, nutrition) Self-determination in social competence and self-perception Identify personal strengths and areas of growth (COP M) Goal setting and monitoring Build self-determination Independence in activities/ skills Use of supports and technology
Strategies Social Learning Theory -peer mediated -video modeling (VM) -Modeling and role play -visual supports (task analysis, my GNO planner, individualized) Cognitive and behavioral -reinforce target skills -differential attention -goal setting and monitoring -in-vivo coaching -specific feedback -token economy GNO bucks/GNO store) -planned generalization (natural/communitybased settings, multiple exemplars)
Related Program Elements and Facilitator Roles Parents or caregivers do not attend sessions with participants. However, a simultaneous PNO (Parents Nigh Out) component emerged based on experiences conducting groups and feedback from families. PNO includes GNO coordinated activities as well as impromptu or parent initiated gathering. Although variable, the goal is for about 50% of sessions to include PNO (formal or informal), with a focus on connecting families, sharing resources, and building infrastructure for continued social opportunities and relationships. A parent volunteer from a previous GNO group
often coordinates these activities based on feedback from families. Past activities included hosting a guest speaker (e.g., transition specialist), connecting families from current and past groups, or shared resources and support during informal social gatherings. Although PNO is optional and not systematically evaluated, families often report benefit from PNO events and seem more likely to participate in future GNO related activities designed to promote skill maintenance and ongoing social experiences. An optimal ratio of approximately four facilitators for a group of 9–12 girls is suggested. GNO facilitators utilize a coaching model to give specific feedback about skill use during role-plays and naturalistic practice opportunities for social and self-care skills. Facilitators whisper specific praise or feedback when participants demonstrate target skills or goal-related behaviors. Facilitators utilize principles of differential attention (Pemberton et al. 2013) by focusing attention and feedback on positive behaviors and minimizing feedback about negative or inappropriate behaviors, gradually introducing some prompting of skills, still followed by specific praise for exhibiting the skill. Facilitator planning and debriefing provide opportunities for strategic feedback that is tailored to participants and peers based on individual goals and areas of need. Unique Qualities of Treatment GNO incorporates unique treatment components along with some common, evidence-based strategies often utilized within social skills programs. One obvious feature is the all-female group designed to address the unique needs of girls with ASD. Although one might argue a 4–5:1 male to female ratio suggests a lesser impact on females as a general population, this discrepancy in diagnosis actually has a residual impact on females with autism, potentially exacerbating social-communication impairments and risk for co-occurring mental health conditions. The greater representation of males within autism-specific programs and special education services (~66% male; Retrieved 13 December
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2017 at https://nces.ed.gov/programs/digest/d16/ tables/dt16_204.50.asp) translates to less time girls with ASD spend with similar-aged female peers, narrowing the pool of potential friends and limiting opportunities to practice social interactions and establish relationships with other girls their age. Unlike many social skills programs or settings that target individuals with ASD, GNO is an all-female group that provides opportunities for social outings and interactions with other girls about the same age and for families to potentially connect around shared experiences. Families often report few girls in previous social programs, support groups, and classrooms and describe the value of participants and families connecting and relating to one another. Use of a coaching model, with peer-mediated strategies for teaching and practicing skills, provides authentic and genuine opportunities for practice within realistic social settings. The model and curriculum reaches beyond skill building and targets overall socialemotional health. Partnerships within the community promote shared expertise between local professionals and facilitators with disability-specific expertise and work towards more inclusive communities.
Efficacy Information Clinical outcome data, based on a combined sample (n ¼ 34) from five groups across the past 4 years, suggests GNO is desirable to families and shows promising results for improved conversation and social-emotional outcomes in adolescent girls with ASD (Jamison and Schuttler 2017). Sample sizes for outcome measures vary (n ¼ 14–34) as evaluation procedures evolved over time. Results show statistically significant improvements following intervention for total social competence (p ¼ 0.035, ES ¼ 0.53) and related subscales, global self-perception (p ¼ 0.008; ES ¼ 0.53), and Quality of life (p ¼ 0.04, ES ¼ 0.52). Participants also reported a significant decrease in internalizing symptoms (p ¼ 04, ES ¼ 0.47). Although samples are small,
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medium to large effect sizes suggest meaningful improvements. Consumer satisfaction (n ¼ 36) is high (satisfaction with program activities ¼ 4.5/ 5.0; positive changes in specific skills ¼ 3.5/5.0) with ratings from parents, participants, and peers signifying the intervention is a service that families value and need. Program activities and targets skills reflect an iterative process informed by the literature and ongoing involvement of participants, peers, and families. Program activities arose initially in part from group discussions with typically developing, adolescent females between the ages of 13–18 years old (n ¼ 10; Caucasian and living in a suburban community in a large Midwestern city in 2008). Subsequent activities and target skills reflect feedback from previous groups and our research on girls with ASD. Examining factors that influence peer acceptance revealed engagement as a critical component in successful conversations (i.e., asking questions, looking and sounding interested, nonverbal encouragers; Jamison et al., poster presented at AUCD 2012), which informed and validated the core components of relationship building skills. Preliminary findings from our qualitative study (Schuttler, Jamison, & Edwards, poster presented at IMFAR 2014) provide evidence for the theoretical framework and curriculum components that foster independence and promote reliance on positive peer influence rather than adults. Typically developing females (n ¼ 46; mean age ¼ 16.4) reported increased social complexity, greater independence, and more reliance on peers compared to family; however, girls with ASD (n ¼ 14; mean age ¼ 15.57) relied significantly more on family members (e.g., parents) for social and self-care support.
Outcome Measurement The program uses informal and formal methods of measurement for key outcome areas of social skills/competence, self-care/adaptive skills, selfperceptions, and social-emotional functioning (including both broad quality of life and more
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specific internalizing/externalizing symptomatology). Current tools used for pre-, post-, and follow-up measurement of key skill areas include the Behavior Assessment System for Children, 3rd Edition (BASC-3), Parent and Self Report, the Social Skills Improvement System (SSIS), Social Responsiveness Scale-Second Edition (SRS-2), Self-Perception Profile for Adolescents, 2nd Edition (SPPA-2), Autism Social Skills Profile, Youth Quality of Life (research version), and a self-care checklist developed concurrent with the program. Interview and ratings using the Canadian Occupational Performance Measure (COPM) inform goal development and attainment. Data is collected throughout the session for some skills. Participants and peers selfmonitoring specific skills related to social competence, adaptive skills (self-care, hygiene), and individual goals throughout the session. A few previous groups included evaluation of specific conversation skills as part a research study using a conversation coding system. Satisfaction data is collected at the conclusion of the program from peers, participants, and parents using Likert scale ratings of activities and skill levels and as an opportunity for families to provide written feedback about their experience.
Qualifications of Treatment Providers Sessions typically involve two kinds of facilitators: lead and support. The lead facilitator is responsible for guiding the session and supervising overall session activities. Support facilitators help with setup and follow up post session. Both types of facilitators are responsible for in vivo coaching and feedback, teaching and supporting skills within session. The lead facilitator (1) should have background in implementing social skills programming and have experience with individuals with developmental disabilities. Support facilitators (2–3) usually have at least a bachelor’s degree related to education, behavioral sciences, psychology, or communication sciences. Experience with individuals with ASD and other
Girls Night Out Model, The
DD is very helpful. For more information on individualized training for local implementation of the program, readers are encouraged to contact the authors of this chapter.
See Also ▶ Behaviorally Based Social Skill Groups ▶ Peer-Mediated Intervention ▶ Social Skill Interventions
References and Reading Bandura, A. (1971). Social learning theory. New York: General Learning Corporation. Bauminger, N., & Kasari, C. (2000). Loneliness and friendship in high-functioning children with autism. Child Development, 71(2), 447–456. https://doi.org/ 10.1111/1467.8624.00156 Begeer, S., Mandell, D., Wijnker-Holmes, B., et al. (2013). Sex differences in the timing of identification among children and adults with autism spectrum disorders. Journal of Autism and Developmental Disorders, 43, 1151–1156. Ditterline, J. & Oakland, T. (2009). Relationships between adaptive behavior and impairment. Assessing Impairment, 24(2), 31–48. Galambos, N. L., Barker, E. T., & Almeida, D. M. (2003). Parents do matter: Trajectories of change in externalizing and internalizing problems in early adolescence. Child Development, 74, 578–594. Hannah, A., & Murachver, T. (1999). Gender and conversational style as predictors of conversational behavior. Journal of Language and Social Psychology, 18(2), 153–174. https://doi.org/10.1177/02691927X9901 8002002 Hiller, R. M., Young, R. L., & Weber, N. (2014). Sex differences in autism spectrum disorder based on DSM-5 criteria: Evidence from clinician and teacher reporting. Journal of Abnormal Child Psychology, 42, 1381–1393. Jamison, T. R., & Schuttler, J. O. (2017). Overview and preliminary evidence for a social skills and self-care curriculum for adolescent females with autism: The girls night out model. Journal of Autism and Developmental Disorders. https://doi.org/10.1007/s10803-0162939-6 Jamison, T. R., & Schuttler, J. O. (2015). Examining social competence, self-perception, quality of life, and internalizing and externalizing symptoms in adolescent females with and without autism spectrum disorder: A quantitative design including between-groups and correlational analyses. Molecular Autism, 6, 53.
Glasgow Sensory Questionnaire (GSQ) Kendall, P. C. (2006). Guiding theory for therapy with children and adolescents. In P. C. Kendall (Ed.), Child and adolescent therapy: Cognitive-behavioral procedure (3rd ed., pp. 3–30). New York: Guilford Press. Kirkovski, M., Enticott, P. G., & Fitzgerald, P. B. (2013). A review of the role of female gender in autism spectrum disorders. Journal of Autism and Developmental Disorders, 43(11), 2584–2603. https://doi.org/10. 1007/s10803-013-1811-1 Koenig, K., & Tsatsanis, K. D. (2005). Pervasive developmental disorders in girls. In D. J. Bell & S. L. Foster (Eds.), Handbook of Behavioral and emotional problems (pp. 211–237). New York: Kluwer Academic/Plenum Publishers. Lai, M. C., Baron-Cohen, S., & Buxbaum, J. D. (2015). Understanding autism in the light of sex/gender. Mol Autism, 6, 24. Mandy, W., Chilvers, R., Chowdhury, U., et al. (2012). Sex differences in autism spectrum disorder: Evidence from a large sample of children and adolescents. Journal of Autism and Developmental Disorders, 42, 1304–1313. Pemberton, J. R., Borrego, J., & Sherman, S. (2013). Differential attention as a mechanism of change in parent– child interaction therapy: Support from time-series analysis. Journal of Psychopathology and Behavioral Assessment, 35(1), 35–44. Skinner, B. F. (1988). Methods and theories in the experimental analysis of behavior. In A. Charles & S. Harnad (Eds.), The selection of behavior: The operant behaviorism of B.F. Skinner: Comments and consequences (pp. 77–149). New York: Cambridge University Press. Solomon, M., Miller, M., Taylor, S. L., Hinshaw, S. P., & Carter, C. S. (2012). Autism symptoms and internalizing psychopathology in girls and boys with autism spectrum disorders. Journal of Autism and Developmental Disorders, 48, 48–59. https://doi.org/10.1007/ s10803-011-1215-z Van Wijngaarden-Cremers, P. J., van Eeten, E., Groen, W. B., et al. (2014). Gender and age differences in the core triad of impairments in autism spectrum disorders: A systematic review and meta-analysis. Journal of Autism and Developmental Disorders, 44, 627–635. Ward, M. J., & Meyer, R. N. (1999). Self-determination for people with developmental disabilities and autism: Two self-advocates’ perspectives. Focus on Autism and Other Developmental Disabilities, 14, 133–139. https://doi.org/10.1177/108835769901400302 Watkins, E. E., Zimmermann, Z. J., & Poling, A. (2014). The gender of participants in published research involving people with autism spectrum disorders. Research in Autism Spectrum Disorders, 8(2). https:// doi.org/10.1016/j.rasd.2013.10.010 Wehmeyer, M. L. (1998). Self-determination and individuals with significant disabilities: Examining meanings and misinterpretations. Research and Practice for Persons with Severe Disabilities, 23(1), 5–16.
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Glasgow Sensory Questionnaire (GSQ) Ashley E. Robertson1 and David R. Simmons2 1 School of Psychological, Social and Behavioural Sciences, Faculty of Health and Life Sciences, Coventry University, Coventry, UK 2 School of Psychology, University of Glasgow, Glasgow, UK
Synonyms Adult/Adolescent Sensory Profile (AASP); Inventory of Interpersonal Situations (IIS); Principal component analysis (PCA); Sensory Sensitivity Questionnaire (SSQ)
Description The Glasgow Sensory Questionnaire is a 42-item measure, which captures self-rated hyper- and hypo-sensory sensitivity to stimuli across seven sensory domains (Robertson and Simmons 2013). It was initially constructed based on reports of common sensory signs and symptoms associated with ASD. The sensory domains represented in the measure are visual, auditory, gustatory, olfactory, tactile, vestibular, and proprioception. Items are equally distributed among sensory modalities, with three questions assessing reported hypersensitivity and three determining hyposensitivity. All questions ask how frequently certain sensory events were experienced, with participants responding using the scale: “NeverRarely-Sometimes-Often-Always.” Responses are coded on a scale from 0 to 4, with possible scores ranging from 0 to 168. Questions are phrased to ask if participants currently encounter the sensory events described. However, if the frequency with which they experience such events has changed over their lifetime, they are asked to choose the response that best matches their experiences over the preceding 12 months. Sample questions are included at the
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start of the questionnaire in order to highlight these points. The questionnaire has been translated into six languages, with the French (Sapey-Triomphe et al. 2018), Japanese (Takayama et al. 2014), and Dutch (Kuiper et al. 2019) versions being published thus far. Our key results have been replicated in a larger sample (e.g., Horder et al. 2014). The GSQ is a useful measure of sensory processing beyond autism and has, for example, been used by researchers exploring synesthesia (Ward et al. 2017, Scientific Reports) and traits in ADHD (Panagiotidi et al. 2018). Informal reports by users of the GSQ for research purposes suggest that this questionnaire provides a quick and useful characterization of sensory sensitivities in both autistic people and non-autistic individuals. The original GSQ was designed as a self-report questionnaire for adults, but recently efforts have been made to validate a caregiver report version (Smees et al. 2019) and a self-report version for children (Simmons et al. 2016; Millington and Simmons 2019). In addition to replications of the original study in different populations and cultures, the GSQ has come to be used as a useful instrument for characterizing sensory sensitivities in empirical studies, especially when the experiment has a sensory component, for example, Freyberg et al. (2015) and Poole et al. (2017), in their comparisons of binocular rivalry and temporal order judgments, respectively, between autistic and control groups.
Historical Background The Glasgow Sensory Questionnaire was initially developed with 70 items (version 1.0); this was subsequently reduced to 42 items after a principal component analysis (PCA) (version 1.1). This is reported in Robertson and Simmons (2013). The reduction affected all modalities equally (the number of items for each modality was reduced from 10 to 6, with an even split between questions targeting hypersensitivity and hyposensitivity). Version 1.2 was released in 2018, correcting an error that existed in the coding sheet.
Glasgow Sensory Questionnaire (GSQ)
Psychometric Data In the original study (Robertson and Simmons 2013), reliability analyses for the 42 items of the questionnaire were reported: Cronbach’s alpha (0.935) and Guttman’s split-half technique (0.929). These scores indicated acceptable levels of reliability. Principal component analysis was used to reduce the number of items from 70 to 42 and also explore the factor structure, with a single underlying factor being identified. Ujie and Wakabayashi (2015) reported psychometric features of the Japanese version of the GSQ. Internal consistency using Cronbach’s alpha was acceptable (0.84). A joint factor analysis of the GSQ and AQ showed a two- factor solution, with the GSQ loading onto the first factor and AQ (minus the “attention to detail” subscale) loading onto the second factor. The authors highlighted that there are some differences in performance between high and low AQ scorers, something that warrants further analysis. Sapey-Triomphe et al. (2018) conducted a comprehensive psychometric analysis of the French version of the GSQ, comparing to the original version (Robertson and Simmons 2013). They found that the GSQ score was very similar between the two studies, with all but one of the items in the French version (number 39) being within 1 standard deviation of the original English version. Internal consistency was assessed using Cronbach’s alpha (0.95 for all 42 items; 0.94 for 14 subscales). They used factor analysis to assess the GSQ and found evidence of two factors, one consisting mainly of hypersensitive items and the second of hyposensitive items. This was observed in both high AQ and low AQ groups, but there were some fine-grain differences between the groups (e.g., more variance was explained for the high AQ group than the low AQ group). Kuiper et al. (2019) investigated the psychometrics of the Dutch version of the GSQ, comparing those with a diagnosis on the autistic spectrum and those without. They reported excellent levels of reliability in both groups (autistic, 0.91; non-autistic, 0.90) for the whole questionnaire and good levels for the hyper (autistic, 0.87;
Glasgow Sensory Questionnaire (GSQ)
non-autistic, 0.85)- and hyporesponsiveness (autistic, 0.85; non-autistic, 0.81) scales. Reliabilities for the individual modalities were mixed. Test-retest reliability was high for both groups (autistic, r ¼ 0.92, p < 0.001; non-autistic, r ¼ 0.83, p < 0.001). Convergent and divergent validity was also assessed, by comparing data from the GSQ to (a) established self-report measures of sensory responsiveness and (b) non-sensory measures, for example, those measuring social skills. To measure convergent validity, the Dutch version of the GSQ was compared to the Adolescent/Adult Sensory Profile (AASP; Brown and Dunn 2002) and Sensory Sensitivity Questionnaire (SSQ; Minshew and Hobson 2008). Between the GSQ and AASP, a strong correlation was reported for the autistic group (r ¼ 0.77, p < 0.001), with a moderate correlation for the non-autistic group (r ¼ 0.66, p < 0.001). A moderate correlation was observed when compared to the SSQ (autistic, r ¼ 0.51, p < 0.001; non-autistic, r ¼ 0.56, p < 0.001). To assess divergent validity, the GSQ was compared to the Inventory of Interpersonal Situations (IIS; Van Dam-Baggen and Kraaimaat 1999). A small nonsignificant correlation existed for the autistic group (r ¼ 0.03, p ¼ 0.78) and a small negative correlation with the non-autistic group (r ¼ 0.25, p ¼ 0.04).
Clinical Uses At this time, widespread clinical use is not recommended since further validation studies are needed. However, clinicians have obtained the GSQ for using in a clinical setting. The main purpose is reportedly to help identify areas of sensory need. It is important to highlight that diagnostic or eligibility decisions should not be based upon GSQ scores. However, on a caseby-case basis, the GSQ might help indicate which individuals need more extensive evaluation and perhaps offer some ideas about intervention. Professionals experienced in treating sensory processing problems in adults with ASD or related conditions should contact the authors before using the GSQ to discuss its appropriateness.
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See Also ▶ Sensory Features as a Marker of Autism Spectrum Disorders ▶ Sensory Processing ▶ Sensory Stimuli
References and Reading Brown, C. E., & Dunn, W. (2002). Adolescent/adult sensory profile: User’s manual. San Antonio: Psychological Corporation. Freyberg, J., Robertson, C. E., & Baron-Cohen, S. (2015). Reduced perceptual exclusivity during object and grating rivalry in autism. Journal of Vision, 15(13), 1–12. https://doi.org/10.1167/15.13.11. Horder, J., Wilson, C. E., Mendez, M. A., & Murphy, D. G. (2014). Autistic traits and abnormal sensory experiences in adults. Journal of Autism and Developmental Disorders, 44(6), 1461–1469. Kuiper, M. W., Verhoeven, E. W., & Geurts, H. M. (2019). The Dutch Glasgow Sensory Questionnaire: Psychometric properties of an autism-specific sensory sensitivity measure. Autism, 23(4), 922–932. Millington, E., & Simmons, D. R. (2019). A comparison of the children and parent Glasgow Sensory Questionnaire. Open Science Framework. https://osf.io/xu9ym/ Minshew, N. J., & Hobson, J. A. (2008). Sensory sensitivities and performance on sensory perceptual tasks in high-functioning individuals with autism. Journal of Autism and Developmental Disorders, 38(8), 1485–1498. Panagiotidi, M., Overton, P. G., & Stafford, T. (2018). The relationship between ADHD traits and sensory sensitivity in the general population. Comprehensive Psychiatry, 80, 179–185. Poole, D., Gowen, E., Warren, P. A., & Poliakoff, E. (2017). Brief report: Which came first? Exploring crossmodal temporal order judgements and their relationship with sensory reactivity in autism and neurotypicals. Journal of Autism and Developmental Disorders, 47, 215–223. Robertson, A. E., & Simmons, D. R. (2013). The relationship between sensory sensitivity and autistic traits in the general population. Journal of Autism and Developmental Disorders, 43(4), 775–784. Sapey-Triomphe, L. A., Moulin, A., Sonié, S., & Schmitz, C. (2018). The Glasgow Sensory Questionnaire: Validation of a French language version and refinement of sensory profiles of people with high autism-spectrum quotient. Journal of Autism and Developmental Disorders, 48(5), 1549–1565. Simmons, D. R., Robertson, A. E., & Brown, L. (2016). The P-GSQ: A new self-report sensory questionnaire for children. Poster presented at the International Meeting for Autism Research (IMFAR), Baltimore, MD, May 11–14, 2016.
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Global and Regional Asperger Syndrome Partnership (GRASP)
Smees, R., Rinaldi, L. J., Simmons, D. R., & Simner, J. (2019). Measuring sensory sensitivities in children: The parent-completed Glasgow Sensory Questionnaire (GDQ-P). (in preparation). Takayama, Y., Hashimoto, R., Tani, M., Kanai, C., Yamada, T., Watanabe, H., . . . Iwanami, A. (2014). Standardization of the Japanese version of the Glasgow Sensory Questionnaire (GSQ). Research in Autism Spectrum Disorders, 8(4), 347–353. Ujie, Y., & Wakabayashi, A. (2015). Psychometric properties and overlap of the GSQ and AQ among Japanese university students. International Journal of Psychological Studies, 7(2), 195. Van Dam-Baggen, C. M. J., & Kraaimaat, F. W. (1999). Assessing social anxiety: The Inventory of Interpersonal Situations (IIS). European Journal of Psychological Assessment, 15(1), 25–38. Ward, J., Hoadley, C., Hughes, J. E., Smith, P., Allison, C., Baron-Cohen, S., & Simner, J. (2017). Atypical sensory sensitivity as a shared feature between synaesthesia and autism. Scientific Reports, 7, 41155.
Global and Regional Asperger Syndrome Partnership (GRASP) Kate Palmer GRASP, New York, NY, USA
“Global and Regional Asperger Syndrome Partnership (GRASP)” was created by a group of individuals with Asperger’s syndrome in New York City seeking to form an organization run by people with Asperger’s and autism spectrum disorder in order to help their peers. In 2003, GRASP was gifted a generous grant by the FAR fund and subsequently filed for 501c-3 status to become a nonprofit. Since then, the GRASP team has expanded the organization to include more programming specific to increasing the independence of autistics and individuals with autism spectrum disorder. GRASP’s team has established structured workshops, social events and groups, as well as increasing the visibility of ASD adults within society through collaborative work with high-profile regional, national, and international organizations. GRASP’s mission is to improve and enrich the quality of life of autistic teens and adults and individuals on the autism spectrum and their
families through community advocacy and outreach, education, peer supports, programming and services, at no cost to our members. GRASP’s vision is to actualize and work toward a world where all autistic individuals and individuals on the autism spectrum are selfdetermined, respected, valued, and fairly represented, where appropriate supports and services are readily available to those in need, where people on the spectrum are empowered to participate in personal decisions that affect their lives and to achieve their dreams. With over 20,000 members – over 40 in-person groups, online support groups, and real-time chats with GRASP staff, and information and referral services for individuals and families – GRASP is the largest organization for individuals with ASD. GRASP has a strong presence on social media through Facebook, Twitter, LinkedIn, and through website information and support. For more information, visit www.grasp.org or contact by email at [email protected].
Global Aphasia Elizabeth R. Eernisse Department of Language and Literacy, Cardinal Stritch University, Milwaukee, WI, USA
Synonyms Dysphasia
Definition Global aphasia is a disorder of language characterized by a severe impairment in all aspects of spoken and written language across comprehension and production. When considering types and categories that fall under the broader term “aphasia,” global aphasia is among the most severe of the nonfluent aphasias as both comprehension and production of language are involved.
Global Versus Local Processing
Estimates of global aphasia in the larger population are largely unknown, though it has been estimated that 80,000 people develop aphasia in the United States each year. The prognosis for individuals with global aphasia is largely dependent upon the extent of the damage to the brain and the severity of the impairments they encounter. For some individuals, an initial global aphasia resolves as swelling in the brain decreases and the patient is left with residual symptoms that are more consistent with Broca’s or Wernicke’s aphasia. For others, the impairments across comprehension and production persist. Factors including age of onset, health, education level, and how soon treatment takes place after brain damage have been shown to be predictive of recovery in aphasia. Individuals with global aphasia have often experienced significant damage to the majority of the “language centers” of the brain, including the arcuate fasciculus, Broca’s area, and Wernicke’s areas of the left hemisphere. Language skills are typically impaired across comprehension and production, and individuals are often rendered without functional speech or produce repetitive/rote speech only. Intervention for individuals with global aphasia largely depends on the severity of the symptoms experienced. As both comprehension and production are impaired, treatment often focuses on providing the individual with maximal support and compensatory strategies. In addition, family and caregiver training and supports are critical to the recovery and management of global aphasia. Please also see ▶ “Aphasia” for a list of caregiver support strategies.
See Also
2251 Barresi, B., Goodglass, H., & Kaplan, E. (2001). The assessment of aphasia and related disorders. Hagerstown: Lippincott Williams & Wilkins. Chapey, R. (2008). Language intervention strategies in aphasia and related neurogenic communication disorders. Philadelphia: Wolters Kluwer/Lippincott Williams & Wilkins. Goodglass, H., Kaplan, E., & Barresi, B. (2000). Boston diagnostic aphasia examination (BDAE-3) (3rd ed.). Austin: Pro-Ed. Kaplan, E., Goodglass, H., & Weintraub, S. (1983). The Boston naming test. Philadelphia: Lea and Febiger. Kent, R. D. (1994). Reference manual for communicative sciences and disorders: Speech and language. Austin: Pro-Ed. Kertesz, A. (2006). Western aphasia battery- revised (WAB-R). Austin: Pro-Ed. Lapointe, L. L. (2004). Aphasia and related neurogenic language disorders. New York: Thieme Medical Publishers. National Institute on Deafness and Other Communication Disorders. (2008). Aphasia. Retrieved 1 May 2011 from http://www.nidcd.nih.gov/health/voice/aphasia. htm
Global Versus Local Processing Laurent Mottron1,2 and Isabelle Soulières3,4 1 Center of Excellence in Pervasive Developmental Disorders of University of Montreal, Montreal, QC, Canada 2 Department of Psychiatry, Riviere-des-Prairies Hospital, University of Montreal, Montreal, QC, Canada 3 Centre d’excellence en troubles envahissants du développement de l’université de Montréal, Hôpital Riviére-des-Prairies, Montréal, QC, Canada 4 Department of Psychology, Université du Québec à Montréal, Montréal, QC, Canada
▶ Aphasia
Definition References and Reading American Speech-Language-Hearing Association (ASHA). (2008). Incidence and prevalence of speech, voice, and language disorders in adults in the United States. Retrieved 1 May 2011 from: www.asha.org/ research/reports/speech_voice_language.htm
Local and global processing refer to hierarchical dimensions within perceptual patterns. Any spatially or temporally extended structure can be dichotomized in its embedding component or global level, and its embedded components or
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local elements. Local and global dimensions are interdefined and are therefore only relative concepts: the same level can be considered as local relatively to its embedding structure and as global if it can itself be decomposed into embedded elements. The classic local–global tasks use “Navon-type” stimuli, which are large patterns (e.g., letters, geometrical figures) composed of small patterns of same or different nature (see Fig. 1). In the auditory modality, hierarchical stimuli can be implemented in melodies where local elements are single pitches or intervals, and global elements are melody or contour. Global level has to be distinguished from holistic level, which is a pattern emerging only from the combination of local and global elements. For example, in a face, global level would be the face outline, parts would be eyes or mouth, but holistic (or gestalt) level would be the information that it is a face, which emerges only from the integration of parts and whole together. Whereas local versus global terminology is technically related to laboratory studies of pattern construction, whole and parts are akin to the gestalt tradition and refer, when empirically studied, to the relation between perception and understanding/recognition processes. Differences in priority, speed, and accuracy of access to global versus local level information
Global Versus Local Processing
define global or local advantage. The facilitating or detrimental effect that the processing of one level has on processing of the other is called interference. For example, dissimilarity between local and global level has typically more detrimental effect on the local than on the global processing: global-to-local interference is usually larger than local-to-global interference. Combined global advantage and superior global-tolocal interference is called global precedence in cognitive literature (Robertson and Lamb 1991). The term precedence is usually replaced by bias in the autism literature. Local and global levels differ not only in their logical and spatial relationships or hierarchical status but also in their respective psychophysical dimensions, neural representation, and relation with further recognition and labeling processes. For visually presented information, spatial frequencies are the average number of elements per unit of length. Global level is therefore composed of lower spatial frequencies and local level of higher spatial frequencies. In terms of neural representation, the local–global distinction is represented within the feed-forward flow of information of visual processing, in the form of a smaller versus larger amount of cells required to code the information (Grill-Spector and Malach 2004). Local elements require the integration of multiple psychophysical dimensions (contrast, luminance, and color). Local processing level is therefore anatomically and functionally more akin to low-level perception, level at which orientation or luminance discontinuities are coded. Conversely, global elements are anatomically and functionally closer to further levels of processing, as object recognition and labeling require the construction of a global representation (Grill-Spector and Malach 2004). Emotional value, for example, is mostly conveyed by global aspects of socially relevant material (Pourtois et al. 2005).
Historical Background
Global Versus Local Processing, Fig. 1 Navon-type stimulus
The local–global issue in autism goes back to Kanner’s initial description of various autistic behaviors apparently related to “irrelevant” or
Global Versus Local Processing
“minute” aspects of their surroundings. Kanner’s first writings emphasized a dependence of the access to global dimension of an object or situation on integrity of all its parts. Thus, autistics would present an “inability to experience wholes without full attention to the constituent parts.” Kanner therefore did not postulate a deficit in the perception of global aspects of objects, but rather a mandatory consideration of details in the recognition/manipulation of entire objects or situations. Kanner’s reference to a perceptual hyperaccuracy and to superior performance of perceptual memory, “photographic or phonographic identity,” was explicit in this context (Kanner 1951). The detail-whole distinction, which may be considered as the ancestor of the “local–global” distinction in autism, had initially two meanings, which, although conceptually and ecologically interconnected, should be distinguished. First, details are opposed to entire objects and refer to a hierarchical relation of inclusion between levels within a perceptual pattern. This relation occurs within perception: local and global aspects are both perceptual features. This interpretation will be referred to as the withinperception meaning of the local–global distinction. Second, details refer to the entire domain of phenomenal appearance or perception, and whole refers to meaning, function, or use of a perceptually distinct pattern or object. In this latter case, “attention to details” refers to an atypical topdown relationship between perception and other elements of cognitive architecture involving higher-order processes. This constitutes the topdown meaning of the local–global distinction. Both meanings are contained in Kanner’s statements on the relation between sameness and detail, in that sameness is the preservation of detailed perceptual appearance of the world, as opposed to its meaning or function. In contrast, the default level of conscious cognitive functioning in typical individuals is that of meaning, which bypasses the detailed appearance (Kanner 1951). The ambiguity between a withinperception and a top-down approach of local– global issues in autistic perception has obscured empirical research on perception in autism for 50 years.
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After Kanner, the top-down approach was the first to be developed. Creak (1963) described in autism an interest for object characteristics independently of their function. Hermelin and O’Connor (1970) expanded the idea that autistics have access to “raw processing” which, according to the dichotomy in use at that time, corresponds to the idea of uncategorized, perceptual material. The top-down approach is also the basis for the weak central coherence model, which emerged during the late 1960s and 1970s through studies from Hermelin’s student, Uta Frith, before its first formal description in 1989. In one of her landmark papers, Frith opposed gestalt to perceptual detail and to raw material (Shah and Frith 1983). The Embedded Figures Test used in this paper, which contains two embedded perceptual levels, bridges the top-down to the within-perception interpretations, though it was not exploited as such. The enhanced detection of embedded figures was attributed to autistics being “less captured by meaning.” The gestalt was not clearly considered as a perceptual impairment in autism since autistics could label global pictures. The local–global conceptual distinction appears in these terms in 1993, simultaneously in Shah and Frith’s study on Block Design task, with a mostly top-down approach, and in Mottron and Belleville (1993) with a strictly perceptual approach. For Shah and Frith, “a relative preference for processing local as opposed to global features” is attributed to a deficit in global processing, understood in a top-down sense. Therefore, the hypothesized deficit is not primarily perceptual, but refers to diminished or absent top-down influence on the entire field of perception. Tasks used to support these assumptions were also not primarily perceptual (they involved response times of several tens of seconds) despite presenting a hierarchical component. In contrast, Mottron and Belleville (1993) investigated hierarchical effects within perception with the description of perceptual analysis in an autistic savant draftsman, with a mixture of strictly perceptual (hierarchical stimuli, perception of impossible figures) and visuoconstructive tasks (graphic construction). They reported that the hierarchical processing and graphic construction of visual
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representation did not present the global precedence effects evident in typical individuals. In the following years, this ambiguity remained, resulting in two partly overlapping, partly antagonist trends of research: one on perceptual laws, the enhanced perceptual functioning model, with a late incursion in top-down influence on perception; the other on top-down effects or weak central coherence, with a late incursion in perceptual laws. Both are unfortunately confounded in the “local–global” issue. Consequently, the question “is there a global deficit” and “is there a local bias” in autism received two empirically and theoretical answers, according to the within-perception or top-down approach of these terms. The cliché contrasting enhanced perceptual functioning and weak central coherence models, with the first emphasizing intact global processing and the other supporting impaired global processing, is misleading. The term global has a within-perception meaning in the enhanced perceptual functioning model and a mainly topdown meaning in the weak central coherence account.
Current Knowledge Only the within-perception meaning of the local– global distinction will be presented here. Visual perceptual hierarchy has been studied in autism through perceptual hierarchical tasks, in which response times are in the range of hundreds of milliseconds, thereby more prone to tap specifically perceptual processes, and visuospatial hierarchical tasks, in which the answer requires several tens of seconds. Perceptual hierarchical tasks include Navontype hierarchical stimuli, part-whole relationships in patterns and faces, and attentional search tasks. A review by Wang et al. (2007) of nine studies using hierarchical stimuli in autism, for a total of 96 autistic spectrum individuals and 126 typical individuals, reveals that group differences, when found, involve superior local bias or local-toglobal interference in autistics. Response times
Global Versus Local Processing
to global level stimuli among autistics are more affected by incongruent stimuli at the local level than are the response times of typical individuals. The default setting of hierarchical autistic perception can also be assessed by prompting the participant to the likelihood of the occurrence of the stimulus at one level or the other. Autistics present a shorter response time for local element similarity than for global similarity, contrary to typical individuals, with equivalent accuracy in both groups. Another consistent result in this type of tasks is autistics’ typical performance level for various aspects of multi-features, global pattern dimensions when constrained to do so (except in specific cases that cannot be considered as representative of the autistic population). Typical holistic processing is manifested by standard effect of “good form” on visual target detection; typical global advantage under various visual angles; faster response to global as compared to local configurations; similar influence of structural global bias, although to a lesser extent than typically developing individuals; and slower response to local stimuli in global incongruence condition or global interference (see Happe and Frith 2006; Mottron et al. 2006, for reviews). A neighboring approach of the local–global distinction is represented by attentional visual search tasks, in which the participant has to disembody targets from surrounding task-irrelevant distracters which share one or several features with the targets. Autistics usually display faster target detection, more accurate discrimination of elementary stimuli differing at the feature level, and diminished influence of increasing number of distracters (Plaisted et al. 1998). For social material (e.g., faces), autistics display a preference for local information in identity matching, a superior priming effect of face parts, and a generally superior processing of high spatial frequencies in tasks involving faces. Integrity of global level analysis is demonstrated by typical gains from the addition of configural information, mostly typical inversion effect, and by superior recognition of an embedded facial target compared to an isolated one.
Global Versus Local Processing
Visuospatial hierarchical tasks in the autism literature comprise mainly the Block Design subtest of the Wechsler scales of intelligence, graphic reproduction of possible and impossible figures, and the Embedded Figures Test. Autistics display a constant pattern of enhanced performance in these tasks. When the processing of a global aspect conflicts with a local analysis among typical individuals (e.g., perceptual cohesiveness in Block Design, impossibility of a figure in graphic reproduction, and visual context in embedded figures), autistics perform better than typical individuals. In contrast, when this conflict is diminished, for example, by segmentation to diminish the perceptual cohesiveness in Block Design or by copying possible instead of impossible figures, autistics are brought back to a level of performance equivalent to that of typical individuals. Conversely, autistics are not rigidly bound to a local strategy, as demonstrated by their capacity to switch to a global strategy when required by the task (Caron et al. 2006). Analogues of Navon-type stimuli in the auditory modality allow investigating hierarchical effects in auditory modality, by using the single note versus melody or chord perception, with the same message of enhanced perception of local elements and intact perception of global auditory patterns, despite diminished detrimental effects of pitch and timing interference on judgments of pitch change. Even when considered in isolation, some psychophysical properties of auditory elements such as pitch are processed more accurately in autistics (Bonnel et al. 2010). The same applies to visual modality, in that lower thresholds for luminance-defined features are reported in autistics (Bertone et al. 2005).
Main Accounts Despite the apparent homogeneity of autistic “local bias,” the large array of tasks grouped under the umbrella term of local–global tasks renders poorly plausible the notion of a unitary mechanism. Explanations are centered either on
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the modification of within-perception hierarchy under the constraint of altered processing of elementary visual and auditory properties or on the modification of top-down influences on perception. Within-perception accounts: Visual and auditory hierarchical tasks share the requirement to detect, identify, match, or reproduce a local target embedded in a larger probe. The enhanced perceptual functioning model proposes that an intrinsic superiority in pattern detection may have the effect of a local bias in comparison to typical individuals. This superiority can be traced back to an enhanced processing performance for the psychophysical properties of the patterns for which autistics display superiority. This is well demonstrated in the auditory modality (superior pitch perception), with some correspondent in the visual modality (enhanced perception of luminance-defined gratings) or in intermodal perception (enhanced perception of audiovisual synchrony). In some tasks like melody and verbal pattern discrimination, visual search, block design, and face perception, the local bias can be directly related to enhanced feed-forward processing of elementary psychophysical visual features. In support of this account, the same autistics who display the higher level of “local bias” also display superiority in low-level discrimination tasks as well as in a wide range of pattern identification tasks (Caron et al. 2006). Conversely, a multimodal local bias may be involved in atypical perception of social releasers. For example, the preference for high-spatial frequencies may modify face scanning as well as the route of emotion interpretation, as most of the emotional aspects of faces are conveyed by low spatial frequencies (Vlamings et al. 2010). Top-down accounts: Another series of accounts attributes the local bias, evident in autistics, to a diminished global precedence within hierarchical patterns. The type of limitation of a top-down intervention favoring global over local processing varies according to accounts. The weak central coherence account, initially proposed for the Embedded Figures Test and the
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Block Design task, hypothesizes a deficit in the typical tendency to favor a gestalt approach. For example, increasing the perceptual cohesiveness of the figure to be reproduced double the execution time in typical individuals, without influencing response times in autistics with a Block Design peak. The concomitant superiority of these autistics in a global task discounts the possibility that their remarkable performance on the Block Design task derived from a deficit in analyzing global aspects of a figure (Caron et al. 2006). Within the enhanced perceptual functioning account, M. Dawson’s optionality model states that autistics are not obligated to use a global strategy when a global approach to the task is detrimental to performance (Soulières et al. 2007). For example, autistic persons are better able than typically developing persons to copy globally impossible figures, but as able to identify that these figures are impossible. Optionality may apply both to the competition between local and global level within pattern construction and between meaningless and meaningful processing of perceptual material.
Conclusion Two findings clearly emerge from the now extensive investigation of local–global processing in autism. First, when the processing of the global aspect conflicts with a local analysis among typically developing persons, autistics either display superiority in local detection or present a diminished influence from the global level on the local one. Second, when the global level is tested in isolation, it is unremarkable; when the local level is tested in isolation, it is either unremarkable or superior. When it can be disentangled from other executive, intentional or motor components, the active ingredient in autistics’ atypical performance is invariably of a perceptual nature. Local bias has two sources: an altered low-level input shared by most perceptual tasks in which autistics display superiority, and a diminished influence on local processing from either global perceptual
Global Versus Local Processing
channel or top-down, nonperceptual processes. These findings appear consistent across visual and auditory modalities, for short as well as long exposure tasks. Local bias in autism is now consensual and represents one of the strongest arguments for an atypical perception in autism as well as a key path to the understanding of overall autistic cognition.
Future Directions • Which low-level property is responsible of enhanced pattern detection, when observed? • Are elementary grouping processes intact, atypical, or impaired? • Lateral interaction and crowding as possible contour enhancers • Role of altered spatial frequencies in face perception • Role of pitch processing in language acquisition • How do hierarchical atypicalities cluster in the same individuals?
See Also ▶ Perception ▶ Weak Central Coherence
References and Reading Bertone, A., Mottron, L., Jelenic, P., & Faubert, J. (2005). Enhanced and diminished visuo-spatial information processing in autism depends on stimulus complexity. Brain, 128(10), 2430–2441. Bonnel, A., McAdams, S., Smith, B., Berthiaume, C., Bertone, A., Ciocca, V., et al. (2010). Enhanced puretone pitch discrimination among persons with autism but not Asperger syndrome. Neuropsychologia, 48(9), 2465–2475. Caron, M. J., Mottron, L., Berthiaume, C., & Dawson, M. (2006). Cognitive mechanisms, specificity and neural underpinnings of visuospatial peaks in autism. Brain, 129(Pt 7), 1789–1802. Creak, M. (1963). Childhood psychosis: A review of 100 cases. The British Journal of Psychiatry, 109, 84–89.
Globus Pallidus Frith, U. (1989). Autism: Explaining the enigma. Oxford, UK/Cambridge, MA: Basil Blackwell. Grill-Spector, K., & Malach, R. (2004). The human visual cortex. Annual Review of Neuroscience, 27, 649–677. Happe, F., & Frith, U. (2006). The weak coherence account: Detail-focused cognitive style in autism spectrum disorders. Journal of Autism and Developmental Disorders, 36(1), 5–25. Hermelin, B., & O’Connor, N. (1970). Psychological experiments with autistic children. Oxford: Pergamon Press. Kanner, L. (1951). The conception of wholes and parts in early infantile autism. The American Journal of Psychiatry, 108(1), 23–26. Mottron, L., & Belleville, S. (1993). A study of perceptual analysis in a high-level autistic subject with exceptional graphic abilities. Brain and Cognition, 23(2), 279–309. Mottron, L., Dawson, M., Soulieres, I., Hubert, B., & Burack, J. (2006). Enhanced perceptual functioning in autism: An update, and eight principles of autistic perception. Journal of Autism and Developmental Disorders, 36(1), 27–43. Plaisted, K., O’Riordan, M., & Baron-Cohen, S. (1998). Enhanced visual search for a conjunctive target in autism: A research note. Journal of Child Psychology and Psychiatry, 39(5), 777–783. Pourtois, G., Dan, E. S., Grandjean, D., Sander, D., & Vuilleumier, P. (2005). Enhanced extrastriate visual response to band pass spatial frequency filtered fearful faces: Time course and topographic evokedpotentials mapping. Human Brain Mapping, 26(1), 65–79. Robertson, L. C., & Lamb, M. R. (1991). Neuropsychological contributions to theories of part/whole organization. Cognitive Psychology, 23(2), 299–330. Shah, A., & Frith, U. (1983). An islet of ability in autistic children: A research note. Journal of Child Psychology and Psychiatry, 24(4), 613–620. Shah, A., & Frith, U. (1993). Why do autistic individuals show superior performance on the block design task? Journal of Child Psychology and Psychiatry, 34(8), 1351–1364. Soulières, I., Mottron, L., Saumier, D., & Larochelle, S. (2007). Atypical categorical perception in autism: Autonomy of discrimination? Journal of Autism and Developmental Disorders, 37(3), 481–490. Vlamings, P. H., Jonkman, L. M., van Daalen, E., van der Gaag, R. J., & Kemner, C. (2010). Basic abnormalities in visual processing affect face processing at an early age in autism spectrum disorder. Biological Psychiatry, 68(12), 1107–1113. Wang, L., Mottron, L., Peng, D., Berthiaume, C., & Dawson, M. (2007). Local bias and local-to-global interference without global deficit: A robust finding in autism under various conditions of attention, exposure time, and visual angle. Cognitive Neuropsychology, 24(5), 550–574.
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Globus Pallidus Wouter Staal Neuroscience, Radboud University Nijmegen Medical Centre Karakter, Nijmegen, The Netherlands
Synonyms Paleostriatum
Definition Its name derived from the Latin for “pale globe” and also termed the paleostriatum the globus pallidus (GP) is a subcortical structure of the brain and part of the basal ganglia. For some time it was associated with the putamen and referred to as the lentiform nucleus. Topographically, the globus pallidus it is part of the telencephalon and a major component of the extrapyramidal motor system. It connects with the substantia nigra, which is quite similar in neuronal architecture. The GP is divided into two parts – termed the internal and external GP – by the medial medullary lamina. These nuclei receive GABAergic axonal terminal branches from the striatum. Damage to the GP may result in stereotyped behavior and impaired extrapyramidal motor functioning. The GP is a target for deep brain stimulation therapy in Parkinson’s disease.
References and Reading Bronstein, J. M., Tagliati, M., Alterman, R. L., Lozano, A. M., Volkmann, J., Stefani, A., et al. (2011). Deep brain stimulation for Parkinson disease: An expert consensus and review of key issues. Archives of Neurology, 68(2), 165. Epub October 11, 2010. Langen, M., Kas, M. J., Staal, W. G., van Engeland, H., & Durston, S. (2011). The neurobiology of repetitive behavior: Of mice. . .. Neuroscience and Biobehavioral Reviews, 35(3), 345–355. Schuenke, M., Schulte, E., Ross, L. M., Lamperti, E. D., Schumacher, U., & Rude, J. (2010). Head and neuroanatomy (THIEME atlas of anatomy series) (pp. 211–214). Stuttgart: Thieme.
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Glu ▶ Glutamate
Glutamate Eunice Yuen Yale Child Study Center, New Haven, CT, USA
Glu
while animals expressing these mutated genes show abnormal Glu channel properties and glutamatergic synapse morphology. Clinically, both agonists and antagonists of NMDAR have been reported to ameliorate ASD-related symptoms in human, suggesting that deviation of NMDAR activity in either direction may be implicated in ASD. Most significantly, alteration of glutamate synaptic transmission may lead to excitation-inhibition imbalance within the neuronal network, which has been postulated as pathophysiology of ASD.
Synonyms
See Also
Glu; Glutamatergic
▶ Shank 3
Definition Glutamate (Glu) is a neurotransmitter that governs the excitatory synaptic transmission in the central nervous system. It plays a key role in brain development, such as migration, differentiation, survival, synaptic pruning, and plasticity. Glu activates ionotropic receptors including α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR) and N-methyl-D-aspartate receptor (NMDAR), as well as metabotropic glutamate receptors (mGluR 1-7). Aberrant Glu system has been implicated in autism spectrum disorder (ASD). ASD patients show elevated serum levels of Glu, and increased brain expression of glutamate receptor and transporters. Increased densities of dendritic spine, a specialized compartment of glutamatergic synapse, correlate with decreased brain weight and severity of ASD symptomatology. In ASD-like animal models, abnormal pre-synaptic glutamate release, glutatmate-mediated neurotransmission, and plasticity are found, and dysfunction of glutamate transporter leads to overproduction of glutamate and NMDAR-mediated excitotoxicity. At the genetic level, missense mutations of genes encoding Glu receptor are found in ASD patients,
References and Reading Aida, T., Yoshida, J., Nomura, M., Tanimura, A., Iino, Y., Soma, M., & Tanaka, K. (2015). Astroglial glutamate transporter deficiency increases synaptic excitability and leads to pathological repetitive behaviors in mice. Neuropsychopharmacology, 40(7), 1569–1579. Hosenbocus, S., & Chahal, R. (2013). Memantine: A review of possible uses in child and adolescent psychiatry. Journal of the Canadian Academy of Child and Adolescent Psychiatry, 22(2), 166–171. Hutsler, J. J., & Zhang, H. (2010). Increased dendritic spine densities on cortical projection neurons in autism spectrum disorders. Brain Research, 1309, 83–94. O’Roak, B. J., Deriziotis, P., Lee, C., Vives, L., Schwartz, J. J., Girirajan, S., & Eichler, E. E. (2011). Exome sequencing in sporadic autism spectrum disorders identifies severe de novo mutations. Nature Genetics, 43(6), 585–589. Purcell, A. E., Jeon, O. H., Zimmerman, A. W., Blue, M. E., & Pevsner, J. (2001). Postmortem brain abnormalities of the glutamate neurotransmitter system in autism. Neurology, 57(9), 1618–1628.
Glutamatergic ▶ Glutamate
Gluten-Free Diet
Gluten-Free Diet Susan Hyman Developmental and Behavioral Pediatrics, Division Chief Neurodevelopmental and Behavioral Pediatrics, University of Rochester Golisano Children’s Hospital, Rochester, NY, USA
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individuals with symptoms of ASD or schizophrenia seemed to improve with elimination of gluten and casein from their diets (Milward et al. 2008). To explain this clinical observation, they examined urinary excretion of peptides (proteins) that they thought were related to these foods. The GFCF diet gained popularity in the late 1990s through anecdotal reports of success, books for families in the popular press (Lewis 1998), and web-based organizations that supported families in implementing the GFCF diet.
Definition The gluten-free and casein-free diet (GFCF diet) is a popular dietary intervention used by families of people with autism spectrum disorders (ASD) in the effort to lessen core symptoms of ASD and/or gastrointestinal problems like bloating, diarrhea, and discomfort that may impact behavior. Gluten is a peptide (protein) found in grains such as barley, rye, and wheat, which provides elasticity to baked goods. Casein is a peptide found in all milk and milk products. The GFCF diet eliminates grain and dairy foods containing these peptides and processed food products containing them. Current use of a diet eliminating gluten and/or casein is reported by less than 20% of families participating in the Autism Treatment Network (Perrin et al. 2012).
Historical Background Elimination diets have been investigated as means of altering behavior in people with developmental, behavioral, and medical diagnoses for many years. Case reports in the 1970s described individuals with symptoms of ASD whose behaviors improved with elimination of multiple foods including wheat products (Bird et al. 1977; O’Banion et al. 1978). In the 1970s and 1980s, the potential for dietary intervention to impact attention and hyperactivity was studied in clinical trials with largely negative results in the USA (see ▶ Feingold Diet). During this period, investigators in the UK and Norway observed that some
Rationale or Underlying Theory To explain the observation that some people with ASD seemed to have improvement in behavior or medical symptoms with elimination of gluten and/or casein, Shattock et al. (1990) and Reichelt et al. (1991) suggested the hypothesis that people with ASD had a “leaky” intestinal lining that allowed proteins to be absorbed from food that typically were not absorbed and that these substances subsequently acted like opiates in the brain. An additional hypothesis has been proposed that increased absorption of peptides related to casein and gluten may be due to altered gut peptidase activity (Christison and Ivany 2006). Urine peptide measurement in people with autism on and off GFCF diets was published that suggested an increase in urine metabolites of gluten and casein that could be decreased with dietary intervention (reviewed in Christison and Ivany 2006). Measurement techniques for urinary peptide identification were less precise in the 1980s, so the compounds separated in the urine could not be specifically identified. Subsequent studies using newer and more accurate measurement techniques did not identify increased excretion of opiate-like peptides related to gluten and casein in children with ASD compared to controls (Cass et al. 2008; Hunter et al. 2003). Although the original “opiate hypothesis” has not been supported by the more recent studies of urine peptides or current understanding of the neurobiology of ASD, it is plausible that the
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empiric observation of behavioral improvement with dietary restriction may have an alternative explanation in subgroups of children with ASD. Celiac disease is an autoimmune disorder found in up to 1% of the population. Antibodies to gluten alter the intestinal lining in response to gluten exposure. This results in characteristic findings on blood testing for antibodies and on biopsy of the intestine performed during diagnostic endoscopy. Elimination of gluten from the diet in people with celiac disease results in improvement in gastrointestinal symptoms (e.g., pain, diarrhea) that might lead to irritability. Neurological symptoms associated with celiac disease include ataxia and peripheral neuropathy with anecdotal reports of impaired attention. Other hypotheses have been proposed regarding primary immunologic abnormalities in the intestine in people with ASD (Buie et al. 2010). Another potential explanation for improvement in behavioral symptoms in a subgroup of children with ASD is lactose intolerance. Lactose is an enzyme that allows the intestine to break down milk sugars found in cow’s milk. The genetic predisposition to lactose intolerance is especially common in certain ethnic groups. Lactose intolerance may also occur after gastrointestinal infections from viruses or bacteria. If someone is lactose intolerant, elimination of milk sugar might decrease irritability from bloating and decrease diarrhea. Some people might benefit from an elimination diet is true allergy or dietary intolerance to milk protein or other foods. The most common food allergies include milk, wheat, eggs, tree nuts, and soy. It is also plausible that the elimination of processed foods containing gluten and casein changes the diet in other ways such as removal of food dyes and preservatives (see ▶ Feingold Diet). Alteration of the foods eaten may change the microbial environment in the gut which could potentially alter absorption of nutrients that are precursors to neurotransmitters (Dinan and Cryan 2017).
Goals and Objectives The GFCF diet is typically trialed by families to determine if the symptoms of ASD or of gastrointestinal distress can be lessened with total
Gluten-Free Diet
elimination of casein- and gluten-containing foods. Clinical studies to date have attempted to examine outcome criteria related to the core symptoms of ASD, language, and nutritional status.
Treatment Participants The published studies in the scientific literature have used different criteria for characterizing the participants, controlling for behavioral and medical interventions that might impact change in symptoms over time, age of participants, the rigor with which the diet was monitored, and medical conditions which might impact outcome. Several case series (reviewed in Christison and Ivany 2006; Mulloy et al. 2010; Millward et al. 2009) and a single-blind study (Knivsberg et al. 2002) report on children with clinical diagnoses of ASD. The children in these studies ranged in age from 3 to 17 years. No attempt was made to control for other interventions. A larger singleblind trial (Whitely et al. 2010) reported on children whose diagnosis of ASD was supported by the Autism Diagnostic Observation Schedule. A smaller double-blind study supported the diagnosis with the Childhood Autism Rating Scale (CARS) (Elder et al. 2006). A small double blind, placebo-controlled challenge study studied 14 children between 2 and 5 years of age who all were getting similar Early Intense Behavioral Intervention (Hyman et al. 2016). Analysis of parent report data on a 90 item survey describing subjective response to the GFCF diet in 387 children included history of gastrointestinal problems (Pennesi and Klein 2012).
Treatment Procedures The studies published in the scientific literature to date have used different procedures to educate families about implementing and maintaining the GFCF diet. The majority provided educationrelated implementation and ongoing support but did not describe dietary adherence. Elder et al. (2006) provided a complete diet to the families for the child under study. Hyman et al. (2016) had
Gluten-Free Diet
a dietitian collect and examine 24-h diet diaries throughout the trial. Study design also varied, making it difficult to compare data (Buie et al. 2010; Christison and Ivany 2006; Mulloy et al. 2009; Millward 2008). Several case series reported on open trials in which children were placed on GFCF diets (Pennesi and Klein 2012). Individual services varied, and medications and supplements that might affect behavior were permitted. Subjective and nonstandardized assessments further compromise interpretation. In two single-blind trials (Knivsberg et al. 2002; Whitely et al. 2010), the study evaluators were blinded to the group assignment, but the reporters of behavior were not. This has the potential for the parents and school personnel who know if the child is on the diet or not to report based on their bias. Double-blind, placebo-controlled approaches included a crossover of 6 weeks on GFCF diet and 6 weeks off (Elder et al. 2006). Hyman et al. (2016) reported on double-blind placebo-controlled challenges of gluten, casein, and both and placebo in children maintained on a GFCF diet.
Efficacy Information The efficacy of the GFCF diet was not consistent across studies. The utility of the diet varied between reported studies based on the outcome measures selected, study population, and interpretation of the data. The outcome measures are summarized in the section below. While the popular press and Internet are replete with anecdotal endorsements of the GFCF diet, the scientific literature to date is not supportive of its use based on the clinical trials that have been published (Buie et al. 2010; Millward 2009). The studies typically are of small size. Negative studies usually need to be quite large to establish that a treatment is not useful. The largest study to date (Whitely et al. 2010) has over 50 participants but is single blind and does not control for other interventions that might have impacted change in behaviors. It did not document medical comorbidities like celiac disease or food allergies that might have impacted the response to dietary intervention. Since educational and behavioral
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interventions may impact development and behavior as well (see ▶ ABA), interventions that are used for prolonged periods need to be evaluated in concert with other approaches that are also being used to impact symptoms. For families who desire to implement an empiric trial of the GFCF diet, the scientific literature does not provide sufficient information to recommend a length of dietary trial or specific workup that would identify children with increased likelihood of response. The longer that a child is exposed to an intervention, the greater the likelihood that ongoing development and the other educational and behavioral interventions in place will also impact developmental gain. Many advocates of the diet recommend removal of one factor (e.g., casein) then removal of the other after an adjustment period. There is no evidence of an “addiction” to either casein or gluten to support a “withdrawal” effect. However, if an underlying intolerance to either component is the cause of irritability or GI distress, then a stepwise removal of casein and gluten might help a family determine the least restrictive diet necessary for symptomatic improvement. If a family desires to undertake a trial of the GFCF diet, they should consult with their healthcare provider regarding the potential for celiac disease, food allergy, or lactose intolerance. General allergy testing and measurement of urine peptides are not likely to be helpful. The family and therapy team can identify target behavioral data that can be collected during implementation of a trial of the GFCF diet to help determine if there is benefit. It can be as simple as structured collection of a Clinical Global Index, behavioral rating scales such as the Aberrant Behavior Checklist (see ▶ ABC), sleep diaries or rating scales, or formal language or cognitive testing. Some measures are not designed to measure change over short periods (e.g., ▶ Autism Diagnostic Observation Schedule) or cannot be administered at a frequent interval without introducing a practice effect (e.g., formal language and IQ tests). These measures may not be sensitive to subtle differences in behavior.
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Outcome Measurement The approach to measurement of behavioral effect varies between published studies, making comparison difficult. Core symptoms of ASD have been reported as subjective parent impression (Pennesi and Klein 2012) as well as through parent report using behavioral scales (GARS, Vineland) (Millward 2009; Whitely et al. 2010). Parent report captures important information; however, it also allows for significant bias if the parents are not blinded to the intervention. They, of course, knew that the diet was implemented in all but the double-blind, placebo-controlled study (Elder et al. 2006). Core symptoms of ASD were addressed by varied approaches as well. While subjective ratings cannot be dismissed, they are not readily interpreted. Whitely et al. (2010) used the GARS to quantify the report of behaviors observed by the parents and the ADOS to document improvement over time. The ADOS as used was not developed to be used as a measure of change for symptoms of ASD (see ▶ Autism Diagnostic Observation Schedule). Elder et al. (2006) used the Childhood Autism Rating Scale to quantify improvement. While the families were blind as to dietary status, this instrument was not developed to measure improvement with repeat administration either (see ▶ CARS). The double-blind, placebocontrolled, randomized study did not identify exposure to gluten/casein as altering core symptoms of autism in the short run by the measures used. Language and development was identified as an outcome measure in several studies. Knivsberg et al. (2002) suggested there was an improvement in formal language scores that did not reach statistical significance. Whitely et al. (2010) suggested developmental improvement over time on the Vineland during the first 6 months of intervention. Elder et al. (2006) found no change in language over the 6-week study interval. Other behaviors that might be affected by diet were considered using several approaches. Elder et al. (2006) looked at global change without demonstrable effect. Nutritional outcome of the GFCF diet has been examined as well. Cornish (2002) reviewed diet diaries collected by families recruited through a
Gluten-Free Diet
postal survey. She suggested that children on restricted diets were not found to be at increased risk for nutritional deficiencies. The nutritional implications of the GFCF diet in community use over time are unknown. Hediger et al. (2008) noted decreased cortical bone density in boys with ASD that was further decreased in the presence of dietary limitation of dairy products. Stewart et al. (2015) did not identify nutritional compromise in children with ASD given a GFCF diet who were supplemented with calcium and vitamin D, but not all children on limited diets are appropriately supplemented (Marí-Bauset et al. 2016; Srinivasan et al. 2017). Ghalichi et al. (2016) used the Rome III criteria to document improvement in the gastrointestinal symptoms of children with ASD randomized to the GFCF diet. However, the report of behavioral improvement is difficult to interpret because the outcome measure was the GARS-2, a tool not designed to measure behavioral change.
Qualifications of Treatment Providers Families considering a trial of the GFCF diet should discuss their plans with their health-care provider. Since dairy products are a major source of calcium and vitamin D for children, another source of these nutrients will need to be identified if milk and milk products are eliminated from the diet. Grain-containing products are typically supplemented with B vitamins and other nutrients and as such provide many essential nutrients in children’s diets. Referral of families considering a trial of this diet to a registered dietitian for education may be helpful regarding education on foods containing gluten and casein, how to introduce new foods, nutritional monitoring, and advice regarding prudent supplementation as needed. An RD is a professional with graduate training in nutrition and state licensure. Health insurance may cover nutrition consultation depending on diagnosis. In addition to counseling regarding nutritional adequacy of the foods their child will consume, families pursuing a GFCF diet need to be educated on reading food labels to determine which products contain gluten and casein. Gluten is present in foods labeled as containing wheat, barley, and rye. Other
Gluten-Free Diet
ingredients containing gluten include malt, modified food starch, and others. Hidden sources of gluten may include spices dusted in flour to prevent clumping and foods fried in oil also used for glutencontaining products (e.g., oil in a restaurant used to cook both French fried potatoes and chicken nuggets with bread coating). All dairy products contain casein including milk, cheese, yoghurt, ice cream, sherbet, butter, and powdered milk. Milk proteins like casein and whey may be ingredients in packaged foods. Some vegan products like cheese substitute may contain casein. Lactose-free dairy products may contain casein or whey. Clinicians who practice complementary, holistic, and integrative medicine may advocate for a trial of the GFCF diet with or without use of other nutritional supplements. The studies in the scientific literature do not report on combined interventions. The long-term health effects of high intake of supplements above recommended intake is unknown.
See Also ▶ Autism Diagnostic Observation Schedule ▶ Childhood Autism Rating Scale ▶ Feingold Diet ▶ Gastrointestinal Disorders and Autism
References and Reading Bird, B., Russo, D., & Cataldo, M. (1977). Considerations in the analysis and treatment of dietary effects on behavior: A case study. Journal of Autism and Childhood Schizophrenia, 7, 373–382. Buie, T., Campbell, D. B., Fuchs, G. J., Furuta, G. T., Levy, J., VandeWater, J., et al. (2010). Evaluation, diagnosis, and treatment of gastrointestinal disorders in individuals with ASDs: A consensus report. Pediatrics, 125S, S1–S18. Cass, H., Gringras, P., March, J., McKendrick, I., O’Hare, A. E., Owen, L., et al. (2008). Absence of urinary opioid peptides in children with autism. Archives of Disease in Childhood, 93, 745–750. Christison, G., & Ivany, K. (2006). Elimination diets in autism spectrum disorders: Any wheat amidst the chaff? Journal of Developmental and Behavioral Pediatrics, 27, S162–S171. Cornish, E. (2002). Gluten and casein free diets in autism: A study of the effects on food choice and nutrition.
2263 Journal of Human Nutrition and Dietetics, 15(4), 261–269. Dinan, T. G., & Cryan, J. F. (2017). The microbiome-gutbrain axis in health and disease. Gastroenterology Clinics of North America, 46(1), 77–89.PMID: 28164854. Elder, J. H., Shankar, M., Shuster, J., Theriaque, D., Burns, S., & Sherrill, L. (2006). The gluten-free, casein-free diet in autism: Results of a preliminary double-blind clinical trial. Journal of Autism and Developmental Disorders, 36, 413–420. Ghalichi, F., Ghaemmaghami, J., Malek, A., & Ostadrahimi, A. (2016). Effect of gluten free diet on gastrointestinal and behavioral indices for children with autism spectrum disorders: A randomized clinical trial. World J of Pediatrics: WIP, 12(4), 436–442.PMID: 27286693. Hediger, M., England, L., Molloy, C., Yu, K., ManningCourtney, P., & Mills, J. (2008). Reduced bone cortical thickness in boys with autism or autism spectrum disorder. Journal of Autism and Developmental Disorders, 38, 848–856. Hunter, L. C., O’Hare, A., Herron, W. J., Fisher, L. A., & Jones, G. E. (2003). Opioid peptides and dipeptidyl peptidase in autism. Developmental Medicine and Child Neurology, 45(2), 121. Hyman, S. L., Stewart, P. A., Foley, J., Cain, U., Peck, R., Morris, D. D., Wang, H., & Smith, T. (2016). The gluten-free/casein-free diet: A double-blind challenge trial in children with autism. Journal of Autism and Developmental Disorders, 46(1), 205–220. PMID: 26343026. Knivsberg, A., Reichelt, K., Hoien, T., & Nodland, M. (2002). A randomized, controlled study of dietary intervention in autistic syndromes. Nutritional Neuroscience, 5, 251–261. Lewis, L. (1998). Special diets for special kids. Arlington: Future Horizons. Marí-Bauset, S., Llopis-González, A., Zazpe, I., Marí-Sanchis, A., & Suárez-Varela, M. M. (2016). Nutritional impact of a gluten-free casein-free diet in children with autism spectrum disorder. Journal of Autism and Developmental Disorders, 46(2), 673–684. PMID: 26428353. Milward, C., Ferriter, M., Calver, S. J., & Connell-Jones, G. G. (2008). Gluten-and casein-free diets for autistic spectrum disorder. Cochrane Database of Systematic Reviews, 1, 2–28. Mulloy, A., Lang, R., O’Reilly, M., Sigafoos, J., Lancioni, G., & Rispoli, M. (2009). Gluten and casein free diets in the treatment of autism spectrum disorders: A systematic review. Research in Autism Spectrum Disorders, 4, 328–339. O’Banion, D., Armstrong, B., Cummings, R., & Stange, J. (1978). Disruptive behavior: A dietary approach. Journal of Autism and Childhood Schizophrenia, 8, 325–337. Pennesi, C. M., & Klein, L. C. (2012). Effectiveness of the gluten-free, casein-free diet for children diagnosed with autism spectrum disorder: Based on parental report.
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Nutritional Neuroscience, 15(2), 85–91. PMID: 22564339. Perrin, J. M., Coury, D. L., Hyman, S. L., Cole, L., Reynolds, A. M., & Clemons, T. (2012). Complementary and alternative medicine use in a large pediatric autism sample. Pediatrics, 130(Suppl 2), S77–S82. PMID: 23118257. Reichelt, K., Knivsberg, A., Lind, G., & Nodland, M. (1991). Probable etiology and possible treatment of childhood autism. Brain Dysfunction, 4, 308–319. Seung, H., Rogalski, Y., Shankar, M., & Elder, J. (2007). The gluten- and casein-free diet and autism: Communication outcomes from a preliminary double-blind clinical trial. Journal of Medical Speech-Language Pathology, 15, 337–345. Shattock, P., Kennedy, A., Rowell, F., & Berney, T. (1990). Role of neuropeptides in autism and their relationships with classical neurotransmitters. Brain Dysfunction, 3, 328–345. Srinivasan, S., O’Rourke, J., Bersche Golas, S., Neumeyer, A., & Misra, M. (2017). Calcium and vitamin D supplement prescribing practices among providers caring for children with autism spectrum disorders: Are we addressing bone health? Autism Research and Treatment, 2016, 6763205. PMID: 27042348. Stewart, P. A., Hyman, S. L., Schmidt, B. L., Macklin, E. A., Reynolds, A., Johnson, C. R., James, S. J., & Manning-Courtney, P. (2015). Dietary supplementation in children with autism spectrum disorders: Common, insufficient, and excessive. Journal of the Academy of Nutrition and Dietetics, 115(8), 1237–1248. PMID: 26052041. Whitely, P., Haracopos, D., Knivsberg, A. M., Reichelt, K. L., Palar, S., Jacobsen, J., et al. (2010). The ScanBrit randomized, controlled, single blind study or a gluten and casein free dietary intervention for children with autism spectrum disorders. Nutritional Neuroscience, 13(2), 87–100.
based parent-teacher consultation and coaching intervention that roughly doubles the Special Education IEP goal attainment outcomes of children and youth with autism spectrum disorder (ASD). COMPASS brings together parents and teachers, with guidance and support from the COMPASS consultant, to create a shared understanding of the child with ASD that integrates the essential and critical information from home and school. This holistic and ecologically informed view leads to the identification of pivotal goals within the critical social, communication, and learning skill domains that leverage other areas of development. Following the identification of contextualized and socially validated goals, an intervention plan is developed based on the child’s personal and environmental challenges and supports, consistent with the stipulations of the Evidence-Based Practice in Psychology (EBPP) tripartite framework. There is experimental evidence that COMPASS results in better education goal attainment outcomes for children and youth.
Goal Attainment of Students with ASD Using COMPASS Lisa Ruble1 and John McGrew2 1 Educational School and Counseling Psychology, University of Kentucky, Lexington, KY, USA 2 Indiana University – Purdue University at Indianapolis, Indianapolis, IN, USA
Definition The Collaborative Model for Promoting Competence and Success (COMPASS) is an evidence-
Historical Background Developed by Ruble et al. (2012a), COMPASS is a framework for helping individuals with ASD achieve optimal outcomes. The model represents an accumulation of more than 80 years of combined experience of the authors in collaboration with parents, teachers, administrators, and other personnel in the field. Developed and refined over 25 years, COMPASS is a well-validated consultation intervention to systematically develop, implement, and monitor programs for students with ASD and remains one of the few consultation approaches experimentally tested in several randomized controlled studies evaluated by an independent assessment of student progress. The COMPASS consultation approach is based on educational research that shows that empirically validated professional development models that result in sustainable changes in teacher behavior and therapeutic instruction requires ongoing coaching support that occurs within the teacher’s own instructional setting and is repeated over time.
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The model was adapted originally from the work of August, Anderson, and Bloomquist and was first promulgated in 1992 under the title Minnesota Competence Enhancement Program. From 1978 until 1992, with both state and federal funding and under the leadership of Nancy Dalrymple at the Indiana University (Bloomington) University Affiliated Program (UAP), the model was utilized within the UAP-based residential programs for children and youth with autism and subsequently as part of a state-wide training initiative in Indiana. During this period, after extensive data gathering, the model was modified to include the concept of balancing risk factors with protective factors for developing competency. That concept was a key to the publication of “An alternative view of outcome” (Ruble and Dalrymple 1996), which advocated for new and different ways to measure outcome by focusing on the development of competence and quality of life as central outcomes and linking these to accommodations and social and family support networks. This work helped to reaffirm the evolving model’s emphasis on collaboration and building environmental and personal supports rather than emphasizing deficits. Extensive field testing, including the development and validation of a COMPASS professional development training package, has continued from 1992 to the present time. In 1996 the model was used as the basis of the Autism Technical Assistance Manual for Kentucky Schools, which Dr. Ruble and Dalrymple authored. School systems throughout Kentucky had the opportunity to be trained with the manual, and the Kentucky Western Education Cooperative took the lead in incorporating the model in extensive training of all their school systems over several years. This training was always specific to individual students with autism, although the underlying framework can be readily applied to children with other types of developmental disabilities that could benefit from holistic approaches to assessment and intervention. The model was used for planning purposes, to address specific behavioral problems, and to help with transition planning from preschool to elementary school, to middle school, to high school, and to postsecondary activities. In
1998, the model served as the consultation framework for TRIAD at Vanderbilt University as part of a contract to provide consultation across the state of Tennessee and was renamed the Collaborative Model for Promoting Competence and Success of Persons with Autism Spectrum Disorder (COMPASS). Since 2004, federal funding from several National Institute of Health grants has enabled the authors to continue to evaluate the effectiveness of COMPASS for young children and for youth preparing for postsecondary activities. COMPASS has also been studied using webbased delivery. A professional development package for training COMPASS consultants is available to support its widespread use.
Rationale or Underlying Theory Unlike most ASD evidence-based practices that focus on a specific skill, the COMPASS framework for improved goal attainment outcomes is a comprehensive program that emphasizes interventions for the core underlying areas of social, communication, and learning skills instruction that can be targeted throughout the school day, over multiple activities and learning opportunities. Designed to bring together the people with the most frequent interactions with the student – parents, teachers, therapists, etc., COMPASS helps the team to jointly identify the key social, communication, and independent work/learning skills often missing in the educational programs of students with ASD yet have a pivotal impact on other areas of development. For example, a skill such as initiation impacts asking for help, starting a work task, and making social greetings. These pivotal goals must be identified and carefully crafted for the individual student and an evidenced-based intervention plan developed and modified based on the student’s needs, preferences, and strengths. Thus, COMPASS explicitly embraces and applies an Evidence-Based Practice in Psychology (EBPP) approach (McGrew et al. 2016), combining identification of the best empirically supported intervention with a consideration of the student’s needs and
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preferences, together with the personnel and general resources available within the school, home, and community. What differentiates COMPASS from traditional behavioral consultation is the focus on quality of life (QOL). Longitudinal outcome research in autism typically defines a good outcome when an individual achieves normal social development and independence. These outcomes, however, tend to misrepresent and underestimate the competencies and critical gains in ability that impact QOL. COMPASS is based on the developmental theory that competency is the result of reciprocal and dynamic interactions between individuals and their environments (i.e., transactional) (Ruble and Dalrymple 1996). Thus, by carefully examining and identifying environmental factors and introducing modifications to reduce individual risk factors and enhance protective factors, we can influence the development of important QOL skills (Fig. 1). Competence looks different across the lifespan and is person-specific. For example, transition to adulthood brings with it vocational decisions, as well as demands for more independent living skills and for establishing and creating lifelong supportive social connections. This requires specific instruction across social, communication, and emotional competencies to continue to be refined and utilized throughout adult life. Further, the extreme heterogeneity in ASD
requires a framework, like COMPASS, that can be helpful independent of the vast differences in language, cognitive, or social abilities that characterize the autism spectrum. A consultation intervention is complex because it is a multilevel EBP. To conceptualize the levels, we have applied Dunst and Trivette’s (2012) Framework for Evidence-Based Implementation and Intervention Practices (FEBIIP) as a model to understand COMPASS (Ruble and McGrew 2015). The FEBIIP includes three levels of assessment that differentiate between the three critical actors in consultation: the consultant, the consultees (parent, teacher, service provider), and the client (student). The first level focuses on quality assessment of the implementation practice or what the consultant does to impact the second level of the intervention practice or what the teacher, parent, and service provider does as a result of the consultant. This second level, the actual intervention, then impacts the practice outcome, indirectly in terms of what the student does as a result of the interventions delivered by the parent, teacher, and service provider, as well as the direct impacts of the three consultee actors on outcomes. These multilevel aspects account for the complexity within a consulting intervention such as COMPASS. For example, in prior research on COMPASS, we showed using serial mediation that COMPASS has an indirect impact on student IEP outcomes through changes in what the teacher does (i.e., teacher adherence) and in the identification of pivotal goals (i.e., IEP quality) (Wong et al. 2018) (Fig. 2).
Goals and Objectives
Goal Attainment of Students with ASD Using COMPASS, Fig. 1 Building competency by balancing challenges and supports
The goal of COMPASS is to support teachers, parents, and other service providers with the implementation of high-quality and effective comprehensive interventions able to be provided in the community. As a consultation intervention, COMPASS helps spread expertise to where it is needed most so that children, youth, and adults achieve their optimal outcomes though the attainment of skills in the areas of social, communication, and learning.
Goal Attainment of Students with ASD Using COMPASS Goal Attainment of Students with ASD Using COMPASS, Fig. 2 FEBIIP
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Implementation Practice
Intervention Practice
Practice Outcome
COMPASS
Program Quality
IEP / Postsecondary Outcomes
Consultant
Teacher, Parent, etc.
Child, Youth, Adult
Treatment Participants
Treatment Procedures
Everyone agrees that informed teachers and parents need support and access to ASD-specific research-based interventions that can be individualized for each student with ASD and result in meaningful outcomes. One means to this end is trained consultants, who can provide the “glue” that assembles the team to together. COMPASS assists parents and teachers in working together to create positive, meaningful, effective, and personalized programs for children and youth with ASD. The personalized approach of COMPASS supports the use of research-supported interventions. Effective implementation of COMPASS requires consultants who are competent about ASD and also able to provide effective consultation to successfully support parents and teachers and other service providers. We have examined implementation factors associated with COMPASS outcomes at the teacher and student levels (Ruble and McGrew 2015). For example, teacher factors associated with greater goal attainment progress of children who receive COMPASS are fidelity of implementation of intervention plans, teaching quality, and decreased teacher burnout. With respect to student factors, for children with autism, child engagement, rather than IQ, language, ASD severity, or adaptive skills, accounted for goal attainment outcomes, suggesting COMPASS works equally for all children (Ruble and McGrew 2013). We also have evidence that COMPASS results in less child-related parent stress (Krakovich et al. 2016). COMPASS is effective for improving educational outcomes for young children between 3 and 8 years of age and with high school-age students with ASD.
The detailed procedures for implementing and evaluating COMPASS are manualized (Ruble et al. 2012a). COMPASS begins with an initial 3-hour joint session that allows for discussion between the team. Starting with the COMPASS Profile or joint summary form that summarizes independently obtained parent and teacher ratings on the student’s challenges and strengths related to social skills, adaptive/self-management, communication, problem behaviors, learning skills, and sensory avoidances and preferences, the consultant facilitates in-depth information sharing. This discussion helps pinpoint critical social, communication, and work behavior/learning goals and informs the intervention plans that are generated for each goal. Following this initial consultation are four 1-hour coaching sessions with the teacher that includes progress assessment and performance feedback monitoring. Each session is standardized and allows for assessment of student progress so that intervention modification and teacher self-reflection on the implementation of the intervention plans occurs. During coaching, teachers and students provide a video or artifact (grades, diaries) to determine student progress using psychometric equivalence tested goal attainment scaling (PET-GAS; Ruble et al. 2012b). PET-GAS represents an improvement over typical GAS, in that it controls for factors that may result in biased assessment of outcomes such as level of difficulty of the skill, measurability of the skill, and the distance between the benchmarks. After progress is determined, the implementation of the teaching plan and fidelity are reviewed. Supportive problem-solving occurs based on the performance feedback and fidelity
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Discuss post-secondary goals
IEP goals linked to post-secondary goals
Student/Parent goals for post-secondary outcomes
Update IEP
Student involvement as much as possible
Videotape, work samples, data
Review Progress toward Post-Secondary Goals
Goal Attainment of Students with ASD Using COMPASS, Fig. 3 COMPASS intervention
monitoring. Figure 3 shows the COMPASS activities in the white boxes for young children, and the black boxes show the adaptations for high school students.
Efficacy Information COMPASS originally focused almost exclusively on the school setting and was developed and tested for young children with ASD. In two NIH-funded RCTs of COMPASS for young children (ages 3–8) with autism (Ruble et al. 2010; Ruble et al. 2013), COMPASS resulted in large effect sizes (Cohen’s d¼1.5; 1.1). Importantly, we were able to demonstrate success in extending COMPASS to geographically isolated areas through videoconferencing, addressing the need for service access for those in underserved areas. Next, we tested a parent-mediated version of COMPASS called COMPASS for Hope (C-HOPE). C-HOPE was designed for parents of children with ASD and challenging behavior. C-HOPE was effective for decreasing child problem behavior (p < 0.001), increasing parent competency (p ¼ 0.02), and decreasing parent stress
(p < 0.001) (Kuravackel et al. 2018). The third intervention COMPASS for Transition (COMPASS-T) was tested in an RCT with high school students in their final year of school with a focus on improving IEP and postsecondary outcomes (Ruble et al. 2018). Students whose teachers received COMPASS made more progress on their IEP goals based on blinded goal attainment ratings, with a very high effect size (Cohen’s d ¼ 2.1) Consultants were able to deliver COMPASS-T with high fidelity, and similar to our studies of young children, teacher adherence to the implementation of teaching plans increased over time, meaning coaching sessions were essential for positive student outcomes. Further, parents and teachers reported high satisfaction; teachers reported COMPASS-T was acceptable, feasible, and not burdensome (Ruble et al. 2018).
Outcome Measurement Data collection and progress monitoring are critical features of evidence-based interventions like COMPASS. But in special education, goals are individualized, and often the measurement
Goal Attainment of Students with ASD Using COMPASS
-2 Present Level of Performance
-1 Progress
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0 Expected level of outcome (goal)
+1 Somewhat more than expected
+2 Much more than expected
Goal Attainment of Students with ASD Using COMPASS, Fig. 4 Example of a Goal Attainment Scale
approaches using standardized assessments are insensitive to interventions for school-age and older individuals (see Bacon et al. 2014) and are, therefore, not helpful for making data-informed decisions in practice. This is an issue that was recognized decades ago when special education researchers called for an alternative approach that allowed for assessment that could be repeated frequently and used for progress monitoring and instructional decision-making for a student with individualized goals (Carr 1979; Shuster et al. 1984). When designing the randomized controlled trials to evaluate COMPASS, an outcome measurement tool was needed that not only had good psychometric properties (reliability, validity) when used to compare groups but also good sensitivity for detecting change and growth in skills as a result of personalized instructional plans developed from COMPASS (Ruble et al. 2019). Because of a need for a measurement approach that was broad, able to be used for any age, useful for goals that varied from child to child, meaningful, and socially valid for community-based interventions such as educational settings, goal attainment scaling (GAS) was selected. GAS is a common outcome assessment approach for intervention studies from both physical and mental health sciences and was described as a method for evaluating special education outcomes more than 40 years ago (Carr 1979). For consultation research GAS is the most frequently used method (Hurwitz et al. 2015; Sladeczek et al. 2001) for several types of research designs including singlecase, quasi-experimental, and randomized controlled designs. For ASD research, GAS has been applied to several group design research
studies (c.f. Odom et al. 2013; Ruble et al. 2010; Ruble et al. 2013; Ruble et al. 2019). As mentioned earlier, we developed PET-GAS to resolve potential problems with measurement bias when using GAS when making between group comparisons. A goal attainment scale traditionally has five benchmarks that range from 2 (present level of performance) to +2 (much more than expected). Details on how to create a GAS are available from Ruble et al. (2012a). After careful assessment of the student that is based on parent and teacher input, rating scales, observations, and other sources, a goal is developed that should be achievable by the end of the school year (0 level or expected level of outcome). After the goal is identified and developed, then benchmarks are written related to the goal (progress or 1; somewhat more than expected or +1; much more than expected or +2). Figure 4 shows an example of GAS.
Qualifications of Treatment Providers Psychologists (including Rehabilitation, Clinical, Counseling, and School Psychologists); Speech and Language Pathologists; Occupational Therapists; Cognitive Rehabilitation Specialists; General and Special Educators; Vocational Rehabilitation Counselors and Life Care Planners
See Also ▶ COMPASS for Hope Training Program ▶ Education
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▶ Family Burden ▶ Free Appropriate Public Education ▶ National Research Council ▶ Parent-Professional Partnership ▶ Social Interventions
Ruble, L.-A., & Dalrymple, N.-J. (1996). An alternative view of outcome in autism. Focus on Autism and Other Developmental Disabilities, 11(1), 3–14. Ruble, L., & McGrew, J. (2013). Teacher and child predictors of achieving IEP goals of children with autism. Journal of Autism and Developmental Disorders, 43, 2748–2763. Ruble, L., & McGrew, J. (2015). COMPASS and implementation science: An evidence-based practice in psychology approach for improving educational outcomes of children with autism spectrum disorder. New York: Springer. Ruble, L. A., Dalrymple, N. J., & McGrew, J. H. (2010). The effects of consultation on Individualized Education Program outcomes for young children with autism: The collaborative model for promoting competence and success. Journal of Early Intervention, 32(4), 286–301. Ruble, L., Dalrymple, N. J., & McGrew, J. H. (2012a). Collaborative model for promoting competence and success for students with ASD. New York: Springer. Ruble, L., McGrew, J. H., & Toland, M. D. (2012b). Goal attainment scaling as an outcome measure in randomized controlled trials of psychosocial interventions in autism. Journal of Autism and Developmental Disorders, 42(9), 1974–1983. https://doi.org/10.1007/ s10803-012-1446-7. Ruble, L. A., McGrew, J. H., Toland, M. D., Dalrymple, N. J., & Jung, L. A. (2013). A randomized controlled trial of COMPASS web-based and face-to-face teacher coaching in autism. Journal of Consulting and Clinical Psychology, 81(3), 566–572. https://doi.org/10.1037/ a0032003. Ruble, L. A., McGrew, J. H., Toland, M., Dalrymple, N., Adams, M., & Snell-Rood, C. (2018). Randomized control trial of COMPASS for improving transition outcomes of students with autism spectrum disorder. Journal of Autism and Developmental Disorders. https://doi.org/10.1007/s10803-018-3623-9. Ruble, L., Wong, W. H., & McGrew, J. (2019). Data collection in education and measurement of progress. In R. Jordan, J. M. Roberts, & K. Hume (Eds.), The SAGE handbook of autism and education (pp. 578–592). Los Angeles: Sage. Shuster, S. K., Fitzgerald, N., Shelton, G., Barber, P., & Desch, S. (1984). Goal attainment scaling with moderately and severely handicapped preschool children. Journal of Early Intervention, 8(1), 26–37. https://doi. org/10.1177/105381518400800104. Sladeczek, I. E., Elliott, S. N., Kratochwill, T. R., Robertson Mjaanes, S., & Stoiber, K. C. (2001). Application of goal attainment scaling to a conjoint behavioral consultation case. Journal of Educational and Psychological Consultation, 12(1), 45–58. Wong, V., Ruble, L. A., McGrew, J. H., & Yu, Y. (2018). An empirical study of multidimensional fidelity of COMPASS consultation. School Psychology Quarterly, 33(2), 251–263. https://doi.org/10.1037/ spq0000217.
References and Reading August, G. A., Anderson, D., & Bloomquist, M. L. (1992). Competence enhancement training for children: An integrated child, parent, and school approach. In S.-L. Christenson & J.-C. Conoley (Eds.), Home-school collaboration: Enhancing children’s academic and social competence (pp. 175–192). Silver Spring: National Association of School Psychologists. Bacon, E. C., Dufek, S., Schreibman, L., Stahmer, A. C., Pierce, K., & Courchesne, E. (2014). Measuring outcome in an early intervention program for toddlers with autism spectrum disorder: Use of a curriculum-based assessment. Autism Research and Treatment, 2014, 964704. https://doi.org/10.1155/2014/964704. Carr, R. A. (1979). Goal attainment scaling as a useful tool for evaluating progress in special education. Exceptional Children, 46(2), 88–95. Dunst, C. J., & Trivette, C. M. (2012). Meta-analysis of implementation practice research. In B. Kelly & D. Perkins (Eds.), Handbook of implementation science for psychology in education (pp. 68–91). New York: Cambridge University Press. Hurwitz, J. T., Kratochwill, T. R., & Serlin, R. C. (2015). Size and consistency of problem-solving consultation outcomes: An empirical analysis. Journal of School Psychology, 53(2), 161–178. https://doi.org/10.1016/j. jsp.2015.01.001. Krakovich, T. M., McGrew, J. H., Yu, Y., & Ruble, L. A. (2016). Stress in parents of children with autism spectrum disorder: An exploration of demands and resources. Journal of Autism and Developmental Disorders, 46(6), 2042–2053. https://doi.org/10.1007/ s10803-016-2728-2. Kuravackel, G. M., Ruble, L. A., Reese, R. J., Ables, A. P., Rodgers, A. D., & Toland, M. D. (2018). COMPASS for hope: Evaluating the effectiveness of a parent training and support program for children with ASD. Journal of Autism and Developmental Disorders, 48(2), 404–416. https://doi.org/10.1007/ s10803-017-3333-8. McGrew, J., Ruble, L., & Smith, I. (2016). Autism spectrum disorder and evidence-based practice in psychology. Clinical Psychology: Science and Practice, 23(3), 239–255. Odom, S. L., Cox, A. W., & Brock, M. E. (2013). Implementation science, professional development, and autism spectrum disorders. Exceptional Children, 79(2), 233–251.
Goldenhar Syndrome
Goldenhar Syndrome Carson Kautz Yale Child Study Center, Yale University, New Haven, CT, USA
Synonyms Craniofacial microsomia; Hemifacial microsomia; Oculoauriculovertebral (OAV) spectrum or dysplasia
Short Description or Definition Goldenhar Syndrome is a congenital condition affecting the development of the eye, ear, and spine. In most cases, the syndrome affects only one side of the body, but both sides of the body can be affected. Individuals with Goldenhar Syndrome have facial asymmetry, underdeveloped or absent ear, benign growths in the eye called epibulbar dermoids or lipodermoids, and benign growths or skin tags on the ear, called preauricular appendages. Abnormalities of the spine can manifest in scoliosis, as well as vertebrae or ribs that are missing, partially formed, or abnormally fused. Problems of the heart, lungs, kidneys, and central nervous system are also common. The root cause of the syndrome is unknown, but it is associated with abnormal development of the first and second branchial arches of the fetus, which develop to form the neck and head.
Categorization Goldenhar syndrome is part of a complex group of conditions characterized by underdeveloped jaw, facial asymmetry, benign growths or skin tags on the ear, and underdevelopment or absence of the ear (Heike et al. 2009). The relationship of these conditions to each other is not well defined; they may be distinct conditions or may describe different severities of the same
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condition. Other conditions in this group include hemifacial microsomia and oculoauriculovertebral (OAV) spectrum. Although these terms can be used interchangeably, they can also refer to specific combinations of craniofacial abnormalities. Goldenhar Syndrome is characterized by these craniofacial abnormalities in addition to spinal malformations and benign growths of the eye (Gorlin et al. 2001).
Epidemiology Goldenhar Syndrome is rare; therefore, it is difficult to estimate prevalence. Some have estimated the prevalence as high as 1 in 5600 births (Gorlin et al. 2001; Grabb 1965), while others place it as low as 1 in 45,000 (Morrison et al. 1992). Males are more likely to be affected, with the male to female ratio estimated at 3:2 (Gorlin et al. 2001). Most people with Goldenhar syndrome are the only person in their family to be affected, but there have been cases in which individuals have a chromosome abnormality, in which case the condition might be inherited. Cases of both autosomal dominance and recessive inheritance have been recorded (Heike et al. 2009).
Natural History, Prognostic Factors, Outcomes In 1952, Maurice Goldenhar coined the term Goldenhar syndrome, including in its description the characteristic preauricular appendages, as well as epibulbar dermoids and mandibular hypoplasia, or underdevelopment of the jaw. Since then, additional symptoms have been recognized as representative of Goldenhar syndrome, namely, vertebral abnormalities and underdevelopment or absence of the ear (Feingold and Baum 1978; Gorlin et al. 1963). Current thinking varies on whether Goldenhar syndrome is a distinct syndrome or whether it is simply a severe phenotype on a spectrum of facial abnormalities. Some classify it as a phenotype included under the umbrella term craniofacial microsomia (Heike et al. 2009). Others use the term OAV spectrum to denote a
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“symptom-complex” that includes Goldenhar syndrome as well as hemifacial microsomia (Gorlin et al. 1963, 2001). Goldenhar syndrome can range from mild to severe. More mild cases may need no other treatment than careful monitoring. Children with Goldenhar syndrome have been seen to be shorter than their peers and may have delayed motor development or hearing loss (Bogusiak et al. 2017). Some speech disorders have been observed, as well as psychosocial problems and behaviors commonly associated with autism (Barton and Volkmar 1998; Bogusiak et al. 2017; Maris et al. 1999). Individuals with Goldenhar syndrome may also exhibit breathing problems, including tachypnea and sleep apnea, and feeding problems (Gorlin et al. 2001). Cleft lip, face, or palate is also commonly associated with Goldenhar syndrome. More severe expression of the disorder is associated with cerebral and cranial defects, which often are associated with intellectual disability and higher mortality (Gorlin et al. 2001; Setzer et al. 1981). Congenital heart disease is common in Goldenhar syndrome; tetralogy of fallout (TOF) and atrial and ventricular septal defects have been found to be the most common, but many types of heart problems have been seen (Digilio et al. 2008).
Goldenhar Syndrome
Goldenhar syndrome have also presented with underdevelopment or absence of an eye (Gorlin et al. 1963). Mandibular or maxillary hypoplasia or other developmental issues in the bone structure of the face are often present (Feingold and Baum 1978; Gorlin et al. 2001; Heike et al. 2009). Preauricular or auricular appendages, skin tags near or on the ear, are characteristic of Goldenhar syndrome, and malformation or absence of one or both ears is common (Gorlin et al. 2001; Heike et al. 2009). Additionally, some malformation of the inner or middle ear can be seen, with or without hearing loss. Facial asymmetry is a hallmark of Goldenhar syndrome and is often due to hypoplasia, or underdevelopment, of bones in one side of the face (Bogusiak et al. 2017). Often symptoms appear only on one side of the face, also contributing to facial asymmetry. In some cases, symptoms have appeared on both sides; in these cases, one side is often more severe (Gorlin et al. 2001). Finally, vertebral anomalies are characteristic of Goldenhar syndrome. Other symptoms not previously associated with Goldenhar syndrome, including central nervous system abnormalities, are beginning to be recognized as characteristic of the disorder, providing further evidence to support the idea of a spectrum (Gorlin et al. 2001). Goldenhar syndrome may become more severe as the individual grows older (Bogusiak et al. 2017).
Clinical Expression and Pathophysiology of Goldenhar Syndrome Evaluation and Differential Diagnosis Clinical presentation of Goldenhar syndrome is highly variable and ranges from mild to severe. It is congenital, meaning it is present at birth. The literature is mixed and no clear diagnostic criteria have been defined for Goldenhar syndrome, but the general clinical expression is characterized by abnormalities in the eye, ear, jaw, and spine (Bogusiak et al. 2017; Feingold and Baum 1978; Gorlin et al. 2001; Heike et al. 2009). Goldenhar syndrome is set apart from other craniofacial conditions by the presence of epibulbar dermoids or lipodermoids in the eye. These are benign growths that are yellow or milky white in appearance and can range in size. Some individuals with
Literature is mixed on the differentiation of Goldenhar syndrome from hemifacial or craniofacial microsomia, or whether Goldenhar syndrome is even distinct from these conditions. Some require the presence of epibular dermoids to differentiate Goldenhar syndrome from hemifacial microsomia (Gorlin et al. 1963). Some consider Goldenhar syndrome to be a phenotype under the umbrella term craniofacial microsomia. In this case, this condition is a diagnosis by exclusion, meaning it is diagnosed by ruling out disorders with similar symptoms. A common difference between Goldenhar
Goldenhar Syndrome
syndrome and other syndromes is that Goldenhar syndrome is characterized by facial asymmetry, while other craniofacial abnormalities are symmetric (Heike et al. 2009). Because of the lack of specified diagnostic criteria, many syndromes must be taken into consideration before an individual will receive a diagnosis of Goldenhar syndrome (for a comprehensive review, see Heike et al. 2009). Some examples of these syndromes are Treacher Collins syndrome, CHARGE association, and Branchio-oto-renal syndrome. Treacher Collins syndrome presents similarly to Goldenhar Syndrome, especially in underdevelopment of the ears but lacks the characteristic facial asymmetry. CHARGE association does present asymmetrically but lacks facial or preauricular appendages, as well as vertebral anomalies. Similarly, Branchio-oto-renal syndrome presents asymmetrically without vertebral anomalies; however, it can present with facial or preauricular appendages. It also usually will not include cardiac abnormalities (Heike et al. 2009). In cases such as Cat-eye syndrome and Townes-Brocks syndrome, the phenotype of the syndrome is exactly the same and so can only be differentiated through a genetic test (Heike et al. 2009).
Treatment Because the presentation of Goldenhar syndrome is highly variable, treatment depends on the specific symptoms of the individual and often develops and changes as the individual grows older. Due to craniofacial abnormalities, individuals with Goldenhar syndrome often have difficulty breathing and may suffer from breathing-related conditions such as tachypnea and sleep apnea, among others. A newborn diagnosed with Goldenhar syndrome or any form of craniofacial microsomia should be evaluated immediately for airway obstruction, and airway interventions will be performed in the first year of life. At the time of diagnosis, the individual should have a renal evaluation as well as a cardiac evaluation; some issues
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may require immediate treatment, while some are less urgent (Heike et al. 2009). Surgery may also be necessary early in life to correct cleft lip, cleft palate, or cleft face and to remove facial, preauricular, or auricular appendages. If cleft lip, cleft palate, or cleft face is present, the newborn will often have trouble feeding, so feeding interventions may also be performed (Bogusiak et al. 2017; Heike et al. 2009). The individual will need hearing evaluations and may require interventions such as hearing prosthetics or cochlear implants (Bogusiak et al. 2017). More surgery may be required as the individual grows older to correct any spinal abnormalities or scoliosis, to correct dental issues, to rebuild ears or to create a prosthetic ear, and to remove epibulbar dermoids if they impair vision (Bogusiak et al. 2017). Finally, due to the higher likelihood of an individual with Goldenhar syndrome experiencing psychosocial difficulties, psychiatric or neurological evaluations and treatment may be necessary. The individual may also require speech therapy for any speech problems (Heike et al. 2009).
See Also ▶ CATCH 22 (Chromosome 22q11 Deletion Syndrome) ▶ Developmental Delay ▶ Velocardiofacial Syndrome
References and Reading Barton, M., & Volkmar, F. (1998). How commonly are known medical conditions associated with autism? Journal of Autism and Developmental Disorders, 28(4), 273–278. Bogusiak, K., Puch, A., & Arkuszewski, P. (2017). Goldenhar syndrome: current perspectives. World Journal of Pediatrics, 13(5), 405–415. Digilio, M. C., Calzolari, F., Capolino, R., Toscano, A., Sarkozy, A., de Zorzi, A., . . . & Marino, B. (2008). Congenital heart defects in patients with oculoauriculo-vertebral spectrum (Goldenhar syndrome). American Journal of Medical Genetics Part A, 146(14), 1815–1819.
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2274 Feingold, M., & Baum, J. (1978). Goldenhar’s syndrome. American Journal of Diseases of Children, 132(2), 136–138. Gorlin, R. J., Jue, K. L., Jacobsen, U., & Goldschmidt, E. (1963). Oculoauriculovertebral dysplasia. The Journal of Pediatrics, 63(5), 991–999. Gorlin, R. J., Cohen, M. M., Jr., & Hennekam, R. C. (2001). Syndromes of the head and neck. New York: Oxford University Press. Grabb, W. C. (1965). The first and second branchial arch syndrome. Plastic and Reconstructive Surgery, 36(5), 485–508. Heike, C. L., Luquetti, D. V., & Hing, A. V. (2009). Craniofacial Microsomia Overview. In M. P. Adam, H. H. Ardinger, R. A. Pagon, et al. (Eds.), GeneReviews ® [Internet]. Seattle: University of Washington, Seattle; 1993–2018. Updated 9 Oct 2014. Maris, C. L., Endriga, M. C., Omnell, M. L., & Speltz, M. L. (1999). Psychosocial adjustment in twin pairs with and without hemifacial microsomia. The Cleft Palate-Craniofacial Journal, 36(1), 43–50. Morrison, J., Mulholland, H. C., Craig, B. G., & Nevin, N. C. (1992). Cardiovascular abnormalities in the oculo-auriculo-vertebral spectrum (Goldenhar syndrome). American Journal of Medical Genetics Part A, 44(4), 425–428. Setzer, E. S., Ruiz-Castaneda, N., Severn, C., Ryden, S., & Frias, J. L. (1981). Etiologic heterogeneity in the oculoauriculovertebral syndrome. The Journal of Pediatrics, 98(1), 88–90.
Grammar
as adding an s to indicate a plural (e.g., books) or an ’s to indicate possessive (e.g., mommy’s), the use of prefixes, the use of suffixes, and so forth. Syntax is the system governing the order and combination of words to form sentences and the relationships among the elements within a sentence (e.g., subject, object). Individuals with speech and language disorders may have difficulty – either singly or in combination – with grammar. Speech-language pathologists assess, diagnose, and treat communication and related problems, including grammar problems.
See Also ▶ Syntax
References and Reading American Speech-Language-Hearing Association. (1993). Definitions of communication disorders and variations [Relevant Paper]. Retrieved December 14, 2010, from www.asha.org/policy
Grammar Diane R. Paul Clinical Issues in Speech-Language Pathology, American Speech-Language-Hearing Association, Rockville, MD, USA
Grammatical Morphemes ▶ Speech Morphology
Definition
Grand Mal Seizure Grammar, the framework for a language, consists of syntax (the way words are combined to convey meaning) and morphology (the forms of words) of a particular language. Morphology is the system that governs the structure of words and the construction of word forms (e.g., nouns, verbs, adjectives). Morphology includes how morphemes (the smallest unit of meaning) are used to develop the rules of word formation in English, such
Frank Besag Child and Adolescent Mental Health Services, SEPT. (South Essex Partnership University NHS Foundation Trust), Bedford, UK
Synonyms Tonic-clonic seizure
Grand Mal Seizure
Definition Although this term was replaced by “tonic-clonic seizure” in 1981 (Commission on Classification and Terminology of the International League Against Epilepsy 1981), it is still sometimes used, perhaps by those who are not aware of the change in terminology. The term “tonic-clonic seizure” should be used not only because it is the current accepted terminology but also because it describes what happens. In this context, the word “tonic” means stiff and the word “clonic” means jerking. A tonic-clonic seizure is one in which the person is stiff and jerking. When the stiffening includes the respiratory muscles, a cry may be emitted at the beginning of the seizure and cyanosis may occur. The tongue is sometimes bitten. Incontinence of urine may occur. Injuries may be sustained if the patient falls. In the clonic phase, there may be copious salivation, sometimes with frothing at the mouth. If the tonicclonic seizure is preceded by a simple partial seizure (consciousness fully retained) and/or a complex partial seizure (some impairment of consciousness), it is a secondarily generalized tonic-clonic seizure. In contrast, if there is no preceding partial seizure, it is a primary generalized tonic-clonic seizure. The sequence of events in a tonic-clonic seizure is that typically the stiffening occurs first and lasts for around 20 s, followed by rhythmical jerking, which lasts for around 45 s (Theodore et al. 1994). However, seizures can be very variable. In some people, both the stiffening and jerking appear to be present at the start of the seizure. The duration of tonic-clonic seizures can also vary greatly from one patient to another. The tonic-clonic seizure and indeed epilepsy are clinical diagnoses based on a clear description from someone who has seen the episodes. The EEG can support diagnosis but is no substitute for a good clinical history. The sequence of events in the EEG during a tonic-clonic seizure can vary from one person to another. The electroencephalogram (EEG) can also vary greatly from one individual to another. A typical situation would be that the EEG would be obscured by muscle artifact, but if the filters
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allowed the underlying pattern to become evident, this would show repetitive spikes with increasing amplitude and with increasing frequency all over the brain during the tonic phase. In the clonic (jerking) phase, slow waves appear and the spikes become less frequent. After the seizure, the EEG may show nonspecific slow-wave activity in a generally suppressed record. Prolonged tonic-clonic seizures, lasting 20 min or longer (status epilepticus), are uncommon but constitute a medical emergency requiring prompt treatment to decrease the risk of permanent brain damage. The tonic-clonic seizure is one of the many types of seizures that can occur in people with autism. Estimates of the prevalence of epilepsy in people with autism vary from about 20% to 40%. The question of whether epilepsy is more common than in the general population in people with autism who do not also have learning disability (mental retardation) has been debated, but most of the evidence suggests that epilepsy primarily occurs in those who have both autism and learning disability. The relationships between epilepsy and autism continue to be debated (Besag 2009).
See Also ▶ Autism ▶ Electroencephalogram (EEG) ▶ Epilepsy ▶ Learning Disability
References and Reading Besag, F. M. (2009). The relationship between epilepsy and autism: A continuing debate. Acta Paediatrica, 98, 618–620. Commission on Classification and Terminology of the International League Against epilepsy. (1981). Proposal for revised clinical and electroencephalographic classification of epileptic seizures. Epilepsia, 22, 489–501. Theodore, W. H., Porter, R. J., Albert, P., Kelley, K., Bromfield, E., Devinsky, O., et al. (1994). The secondarily generalized tonic-clonic seizure: A videotape analysis. Neurology, 44, 1403–1407.
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Grandin, Temple Fred R. Volkmar Child Study Center, Irving B. Harris Professor of Child Psychiatry, Pediatrics and Psychology, Yale Child Study Center, School of Medicine, Yale University, New Haven, CT, USA
Name and Degrees Temple Grandin • 1966 B.A. Franklin Pierce College, Concord, New Hampshire • 1975 M.A. Animal Science Arizona State University, Phoenix, Arizona • 1989 Ph.D. Animal Science, University of Illinois, Urbana-Champaign
Major Appointments (Institution, Location, Dates) • Professor of Animal Science, Colorado State University, Fort Collins, CO
Major Honors and Awards • 2010 Honorary Doctoral Degree, Duke University, Durham North Carolina
Landmark Clinical, Scientific, and Professional Contributions Temple Grandin, Ph.D., is an animal scientist and widely known for her writing and lectures on her experience of autism. Her book Thinking in Pictures provides a personal and highly illuminating view of the world from the perspective of a highly able person with autism. She has written vividly about the contribution of sensory experience in autism. In addition to her advocacy, she has had an interest in approaches to treatment that involve sensory experiences. She
Grandin, Temple
has also been a prominent advocate for animal welfare.
Short Biography Born in Boston, Temple Grandin was diagnosed with autism in 1950. At that time, treatment options were limited, but she received a structured school program and communication therapy. By age 5, she was talking but recalls experiences of feeling isolated. She attended a boarding school for gifted children before going on to do undergraduate in psychology and then graduate work in animal science. In addition to her work on animal management, she has written several volumes on autism including books concerned with visual thinking, the use of pressure sensation as an intervention technique, and an autobiography.
See Also ▶ Occupational Therapy (OT)
References and Reading Grandin, T. (1990). Needs of high functioning teenagers and adults with autism: Tips from a recovered autistic. Focus on Autistic Behavior, 5(1)., 16 p. Grandin, T. (1992). Calming effects of deep touch pressure in patients with autistic disorder, college students, and animals. Journal of Child & Adolescent Psychopharmacology, 2(1), 63–72. Grandin, T. (1995). Thinking in pictures: And other reports from my life with autism. New York: Doubleday. Grandin, T. (2005). A personal perspective of autism. In F. R. Volkmar, A. Klin, R. Paul, & D. J. Cohen (Eds.), Handbook of autism and pervasive developmental disorders (Vol. 2, pp. 1276–1286). Hoboken: Wiley. Grandin, T., & Johnson, C. (2005). Animals in translation: Using the mysteries of autism to decode animal behavior. New York: Scribner/Simon & Schuster. Grandin, T., & Scariano, M. M. (1986). Emergence: Labeled autistic. New York: Warner Books.
Grandma’s Rule ▶ Premack Principle
Gross Motor Skills
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Grants
Gray Substance
▶ FAFSA
▶ Gray Matter
Gross Motor Skills Gray Matter Danielle Bolling Yale Child Study Center, New Haven, CT, USA
Synonyms Gray substance; Substantia grisea
Gianluca Esposito1,3,4 and Giacomo Vivanti2 1 Nanyang Technological University, Wako, Saitama, Singapore 2 A.J. Drexel Autism Institute, Drexel University, Philadelphia, PA, USA 3 University of Trento, Rovereto, TN, Italy 4 Kuroda Research Unit, RIKEN Brain Science Institute, Wako-shi Saitama, Japan
Definition Definition Gray matter refers to the portion of the central nervous system composed of neuronal cell bodies, neuropil, glial cells, and capillaries. This region is so named because of its gray color (in contrast to white matter). Gray matter can be found in the cerebral cortex as well as in the thalamus, basal ganglia, and cerebellum.
See Also ▶ Cerebral Cortex
References and Reading Palmen, S. J. M. C., Pol, H. E. H., Kemner, C., Schnack, H. G., Durston, S., Lahuis, B. E., et al. (2005). Increased gray-matter volume in medication-naive high-functioning children with autism spectrum disorder. Psychological Medicine, 35, 561–570. Rojas, D. C., Peterson, E., Winterrowd, E., Reite, M. L., Rogers, S. L., & Tregellas, J. R. (2006). Regional gray matter volumetric changes in autism associated with social and repetitive behavior symptoms. BMC Psychiatry, 6, 56.
Gross motor abilities entail the use of large muscle groups that coordinate body movements to perform activities such as maintaining balance, walking, sitting upright, jumping, throwing objects, etc. Gross motor skills are acquired during infancy and early childhood as part of a child’s motor development and continue to be refined throughout most of the individual’s years of development into adulthood. Factors that contribute to the ability and the rate that children develop their gross motor skills include both genetic and environmental influences. The development of gross motor abilities occurs in the motor cortex, the region of the cerebral cortex that controls voluntary muscle groups. The development of motor autonomy is the infant’s main task in the first year of life. The process can be described as a series of distinct stages, where mastering each stage prepares the infant’s progression to the next one. At the beginning of each stage, movement is characterized by a persistent lack of symmetry. As the stage progresses, movement becomes increasingly symmetric. During each stage, complex reflex
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patterns appear and are integrated with those already existing. By 2 years of age, almost all children are able to stand up, walk, jump, walk upstairs and downstairs, and run. Difficulties in gross motor abilities are observed in a range of conditions and have been documented in individuals with autism spectrum disorders (ASDs; Dewey et al. 2007; Lloyd et al. 2011).
Historical Background Although both Kanner (1943) and Asperger (1944/1991) reported signs of gross motor impairment in their seminal descriptions of patients with autism, the issue of motor difficulties in this population was overlooked, until recently, in favor of research on the social and communicative deficits in this population. After 1990, and for the successive two decades, there has been a growing recognition that children with autism do experience motor difficulties (Ghaziuddin and Butler 1998; Zingerevich et al. 2009). Furthermore, the development of gross motor skills in autism and its utility as precursory sign has increasingly gained attention (Jansiewicz et al. 2006). Currently, the examination of motor abnormalities is considered to be a valuable tool for understanding the neurological basis of autism, as motor signs are easily observable and quantifiable, and they can be examined in a manner that minimizes the potential confounds of social and communicative impairments (Wodka and Mostofsky 2011). Moreover, the importance of developing specific remediation strategies to target motor difficulties is increasingly acknowledged in clinical practice (Bhat et al. 2011). Recent research is focusing on motor development (Kim 2008), motor learning (Dowell et al. 2009), motor control (Rinehart et al. 2006), preambulatory movement (Esposito et al. 2009, 2011), and the relation between motor and social-communicative symptoms in autism (Gallese et al. 2009; Gernsbacher et al. 2008; LeBarton and Iverson 2016; Setoh et al. 2017).
Gross Motor Skills
Current Knowledge A range of motor atypicalities have been documented in individuals with autism spectrum disorders, including delayed walking; abnormalities in gait (e.g., toe walking), posture, balance, coordination, tone, and difficulties in higher-level motor learning tasks involving imitation and pantomime of complex gestures (Dewey et al. 2007; Gowen and Hamilton 2013; Minshew et al. 2004; Staples and Reid 2010). While motor abnormalities are not considered as a crucial feature in the diagnostic process and are not a main focus of most intervention models, literature suggests that motor impairments might be observed in individuals with autism across the spectrum and across developmental levels and chronological ages (Fournier et al. 2010). Whether motor anomalies are a universal and/or specific symptom of autism is still debated (Ozonoff et al. 2008). Similarly, it is still debated whether motor abnormalities are present and identifiable in the first year of life (Esposito et al. 2009, 2011; Ozonoff et al. 2008). This issue has far-reaching relevance with regard to early diagnosis and identification of autism. Studies investigating gross motor abilities in very young children with autism indicated that delays and/or atypical patterns in gross motor development are present between 12 and 36 months and that these differences become more pronounced with age in preschool years (Landa and Garrett-Mayer 2006; Lloyd et al. 2011). A study by Sutera and colleagues found that motor difficulties at age 2 years was the clearest distinguishing factor for identifying the children who continued to meet criteria for an ASD at age of 4 years (Sutera et al. 2007). Additionally, early gross motor skills have been found to predict subsequent development of language in children with ASD and those at risk for ASD (Bedford et al. 2016; Leonard et al. 2015), and preliminary evidence suggests a link between early gross motor abnormalities and social communication development in ASD (Bhat et al. 2012; LeBarton and Iverson 2016;
Gross Motor Skills
see also Barbeau et al. 2015). Given the paucity of studies that investigated the development of motor abilities through childhood and adulthood, it is not clear whether motor difficulties present with different features at different ages and/or whether they improve over time. More empirical research is needed to identify the age of onset and the developmental pattern of motor difficulties in autism, as well as relationship between gross motor development and later social communication abilities in these children. Recent research suggested that motor abnormalities observed in autism might reflect a developmental dyspraxia, that is, a deficit in the execution of skilled movements caused by difficulties in forming the sensory representation of the motor plan that is necessary to execute the actions (Mostofsky and Ewen 2011; Dziuk et al. 2007). While little is known about the etiology of motor impairment in autism, functional and anatomical abnormalities, as well as abnormal connectivity in the brain systems underlying motor planning and execution, have been documented in autism (Mostofsky et al. 2009; Nayate et al. 2005; Nebel et al. 2014; Qiu et al. 2010; Mostofsky et al. 2007).
Future Directions Current studies emphasizing the involvement of the motor system in action understanding and social cognition provide an exciting avenue for future research in the field (Rizzolatti and Sinigaglia 2007; Giorello and Sinigaglia 2007). Moreover, longitudinal studies involving infants at risk for autism is starting to provide crucial information on the nature of motor impairment in autism and its relationship with social and communicative core symptoms and other motor abnormalities such as stereotyped movements (Leonard et al. 2015; Van Waelvelde et al. 2010). The increasing recognition of the importance of motor abilities in autism is leading practitioners in the field to include increasingly refined motor assessments and programs in their clinical practice.
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See Also ▶ Apraxia ▶ Dyspraxia ▶ Early Diagnosis ▶ Imitation ▶ Mirror Neuron System ▶ Motor Control ▶ Motor Planning ▶ Occupational Therapy (OT) ▶ Praxis ▶ Toe Walking
G References and Reading Asperger, H. (1944). Die “Autistischen psychopathen” im kindesalter. Archiv fur Psychiatrie und Nervenkrankheiten, 117, 76–136. Translated by Frith, U. (Ed.). (1991). Autism and Asperger’s syndrome (pp. 37–92). Cambridge: Cambridge University Press. Baranek, G. T. (2002). Efficacy of sensory and motor interventions for children with autism. Journal of Autism and Developmental Disorders, 32(5), 397–422. Barbeau, E. B., Meilleur, A. A. S., Zeffiro, T. A., & Mottron, L. (2015). Comparing motor skills in autism spectrum individuals with and without speech delay. Autism Research, 8(6), 682–693. Bedford, R., Pickles, A., & Lord, C. (2016). Early gross motor skills predict the subsequent development of language in children with autism spectrum disorder. Autism Research, 9, 993–1001. Bhat, A. N., Landa, R. J., & Galloway, J. C. (2011). Current perspectives on motor functioning in infants, children, and adults with autism spectrum disorders. Physical Therapy, 91, 1116–1129. Bhat, A. N., Galloway, J. C., & Landa, R. J. (2012). Relation between early motor delay and later communication delay in infants at risk for autism. Infant Behavior and Development, 35(4), 838–846. Bishop, S. L., Thurm, A., Farmer, C., & Lord, C. (2016). Autism spectrum disorder, intellectual disability, and delayed walking. Pediatrics, 137(3), e20152959. Dewey, D., Cantell, M., & Crawford, S. G. (2007). Motor and gestural performance in children with autism spectrum disorders, developmental coordination disorder, and/or attention deficit hyperactivity disorder. Journal of International Neuropsychological Society, 13(2), 246–256. Dowell, L. R., Mahone, E. M., & Mostofsky, S. H. (2009). Associations of postural knowledge and basic motor skill with dyspraxia in autism: Implication for abnormalities in distributed connectivity and motor learning. Neuropsychology, 23(5), 563–570.
2280 Dziuk, M. A., Gidley Larson, J. C., Apostu, A., Mahone, E. M., Denckla, M. B., & Mostofsky, S. H. (2007). Dyspraxia in autism: Association with motor, social, and communicative deficits. Developmental Medicine and Child Neurology, 49(10), 734–739. Esposito, G., Venuti, P., Maestro, S., & Muratori, F. (2009). An exploration of symmetry in early autism spectrum disorders: Analysis of lying. Brain & Development, 31(2), 131–138. Esposito, G., Venuti, P., Apicella, F., & Muratori, F. (2011). Analysis of unsupported gait in toddlers with autism. Brain Development, 33(5), 367–373. Fournier, K. A., Hass, C. J., Naik, S. K., Lodha, N., & Cauraugh, J. H. (2010). Motor coordination in autism spectrum disorders: A synthesis and meta-analysis. Journal of Autism and Developmental Disorders, 40(10), 1227–1240. Gallese, V., Rochat, M., Cossu, G., & Sinigaglia, C. (2009). Motor cognition and its role in the phylogeny and ontogeny of action understanding. Developmental Psychology, 45(1), 103–113. Gernsbacher, M. A., Sauer, E. A., Geye, H. M., Schweigert, E. K., & Hill Goldsmith, H. (2008). Infant and toddler oral- and manual-motor skills predict later speech fluency in autism. Journal of Child Psychology and Psychiatry, 49(1), 43–50. Ghaziuddin, M., & Butler, E. (1998). Clumsiness in autism and Asperger syndrome: A further report. Journal of Intellectual Disability Research, 42(Pt 1), 43–48. Giorello, G., & Sinigaglia, C. (2007). Perception in action. Acta Bio-Medica, 78(Suppl 1), 49–57. Gowen, E., & Hamilton, A. (2013). Motor abilities in autism: A review using a computational context. Journal of Autism and Developmental Disorders, 43(2), 323–344. Gutman, S. A., Raphael, E. I., Ceder, L. M., Khan, A., Timp, K. M., & Salvant, S. (2010). The effect of a motor-based, social skills intervention for adolescents with high-functioning autism: Two single-subject design cases. Occupational Therapy International, 17(4), 188–197. Jansiewicz, E. M., Goldberg, M. C., Newschaffer, C. J., Denckla, M. B., Landa, R., & Mostofsky, S. H. (2006). Motor signs distinguish children with high functioning autism and Asperger’s syndrome from controls. Journal of Autism and Developmental Disorders, 36(5), 613–621. Kanner, L. (1943). Autistic disturbances of affective contact. The Nervous Child, 2, 217–250. Kim, H. U. (2008). Development of early language and motor skills in preschool children with autism. Perceptual and Motor Skills, 107(2), 403–406. Landa, R., & Garrett-Mayer, E. (2006). Development in infants with autism spectrum disorders: A prospective study. Journal of Child Psychology and Psychiatry, 47(6), 629–638. LeBarton, E. S., & Iverson, J. M. (2016). Associations between gross motor and communicative development
Gross Motor Skills in at-risk infants. Infant Behavior and Development, 44, 59–67. Leonard, H. C., Bedford, R., Pickles, A., Hill, E. L., & BASIS Team. (2015). Predicting the rate of language development from early motor skills in at-risk infants who develop autism spectrum disorder. Research in Autism Spectrum Disorders, 13, 15–24. Lloyd, M., Macdonald, M., & Lord, C. (2011). Motor skills of toddlers with autism spectrum disorders. Autism. doi:10.1177/1362361311402230. Minshew, N. J., Sung, K., Jones, B., & Furman, J. (2004). Underdevelopment of the postural control system in autism. Neurology, 63(11), 2056–2061. Mostofsky, S. H., & Ewen, J. B. (2011). Altered connectivity and action model formation in autism is autism. The Neuroscientist, 17, 437–448. Mostofsky, S. H., Burgess, M. P., & Gidley Larson, J. C. (2007). Increased motor cortex white matter volume predicts motor impairment in autism. Brain, 130 (Pt 8), 2117–2122. Mostofsky, S. H., Powell, S. K., Simmonds, D. J., Goldberg, M. C., Caffo, B., & Pekar, J. J. (2009). Decreased connectivity and cerebellar activity in autism during motor task performance. Brain, 132 (Pt 9), 2413–2425. Nayate, A., Bradshaw, J. L., & Rinehart, N. J. (2005). Autism and Asperger’s disorder: Are they movement disorders involving the cerebellum and/or basal ganglia? Brain Research Bulletin, 67(4), 327–334. Nebel, M. B., Joel, S. E., Muschelli, J., Barber, A. D., Caffo, B. S., Pekar, J. J., & Mostofsky, S. H. (2014). Disruption of functional organization within the primary motor cortex in children with autism. Human Brain Mapping, 35(2), 567–580. Ozonoff, S., Young, G. S., Goldring, S., Greiss-Hess, L., Herrera, A. M., Steele, J., et al. (2008). Gross motor development, movement abnormalities, and early identification of autism. Journal of Autism and Developmental Disorders, 38(4), 644–656. Qiu, A., Adler, M., Crocetti, D., Miller, M. I., & Mostofsky, S. H. (2010). Basal ganglia shapes predict social, communication, and motor dysfunctions in boys with autism spectrum disorder. Journal of the American Academy of Child and Adolescent Psychiatry, 49(6), 539–551, e531–e534. Rinehart, N. J., Tonge, B. J., Iansek, R., McGinley, J., Brereton, A. V., Enticott, P. G., et al. (2006). Gait function in newly diagnosed children with autism: Cerebellar and basal ganglia related motor disorder. Developmental Medicine and Child Neurology, 48(10), 819–824. Rizzolatti, G., & Sinigaglia, C. (2007). Mirror neurons and motor intentionality. Functional Neurology, 22(4), 205–210. Setoh, P., Marschik, P. B., Einspieler, C., & Esposito, G. (2017). Autism spectrum disorder and early motor abnormalities: Connected or coincidental companions? Research in Developmental Disabilities, 60, 13–15.
Group Therapy Staples, K. L., & Reid, G. (2010). Fundamental movement skills and autism spectrum disorders. Journal of Autism and Developmental Disorders, 40(2), 209–217. Sutera, S., Pandey, J., Esser, E. L., Rosenthal, M. A., Wilson, L. B., Barton, M., et al. (2007). Predictors of optimal outcome in toddlers diagnosed with autism spectrum disorders. Journal of Autism and Developmental Disorders, 37(1), 98–107. Van Waelvelde, H., Oostra, A., Dewitte, G., Van Den Broeck, C., & Jongmans, M. J. (2010). Stability of motor problems in young children with or at risk of autism spectrum disorders, ADHD, and/or developmental coordination disorder. Developmental Medicine and Child Neurology, 52(8), e174–e178. Wodka, E., & Mostofsky, S. H. (2011). Motor development and its relation to social and behavioral manifestations. In D. Fein (Ed.), The neuropsychology of autism. New York: Oxford University Press. Zingerevich, C., Greiss-Hess, L., Lemons-Chitwood, K., Harris, S. W., Hessl, D., Cook, K., et al. (2009). Motor abilities of children diagnosed with fragile X syndrome with and without autism. Journal of Intellectual Disability Research, 53, 11–18.
Group Research ▶ Qualitative Versus Quantitative Approaches
Group Therapy Fred R. Volkmar Child Study Center, Irving B. Harris Professor of Child Psychiatry, Pediatrics and Psychology, Yale Child Study Center, School of Medicine, Yale University, New Haven, CT, USA
Definition A treatment modality that makes us of group, rather than individual, format to provide counseling/support/psychotherapy.
Historical Background Group treatments for children have their origins in the attempts to provide rehabilitation services for
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children, for example, those in the juvenile justice system or in some form of congregate care. As with work with adults, approaches to group treatment have become more specialized over time, for example, in autism focusing primarily on social skills or to help children with some common situation or problems. As with other approaches to psychotherapies, there has been an increased emphasis on use of common methods and specific methods and curricula.
Current Knowledge Various approaches to group treatment have been developed (see Moss et al. 2007). Groups can be time limited or ongoing, and they may be focused on a special issue or problem. The role of the leader(s) may also vary so that, for example, highly focused or short-term groups may need a much more active leadership process and be more didactic. Groups can be guided by various theoretical perspectives, and such a perspective may have implications for group composition. Groups for children and adolescents must be cognizant of the ages and developmental levels of the members. Particularly for social skills-based groups, the inclusion of typically developing peers may provide important role models. The leaders provide overall structure and ongoing monitoring and can plan and implement a range of activities. For example, for a group of more cognitively able students with autism, explicit discussion of common tasks for the age group may include an experiential component to provide practice with opportunities for feedback. For the group leaders, awareness of the group’s needs and levels of functioning can guide interventions and group process as well as issues of selection of new members. Group approaches are used in a range of settings (including clinics and schools). For children, it is common to have a standard structured format with some period for group discussion, an activity and snack, and then a brief review/wrap-up. Various techniques can be used to cope with behavioral difficulties including changes in group activities or structure, verbal interpretation, limit setting,
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and so forth. Sometimes an individual will not be able to tolerate the demands of being in a group, and in such cases, removal of a group member should provide an opportunity for explanation as well as discussion. Group leaders can make use of a range of intervention strategies. Time-limited groups can be readily used with less able individuals and provide opportunities to practice social skills in a supportive context. For more cognitively able individuals, extended groups can have a broader focus on a range of issues. If typical peers are included, some degree of (often relatively minimal) preparation is needed. Support groups for parents can also be useful. As with other psychotherapies, considerations for great treatment include the nature of the difficulties and goals, the degree to which a group approach can be helpful, and so forth. Children who are highly disruptive may be difficult to incorporate in a group treatment approach. A small body of work on the efficacy of group treatment now exists with overall results suggesting some moderate level of benefit on average.
Group Work and Class Discussions Dugan, E., Kamps, D., et al. (1995). Effects of cooperative learning groups during social studies for students with autism and fourth-grade peers. Journal of Applied Behavior Analysis, 28(2), 175–188. Kohler, F. W., Strain, P. S., et al. (1990). Promoting positive and supportive interactions between preschoolers: An analysis of group-oriented contingencies. Journal of Early Intervention, 14(4), 327–341. Moss, N. E., Racusin, G. R., & Moss-Racusin, C. (2007). Group therapy. In A. Martin & F. Volkmar (Eds.), Lewis’s child and adolescent psychiatry: A comprehensive textbook (4th ed., pp. 842–854). Philadelphia: Lippincott Williams & Wilkins. Williams, T. I. (1989). A social skills group for autistic children. Journal of Autism and Developmental Disorders, 19(1), 143–155. Philadelphia: Wolters Kluwer.
Group Work and Class Discussions Ruth Eren Center of Excellence on Autism Spectrum Disorders, Southern Connecticut State University, New Haven, CT, USA
Synonyms Future Directions There has been an increased emphasis on implement of specific behavioral and cognitive strategies in group treatment. New approaches focus on such CBT-based approaches. Although frequently used to teach social skills, the body of actual research on group treatments in autism is rather limited.
See Also ▶ Psychotherapy
References and Reading Cappadocia, M., & Weiss, J. A. (2011). Review of social skills training groups for youth with Asperger syndrome and high functioning autism. Research in Autism Spectrum Disorders, 5(1), 70–78.
Cooperative groups
Definition Group work and class discussions are teaching strategies that can be found in a general education classroom. In order for students with ASD to be successful in these two learning environments, some accommodations may be needed. In group work, students are typically assigned a task to be completed by the group. Students may be assigned a specific role (e.g., notetaker) or the group may be directed to have a discussion on a specific topic or problem with the expectation that all students will equally contribute to the discussion. Students with ASD may find group work confusing unless the roles of the group members are clearly defined and written instructions for the task to be accomplished are provided. Often, stepby-step instructions for the task (task analysis) are
Group-Based Early Start Denver Model (G-ESDM)
most helpful to the student with ASD because in addition to identifying specific steps that lead to task completion, the student sees the total process required to complete the task. In addition, the social behavior expected during the group work may need to be clearly defined and practiced through role playing prior to the inclusion of the student in a small work group. Finally, the student with ASD may need to practice discourse skills required for successful participation in a group discussion such as accepting suggestions, changing topics in a discussion, compromising, considering others opinions, negotiating, clarifying, and questioning. Class discussions pose additional challenges for some individuals with ASD. It will be important for the teacher to make prior connections to the topic under discussion with all students, but this will be especially helpful for the student with ASD. In addition, the general topic and related vocabulary may need to be reviewed, pretaught, or “primed” with the student with ASD if they are to keep up and comprehend the discussion as it unfolds in the general classroom setting. During class discussions, the teacher can provide additional accommodations for the student with ASD by creating a visual web of information that highlights specific points and gives the student the entire picture of the topic being discussed.
See Also ▶ Academic Supports ▶ Activity-Based Instruction ▶ Classroom Management ▶ Self-management Interventions ▶ Visual Supports
References and Reading Mith, T. E. C., Polloway, E. A., Patton, J. R., & Dowdy, C. A. (2008). Teaching students with special needs in inclusive settings (5th ed.). Upper Saddle River: Pearson. Volkmar, F. R., Paul, R., Klin, A., & Cohen, D. (Eds.). (2005). Handbook of autism and pervasive developmental disorders (3rd ed.). Hoboken: Wiley.
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Group-Based Early Start Denver Model (G-ESDM) Giacomo Vivanti A.J. Drexel Autism Institute, Drexel University, Philadelphia, PA, USA
Definition The Group-Based Early Start Denver Model (G-ESDM; Vivanti et al. 2017) is an early intervention approach based on the adaptation of the Early Start Denver Model (ESDM; Rogers and Dawson 2010) for delivery within group settings. The G-ESDM emphasizes the importance of providing intensive teaching, drawing from evidence-based educational strategies, individualizing the program, and addressing multiple developmental domains within group-based cooperative learning experiences, with one adult delivering instruction to small groups of 2–3 children with ASD. Principles and strategies incorporate knowledge from developmental science, behavior analysis, and social-affective neuroscience to support learning and development in young children with autism. The group delivery format capitalizes on the feasibility, sustainability, and cost-effectiveness of providing treatment in small groups, the widespread availability of preschool programs in the community, the culturally universal tradition of educating young children in group settings, and the social and learning opportunities provided by peers.
Historical Background The origins of G-ESDM date back to the early 1980s, when Sally Rogers and colleagues at the University of Colorado Health Sciences Center developed the Denver Model (Rogers et al. 1987), an autism intervention model informed by developmental models of autism and typical development (e.g., Rogers and Pennington 1991; Stern 1985). Subsequently, Sally Rogers and Geraldine Dawson created the “Early Start
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Denver Model” (ESDM; Rogers and Dawson 2010), which involves the expansion and adaptation of the original Denver Model curriculum to address the developmental needs of toddlers, as well as the inclusion of a curriculum checklist (ESDM Curriculum Checklist; Rogers and Dawson 2010) to design individualized intervention targets. In 2010, Vivanti and colleagues at La Trobe University in Melbourne, Australia, developed the Group-Based Early Start Denver Model (G-ESDM), an approach for delivering ESDM within group-based settings, such as childcare and preschool programs. The G-ESDM was developed and implemented in the context of a partnership between the La Trobe University Olga Tennison Autism Research Center, the MIND Institute at University of California Davis, and the Victorian Autism Specific Early Learning and Care Centre, an early intervention program located at La Trobe University’s Community Children’s Centre.
Rationale or Underlying Theory The G-ESDM, like the original ESDM, is based on cascade model of ASD (Pennington et al. 2006). According to this model, early emerging autism symptoms create a barrier to the development of the processes that facilitate bodily and emotional engagement during early interactions, such as imitation, reciprocal vocalization, and sharing of affect. Diminished participation in bodily routines that are social and playful in nature, in turn, disrupts the construction of neural specialization and behavioral knowledge in the social communication domain. As recently proposed by Dawson et al. (2005; Dawson 2008), such early disruptions might be linked to a biologically-based deficiency in experiencing social engagement as intrinsically rewarding. A corollary of this model is that targeting early social disruptions during early childhood might counteract the cascading effects of social impairments for individuals with ASD, conferring beneficial effects across different developmental domains. This is particularly important for younger children, as neural plasticity during early
Group-Based Early Start Denver Model (G-ESDM)
developmental stages might allow for a deeper impact of social learning experiences on the developing brain. Additionally, procedures of ESDM and G-ESDM are informed by the naturalistic application of applied behavior analysis (ABA), as articulated by Schreibman, Koegel et al. 1989 in their pioneering work on Pivotal Response Training (PRT), and more recently in the framework of Naturalistic Developmental Behavioral Intervention models of ASD (Schreibman et al. 2015). Finally, the Group-ESDM draws heavily on the literature focused on the beneficial effects of early interaction with peers and preschool experiences for social-cognitive development. This literature indicates that the opportunity to practice complex social and cooperative behaviors during play routines with peers supports the development of social-cognitive and social emotional skills such as empathy and mentalizing (McAlister and Peterson 2007) and that involvement in highquality child care environments that facilitate engagement in joint activities with peers has positive effects on social and communication development (National Institute of Child Health and Human Development Early Child Care Research Network 2000, 2003).
Goals and Objectives The chief objective in the G-ESDM is to provide a sustainable evidence-based early intervention approach for young children with ASD that can be delivered in regular childcare and preschool settings. In the G-ESDM, children with ASD are provided with high-quality social learning experiences within group settings, so that the child can learn from others in their everyday experiences in their preschool program and in any other settings, and build a behavioral repertoire that support further learning and foster socialcognitive development. While this overarching goal is relevant for all children with autism, specific treatment objectives are based on the distinctive profile of strength and weakness of each child, so that learning experiences can be individually tailored to
Group-Based Early Start Denver Model (G-ESDM)
maximize learning progress. Individualized treatment objectives are developed from a careful assessment of the child’s behavioral repertoire based on the ESDM Curriculum Checklist (Rogers and Dawson 2010), which evaluate each child’s skills across multiple developmental domains including receptive communication, expressive communication, social skills, play skills, cognitive skills, fine motor skills, and adaptive skills.
Treatment Participants The G-ESDM is designed for toddlers and preschoolers with ASD. Treatment goals are based on the ESDM curriculum, which addresses developmental skills in the 12–48 months range. The G-ESDM has been tested on children 12– 60 months, with results showing better outcomes in children receiving the ESDM before 48 months of age. It has not been tested with children who have additional diagnoses like Down syndrome or fragile X.
Treatment Procedures In the G-ESDM, individualized measurable learning objectives covering multiple developmental domains are generated every 12 weeks based on the curriculum checklist assessment. Objectives are designed to be addressed within the constraints and opportunities of group settings. Child progress is systematically recorded and mastery of all objectives is assessed every 12 weeks; new learning objectives are then generated based on the assessment results. Individual goals are targeted within group routines. These are organized around clear, predictable, and shared goals, and designed to provide naturalistic learning opportunities, i.e., age-appropriate learning experiences involving routines and materials that children typically encounter in their everyday environments. Adults set up fun routines and play activities that naturally bring children together in the same physical space including art table activities, book activities,
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“sensory” games such as games with water, sand, and shaving cream, group music and movement games such as Ring-around-a Rosie and parachute games as well as table games in which the children need to share and pass materials, or help each other. Children have duplicate objects and are face to face, so they can imitate one another, and the materials and their placement actively facilitate peer interaction. In this context, the adult organizes group-based joint activity routines, in which the children and adults in the small group are coconstructing activities that provide opportunities to do things together and learn from such experiences (Ratner and Bruner 1978). A joint activity routine is articulated in four stages, including (a) a set-up phase, in which the child gets to choose the activity; (b) a theme in which the child and the play-partners participate equally in the activity chosen by the child, creating a predictable and enjoyable routine; (c) a variation or elaboration that expands the theme; and (d) a closing phase, marking smooth ending for the current activity followed by a transition to the opening phase of the next activity. For example, a child might start to play with a piece of material (set-up phase), then the adult joins in, and the adult and child begin playing with material together, while other children are also encouraged to join in (the theme phase). As one child in the group starts engaging in a different action with the same material, the adult points that out and prompts other children to do the same (elaboration phase). When the activity starts to feel repetitive and/or children start to lose interest, the adult encourages children to close the activity and transition to the next activity (closing phase). The goal of joint activity routines is to address both the social difficulties (through the joint engagement component) and the flexibility difficulties (through the systematic introduction of variations on the theme) that characterize autism, while also providing opportunities to target multiple objectives across developmental domains. The repeated engagement in meaningful and rewarding joint activity routines in close proximity to the peers under the guidance of the adult provide the framework for targeting and practicing expressive and receptive
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communication, turn taking, imitation, sharing of affect, joint attention, functional and symbolic play, and motor skills, using evidence-based instructional techniques based on naturalistic developmental behavioral intervention, including the use of “Antecedent–Behavior–Consequence (ABC)” sequences, shaping, fading, prompting, chaining and error correction procedures, peermediated teaching, active management of affect, arousal, and motivation, and the use of warm, playful shared interactions as a context for learning (see Vivanti et al. (2017) for details). A series of “decision trees” are used in the G-ESDM to readjust the program when data show that progress is slower than expected in one or more developmental areas. Modifications might include increasing the amount of teaching episodes delivered to the child during group activities, organizing 1:1 focused teaching sessions in a distraction-free environment, increasing reinforcers strength, and introducing augmentative communication tools. The G-ESDM group activity fidelity tool (Vivanti et al. 2017) is used to determine whether the program is being delivered according to the G-ESDM implementation standards.
Efficacy Information Preliminary evidence for effectiveness of the G-ESDM is documented in a controlled study involving 57 children with ASD ages 18– 60 months, of whom 27 received 12 months of 15+ h per week of G-ESDM with staff ratios of one adult to three children (Vivanti et al. 2014). This group was compared to a carefully matched group of 30 children receiving 15+ h of early intervention for 12 months delivered in highquality preschools that used a combination of empirically based intervention practices. Children in the G-ESDM group showed a significant advantage in overall developmental rate and receptive language scores. Additional research documented that children with younger chronological age and more advanced skills in object play, joint
Group-Based Early Start Denver Model (G-ESDM)
attention, and imitation experienced the largest gains (Vivanti et al. 2013, 2016).
Outcome Measurement Within the G-ESDM, there is a strong emphasis on daily tracking of a child’s progress against each learning objective. Consistent with the ESDM procedures detailed in Rogers and Dawson (2010), child’s response to each learning opportunity is recorded according to a time interval recording system, with adults taking data at between 10 and 15 min intervals depending on the frequency and intensity of teaching opportunities. These data are then summarized on each child’s “data summary sheet” on a daily basis. Measurable progress is expected on every objective within a 1–2-week period and mastery of all objectives is assessed every 12 weeks. Based on the assessment results, new objectives are written and broken down into small teachable steps, and new data sheets are developed. For research purposes, annual progress has been measured through standardized tests measuring cognitive functioning (Mullen Scales of Early Learning; Mullen 1995), adaptive behavior (Vineland Scales of Adaptive Behavior, Sparrow et al. 2005), and severity of autism symptoms (Autism Diagnostic Observation Schedule; Lord et al. 2012).
Qualifications of Treatment Providers Professionals certified as ESDM therapists might deliver the G-ESDM following the manualized guidelines in Vivanti et al. 2017. Certification in ESDM involves completion of multiple levels of training according to the steps detailed here https://www.ucdmc.ucdavis.edu/mindinstitute/ research/esdm/pdf/certification_steps.pdf. Professionals from a wide number of disciplines are eligible to be trained as ESDM therapists, including occupational therapy, physical therapy, speech and language pathology, early
Growth Mixture Modeling
childhood education, early childhood special education, clinical child psychology, and school psychology.
See Also ▶ Applied Behavior Analysis (ABA) ▶ Early Intervention ▶ Early Start Denver Model
References and Reading Dawson, G. (2008). Early behavioral intervention, brain plasticity, and the prevention of autism spectrum disorder. Development and Psychopathology, 20, 775–803. Dawson, G., Webb, S., & McPartland, J. (2005). Understanding the nature of face processing impairment in autism: Insights from behavioral and electrophysiological studies. Developmental Neuropsychology, 27(3), 403–424. Koegel, R. L., Schreibman, L., Good, A. B., Cerniglia, L., Murphy, C., & Koegel, L. K. (1989). How to teach pivotal behaviors to autistic children. Santa Barbara: University of California. Lord, C., Rutter, M., DiLavore, P., Risi, S., Gotham, K., & Bishop, S. (2012). Autism diagnostic observation schedule – 2nd edition (ADOS-2). Los Angeles: Western Psychological Corporation. McAlister, A., & Peterson, C. (2007). A longitudinal study of child siblings and theory of mind development. Cognitive Development, 22(2), 258–270. Mullen, E. M. (1995). Mullen scales of early learning. Circle Pines, MN: American Guidance Service. NICHD Early Child Care Research Network. (2000). The relation of child care to cognitive and language development. Child Development, 71(4), 960–980. NICHD Early Child Care Research Network. (2003). Does amount of time spent in child care predict socioemotional adjustment during the transition to kindergarten? Child Development, 74(4), 976–1005. Pennington, B., Williams, J., & Rogers, S. (2006). Conclusions. In S. Rogers & S. Williams (Eds.), Imitation and the social mind. Autism and typical development (pp. 431–450). New York: Guilford Press. Ratner, N., & Bruner, J. (1978). Games, social exchange and the acquisition of language. Journal of Child Language, 5(03), 391–401. Rogers, S. J., & Dawson, G. (2010). Early start Denver model for young children with autism: Promoting language, learning, and engagement: The curriculum checklist. NY: Guilford Press.
2287 Rogers, S. J., & Pennington, B. F. (1991). A theoretical approach to the deficits in infantile autism. Development and Psychopathology, 3(2), 137–162. Rogers, S. J., Lewis, H. C., & Reis, K. (1987). An effective procedure for training early special education teams to implement a model program. Journal of the Division of Early Childhood, 11, 180–188. Schreibman, L., Dawson, G., Stahmer, A. C., Landa, R., Rogers, S. J., McGee, G. G., et al. (2015). Naturalistic developmental behavioral interventions: Empirically validated treatments for autism spectrum disorder. Journal of Autism and Developmental Disorders, 45(8), 2411–2428. Sparrow, S., Balla, D., & Cicchetti, D. (2005). Vineland adaptive behavior scales (2nd ed.). Circle Pines: American Guidance Service. Stern, D. N. (1985). The interpersonal world of the infant. New York: Basic Books. Vivanti, G., Dissanayake, C., Zierhut, C., Rogers, S. J., & Victorian ASELCC Team. (2013). Brief report: Predictors of outcomes in the early start Denver model delivered in a group setting. Journal of Autism and Developmental Disorders, 43(7), 1717–1724. Vivanti, G., Paynter, J., Duncan, E., Fothergill, H., Dissanayake, C., Rogers, S. J., & Victorian ASELCC Team. (2014). Effectiveness and feasibility of the early start Denver model implemented in a group-based community childcare setting. Journal of Autism and Developmental Disorders, 44(12), 3140–3153. Vivanti, G., Dissanayake, C., & Victorian ASELCC Team. (2016). Outcome for children receiving the early start Denver model before and after 48 months. Journal of Autism and Developmental Disorders, 46(7), 2441–2449. Vivanti, G., Duncan, E., Dawson, G., & Rogers, S. (2017). Implementing the group-based early start Denver model for preschoolers with autism. New York: Springer International Publishing.
Group-Based Organized Sport ▶ Efficacy of Group-Based Organized Physical Activity Participation for Children with Autism Spectrum Disorder
Growth Mixture Modeling ▶ Latent Variable Modeling
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GSH-Px1 and Glutathione Hydrogen-Peroxide Oxido-reductase
GSH-Px1 and Glutathione Hydrogen-Peroxide Oxido-reductase ▶ Erythrocyte Glutathione Peroxidase
Guanfacine Lawrence David Scahill Nursing and Child Psychiatry, Yale Child Study Center, Yale University School of Nursing, New Haven, CT, USA Marcus Autism Center, Children’s Healthcare of Atlanta, Atlanta, GA, USA Department of Pediatrics, Emory University, Atlanta, GA, USA
Synonyms Intuniv; Tenex
Definition Guanfacine is an alpha-2 agonist medication that has properties in common with clonidine. Early studies in adults with hypertension showed that it was less likely to cause sedation. In addition, it appeared to be less potent as antihypertensive medication. Studies done in animals showed that guanfacine also had other properties that are different from clonidine. These studies showed that guanfacine improved performance on delayed memory test in primates. In the early 1990s, studies of guanfacine in children with various conditions targeting hyperactivity and impulsive behavior began to emerge. These trials used the immediate-release product. Most trials were small and few were placebo-controlled. The immediaterelease form of guanfacine has been examined in two studies in children with autism spectrum disorders. These studies suggest that the medication is helpful for reducing hyperactivity,
impulsiveness, and distractibility in these children. However, these studies were small and not definitive. More recently, a long-acting formulation of guanfacine has been studied in two large-scale trials and also has been studied in a long-term extension trial. This new product is approved for the treatment of children with ADHD. The longacting form of guanfacine is now approved for treatment of ADHD. Adverse effects of guanfacine are similar to clonidine. However, it does appear to be less likely to cause sedation that is often observed with clonidine. Midsleep awakening and irritability have been observed in studies of children with guanfacine. As with clonidine, guanfacine does not usually cause problems with hypotension. However, pulse and blood pressure are routinely monitored when children are treated with these compounds.
See Also ▶ ADHD
References and Reading Arnsten, A. F., Scahill, L., & Findling, R. L. (2007). Alpha2-Adrenergic receptor agonists for the treatment of attention-deficit/hyperactivity disorder: Emerging concepts from new data. Journal of Child and Adolescent Psychopharmacology, 17(4), 393–406. Biederman, J., Melmed, R., Patel, A., McBurnett, K., Konow, J., Lyne, A., et al. (2008). A randomized, double-blind, placebo-controlled study of guanfacine extended release in children and adolescents with attention-deficit/hyperactivity disorder. Pediatrics, 121(1), e73–e84. Retrieved from EBSCOhost. Sallee, F., McGough, J., Wigal, T., Donahue, J., Lyne, A., & Biederman, J. (2009). Guanfacine extended release in children and adolescents with attention-deficit/ hyperactivity disorder: A placebo-controlled trial. Journal of the American Academy of Child and Adolescent Psychiatry, 48(2), 155–165. Treatments for Children with Autism (pp. 231–243). Springer (2011). https://doi.org/10.1097/CHI.0b013e318191769e. Scahill, L., & Boorin, S. (2011). Psychopharmacology in children with PDD: Review of current evidence. In B. Reichow, P. Doering, D. Ciccheti, & F. Volkmar (Eds.), Evidence-based practices. New York: Springer.
Guardianship
Guardianship Kenneth Thomas Quinnipiac University School of Medicine, Hamden, CT, USA Yale School of Medicine, New Haven, CT, USA
Synonyms Conservatorship
Definition Guardianship A guardianship is the process whereby one person assumes the legal responsibility for protecting the personal and property interests of another person who is unable to adequately do so due to physical incapacity, mental incapacity, or immaturity of age (infancy). Guardianship effectively replaces the judgment of the legally incompetent or incapacitated person (the ward) with that of the capable person (the guardian). A guardian may be an actual person, professional or nonprofessional, or a “legal person” such as a state agency, a private nonprofit organization, or a private for-profit organization. Regardless of their specific identities, all guardians must scrupulously act in good faith to protect their wards’ interests. If they fail to do so, the courts can remove and replace them. State law controls guardianship regulations and procedures, with each state determining the conditions, provisions, and specific terminology of guardianships within its jurisdiction. Guardianship laws generally prefer that courts appoint qualified and willing family members as guardians but also allow them to appoint nonrelatives when doing so would better serve the needs of the wards. Due to the loss of personal freedom that results from the appointment of a guardian, guardianship proceedings must follow a careful and orderly legal process that ensures that the ward’s legal rights will be fully protected. Guardianships are not always permanent: adult guardianships can be terminated if the adult wards can demonstrate that
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they have regained or acquired the necessary competence to care for their own needs, and guardianships of children can be terminated once the children reach the age of legal maturity. Some types of relevant guardianships include: Guardianship of the person. The guardian is responsible only for the ward’s personal welfare. Guardianship of the estate (or conservatorship). The guardian is responsible only for the ward’s property; such guardians may be called “conservators.” While complete mental incompetence of the property owner is not required, his or her incapacity to adequately manage his or her property must be demonstrated. Plenary guardianship. The guardian is responsible for all aspects of the ward’s personal welfare and property. Limited guardianship. The guardian assumes authority only for those specific areas in which the ward is unable to make his or her own competent decisions. Limited guardianships are rarely created. Standby guardianship. A guardian is chosen in advance by a child’s parents to assume future responsibility for the child’s welfare only if the parents become unable to do so.
See Also ▶ Conservatorship
References and Reading Frolik, L. A., & Barnes, A. M. (2007). Elder law: Cases and materials (4th ed.). Newark: Lexis-Nexis, Matthew Bender. Garner, B. A. (Ed. in Chief). (2009). Black's law dictionary (9th Ed.). St. Paul: West-Thomson Reuters. National Guardianship Association, Inc. Guardianship and developmentally disabled individuals. Retrieved July 5, 2012 from http://www.guardianship.org/pdf/develop mentallyDisabled.pdf National Guardianship Association, Inc. Guardianship/ conservatorship-an overview. Retrieved July 5, 2012 from http://www.guardianship.org/pdf/guardianshipCon servatorship.pdf. National Guardianship Association, Inc. Questions and answers on guardianship issues. Retrieved July 5, 2012 from http://www.guardianship.org/pdf/question.pdf
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Guided Compliance Shaunessy Egan Center for Children with Special Needs, Glastonbury, CT, USA
Definition Guided compliance procedures characteristically involve the systematic delivery of progressively more intrusive prompts. The prompts are delivered contingent upon an individual’s noncompliance. The type of prompts used may include verbal prompting, model prompting, and/or physical guidance. These prompts are methodically increased following noncompliance with less-intrusive prompts, eventually culminating with full physical guidance when necessary.
Guided Compliance
Interventions using guided compliance have been successfully implemented to decrease noncompliance.
References and Reading Cote, C., Thompson, R. H., & McKerchar, P. M. (2005). The effects of antecedent interventions and extinction on toddlers’ compliance during transitions. Journal of Applied Behavior Analysis, 38, 235–238. Horner, R. D., & Keilitz, I. (1975). Training mentally retarded adolescents to brush their teeth. Journal of Applied Behavior Analysis, 25, 491–498. Smith, M. R., & Lerman, D. C. (1999). A preliminary comparison of guided compliance and high-probability instructional sequences as treatment for noncompliance in children with developmental disabilities. Research in Developmental Disabilities, 20, 183–195. Wilder, D., & Atwell, J. (2006). Evaluation of a guided compliance procedure to reduce noncompliance among preschool children. Behavioral Interventions, 21, 265–272.
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H1 Receptor Antagonist ▶ Antihistamines: Definition
H1 Receptor Inverse Agonist ▶ Antihistamines: Definition
Habit Reversal Douglas W. Woods and Matthew R. Capriotti Department of Psychology, University of Wisconsin-Milwaukee, Milwaukee, WI, USA
Definition Habit reversal training (HRT) is a multicomponent treatment package primarily used to treat tic disorders and body-focused repetitive behavior disorders such as skin picking, nailbiting, and hairpulling (trichotillomania; TTM). The first primary component of HRT is awareness training, in which the therapist works with the patient to identify instances of the target behavior, insuring the patient has, or develops, the ability to detect when the behavior occurs in real time. After the patient is adequately aware of the behavior, competing response (CR) training
begins. In CR training, the patient and therapist identify a socially inconspicuous behavior that is physically incompatible with the target behavior, easy to perform in a variety of settings, and acceptable to the patient. The patient is taught to do CR for a prescribed period of time whenever the target behavior begins or is about to begin. The client practices using CR in session. The therapist provides praise for appropriate use of CR and prompts the patient when he or she does the targeted behavior but does not do CR. After the patient is trained to use CR in session, he or she is instructed to use it whenever the target behavior occurs or whenever he or she feels the urge to engage in the target behavior. The final component of HRT is social support. In social support, a parent, spouse, or other individual who has a close relationship with the patient is taught to the use of HRT skills in the patient’s day-to-day life. Social support ensures that HRT skills (especially the use of CR) generalize to settings beyond the therapy session. The support person is taught to praise the patient for using CR exercises and to provide gentle reminders to use CR when the target behavior occurs but CR does not.
Historical Background The first evidence of HRT’s efficacy emerged in a 1973 study by Nathan Azrin and Gregory Nunn. Throughout the 1970s and 1980s, a series of
© Springer Nature Switzerland AG 2021 F. R. Volkmar (ed.), Encyclopedia of Autism Spectrum Disorders, https://doi.org/10.1007/978-3-319-91280-6
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small-n and group design studies built an initial evidence base suggesting that HRT was an efficacious treatment for tics and body-focused repetitive behaviors. More recently, larger controlled trials have lent support for HRT as a viable treatment option (either in place of or as an adjunct to drug therapy) to treat TTM, chronic tic disorders (e.g., Tourette syndrome), and other repetitive behavior problems. Within the field of tic disorders, a series of randomized controlled trials showed that patients who received HRT experienced greater symptom reduction than those on a wait list or those who received active control therapy (i.e., supportive psychotherapy). The largest of these trials showed that eight 1-h sessions of comprehensive behavioral intervention for tics – a manualized treatment centered on HRT’s core components – produced greater tic reductions than education and supportive psychotherapy and yielded gains comparable to those found with standard pharmacological treatments for tics.
Rationale or Underlying Theory Across all problems for which the treatment is used, HRT is believed to increase awareness of behaviors that occur rapidly and often out of conscious awareness. In addition, it is believed that HRT may disrupt the underlying reinforcement processes serving to maintain a particular condition. For example, in persons with tics, aversive sensory experiences known as “premonitory urges” are known to build up immediately prior to a tic and are relieved immediately after the tic. This reduction in the aversive premonitory urge is believed to reinforce the tic. HRT physically prevents the tic from occurring and providing subsequent relief, which then allows the patient to habituate to the urge rather than eliminating it through ticcing. Over time, the urge becomes weaker with respect to eliciting tics. Similar reinforcement patterns are disrupted in the use of HRT to treat TTM. A large reinforcer for pulling involves the tactile stimulation that comes from the act of pulling. When the patient does CR at the beginning of the pulling act, the
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tactile reinforcement that is usually received does not occur, and ultimately the pulling is decreased.
Goals and Objectives HRT is intended to address tic disorders (e.g., Tourette syndrome) along with a variety of body-focused repetitive behavior disorders such as nail-biting, thumb-sucking, TTM, stereotypies, and skin picking. Like behavioral approaches to the treatment of other disorders, HRT takes a pragmatic, present-focused approach whose primary aim is reduction of the target behavior. When used as a treatment for neuropsychiatric conditions such as chronic tic disorders, HRT does not propose to “cure” the affected individual but rather teach skills that can effectively aid symptom management. HRT can be used either alone or in combination with pharmacotherapy or other treatments. As the severity of chronic tic disorders and TTM can wax and wane over time, the frequency with which a patient uses HRT skills at a given point in time may also vary. Evidence suggests that HRT skills are retained long after acute treatment ends (see section “Efficacy Information”) and therefore serve as a long-term tool for self-management of repetitive behavior disorders.
Treatment Participants HRT is effective for both children and adults. Most studies evaluating HRT have been done with patients who have IQ > 80; however, clinical observation suggests that patient’s motivation for change, receptive language abilities, and instruction following skills are critical to the success of HRT. Also, research has shown that general deficits in response inhibition predict poor response to HRT for adults with tic disorders. Patients with deficits in these areas, either due to young biological age or delays in language and/or cognitive development, may struggle with traditional HRT. Nevertheless, the core components of HRT could be implemented in a fashion that does not rely as much on patient self-management skills. For
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example, awareness training could consist of reinforcing a specific, subtle response such as raising a finger immediately after the occurrence of the behavior (a type of tacting) to indicate awareness. After this skill is mastered, chaining procedures could then be used to teach the patient to perform the competing response after the finger-raising response, thus mimicking the typical CRT requirement that patients perform the competing response after the occurrence of the targeted behavior. To date, two published studies have demonstrated the potential effectiveness of modified HRT procedures as part of effective treatments for repetitive behaviors in individuals with intellectual disabilities and/or autism. The modification included physical prompting by the therapist during AT and the use of differential reinforcement and auditory feedback to augment the “selfmanagement” style of HRT intervention. Although more research on the viability and efficacy of such adapted forms of HRT should be conducted, these studies serve to demonstrate the potential utility of HRT in individuals with autism spectrum disorders and intellectual disabilities who may have deficits in language and instruction following skills.
Treatment Procedures HRT is delivered on a flexible outpatient basis, which is typically acceptable to consumers. Research has shown benefits resulting from as little as one or two HRT sessions, but typically HRT has been implemented across 8–10 weekly sessions lasting approximately 1 h each. Treatment is usually delivered in an individualized, face-to-face format, though research has shown that the treatment can be effective in groups and when implemented via remote teleconferencing equipment. Since HRT skills involve a “self-management” repertoire employed by the individual himself or herself (i.e., are ultimately to be performed in the absence of prompts from others), it is important that the treatment be acceptable to the patient and able to be practically implemented in the patient’s
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day-to-day environment. To ensure this, HRT employs open dialogue between the therapist, patient, and parent or others close to the patient. The core role of the HRT therapist is to teach HRT skills involved in AT and CR training, as well as to educate the support person, devise an appropriate reward system, and to work in concert with other treating professionals and parents to ensure smooth implementation of the intervention.
Efficacy Information A large evidence base exists to support the use of HRT for body-focused repetitive behavior disorders. A series of small-n and uncontrolled studies throughout the 1970s–1990s showed that HRT tends to provide rapid reductions in symptom severity that are fairly durable following the cessation of treatment. Research has also shown that relapses do sometimes occur, but can most often be ameliorated by the inclusion of one or more posttreatment “booster sessions” in which HRT skills are briefly reviewed and practiced. Throughout the past 20 years, the bulk of research on the efficacy of HRT has shifted to the study of Tourette syndrome and other chronic tic disorders. Researchers have conducted a series of randomized controlled trials, confirming that HRT is superior to wait list and active control treatments. In the largest of these studies, children with chronic tic disorders and Tourette syndrome received either an HRT-based intervention or psychoeducation plus supportive psychotherapy (as a control condition). Fifty-three percent of those receiving HRT showed clinically meaningful improvement following the acute phase of treatment (compared to 17% of those in the supportive psychotherapy condition), and 87% of these patients maintained treatment gains at the 6-month follow-up. The magnitude of the symptom reduction observed in the HRT group was comparable to the reduction of symptoms reported in studies evaluating pharmacological treatments for tics. Although children with autism experience a higher prevalence of chronic tic disorders than typically developing children (Baron-Cohen
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et al. 1999), stereotypies are by far the most common repetitive behavior problem seen in children with autism and related disorders. HRT has been far less studied as a treatment for stereotypy compared to chronic tic disorders, but the single published study investigating HRT for stereotypy in typically developing children found that HRT was an effective treatment. This finding, combined with the finding that HRT can be modified and taught to individuals with language deficits, sets the stage for future research investigating the application of HRT for tics and other repetitive behavior problems in children with autism spectrum disorders.
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above; MIST-C, child version valid for ages 10–17) can be used to assess TTM, while the Skin Picking Scale (SPS; valid for adults) and the Milwaukee Inventory for the Dimensions of Adult Skin Picking (MIDAS; valid for adults) can be used to assess skin picking. The Stereotypy Severity Scale can be used to assess stereotypies. Due to the strengths and weaknesses of each type of assessment, clinicians should use multimodal assessment both at intake and throughout treatment to monitor symptom severity and the patient’s response to the intervention.
Qualifications of Treatment Providers Outcome Measurement The selection of tools to assess outcomes will depend on the age of the patient as well as the target behavior. For some behaviors, such as nailbiting and skin picking, permanent product measures such as the length of fingernails or the size of picking-induced wounds may be used to evaluate the severity of the behavior. However, other behaviors treated with HRT do not yield an obvious product, and other methods of evaluation must be used. Calculating the frequency with which the behavior occurs during a specific observation period has high face validity, but has significant limitations which preclude its use as a stand-alone measure of behavior. In addition, tics and many other repetitive behaviors often occur in the absence of others and are heavily influenced by minute-to-minute contextual variables such as physical setting, fatigue, and stress, thus making direct observation more difficult. To overcome these difficulties, clinical researchers have developed interview-based, parent-report, and self-report assessment tools. Examples of tic assessments include the Yale Global Tic Severity Scale (YGTSS; validated for ages 5 and above), the Parent Tic Questionnaire (PTQ; validated for ages 7–16), and the Premonitory Urge for Tics Scale (PUTS; valid for ages 10 and above). The Massachusetts General Hairpulling Scale (valid for adults), the Milwaukee Inventory for Styles of Trichotillomania (MIST-A, adult version valid for ages 18 and
Like any form of therapy, HRT should be implemented only by adequately trained behavioral health professionals, who are knowledgeable in the presenting problem. Although no formal certification process currently exists, it is recommended that HRT training involve didactic instruction through graduate courses or through one of the many workshops on HRT and related techniques that are regularly offered throughout the United States and other countries. In addition, it is recommended that practitioners interested in learning HRT be supervised for the first few cases by someone well versed in the procedure. HRT-based treatment manuals have been devised for many disorders, and health professionals trained in other forms of behavior therapy may be able to effectively administer HRT with the manuals as therapeutic guides.
See Also ▶ Applied Behavior Analysis (ABA) ▶ Tics ▶ Tourette Syndrome ▶ Trichotillomania
References and Reading Azrin, N. H., & Nunn, R. G. (1973). Habit-reversal: A method of eliminating nervous habits and tics. Behavior Research and Therapy, 11, 619–628. Baron-Cohen, S., Scahill, V. L., Izaguirre, J., Hornsey, H., & Robertson, M. M. (1999). The prevalence of Gilles
Hair Analysis de la Tourette syndrome in children and adolescents with autism: A large scale study. Psychological Medicine, 29, 1151–1159. Conelea, C. A., & Woods, D. W. (2008). Examining the impact of distraction on tic suppression in children and adolescents with Tourette syndrome. Behavior Research and Therapy, 46, 1193–1200. Cook, C. R., & Blacher, J. (2007). Evidence-based psychosocial treatments for tic disorders. Clinical Psychology: Science and Practice, 14(3), 252–267. Deckersbach, T., Rauch, S., Buhlmann, U., & Wilhelm, S. (2006). Habit reversal versus supportive psychotherapy in Tourette’s disorder: A randomized controlled trial and predictors of treatment response. Behavior Research and Therapy, 44, 1079–1090. Himle, M. B., Olufs, E., Himle, J., Tucker, B. T. P., & Woods, D. W. (2010). Behavior therapy for tics via videoconference delivery: An initial pilot test in children. Cognitive and Behavioral Practice, 17, 329–337. Long, E. S., Miltenberger, R. G., Ellingson, S. A., & Ott, S. M. (1999). Augmenting simplified habit reversal in the treatment of oral-digital habits exhibited by individuals with mental retardation. Journal of Applied Behavior Analysis, 32, 35–365. Marcus, A., Sinnott, B., Bradley, S., & Grey, I. (2010). Treatment of idiopathic toe-walking in children with autism using GaitSpot auditory speakers and simplified habit reversal. Research in Autism Spectrum Disorders, 4, 260–267. Piacentini, J., Woods, D. W., Scahill, L., Wilhelm, S., Peterson, A. L., Chang, S., et al. (2010). Behavior therapy for children with Tourette disorder: A randomized controlled trail. Journal of the American Medical Association, 303, 1929–1937.
Hair Analysis Madison Pilato Neurodevelopmental and Behavioral Pediatrics, University of Rochester Medical Center, Rochester, NY, USA
Synonyms Commercial hair analysis; Hair mineral analysis; Hair trace metal analysis
Definition Hair analysis is an approved method for determining trace element concentrations and exposure to heavy metals (World Health Organization 1990).
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Scalp hair is removed and sent to a laboratory for analysis. The most common elements analyzed by laboratories in the United States, as determined by Seidel et al. (2001), include aluminum (Al), arsenic (As), calcium (Ca), cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), iron (Fe), mercury (Hg), potassium (K), magnesium (Mg), manganese (Mn), molybdenum (Mo), sodium (Na), nickel (Ni), phosphorus (P), lead (Pb), selenium (Se), and zinc (Zn). Many children with autism are selective eaters, leading to concerns about possible nutritional deficiencies. Additionally, trace metals are related to neurotransmitter synthesis and brain electrochemistry and therefore have the potential to affect neurological function. These concerns inspired several studies performed during the 1980s in which the hair of children with autism was analyzed and compared to controls for trace elements. The results of these studies did not show consistent differences between individuals with and without autism (Shearer et al. 1982; Wecker et al. 1985; Gentile et al. 1983), and hair analysis became less prevalent in the autism literature. Hair analysis became relevant to autism again because of a proposed relationship between mercury exposure and the development of autism (Bernard et al. 2001). Hair analysis was adopted as one technique to test for unusual mercury levels, in addition to other elements. Holmes et al. (2003) determined that mercury levels were reduced in the results of hair analysis of children with autism compared to control children. The authors explained this observation by claiming that children with autism have a reduced ability to clear mercury from their bodies. In contrast, Fido and Al-Saad (2005) discovered elevated levels of mercury, lead, and uranium in the hair of children with autism. Other studies (Ip et al. 2004; Williams et al. 2008) have found no differences in hair mercury levels between children with autism and control children. Results from commercial hair analysis should be interpreted with extreme caution. Barrett (1985) reported the lack of inter- and intralaboratory reliability of 13 commercial hair analysis laboratories. In 2001, Seidel et al. confirmed the lack of interlaboratory reliability even after considerable technological improvements.
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Similarly, laboratories used different techniques (e.g., atomic emission, mass spectroscopy), and without any existing proficiency testing programs, each laboratory was responsible for its own verification method and criteria for accuracy. At the time of publication, Seidel et al. reported that there were no regulation standards among commercial hair analysis laboratories. Similarly, Steindel and Howanitz (2001) advised clinicians that no standard method for commercial hair analysis exists. However, they give several suggestions for choosing a good laboratory. The best laboratories will give proof of certification or accreditation, an explanation of the specific washing and digestion procedures, and information about recovery rates, minimum detection limits, and internal quality control. The World Health Organization (1990) recommends the neutron activation procedure as the most accurate and sensitive procedure. Lind et al. (1988) found good interlaboratory agreement for this method. Another widely used and accepted procedure is the selective atomic absorption (“Magos”) method.
Hair Mineral Analysis Lind, B., Bigras, L., Cernichiari, E., Clarkson, T. W., Friberg, L., Hellman, M., & Ohlin, B. (1988). Quality control of analyses of mercury in hair. Fresenius Journal of Analytical Chemistry, 332, 620–622. Seidel, S., Kreutzer, R., Smith, D., McNeel, S., & Gilliss, D. (2001). Assessment of commercial laboratories performing hair mineral analysis. Journal of the American Medical Association, 285, 67–72. Shearer, T. R., Larson, K., Neuschwander, J., & Gedney, B. (1982). Minerals in the hair and nutrient intake of autistic children. Journal of Autism and Developmental Disorders, 12, 25–34. Steindel, S. J., & Howanitz, P. J. (2001). The uncertainty of hair analysis for trace metals. The Journal of the American Medical Association, 285, 83–85. Wecker, L., Miller, S. B., Cochran, S. R., Dugger, D. L., & Johnson, W. D. (1985). Trace element concentration in hair from autistic children. Journal of Mental Deficiency Research, 29, 15–22. Williams, P. G., Hersh, J. H., Allard, A., & Sears, L. L. (2008). A controlled study of mercury levels in hair samples of children with autism as compared to their typically developing siblings. Research in Autism Spectrum Disorders, 2, 170–175. World Health Organization. (1990). Methylmercury. In International programme on chemical safety (Environmental health criteria 101). Retrieved from http://www.inchem.org/documents/ehc/ehc101.htm
See Also
Hair Mineral Analysis ▶ Thimerosal
▶ Hair Analysis
References and Reading Barrett, S. (1985). Commercial hair analysis: Science or scam? Journal of the American Medical Association, 254, 1041–1045. Bernard, S., Enayati, A., Redwood, L., Roger, H., & Binstock, T. (2001). Autism: A novel form of mercury poisoning. Medical Hypotheses, 56, 462–471. Fido, A., & Al-Saad, S. (2005). Toxic trace elements in the hair of children with autism. Autism, 9, 290–298. Gentile, P. S., Trentalange, M. J., Zamichek, W., & Coleman, M. (1983). Brief report: Trace elements in the hair of autistic and control children. Journal of Autism and Developmental Disorders, 13, 205–206. Holmes, A. S., Blaxill, M. F., & Haley, B. E. (2003). Reduced levels of mercury in first baby haircuts of autistic children. International Journal of Toxicology, 22, 277–285. Ip, P., Wong, V., Ho, M., Lee, J., & Wong, W. (2004). Mercury exposure in children with autistic spectrum disorder: Case-control study. Journal of Child Neurology, 19, 431–434.
Hair Trace Metal Analysis ▶ Hair Analysis
Halcion ▶ Triazolam and Autism
Haldol™ ▶ Haloperidol
Haloperidol
Haloperidol Carolyn A. Doyle1 and Christopher J. McDougle2,3 1 Indiana University School of Medicine, Indianapolis, IN, USA 2 Lurie Center for Autism, Massachusetts General Hospital, Lexington, MA, USA 3 Nancy Lurie Marks Professorship in the Field of Autism, Harvard Medical School, Boston, MA, USA
Synonyms Alti-Haloperidol; Apo-Haloperidol; Haldol™; Novo-Peridol; Peridol; Pms-Haloperidol; RatioHaloperidol
Definition The brand name of haloperidol in the United States is Haldol. Canadian brand names include Alti-Haloperidol, Apo-Haloperidol, NovoPeridol, Peridol, Pms-Haloperidol, and RatioHaloperidol. It comes in three forms: a pill, an intramuscular injection, and a longacting depot decanoate form and is manufactured by OrthoMcNeil-Janssen Pharmaceuticals, Inc.
Indications The oral and immediate-release injection forms of haloperidol are FDA-approved for the treatment of schizophrenia as well as for tics and vocal utterances associated with Tourette’s disorder (Janssen 2005). These forms are also used as second-line treatment to manage hyperactivity in the pediatric population as well as for severe behavior problems, particularly combativeness, explosiveness, and hyperexcitability (Stahl 2009). The long-acting depot intramuscular decanoate form of haloperidol is approved for the treatment of schizophrenia in situations where prolonged parenteral antipsychotic therapy
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is needed. Haloperidol is not approved for use in the treatment of autism or any other autism spectrum disorder (ASD).
Mechanisms of Action Mechanism of Action, According to Stahl (2008). Haloperidol is a conventional antipsychotic, also known as a “first-generation” or “typical” antipsychotic. The precise mechanism of action is not clearly established, but it is believed to antagonize postsynaptic dopamine-2 (D2) receptors in the brain. One of haloperidol’s most notable roles is D2 receptor antagonism in the mesolimbic pathway is diminishing the symptoms of psychosis. The introduction of this drug class resulted in relief to individuals living with schizophrenia and associated psychotic illnesses by reducing perceptual disturbances, such as hallucinations and delusions. The downside is that the mesolimbic pathway is believed to moderate the brain’s reward and reinforcement system. Blockade here can also result in decreased pleasure, anhedonia, apathy, and reduced social interest, often referred to as “secondary negative symptoms.” This is further exacerbated by dopamine blockade in the brain cortex and mesocortical pathways. Blockade of the dorsolateral prefrontal cortex can lead to worsening cognition, whereas blockade of the ventromedial prefrontal cortex can lead to worsening affective symptoms, like depression. The benefit of D2 receptor antagonism in the nigrostriatal dopamine pathway is reduction of tics and other symptoms of Tourette’s disorder. However, disproportionate dopamine blockade in this area can mimic the pathophysiology of Parkinson’s disease. When the amount of dopamine available to the postsynaptic receptor is excessively diminished, Parkinsonian-like side effects can occur including stiffness, cogwheeling, and tremor. Since the nigrostriatal pathway is part of the brain’s extrapyramidal nervous system, these side effects are referred to as extrapyramidal symptoms or EPS. Continued, long-term blockade of nigrostriatal D2 receptors can result in a hyperkinetic movement disorder
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known as tardive dyskinesia, involving involuntary movements of the face, jaw, tongue, extremities, or torso. This may present like chewing, facial grimacing, jerking of the arms or legs, or even rhythmic, fluid dancing of the body known as chorea. These side effects may be reversible if the drug is stopped early enough, but there may be a point in chronic therapy when it is too late. It is hypothesized that chronic antagonism of D2 receptors may cause them to permanently become hypersensitive or increase in number, leading to irreversible tardive dyskinesia. D2 receptor antagonism in the tuberoinfundibular pathway causes elevated plasma prolactin levels. This can result in amenorrhea (irregular menstruation) in women and galactorrhea (breast secretions) in either men or women. It may also interfere with fertility, particularly in women, as well as bone mineralization in postmenopausal women, raising their risk of osteoporosis. Haloperidol is also selective in its affinity for the alpha-1 adrenergic receptors, which can lead to orthostatic hypotension, dizziness, and drowsiness. Unlike other conventional antipsychotics, it has minimal affinity for cholinergic or histaminic receptors and therefore puts the patient at risk for increased EPS.
Haloperidol
Clinical Use (Including Side Effects)
forms are often used to stabilize acutely agitated patients with moderately severe to very severe symptoms (Janssen 2005). Haloperidol is used in the short- and long-term treatment of psychotic illnesses, particularly in diminishing symptoms of schizophrenia, like hallucinations and delusions (Stahl 2009). It is also used as management for tics and involuntary vocal utterances associated with Tourette’s disorder. In the pediatric population, haloperidol can be used to manage hyperactivity and severe behavioral disturbance, particularly combativeness, explosiveness, and hyperexcitability. Safety and efficacy have not yet been established in children and adolescents, so haloperidol should be considered second-line treatment for these conditions behind the atypical, second-generation antipsychotics that are approved for use in the pediatric population. Oral and intramuscular haloperidol is commonly given to patients once or multiple times daily, depending on their clinical presentation and medication needs. The long-acting depot decanoate form is approved for the treatment of schizophrenia where prolonged parenteral antipsychotic therapy is needed and given as a monthly injection. Caution should be used when administering haloperidol to individuals with cardiovascular disorder due to a risk of hypotension and/or precipitation of anginal pain (Janssen 2005). Haloperidol also decreases the seizure threshold and should therefore be used with caution in patients being treated with anticonvulsants. There are no well-controlled studies with haloperidol in pregnant women, so the effect on the fetus is unknown. Haloperidol is a pregnancy category C drug, meaning there are some animal studies demonstrating adverse effects but no controlled studies in humans. Haloperidol therefore should only be used in pregnancy (or in women likely to become pregnant) if the benefit clearly outweighs the risk.
Haloperidol comes in a generic pill form, an immediate-release intramuscular injectable form called Haldol Injection, and a long-acting injectable form called Haldol Decanoate. Haloperidol can be sedating, so both the oral or intramuscular
Clinical Uses in Autism Spectrum Disorders Haloperidol is the most critically studied antipsychotic medication for managing or reducing symptoms associated with autism (Malone and Waheed 2009). The earliest double-blind,
Specific Compounds and Properties Haloperidol generic and immediate-release injection: 4-[4-(p-chlorophenyl)-4-hydroxypiperidine]40 -fluorobutyrophenone (Janssen 2005). Haloperidol decanoate for long-acting injection: 4-(chlorophenyl)-1-[4-(4-fluorophenyl)-4oxobutyl]-4-piperidinyl decanoate (Janssen 2005).
Haloperidol
placebo-controlled studies examined the efficacy of haloperidol in treating symptoms associated with autism such as repetitive behavior, stereotypies, hyperactivity, mood lability, learning, language acquisition, and sociability in autistic children between the ages of 2 and 7.5 years (Anderson et al. 1984, 1989; Campbell et al. 1978; Cohen et al. 1980). Overall, these studies found haloperidol to be efficacious in alleviating many of these symptoms with minimal side effects, although these were short-term studies that did not extend beyond 4–14 weeks. Other studies have compared the safety and efficacy of haloperidol with other drugs in treating autistic disorder and were overall successful (Faretra et al. 1970; Miral et al. 2008; Naruse et al. 1982; Remington et al. 2001). These latter studies looked at broader age ranges and included older children, adolescents, and some adults. There has been one long-term placebo-controlled study showing the efficacy of discontinuous haloperidol treatment over the course of 6 months in children with autism, but the results are somewhat limited by the study population as it only included children who responded favorably to haloperidol in the past (Perry et al. 1989). The results are intriguing nonetheless and have been included in this summary of double-blinded, placebo-controlled studies examining haloperidol in the treatment of autism. Short-Term Research (4–14 Weeks) In a study by Campbell et al. (1978), haloperidol was found to be superior to placebo in reducing stereotypies and social withdrawal in 40 children diagnosed with autism aged 2–7 years over the course of 12 weeks. Participants received dosages between 0.5 and 4.0 mg/day with a mean of 1.65 mg/day. Interestingly, children who received both medication and behavioral treatment improved the most in acquiring imitative speech, making it one of the first reports showing that combined treatments produced superior results (Malone and Waheed 2009). Excessive, doserelated sedation was the most common side effect. Another study by Cohen et al. (1980) demonstrated decreased stereotypies in autistic children ages 2–7 using haloperidol over the course of
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10 weeks. In both of these studies, acute dystonia was observed in a few participants as an adverse event. Another study by Anderson et al. (1984) showed that haloperidol resulted in significantly reduced behavioral symptoms, general clinical improvement, and greater facilitation and retention of discrimination learning in 40 autistic children ages 2–7 years over 14 weeks. There were no adverse effects noted at therapeutic doses, which ranged from 0.5 to 3.0 mg/day (0.019–0.217 mg/kg/day). Anderson et al. (1989) described haloperidol as a “powerful drug in reducing, both statistically and clinically, maladaptive behaviors” including decreased hyperactivity, temper tantrums, withdrawal, and stereotypies. The sample of 45 children in this study were inpatients whose symptoms ranged from moderate to severe and whose ages were between 2 and 7.5 years. Medication dosages ranged from 0.25 to 4.0 mg/day. During the 4-week course of treatment, no adverse side effects were noted. Haloperidol has been compared to two other conventional antipsychotics in the management of autism: fluphenazine in 1970 by Fraretra et al. and pimozide in 1982 by Naruse et al. Each of these drugs demonstrated efficacy, but in comparison, haloperidol was found to be more effective than fluphenazine in reducing withdrawal, aggression, and stereotypies. Acute dystonic reaction, akathisia, and sedation were observed side effects. A 2001 study by Remington et al. compared haloperidol to clomipramine, a tricyclic antidepressant with potent serotonin reuptake inhibiting (SSRI) properties, in managing withdrawal, stereotypy, hyperactivity, and affective lability in autistic disorder. This study examined 36 children and adults between the ages of 10 and 36 years over 7 weeks. Both drugs demonstrated comparable improvement in symptoms when compared to baseline, but unfortunately, a significant number of participants were unable to complete the study due to side effects and lack of efficacy with clomipramine. The study favored haloperidol over clomipramine in the treatment of autistic disorder, particularly for hyperactivity and irritability but also in global symptom severity. Clomipramine was not better tolerated nor was it superior to
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haloperidol in the management of stereotypy, as was originally hypothesized. In 2008, Miral et al. compared the safety and efficacy of haloperidol to risperidone, a second-generation antipsychotic that is believed to antagonize dopamine-2 receptors, serotonin 2A receptors, and other serotonin receptors in the treatment of autistic disorder. This study examined 30 children aged 8–18 years for 12 weeks, with dosages ranging from 0.01 to 0.08 mg/kg/day. Although both drugs were found to be safe and well tolerated in the treatment of autistic disorder, risperidone was more effective in treating impulsivity, behavioral symptoms, language skills, and impaired sociability. Risperidone led to a greater increase in prolactin, whereas haloperidol resulted in increased alanine amino transferase (ALT). Long-Term Research (6 Months or More) In a 1989 study by Perry et al., haloperidol demonstrated efficacy when dosed continuously and discontinuously (5 days taking haloperidol and 2 days taking placebo) in children who had previously responded favorably to haloperidol. The children ranged in age from 2 to 8 years and showed a reduction in many maladaptive symptoms, with the most response in those with irritability, labile and angry affect, and uncooperativeness. Although the two groups were not matched to placebo, a subset of these children who were switched to placebo after the study has discontinued required additional haloperidol treatment for the recurrence of severe symptoms, supporting the drug’s efficacy. Side Effects, per Ortho-McNeil-Janssen Pharmaceuticals Cardiovascular side effects include hypotension, tachycardia, and hypertension. QT prolongation and ventricular arrhythmias have been reported. Central nervous system (CNS) side effects include Parkinsonian-like symptoms like tremor, rigidity, or cogwheeling; akathisia (inner restlessness); dystonias; or tardive dyskinesia. Dystonias are prolonged contractions of muscles and can include neck muscle spasms, tightening of the throat, dyspnea, or tongue protrusion. These can occur at low doses but are known to occur more frequently and with greater severity at higher
Haloperidol
doses. They are also more often seen in males and younger age groups. Administering anticholinergic medication can limit or reduce these effects. Tardive dyskinesia is the potentially irreversible syndrome of involuntary, dyskinetic movements (see Mechanism of Action). Haloperidol reduces the seizure threshold and therefore increases the risk of seizures. Other CNS side effects can include insomnia, anxiety, euphoria, agitation, drowsiness, depression, headache, confusion, sedation, dizziness, and vertigo. Neuroleptic malignant syndrome (NMS) is a potentially fatal adverse effect of using any antipsychotic medication. It presents as hyperpyrexia, muscle rigidity, altered mental status, and autonomic instability. This includes dysrhythmias, irregular blood pressure, tachycardia, or diaphoresis. It can lead to elevated creatinine phosphokinase, myoglobinuria from muscle breakdown, and acute renal failure. Autonomic reactions can include dry mouth, blurred vision, urinary retention, diaphoresis, and priapism. Dermatologic side effects include hair loss, photosensitivity, and maculopapular or acnelike skin reactions. Visual disturbances can include cataracts or retinopathy. Effects on the gastrointestinal system can include constipation, diarrhea, nausea, vomiting, anorexia, dyspepsia, or hypersalivation. Endocrine effects can include breast engorgement, gynecomastia, menstrual irregularities, impotence, increased libido, hyperglycemia, hypoglycemia, or hyponatremia. Hematologic reactions may include agranulocytosis, anemia, leukocytosis, leucopenia, or lymphomonocytosis. Hepatic side effects can include jaundice or impaired liver function. Respiratory manifestations may include brochospasm, laryngospasm, or increased depth of respiration. Since the release of haloperidol, there has been one case of hyperammonemia in a 5-year-old child with citrullinemia, an inherited disorder of ammonia excretion. Haldol Decanoate may produce a local tissue reaction at the injection site.
See Also ▶ Antipsychotics: Drugs ▶ Dystonia
Halstead-Reitan Neuropsychological Test Battery
▶ Tardive Dyskinesia ▶ Tics ▶ Tourette Syndrome
References and Reading Anderson, L. T., Campbell, M., et al. (1984). Haloperidol in the treatment of infantile autism: Effects on learning and behavioral symptoms. The American Journal of Psychiatry, 141(10), 1195–1202. Anderson, L. T., Campbell, M., et al. (1989). The effects of haloperidol on discrimination learning and behavioral symptoms in autistic children. Journal of Autism and Developmental Disorders, 19(2), 227–239. Campbell, M., Anderson, L. T., et al. (1978). A comparison of haloperidol and behavior therapy and their interaction in autistic children. Journal of the American Academy of Child Psychiatry, 17(4), 640–655. Cohen, I. L., Campbell, M., et al. (1980). Behavioral effects of haloperidol in young autistic children. An objective analysis using a within-subjects reversal design. Journal of the American Academy of Child Psychiatry, 19(4), 665–677. Faretra, G., Dooher, L., et al. (1970). Comparison of haloperidol and fluphenazine in disturbed children. The American Journal of Psychiatry, 126(11), 1670–1673. Janssen-Cilag Pty Limited. (2005). Haldol product information. Retrieved from http://www.janssen.com.au/ files/Products/Haldol_PI.pdf. Malone, R. P., & Waheed, A. (2009). The role of antipsychotics in the management of behavioural symptoms in children and adolescents with autism. Drugs, 69(5), 535–548. Miral, S., Gencer, O., et al. (2008). Risperidone versus haloperidol in children and adolescents with AD: A randomized, controlled, double-blind trial. European Child & Adolescent Psychiatry, 17(1), 1–8. Naruse, H., Nagahata, M., et al. (1982). A multi-center double-blind trial of pimozide (Orap), haloperidol and placebo in children with behavioral disorders, using crossover design. Acta Paedopsychiatrica, 48(4), 173–184. Perry, R., Campbell, M., et al. (1989). Long-term efficacy of haloperidol in autistic children: Continuous versus discontinuous drug administration. Journal of the American Academy of Child and Adolescent Psychiatry, 28(1), 87–92. Remington, G., Sloman, L., et al. (2001). Clomipramine versus haloperidol in the treatment of autistic disorder: A double-blind, placebo-controlled, crossover study. Journal of Clinical Psychopharmacology, 21(4), 440–444. Stahl, S. M. (2008). Stahl’s essential psychopharmacology: Neuroscientific basis and practical applications (3rd ed., pp. 328–342). New York: Cambridge University Press. Stahl, S. M. (2009). Stahl’s essential psychopharmacology: The prescriber’s guide (3rd ed., pp. 237–242). New Delhi: Cambridge University Press.
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Halstead-Reitan Neuropsychological Test Battery Beth Springate1 and Deborah Fein2 1 Department of Psychology, University of Connecticut, Storrs, CT, USA 2 Department of Psychological Sciences, University of Connecticut, Storrs, CT, USA
Synonyms HRB
Description The HRB represents a fixed battery approach to neuropsychological assessment. The HRB is composed of three separate batteries: the adult battery (Halstead Neuropsychological Test Battery for Adults; ages 15+), an older children’s battery (Halstead Neuropsychological Test Battery for Children; ages 9–14), and a younger children’s battery (Reitan-Indiana Neuropsychological Test Battery for Children; ages 5–8). The adult battery is composed of the Category Test, Tactual Performance Test, Seashore Rhythm Test, Speech Sounds Perception Test, Finger Oscillation Test, Trail-Making Test, Aphasia Screening Test, Sensory-Perceptual Disturbances Test, Lateral Dominance Test, and Grip Strength Test. In creating the older children’s battery, Reitan simplified and/or shortened the Category Test, TrailMaking Test, Tactual Performance Test, and Speech Sounds Perception Test (Reitan and Wolfson 1992a). Several of these tests were simplified by Reitan when he created the older children’s battery (Horton 2008). Additional modifications were made for the younger children’s battery, including addition of the Progressive Figures Test (a visual modification of the Trail-Making Test), use of an electronic finger tapper, and simplification of the Category Test, Tactual Performance Test, and SensoryPerceptual Examination (Reitan and Wolfson 1992b). An age-appropriate Wechsler intelligence
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measure, as well as a personality measure such as the Minnesota Multiphasic Personality Inventory, is also typically administered (Horton 2008).
Historical Background Development of the HRB began in the 1940s when Dr. Ward Halstead aimed to develop a series of tests sensitive to the presence of brain damage, in contrast to the single-test assessment approaches which were prevalent at the time. In order to develop his test battery, Halstead observed brain-damaged patients in their everyday lives to discern which aspects of their behavior were different from the behavior of unimpaired individuals. Given the heterogeneity of their deficits, it was clear to Halstead that a single test would likely not be able to adequately identify and evaluate the nature of their deficits (Reitan and Wolfson 2009). Halstead ultimately developed 10 tests he considered to be sensitive to the presence of brain damage (Halstead 1947). In a fact-driven validation procedure over the course of 15 years, Dr. Ralph Reitan utilized Halstead’s original 10 tests to examine patients and blindly interpreted the results without knowledge of their histories or neurological findings. After a report was written, it was compared with an independent summary of the medical and neurological findings in order to examine inaccuracies in the neuropsychological test results. Tests were accepted or rejected solely on the basis of their accurate prediction of the presence of brain damage for thousands of patients with various types of injuries. Although Halstead’s tests typically resulted in a correct conclusion about whether a patient had brain damage, conclusions regarding the nature of the damage (e.g., lateralization, type of lesion) could not be done in a highly accurate manner. Reitan experimentally evaluated additional tests for their ability to provide information about the nature of the brain damage. Three of Halstead’s original 10 tests were ultimately excluded as a result of this procedure (Reitan and Wolfson 2009).
Halstead-Reitan Neuropsychological Test Battery
Psychometric Data Interpretation of results from the HRB typically occurs along several different levels of analysis: level of performance, pathognomonic signs, score patterns, and right-left differences. Analysis at the level of performance involves the comparison of patients to normative standards. However, many other factors besides brain injury can impact test performance (e.g., limited education, mood disorders), and the patients’ degree of decline from premorbid function is not taken into account. Use of composite indices generated from test results, as well as comparisons to performance on Wechsler intelligence measures, can occur at this level of interpretation. Interpretation at the level of pathognomonic signs involves examining for evidence of aphasia, agnosia, and apraxia. Limitations at this level of analysis include the reliance upon the examiner’s judgment for the presence of signs, and depending on the location of their brain injury, individuals do not necessarily show signs. Score patterns are examined to determine the relative integrity of the two cerebral hemispheres and to allow for intraindividual comparisons. Finally, analysis of left-right differences involves the comparison of right and left hands on various sensory and motor tasks, although the utility of this can be limited in cases involving significant peripheral injury of the arms/hands (Sweeny et al. 2007; Horton 2008). Screening tests, requiring only 30–45 min to administer, have also been developed in order to more quickly identify those individuals who would benefit from administration of the complete HRB battery. Results from young children (Reitan and Wolfson 2008a) indicate the brief screening tests have 92% sensitivity and 85% specificity in detecting individuals with brain damage. Results from older children (Reitan and Wolfson 2008b) indicate 92% sensitivity and 88% specificity in detecting individuals with brain damage. Reitan and Wolfson argue that many of the misclassified children had borderline scores, and recommendations to take into account additional sources of information were made. For adults, the screening tests had a sensitivity of 96% and a specificity of 82%, with
Handicaps, Behavior and Skills Schedule (HBSS)
older adults more likely to be misclassified as brain injured (Reitan and Wolfson 2008c).
Clinical Uses The HRB does have several limitations: It is unwieldy and difficult to transport, takes a relatively long time to administer and score, and is not suitable for thorough examination of patients with sensory or motor handicaps. It contains no formal memory tests, and supplemental language measures may also be needed. Few ecological validity studies of the HRB have been done, and adequate interpretation requires a high level of expertise and years of experience (Horton 2008). In addition, with modern neuroimaging methods, neuropsychologists are rarely called upon to identify the presence of brain injury and its location, although they may be asked to judge the integrity of localized cognitive functions (Lezak et al. 2004). Finally, the need to use demographic normative corrections with test scores has been raised as an issue, although Reitan and Wolfson (1995) argue the effects of brain damage may reduce/eliminate age and education effects. Most recently, Heaton et al. (2004) revised the demographic age, education, and gender-corrected norms to include normative data for Caucasians and African Americans. Finally, several authors have argued the greatest contribution of the HRB may be with its impact upon the practice of neuropsychological assessment more generally and its push to make psychologists cognizant of the need to assess many different aspects of behavior and cognitive function during neuropsychological evaluations (Lezak et al. 2004; Horton 2008).
References and Reading Halstead, W. C. (1947). Brain and intelligence: A quantitative study of the frontal lobes. Chicago: University of Chicago Press. Heaton, R. K., Miller, S. W., Taylor, M. J., & Grant, I. (2004). Revised comprehensive norms for an expanded Halstead-Reitan Battery. Odessa: Psychological Assessment Resources.
2303 Horton, A. M. (2008). The Halstead-Reitan neuropsychological test battery: Past, present, and future. In A. M. Horton & D. Wedding (Eds.), The neuropsychology handbook (3rd ed.). New York: Springer. Lezak, M. D., Howieson, D. B., & Loring, D. W. (2004). Neuropsychological assessment. New York: Oxford University Press. Reitan, R. M., & Wolfson, D. (1992a). Neuropsychological evaluation of older children. Tucson: Neuropsychology Press. Reitan, R. M., & Wolfson, D. (1992b). Neuropsychological evaluation of younger children. Tucson: Neuropsychology Press. Reitan, R. M., & Wolfson, D. (1995). Influence of age and education on neuropsychological test results. The Clinical Neuropsychologist, 9, 151–158. Reitan, R. M., & Wolfson, D. (2008a). The use of serial testing to identify young children in need of comprehensive neuropsychological evaluation. Applied Neuropsychology, 15, 1–10. Reitan, R. M., & Wolfson, D. (2008b). Serial testing of older children as a basis for recommending comprehensive neuropsychological evaluation. Applied Neuropsychology, 15, 11–20. Reitan, R. M., & Wolfson, D. (2008c). The use of serial testing in evaluating the need for comprehensive neuropsychological testing of adults. Applied Neuropsychology, 15, 21–32. Reitan, R. M., & Wolfson, D. (2009). The HalsteadReitan neuropsychological test battery for adults–theoretical, methodological, and validational bases. In I. Grant & K. M. Adams (Eds.), Neuropsychological assessment of neuropsychiatric and neuromedical disorders (3rd ed.). New York: Oxford University Press. Sweeny, J. E., Slade, H. P., Ivins, R. G., Nemeth, D. G., Ranks, D. M., & Sica, R. B. (2007). Scientific investigation of brain-behavior relationships using the Halstead-Reitan battery. Applied Neuropsychology, 14(2), 65–72.
Handicaps, Behavior and Skills Schedule (HBSS) Maretha de Jonge Department of Psychiatry, University Medical Center, Utrecht, Netherlands
Synonyms HBSS
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Description The Handicaps, Behavior and Skills schedule (HBS, Wing and Gould 1978) is a semistructured, investigator-based interview schedule. It is the precursor of the Diagnostic Interview for Social and Communication Disorders (DISCO) (Wing 1999). The HBS was developed to be used for assessing autism symptoms in children with mental retardation. It was designed to elicit a detailed description of the child’s skills, impairments, and behavior, in order to assist clinicians in their judgment of the level of development and special needs of a child in different domains. The HBS measures skills and deficits as shown in daily life instead of during formal intelligence tests. It provides the clinician with a profile of the child’s pattern of development and behavior and its strengths and difficulties which can be used for treatment planning. The interview is completed with parents or other caregivers and inquires about specific, easily observable aspects of behavior during the preceding month. It takes from 45 min to 2.5 h to complete, depending on the informant and the complexity of the child’s behavior. The schedule consists of items covering behavioral abilities, such as social skills, language and communication, imaginative play, self-care, domestic skills, visuospatial abilities, and school work. In addition, there are items covering challenging behavior often seen in autism, such as sleep disturbance, echolalia, or stereotypic movements. The items within the developmental sections are hierarchically ordered according to the steps in normal development. A higher total score represents a higher level of development. A high total score on the part of the schedule covering specific abnormalities signifies less severe behavioral difficulties.
Historical Background The HBS was developed in the early 1970s, by Lorna Wing and Judith Gould. It was constructed
Handicaps, Behavior and Skills Schedule (HBSS)
to be used in an epidemiological study of all children under 15 years of age with intellectual disability and/or autistic characteristics in the former London Borough of Camberwell. One of the aims of the Camberwell study was to identify children with features of autism and to see if any patterns could be discovered among the clinical pictures thus found. At that time, several checklists, questionnaires, and behavior observations scales were available for the assessment of autistic individuals, but none of those was suitable for this study. Most instruments were designed to identify autism, rather than to collect detailed information about skills and difficulties necessary to investigate patterns of behavior. Therefore, the Handicaps, Behavior and Skills schedule was constructed. The developmental and self-care items were largely based on an unpublished research interview schedule by Williams and Kushlick and on the Vineland Social Maturity Scale (Doll 1965). The behavioral items were in part derived from a previous rating scale by Wing (1969) and based on the work of DeMyer (1971), Rutter et al. (1971), and Sheridan (1969). The schedule enabled the researchers to delineate skills, challenging behaviors, and the three specific impairments that were found to co-occur: social problems, communication deficits and repetitive activities, and a lack of imaginative play (later known as the triad of impairments; Wing and Gould 1979). In 1990, the Elliot House Centre for Social and Communication Disorders was set up by the National Autistic Society (UK). Both children and adults with a variety of developmental disorders and cognitive abilities, ranging from profound mental retardation to superior cognitive skills, were referred to this center. The HBS, which had been designed for children with intellectual disability, did not fulfill the requirements in clinical practice. It was adapted to cover all ages and levels of ability and to record not only current behavior, but also developmental history. The adapted schedule was renamed the Diagnostic Interview for Social and Communication Disorders (DISCO).
Hand-Over-Hand Assistance
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Psychometric Data
References and Reading
Very few data are available on the psychometric properties of the HBS. In the original study of Wing and Gould (1978), the inter-informant reliability was investigated. Both parents and nurses/ teachers of 104 children were interviewed. The mean Spearman correlation coefficient was 0.8 (ranging from 0.2 to 0.9), and the mean kappa was 0.5 (ranging from 0.1 to 0.8). In general, parents and teachers were in agreement, except for a few behavioral features for which the correlations were weak, such as echolalia, intonation, or special skills. Age and sex were not significantly associated with the level of agreement between parents and nurses/teachers. The severity of the mental retardation and type of disability were significantly associated with level of agreement. There was better agreement for children with lower developmental levels and fewer behavior problems. Inter-rater reliability was calculated on 20 interviews. When raw scores were collapsed into a 3-point scale, there was maximum overall agreement between the raters on 30 of the 31 sections in all children. Internal consistency was investigated in a Dutch study with 72 children (Van BerckelaerOnnes and van Duijn 1993). Cronbach’s alpha coefficients were considered satisfactory, ranging from 0.4 to 0.7, and slightly higher when only the autistic individuals were included, expect for the imagination/social play subscale.
Rutter, M., Bartak, L., & Newman, S. (1971). Autism – A central disorder of cognition and language? In M. Rutter (Ed.), Infantile autism: Concepts, characteristics and treatment. London: Churchill Livingstone. Sheridan, M. (1969). Playthings in the development of language. Health Trends, 1, 7–10. Van Berckelaer-Onnes, I., & van Duijn, G. (1993). A comparison between the handicaps behaviour and skills schedule and the psychoeducational profile. Journal of Autism and Developmental Disorders, 23(2), 263–272. Wing, L. (1969). The handicaps of autistic children: A comparative study. Journal of Child Psychology and Psychiatry, 10, 1–40. Wing, L. (1999). Diagnostic interview for social and communication disorders. Manual. Bromley: Centre for Social and Communication Disorders. Wing, L., & Gould, J. (1978). Systematic recording of behaviors and skills of retarded and psychotic children. Journal of Autism and Childhood Schizophrenia, 8(1), 79–97.
Hand-Over-Hand Assistance Brian Reichow Child Study Center, Yale University School of Medicine, New Haven, CT, USA Anita Zucker Center for Excellence in Early Childhood Studies, University of Florida, Gainesville, FL, USA
Synonyms Controlling prompt; Full physical prompt
Clinical Uses The HBS has been adapted and reconstructed to make it suitable for clinical use with individuals of all ages and levels of ability. In addition to present functioning, it now covers also developmental history. This revised interview is named the Diagnostic Interview for Social and Communication Disorders (DISCO) (see DISCO).
Definition Hand-over-hand assistance involves placing ones hands over an individual with autism’s hands to help them complete a movement. When using hand-over-hand assistance, the adult is controlling the movements of the child’s hands. For example, hand-over-hand assistance can be used to help teach a child how to wash their hands. In this
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case, the adult would take the child’s hands in their hands and complete all of the steps of hand washing using the child’s hands; i.e., the adult would use the child’s hands to turn on the water, pump the soap, scrub hands. . .dry hands. Handover-hand assistance, also referred to as a full physical prompt, is the most intrusive prompt that you can use, and care should be taken to ensure the adult is not exerting too much force on the child. Because it is the most restrictive prompt, it should be used sparingly when other options are not effective and systematically faded to less intrusive prompting techniques (e.g., partial physical prompts, models).
See Also ▶ Prompt Hierarchy ▶ Prompting
Handwriting Difficulty
TalkAbility™ – The Hanen Program ® for Parents of Verbal Children on the Autism Spectrum. The Hanen approach includes seven programs, four for parents and three for early childhood educators. These programs are: • It Takes Two to Talk ® – The Hanen Program ® for Parents of Children with Language Delays • Target Word ® – The Hanen Program ® for Parents of Children who are Late Talkers • More Than Words ® – The Hanen Program ® for Parents of Children on the Autism Spectrum • TalkAbility™ – The Hanen Program ® for Parents of Verbal Children on the Autism Spectrum • Learning Language and Loving It™ – The Hanen Program ® for Early Childhood Educators • Teacher Talk™ Training Series • ABC and Beyond™ – The Hanen Program ® for Building Emergent Literacy
Handwriting Difficulty
Historical Background
▶ Dysgraphia
The Hanen Centre is a Canada-based nonprofit center. The first Hanen program was designed in 1975 to teach parents to take a primary role in helping their young children develop communication. This original program became It Takes Two to Talk. The Hanen approach now includes seven programs, four for parents and three for early childhood educators, designed to teach parents and educators of young children to promote communication, social skills, and literacy in everyday activities. Hanen programs are now offered in Canada, the United States, and other countries around the world.
Hanen Approach Elizabeth Spencer College of Education and Human Ecology, The Ohio State University, Columbus, OH, USA
Definition The Hanen approach is an intervention approach in which parents and educators are taught to facilitate communication development in young children. The Hanen Centre has developed a number of programs designed to teach parents and educators of young children to promote communication, social skills, and literacy in everyday activities. Two specific approaches have been developed for parents of children with autism: More Than Words ® – The Hanen Program ® for Parents of Children on the Autism Spectrum and
Rationale or Underlying Theory Hanen programs reflect a family-centered model of intervention based on a social-interactionist perspective of language development. The Hanen approach is based on the principles that parents should be involved in their child’s intervention process and that communication should be facilitated in naturalistic contexts. Hanen
Hanen Approach
programs emphasize the critical role of parents, the importance of early intervention, and a focus on everyday routines and activities. The Hanen programs are designed to provide social support for parents in small-group interactions with other parents. The strategies that parents are taught in It Takes Two to Talk are based on the responsivity hypothesis. According to this hypothesis, when parents are taught to respond to child’s communication and focus on the child’s interest, language input is more easily processed by the child. The strategies that parents are taught in More Than Words are based on a social-pragmatic theory approach. Social communication is facilitated by structuring the environment to promote communication and by responding to the child’s communication. The strategies that parents are taught in the TalkAbility program are based on research on development of theory of mind. Specifically, the program emphasizes a connection between the development of language and the development of theory of mind.
Goals and Objectives The goals of the Hanen approach are to teach parents and educators to promote communication with young children during daily routines and activities. It Takes Two to Talk is designed to teach parents to help their young children with language delays develop language skills. More Than Words is designed to teach parents to help their children with autism improve social skills, increase receptive language, and engage in interactions. TalkAbility is designed to teach parents to help their verbal children with autism improve social skills, participate in conversations, and understand the meaning of nonverbal cues like body language and facial expressions.
Treatment Participants The Hanen approach is designed for parents, early childhood educators, and young children. It Takes
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Two to Talk is designed for parents of children with a language delay who are 5 years and younger. More Than Words is designed for parents and their children with autism 5 years old and younger. TalkAbility is designed for parents and their verbal children with autism between 3 and 7 years old.
Treatment Procedures The Hanen programs are led by Hanen-certified speech-language pathologists. Small groups of parents or educators participate in group teaching sessions and individual feedback sessions. Individual feedback sessions use videotapes of parentchild or educator-child interactions. It Takes Two to Talk is a program for parents of children with language delay and is conducted by a Hanen-certified speech-language pathologist. The program includes a preprogram consultation, 6–8 small-group teaching sessions, and three individual visits in which the parent and the speechlanguage pathologist review videotapes of the parent and child interacting. It Takes Two to Talk is designed to teach parents to be responsive to their child. Parents are taught to respond promptly and positively when their child communicates. Parents are also taught to follow the child’s lead by talking and paying attention to what the child is interested in. The focus of the program is on teaching practical strategies that parents can use in daily routines and activities. For example, parents are taught to OWL: observe, wait, and listen to follow their child’s lead during daily routines such as meal time. It Takes Two to Talk also teaches parents to use strategies during play, when reading books, and with music. Parents are also taught to identify and respond to their child’s individual communication ability. For example, parents of a child who is a Communicator learn to recognize and respond to their child’s gestures and sounds. More Than Words is an 11-week program for parents of children with autism and is conducted by a Hanen-certified speech-language pathologist. The program includes a preprogram consultation, eight small-group teaching sessions, and three
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individual visits in which the parent and speechlanguage pathologist review videotapes of the parent and child interacting. The More Than Words program is designed to guide parents in adjusting the way they talk to their child with autism during daily activities. Materials include a guidebook and accompanying DVD. The 4-S acronym identifies the key elements of the approach: Say Less, Stress, Go Slow, and Show. Parents learn to simplify what they say to their children, to talk about what their child is paying attention to and stress key words, and to talk slowly and use pictures, gestures, and objects. The program is designed to teach parents to use strategies in daily routines (e.g., meals, bath time). TalkAbility is conducted by a Hanen-certified speech-language pathologist. The program includes a preprogram consultation, small-group teaching sessions, and individual visits in which the parent and speech-language pathologist review videotapes of the parent and child interacting and videotapes of the child interacting with another child. The TalkAbility program is designed to teach parents to facilitate social communication and social skills with their verbal children with autism. Materials include a guidebook and accompanying DVD. The TalkAbility program emphasizes social communication skills like conversational turn-taking, storytelling and imaginative play, and peer relationships. In the program, parents are taught to identify their child’s communication strengths and weaknesses. For example, the guidebook provides a Conversation Checklists for parents to use to identify the stages of conversations that their child struggles with. Parents are taught strategies to teach their child to have successful conversations. For example, for a child who has trouble initiating a conversation, a parent is taught to interpret the child’s nonverbal messages (e.g., watching a peer play with a toy) and provide a verbal model (e.g., You want to play.).
Efficacy Information Efficacy studies have been conducted on some of the Hanen programs, but empirical support is limited. In most of these studies, a quasiexperimental design was employed. Participants
Hanen Approach
in the treatment groups have been parents participating in a Hanen program. Participants in the control groups have been groups of parents on the wait list to participate in a Hanen program. In a quasi-experimental study, parents who participated in It Takes Two to Talk demonstrated changes in interaction with their children including increases in responsivity and decrease in complexity of language directed at child. Children whose parents participated in It Takes Two to Talk demonstrated an increase in expressive language. Other studies have examined effectiveness of approaches that use procedures modified from the Hanen approach. In a quasi-experimental study, parents were taught a modified Hanen approach that emphasized target vocabulary words. Parents demonstrated a change in parental interaction and children demonstrated an increase in expressive vocabulary. Case studies and quasi-experimental design have been used to examine the outcomes of parents and children who have participated in the More Than Words program. Case studies have reported an increase in parental interaction and child social interaction following participation in the More Than Words Program. In a quasi-experimental study, parents who participated in the More Than Words program had higher scores on measures of parental interaction and children had higher expressive vocabulary relative to the control group. There are no published efficacy studies of TalkAbility.
Outcome Measurement The Hanen Approach does not specify outcome measurement. Research studies that have examined It Takes Two to Talk have measured outcomes of parent interaction and child communication. Parent interaction has been measured using researcher-developed coding systems. Child communication has been measured using standardized measures (e.g., The Sequenced Inventory of Communication Development), researcher-developed tools, and analysis of language samples. Research studies that have examined the More Than Words program have used the following tools to measure outcomes: the Autism Diagnostic
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Observation Schedule, the MacArthur-Bates Communicative Development Inventory, the Behavior Screening Questionnaire, the Questionnaire on Resources and Stress, and the Parent Feelings Questionnaire. Researcher-developed measures have included the Joy and Fun Assessment and other parent report measures. Outcomes also have been measured using analysis of parentchild interactions, spontaneous language samples, researcher-child interactions, and child social initiations.
Qualifications of Treatment Providers For the parent-teaching programs, including It Takes Two to Talk, More Than Words, and TalkAbility programs, qualified treatment providers are speech-language pathologists who have received certification from the Hanen Centre. Certification in It Takes Two to Talk requires participation in a 3-day workshop led by a Hanen Instructor. Certification in the It Takes Two to Talk program is a prerequisite for certification in all other programs. Certification in More Than Words requires participation in another 3-day workshop led by a Hanen Instructor. Certification in TalkAbility requires certification in both It Takes Two to Talk and More Than Words and participation in a 2-day workshop led by a Hanen Instructor. Early childhood professionals can become qualified treatment providers for the Learning Language and Loving It and ABC and Beyond programs.
See Also ▶ Language Interventions ▶ Parent Training ▶ Speech-Language Pathologist (SLP)
References and Reading Aldred, C., Green, J., & Adams, C. (2004). A new social communication intervention for children with autism: Pilot randomized controlled treatment study suggesting effectiveness. Journal of Child Psychology and Psychiatry, 40, 1–11.
2309 Awcock, C., & Habgood, N. (1998). Early intervention project: Evaluation of WILSTAAR, Hanen and specialist playgroup. International Journal of Language & Communication Disorders, 33(S1), 500–505. Bohannon, J., & Bonvillian, J. (1997). Theoretical approaches to language acquisition. In J. B. Gleason (Ed.), The development of language (4th ed., pp. 259–316). Boston: Allyn & Bacon. Brady, N., Warren, S. F., & Sterling, A. (2009). Interventions aimed at improving child language by improving maternal responsivity. International Review of Research in Mental Retardation, 37, 333–357. Dominey, P. F., & Dodane, C. (2004). Indeterminacy in language acquisition: The role of child directed speech and joint attention. Journal of Neurolinguistics, 17, 121–145. Giroalmetto, L., Pearce, P., & Weitzman, E. (1996a). The effects of focused stimulation for promoting vocabulary in children with delays: A pilot study. Journal of Childhood Communication Development, 17, 39–49. Giroalmetto, L., Pearce, P., & Weitzman, E. (1996b). Interactive focused stimulation for toddlers with expressive vocabulary delays. Journal of Speech and Hearing Research, 39, 1274–1283. Girolametto, L. E. (1988). Improving the socialconversational skills of developmentally delayed children: An intervention study. The Journal of Speech and Hearing Disorders, 53, 156–167. Girolametto, L., & Weitzman, E. (2006). It takes two to talk ® – The Hanen program® for parents: Early language intervention through caregiver training. In R. McCauley & M. Fey (Eds.), Treatment of language disorders in children (pp. 77–103). Baltimore: Paul H. Brookes. Girolametto, L., Tannock, R., & Siegel, L. (1993). Consumer-oriented evaluation of interactive language intervention. American Journal of Speech-Language Pathology, 2, 41–51. Girolametto, L., Verbey, M., & Tannock, R. (1994). Improving joint engagement in parent-child interaction. Journal of Early Intervention, 18, 155–167. Girolametto, L., Pearce, P., & Weitzman, E. (1997). Effects of lexical intervention on the phonology of late talkers. Journal of Speech, Language, and Hearing Research, 40, 338–348. Girolametto, L., Weitzman, E., & Greenberg, J. (2003). Training day care staff to facilitate children’s language. American Journal of Speech-Language Pathology, 12, 299–311. Girolametto, L., Sussman, F., & Weitzman, E. (2007). Using case study methods to investigate the effects of interactive intervention for children with autism spectrum disorders. Journal of Communication Disorders, 40, 470–492. Ingersoll, B., & Dvortcsak, A. (2006). Including parent training in the early childhood special education curriculum for children with autism spectrum disorders. Journal of Positive Behavior Interventions, 8, 79–87. Ingersoll, B., Dvortcsak, A., Whalen, C., & Sikora, D. (2005). The effects of a developmental, socialpragmatic language intervention on rate of expressive
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2310 language production in young children with autistic spectrum disorders. Focus on Autism and Other Developmental Disabilities, 20, 213–222. Manolson, A. (1992). It takes two to talk: A parent’s guide to helping children communicate (2nd ed.). Toronto: Hanen Centre. McConachie, H., Randle, V., & Couteur, L. (2005). A controlled trial of a training course for parents of children with suspected autism spectrum disorder. The Journal of Pediatrics, 147, 335–340. Pepper, J., & Weitzman, E. (2004). It takes two to talk ®: A practical guide for parents of children with language delays (2nd ed.). Toronto: Hanen Centre. Prelock, P. A., Paul, R., & Allen, E. M. (2011). Evidencebased treatments in communication for children with autism spectrum disorders. In B. Reichow, P. Doehring, D. Cicchetti, & F. Volkmar (Eds.), Evidence-Based Practices and Treatments for Children with Autism (pp. 93–169). New York: Springer. Prizant, B. M., Wetherby, A. M., & Rydell, P. J. (2000). Communication intervention issues for children with autism spectrum disorders. In A. M. Wetherby & B. M. Prizant (Eds.), Autism spectrum disorders: A transactional developmental perspective (pp. 193–224). Baltimore: Brookes. Sheldon, M. L., & Rush, D. D. (2001). The ten myths about providing early intervention services in natural environments. Infants and Young Children, 14, 1–13. Sussman, F. (1999). More than words: The Hanen program for parents of children with autism spectrum disorder. Toronto: Hanen Centre. Tannock, R., Girolametto, L., & Siegel, L. (1992). Language intervention with children who have developmental delays: Effects of an interactive approach. American Journal of Mental Retardation, 97, 145–160. Weitzman, E., Girolametto, L., & Greenberg, J. (2006). Adult responsiveness as a critical intervention mechanism for emergent literacy: Strategies for early childcare educators. In L. Justice (Ed.), Clinical approaches to emergent literacy intervention. San Diego: Plural.
Harris, Sandra
internship at St. Elizabeths Hospital in Washington, DC, after which she accepted a position as assistant professor at Douglass College, Rutgers, The State University of New Jersey. Dr. Harris has been at Rutgers ever since, retiring in 2007.
Major Appointments (Institution, Location, Dates) • Douglass College, Rutgers, The State University of New Jersey: 1969–1979. Assistant and associate professor • Graduate School of Applied and Professional Psychology, Department of Clinical Psychology and Department of Psychology, Faculty of Arts and Sciences, Rutgers, The State University of New Jersey: 1979–2007. Professor I and professor II • Graduate School of Applied and Professional Psychology, Rutgers, The State University of New Jersey: 1992–1993, acting dean; 1993–2001, dean • Board of Governors Distinguished Service Professor, Rutgers, The State University of New Jersey: 2003–2007 • Board of Governors Distinguished Service Professor Emerita, Rutgers, The State University of New Jersey: 2007 • Douglass Developmental Disabilities Center, Rutgers, The State University of New Jersey 1972–2007: Founder and executive director
Major Honors and Awards
Harris, Sandra Michael D. Powers The Center for Children with Special Needs, Glastonbury, CT, USA
She is the recipient of numerous awards for distinguished service and contributions to science including the Daniel Gorenstein Memorial Award, the President’s Award for Research in Service to New Jersey, the Anne Klein Advocate Award, and the Distinguished Alumni Award, SUNY Buffalo.
Name and Degrees Sandra L. Harris (1943–) received her BA from the University of Maryland and PhD in clinical psychology from the State University of New York at Buffalo. She completed her clinical
Landmark Clinical, Scientific, and Professional Contributions Dr. Harris’ scientific and clinical interests have been at the forefront of the evolution of
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evidence-based treatment for those with autism and their families for over 40 years. From an early interest in teaching language and the generalizability of teaching strategies to families to treatment of core behavioral and social deficits of autism, through research on the effect of autism on parents, grandparents, and siblings, and to demonstrating the effectiveness and sustainability of intensive early behavioral intervention, her work with colleagues and students has been both robust and pragmatic, emphasizing the development of new science but also the training of new professionals in those endeavors. Dr. Harris established one of the first behavioral treatment centers for children with autism in the United States at the Douglass Developmental Disabilities Center and, in so doing, created both a nationally recognized center of excellence for educating individuals with autism across the life span and also a training facility for a generation of psychologists, teachers, and other health-care professionals working in the field.
Short Biography As a clinical psychologist in the best tradition of the scientist-practitioner model and as an educator, Dr. Harris has mentored a generation of leaders in the field of evidence-based behavioral treatment of those with autism spectrum disorders. While initially devoting her efforts for many years to research and teaching in the graduate program in clinical psychology at Rutgers and simultaneously serving as executive director of the Douglass Developmental Disabilities Center, Dr. Harris became the third dean of the Graduate School of Applied and Professional Psychology (GSAPP), the nation’s first university-based school of professional psychology, in the early 1990s. For a period of nearly 10 years, she oversaw a substantial expansion of GSAPP’s training, clinical, and community-based programs, in the process of helping to reshape the mission of this flagship program. By honoring her as the Board of Governors distinguished service professor in 2003, the Rutgers community recognized her contributions to research and service
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to the children and families of New Jersey and beyond. While she retired as professor emerita in 2007, Dr. Harris continued to serve as executive director of the Douglass Developmental Disabilities Center and to mentor new professionals for nearly 10 additional years, retiring recently.
References and Reading Ferraioli, S. J., & Harris, S. L. (2011). Treatments to increase social awareness and social skills. In B. Reichow, P. Doering, D. V. Cicchetti, & F. R. Volkmar (Eds.), Evidence-based practices and treatments for children with autism (pp. 171–196). New York: Springer. Gill, M. J., & Harris, S. L. (1991). Hardiness and social support as predictors of psychological discomfort in mothers of children with autism. Journal of Autism and Developmental Disorders, 21, 407–416. Harris, S. L. (1975). Teaching language to nonverbal children-with emphasis on problems of generalization. Psychological Bulletin, 82, 564–580. Harris, S. L. (1983). Families of the developmentally disabled: A guide to behavioral intervention. Elmsford: Pergamon. Harris, S. L. (1984). Intervention planning for the family of the autistic child: A multilevel assessment of the family system. Journal of Marital and Family Therapy, 10, 157–166. Harris, S. L., & Glasberg, B. (2003). Siblings of children with autism (2nd ed.). Bethesda: Woodbine House. Harris, S. L., & Handleman, J. S. (2000). Age and IQ at intake as predictors of placement for young children with autism: A four to six year follow-up. Journal of Autism and Developmental Disorders, 30, 137–142. Harris, S. L., & Weiss, M. J. (2007). Right from the start: Intensive behavioral intervention for young children with autism (2nd ed.). Bethesda: Woodbine House. Harris, S. L., Wolchik, S., & Weitz, S. (1981). The acquisition of language skills by autistic children: Can parents do the job? Journal of Autism and Developmental Disorders, 11, 373–384. Harris, S. L., Handleman, J. S., & Palmer, C. (1985). Parents and grandparents view the autistic child. Journal of Autism and Developmental Disorders, 15, 127–137. Harris, S. L., Handleman, J. S., Kristoff, B., Bass, L., & Gordon, R. (1990). Changes in language development among autistic and peer children in segregated and integrated preschool settings. Journal of Autism and Developmental Disorders, 20, 23–31. Harris, S. L., Handleman, J. S., Gordon, R., Kristoff, B., & Fuentes, F. (1991). Changes in cognitive and language functioning of preschool children with autism. Journal of Autism and Developmental Disorders, 21, 281–290.
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Hate Crimes/Victimization Mark Sherry Department of Sociology and Anthropology, University of Toledo, Toledo, OH, USA
Definition Many people with autism experience abuse, maltreatment, and other forms of victimization including hate crimes. Experiences of victimization can take many forms, including physical, sexual, emotional, medical, and financial abuse. Physical abuse can include hitting, slapping, and pushing; sexual abuse can involve unwanted touching, sexual contact, or rape; emotional abuse can including bullying, threatening, and intimidating a person; medical abuse can involve overmedicating a person or denying them appropriate medications; and financial abuse involves wrongfully using someone else’s finances. These forms of abuse are sometimes called maltreatment, bullying, or neglect. As well as the forms of abuse listed above, neurodiverse people may be subjected to disability hate crimes. Disability hate crimes involve the deliberate targeting of an individual because of his or her disability.
Historical Background Studies of abuse and victimization have been conducted for decades, consistently indicating that there is an unacceptably high rate of victimization of people across the autistic spectrum in terms of abuse, harassment, bullying, and other forms of victimization. However, the actual estimations of the overall rate differ greatly. These different prevalence rates possibly reflect different methodologies: some studies ask caregivers, others ask parents, and others ask neurodiverse people. The size of the sample (i.e., the number of people questioned in the survey) also is a major factor in terms of the generalizability of the study – smaller samples tend to mean that the
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results cannot automatically be assumed to apply to different groups across a broad range of social and cultural contexts. One study of the experiences of 156 children with autism asked caregivers to indicate whether their children with autism had been abused (Mandell et al. 2005). These caregivers reported that 18.5% of children with autism had been physically abused and 16.6% had been sexually abused. Such abuse frequently resulted in selfharming strategies, such as suicide attempts, by the victims of abuse. Other effects included sexually acting out, running away from home, and psychiatric hospitalization. A similar study, which asked parents of people with Asperger’s syndrome whether their child had been abused, reported that 100% of the children had been victimized, on average 1.25 times per week, but for 23% of the children, such victimization occurred on average twice or three times per week (Konstantareas 2005). In the United Kingdom, the National Autistic Society has reported that 56% of people with autistic spectrum disorder have been bullied or harassed as adults, a figure which rises to 70% for those people with autistic spectrum disorder who live alone (Rosenblatt 2008). This organization wrote a response to the UK Government’s “Anti-social behavior: policy and procedure” document which contained one story about a person who was given the pseudonym of Matthew. The document explained that Matthew had experienced verbal abuse, graffiti on his home, and was hospitalized after being physically assaulted within his own home. Matthew’s autistic spectrum disorder made it harder to communicate effectively with police. He lacked support workers or family who could help him in lodging such criminal complaints (The National Autistic Society 2009). This problem raises another issue: the importance of support programs that assist disabled people in accessing the criminal justice system. And there is obviously a need to raise awareness of the problems that such barriers exist in the first place. People with Asperger’s syndrome have been called “perfect targets” for bullying and
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victimization (Heinrichs and Myles 2003). For instance, when students with Asperger’s syndrome are constantly reminded that they do not “fit in” and that their social, academic, or communication skills make them somehow “abnormal,” they become not only socially marked but socially isolated and victimized as well. When a potential target is seen as socially isolated and having little peer support, a bully might assume that there will be little or no consequences for their actions. Additionally, many people with Asperger’s syndrome are unable to predict the social behavior of others, making it even more difficult for them to avoid potential setups for violence, intimidation, or bullying.
Current Knowledge There have been some suggestions that certain behaviors associated with autism and with the processes used to interview victims of crimes reduce the likelihood of sexual abuse against an autistic person being recognized. For instance, one author suggests that “because of the use of lengthy, one-time interviews and the need for sustained reciprocity and verbal exchanges,” autistic people may face extreme barriers in interviews with law enforcement personnel (Edelson 2010). Additionally, the behaviors which an autistic person might demonstrate in response to sexual abuse may be misattributed to autism rather than a sex crime. Nevertheless, psychological evaluations of people across the autistic spectrum who have experienced various types of trauma, abuse, and harassment consistently indicate that they have lower self-esteem and higher rates of depression and anxiety than those who have not been victimized (Brownlie et al. 2007; Mandell et al. 2005; Shtayermman 2007). A number of studies suggest that the vast majority of perpetrators of abuse are male and are known to the victim (National Center for Injury Prevention and Control 1998). Perpetrators of abuse include caregivers, family members, other people with disabilities, health-care providers, and acquaintances. The fact that many
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people with disabilities have a number of caregivers in their lives, whose work often involves rather intimate tasks, may be one of the factors which puts them at increased risk of abuse. Social and personal boundaries are often at risk of being blurred in the provision of personal assistance (Saxon et al. 2001; Saxton et al. 2006). The following cases involving crimes against people with autism gained national attention in the media. They involve two US cases: threats to burn an autistic boy’s home and the deliberate hitting of an autistic boy in a T-ball game. In Seattle in July 2008, Mark Joe Levison was charged with malicious harassment after threatening to burn the home of an autistic child. In July 2008, he is alleged to have told the child’s mother “Keep your fucking son in the house or the backyard like a dog. If you don’t I’ll burn the (child’s bedroom) down” (Jones 2008). The family had only moved into the house 2 weeks earlier and the child’s mother, Acquinnette Engen, said “I was terrified.” According to police, Levison, 48, also said “I pay $1,000 a month rent and shouldn’t have to see that idiot spinning around and staring at my house” (Pulkkinen 2008). Levison was again arrested a few months later for similar behavior. One report indicated that he had repeatedly threatened to burn down the home, had also taken to imitating the autistic child, and had challenged the child’s father to a fight. He pleaded guilty in November 2008 to one count of malicious harassment, a felony, and one count of attempted malicious harassment, a misdemeanor (Sherry 2010). Levison was sentenced to nearly 4 months in jail, as well as a 1-year suspended sentence that could be imposed if he violated the conditions of his release during the next 2 years. Levison was also ordered to undergo substance abuse treatment and anger management classes; he was a self-described “alcoholic” and told the judge “When I’m sober, I would never say words like that” (Seattle Times Staff 2008). The case of Mark Reed Downs, a 27-year-old T-ball coach from Philadelphia, illustrates the ways in which people with autism can be attacked in social situations. (T-ball is a children’s version of baseball). Downs was coaching a children’s
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team in 2005 when he offered one of his players $25 to hit Harry Bowers, Jr., an 11-year-old autistic boy on their own team, in the head with a baseball during warm-ups so that he would be unable to play that day. After speaking to the coach, the player threw a ball which bounced and then hit the disabled child in the groin with a pitch. Bowers ran off crying to his mother. She sent him back out to practice and the other child hit him again, this time in the ear. Bowers was unable to play in the game and was taken to the Uniontown Hospital. Bowers was targeted because his autism meant that he was not a great player, and every player is required to play three innings unless they are injured. The boy who threw the balls, Keith Reese, Jr., was one of the better pitchers in their team and the team was in the play-offs (finals) that day. He testified in court that after he had struck Bowers on the first occasion, “He (Downs) told me to go out there and hit him harder. So I went out there and hit him in the ear.” Reese, Jr., testified that he did not receive the $25 from Downs. Downs was convicted of criminal solicitation to commit simple assault and corruption of minors but was found not guilty of the more serious charge of criminal solicitation to commit aggravated assault, and the jury could not reach agreement on whether his actions constituted reckless endangerment. Downs was sentenced to 1–6 years in prison but was granted bond while appealing to the state Superior Court (Sherry 2010). Awareness of disability hate crimes, a particular form of victimization, is growing internationally (Sherry 1999, 2000a, b, 2002, 2003, 2004, 2010). In these crimes, the perpetrator targets a person because of their disability identity. They are called “hate crimes” or “bias crimes,” depending on the particular local legislation. Perpetrators often receive additional sentencing for the hate crime, as well as the original crime, because hate crimes not only affect the individual but also the whole community to which that person belongs. Hate crimes send a message of intolerance that might mean people in the group consider a particular place unsafe and a “no go zone,” so the perpetrator is punished for that effect of their actions as well.
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Future Directions The future direction for dealing with and preventing abuse, maltreatment, and other forms of victimization involves the development of a multidimensional response. One author recommends six different areas which could be addressed to eliminate bullying of children with Asperger’s syndrome: empowering victims, empowering bystanders, empowering teachers, understanding bullies, empowering parents, and empowering schools (Dublin 2007). Presumably, the responses for adults would be somewhat different, given that they are not in school. Some of the more general responses which have (Sherry 1999, 2000a, b, 2002, 2003, 2004, 2010) been developed to prevent abuse include training programs for both potential victims and caregivers to increase awareness of abuse issues; sex education programs which emphasize choice-making, personal rights, and assertiveness training; and staff screening programs involving improved reference and police checks to weed out convicted sex offenders from caregiving positions (Sobsey 1994). It is essential that child protection workers, law enforcement personnel, and educators (particularly in special education settings) be provided with sufficient training in order to appropriately respond to cases of disability abuse. Unfortunately, many child protection workers lack knowledge about disability issues. This lack of confidence dealing with disability issues has led to the situation where disabled children are overrepresented among victims of abuse but underrepresented among the caseloads of child protection workers (Orelove et al. 2000). As a result, disabled victims of abuse often experience significant difficulty in accessing appropriate services. Many professionals also report a lack of training in dealing with abuse histories of male clients, which may compound these problems (Lab et al. 2000). Another specific challenge for investigators may be the use of assisted communication by some people with autism. Assisted communication may involve the use of symbols, pointing devices, computer aids, a communication partner, word prediction software, and many other
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individualized approaches. These assisted communication tools are necessary because up to 25% of people with autism have communication impairments (Howlin 2006). However, many law enforcement officers and court systems are unfamiliar with these communication techniques, creating an additional barrier for victims who seek redress through the judicial system. Abuse, harassment, vilification, and maltreatment are unfortunately common experiences for people with autism. They can have devastating and lifelong effects on self-esteem and leave a person struggling with depression and anxiety. Awareness of this problem is growing within the community and within the criminal justice system, as more victims come forward and discuss their experiences. However, many cases are not reported, for fear of retaliation or for fear that the victim will not be believed. Also, there are challenges associated with limited awareness about assisted communication devices within the criminal justice system. There is a definite need for more advocacy, support, and awareness of this problem.
See Also ▶ Bullying
References and Reading Brownlie, E. B., Jabbar, A., Beitchman, J., Vida, R., & Atkinson, L. (2007). Language impairment and sexual assault of girls and women: Findings from a community sample. Journal of Abnormal Child Psychology, 35(4), 618–626. Dublin, N. (2007). Asperger syndrome and bullying: Strategies and solutions. London: Jessica Kingsley. Edelson, M. G. (2010). Sexual abuse of children with autism: Factors that increase risk and interfere with recognition of abuse (Electronic Version). Disability Studies Quarterly, 30. Retrieved May 15, 2011, from http://www.dsq-sds.org/article/view/1058/1228 Heinrichs, R., & Myles, B. S. (2003). Perfect targets: Asperger syndrome and bullying – Practical solutions for surviving the social world. Shawnee Mission: Autism Asperger. Howlin, P. (2006). Augmentative and alternative communication systems for children with autism. In
2315 T. Charman & W. Stone (Eds.), Social and communication development in autism spectrum disorders: Early identification, diagnosis, and intervention (pp. 236–266). New York: Guilford Press. Jones, L. A. (2008). South Seattle man accused of harassing autistic child, threatening arson. The Seattle Times. Konstantareas, M. (2005). Anxiety and depression in children and adolescents with Asperger syndrome. In K. Stoddart (Ed.), Children, youth and adults with Asperger syndrome: Integrating multiple perspectives (pp. 47–59). London: Jessica Kingsley. Lab, D. D., Feigenbaum, J. D., & De Silva, P. D. (2000). Mental health professionals’ attitudes and practices towards male childhood sexual abuse. Child Abuse & Neglect, 24(3), 391–409. Mandell, D. S., Walrathm, C. M., Manteuffel, B., Sgro, G., & Pinto-Martin, J. A. (2005). The prevalence and correlates of abuse among children with autism served in comprehensive community-based mental health settings. Child Abuse & Neglect, 29(12), 1359–1372. National Center for Injury Prevention and Control. (1998). Sexual violence against people with disabilities. Atlanta: Centers for Disease Control and Prevention, Division of Violence Prevention. Orelove, F. P., Hollahan, D. J., & Myles, K. T. (2000). Maltreatment of children with disabilities: Training needs for a collaborative response. Child Abuse & Neglect, 24(2), 185–194. Pulkkinen, L. (2008). Neighbor sentenced for directing drunken tirades at autistic boy. Retrieved August 1, (subsequently made a private post), 2009, from http:// www.seattlepi.com/local/395505_levinson10.html Rosenblatt, M. (2008). I Exist: The message from adults with autism in England. Bristol: The National Autistic Society. Saxon, M., Curry, M. A., Powers, L. E., Maley, S., Eckels, K., & Gross, J. (2001). Bring my scooter so I can leave you. Violence Against Women, 7(4), 393–417. Saxton, M., McNeff, E., Powers, L., Limont, M., & Benson, J. (2006). “We’re all little John Waynes”: A study of disabled men’s experiences of abuse by personal assistants. Journal of Rehabilitation, 72(4), 3–13. Seattle Times Staff. (2008, July 24). South Seattle man accused of harassing autistic child, threatening arson. The Seattle Times. Sherry, M. (1999). Hate crimes against people with disabilities. Hate Crime Conference, The University of Sydney. Sherry, M. (2000a). Hate crimes and disabled people. Social Alternatives, 19(4), 23–30. Sherry, M. (2000b, July 1). Talking about hate. Bent: A Journal of Crip Gay Voices Retrieved 5 May, 2011. Sherry, M. (2002). Don’t ask, Tell or respond: Silent acceptance of disability hate crimes. Paper presented at the Ed Roberts Post doctoral fellowship in disability studies at the University of California at Berkeley Public Lecture Series 21 November. Sherry, M. (2003). Exploring disability hate crimes. Paper presented at the 16th Annual Meeting of the Society for Disability Studies.
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2316 Sherry, M. (2004). Exploring disability hate crimes. Review of Disability Studies: An International Journal, 1(1), 51–60. Sherry, M. (2010). Disability hate crimes: Does anyone really hate disabled people? London: Ashgate. Shtayermman, O. (2007). Peer victimization in adolescents and young adults diagnosed with Asperger’s syndrome: A link to depressive symptomatology, anxiety symptomatology and suicidal ideation. Issues in Comprehensive Pediatric Nursing, 30(3), 87–107. Sobsey, D. (1994). Violence and abuse in the lives of people with disabilities: The end of silent acceptance? Baltimore: Paul H Brookes. The National Autistic Society. (2009). A response from The National Autistic Society: NAS comments on the anti-social behaviour bill part 2 (Housing). Retrieved October 25, 2009.
Hatred of Sound ▶ Misophonia
Hay Fever ▶ Allergies
HBSS ▶ Handicaps, Behavior and Skills Schedule (HBSS)
HEA 2008 Paul K. Cavanagh Vocational Independence Program, New York Institute of Technology, Central Islip, NY, USA
Definition The Higher Education Opportunity Act of 2008 is also known as Public Law 110–135. It is the latest reauthorization of the Higher Education Act of
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1965 (Pub. L. No. 89–329), originally signed into law by President Lyndon Johnson on November 8, 1965. The Higher Education Opportunity Act of 2008 is important for individuals with a diagnosis of autism or an autism spectrum disorder because it creates new legislative requirements for access to postsecondary education for individuals with an intellectual disability.
Historical Background The real importance of the passage of the Higher Education Opportunity Act of 2008 (HEOA) for individuals with a diagnosis on the autism spectrum is in its legislative mandates for higher educational opportunities for individuals with an intellectual disability. The original Higher Education Act (HEA) of 1965 does not specifically provide for educational opportunities for individuals with a disability. Nevertheless, the history of the Higher Education Act of 1965 and its subsequent amendments and reauthorizations provides a context for understanding the new legislative provisions in the HEOA of 2008. While the Education Act of 1965 and its subsequent revisions has provided for federal support for many aspects of higher education in the United States, the most extensive impact of the legislation has come from Title IV of the Act which provided for direct federal financial aid to students pursuing higher education as well as federally subsidized loans for these students. The limits on access to this financial aid is central to the new legislative mandates in HEOA of 2008 which support access to higher education for individuals with an intellectual disability. This section will provide a brief historical context to the original passing of the Higher Education Act and examine the major developments in the legislation in its nine subsequent reauthorizations since 1965. In the United States, the establishment of institutes of higher education has always been a private or state responsibility. The United States has never established a national university. The federal role in higher education has been via certain federal laws and regulations and limited, targeted federal aid. The early aspect of direct federal aid to
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higher education came via legislation that mandated that certain lands be set aside for the establishment of institutes of higher education. The first provision for this was as part of the Northwest Territories Act of 1787 and then similar provisions where the central purpose of the Morrill Acts of 1862 and 1890 creating the so-called Land Grant Colleges. Probably, the most significant foray of the federal government into support for higher education was the Servicemen’s Readjustment Act of 1944, more commonly known as the “GI Bill.” The GI Bill provided financial support to honorably discharged veterans for from 1 to 4 years of study in an institute of higher education. Prior to the passage of the Higher Education Act, congress passed the National Defense Education Act in 1958. This Act used a needs-based formula to provide lowinterest loans to college students studying science, math, or foreign languages. Students who became teachers had an option to have their loan forgiven. As part of President Lyndon Johnson’s War on Poverty, he commissioned a taskforce on higher education and the role of the federal government in student aid. The recommendations from this taskforce ultimately become the major provisions of the Higher Education Act (HEA). The HEA was sponsored in congress by Wayne Morse in the senate and by Edith Green in the house, both representing the state of Oregon. The Act was signed into law by President Lyndon Johnson on November 8, 1965, at his alma mater, the Southwest Texas State College (now known as Texas State University). The Act had seven major sections identified as Title I–Title VII. Title I of the Act provided for grants to strengthen community service projects in particular those addressing such problems as poverty, housing, transportation, and youth opportunities. Title II provided grants for the expansion of college library collections as well as for an increase in the education and training of college librarians. Title III provided for aid to developing institutions with its aim primarily to support the growth of traditional African-American colleges. Technical and 2-year colleges could also receive support under Title III. Title IV is probably the best known aspect of the HEA and will be discussed in more detail below.
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Title IV provided for both direct federal financial aid as well as federally insured loans for eligible college students enrolled full-time. Title V established a Teacher Corps for the development of better teacher education. Title V is later moved to a reauthorization of the Elementary and Secondary Education Act. Title VI established grants for the use of technology in the improvement of undergraduate education. Title VII afforded institutes of higher education with funding for new construction. Title VII amended and incorporated the Higher Education Facilities Act of 1963 into the HEA. Title IV has been the most influential aspect of the HEA having substantially increased access to higher education for a large segment of American society. Title IVof the HEA of 1965 had four parts labeled A–D. Part A itself and two main provisions were the first created Educational Opportunity Grants, which are now known as Supplemental Educational Opportunity Grants. The Educational Opportunity Grants where funneled through the states to the institutes of higher education who could then distribute them to students in need based on their own criteria. The second provision in Part A of Title IV incorporated an existing program, Upward Bound, with a new program called Talent Search in order to encourage college attendance among disadvantaged youth. Part B of Title IV established Guaranteed Students Loans, loans that would be funded by private lenders but backed by federal guarantees. There have been numerous changes in this program over the years, but the essential features of the original legislation still exist: loans are made directly to student families from private lenders, but the lenders have the promise of payment from the federal government if the recipient defaulted on the loan. In addition, based on need, the loan recipient may have some of her or his interest subsidized while attending school. A third aspect of Title IV was the incorporation of the Federal Work Study Program into the HEA, which was authorized in Part C of the Title. A Federal Work Study Program had already been started as part of the War on Poverty, but would now be moved to the Department of
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Education from the Office of Economic Opportunity. Federal Work Study is funded by the institute of higher education with matching funds from the federal government. The final provision under Title IV of the HEA was to move the National Defense Student Loan (NDSL) Program from the Office of Economic Opportunity to Department of Education. The NDSL program is now known as the Perkins Student Loan program. It is a direct loan program and is funded by both the federal government and the individual institutions of higher education. It provides low-interest loans to financially needy undergraduate, graduate, and professional students.
Current Knowledge The Higher Education Opportunity Act (HEOA) of 2008 (Public Law 110–135) is the ninth reauthorization of the original 1965 law (Pub. L. No. 89–329). The 2008 law was passed on July 31, 2008, and signed into law on August 14, 2008. The reauthorization was 5 years late and completed after congress passed 14 extensions of the statutory deadline. Originally, the 1998 reauthorization only extended until September 30, 2003. While the reauthorization addresses literally hundreds of different provisions within the law, the essential aspects of the new law that have a new and unique impact for individuals with a diagnosis on the autism spectrum are the provisions related to postsecondary education for individuals with an intellectual disability. Thus, this section will primarily explicate the new rights and mandates concerning postsecondary education for individuals with an intellectual disability. The end of the section will briefly overview the other main provisions of the new law. There are four main features of the HEOA of 2008 that address students with an intellectual disability. The law provides for a competitive federal grant program for institutes of higher education to create model comprehensive postsecondary transition programs for students with an intellectual disability. HEOA also provides authorization for the creation of two centers to support the development of transition programs. One initiative is to create a National Center for
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Information and Technical Support for Postsecondary Students with Disabilities. The second is to create a Coordinating Center for institutes of higher learning offering postsecondary transition programs for individuals with an intellectual disability. And finally, the law opens the eligibility for receiving Pell Grants, Federal Supplemental Education Opportunity Grants, and Federal Work Study for students with an intellectual disability. Importantly, for students enrolled in an approved comprehensive postsecondary transition program, the law modifies the requirements for full-time matriculation in a degree-granting program and for demonstrating satisfactory academic progress. The Higher Education Opportunity Act of 2008 expands access to higher education for students with an intellectual disability. Probably, the most substantial change in the law for students with an intellectual disability is modifications to Title IV of the law addressing student financial aid. Prior to this, it was difficult for a student with an intellectual disability to be eligible for any type of federal financial aid because they often lacked a regular high school diploma (or General Educational Development equivalent). Even if they did meet the diploma requirement, they often failed the Title IV “ability to benefit” test, based on satisfactory academic progress as a full-time student in a degree-granting program. Changes to Title IV give the secretary of education broad authority to waive restrictions on eligibility for federal financial aid for students with an intellectual disability if they meet two general criteria. The student must be enrolled in an approved comprehensive transition program at an institution of higher education, and they must maintain satisfactory progress as determined by the institution. Title VII (Part D, Section 760) of the HEOA defines the criteria for a comprehensive transition and postsecondary program for students with intellectual disabilities as well as defining the term “a student with an intellectual disability.” The law identifies a comprehensive postsecondary transition program as a degree, certificate, or nondegree program offered by an institution of higher education that is designed to support students with an intellectual disability
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who are seeking to continue academic, career and technical, and independent living instruction in order to prepare for gainful employment; the program includes an advising and curriculum structure and requires students to participate on not less than a half-time basis, as determined by the institution and focusing on academic components. The academic components must include regular involvement with nondisabled individuals via one or more of the following: regular enrollment in credit-bearing courses, auditing or participating in credit-bearing courses, enrollment in noncreditbearing or nondegree courses, or participation in internships or work-based training. The change in Title VII defines the meaning of the term a “student with an intellectual disability.” Specifically, the law indicates that such a student is a student with mental retardation or a cognitive impairment, characterized by significant limitation in intellectual and cognitive functioning, and adaptive behavior as expressed in conceptual, social, and practical adaptive skills. In addition, a student with an intellectual disability is a student who is currently or formally eligible for a free appropriate public education (FAPE) under the Individuals with Disabilities Education Act (IDEA). Subpart of 2 of Part D of Title VII of the HEOA creates the authorization of model comprehensive transition and postsecondary programs for students with intellectual disabilities. On a competitive basis, institutes of higher education will be able to apply for federal matching grants to support comprehensive transition programs. The grants, once awarded, will last 5 years. The first set of grants was awarded in October of 2010 to 27 institutes of higher learning. The list of all grantees is available via the National Coordinating Center which is housed at the Institute for Community Inclusion at the University of Massachusetts Boston (http://www.thinkcollege.net/ tpsid-coordinating-center). The main criteria for institutes of higher education receiving funding is as follows: The program must serve students with an intellectual disability; it must provide for the inclusion of students with an intellectual disability with the regular academic and extracurricular activities of the institution; it must use a personcentered planning approach; and it must provide a focus on academic enrichment, socialization,
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independent living skills, and integrated work experiences that lead to gainful employment. In October of 2010, an award was also made to establish a National Coordinating Center for institutions of higher education offering inclusive comprehensive postsecondary transition programs for students with intellectual disabilities. This National Coordinating Center is housed at the Institute for Community Inclusion at the University of Massachusetts Boston (http://www. thinkcollege.net/tpsid-coordinating-center). The HEOA stipulates several key responsibilities for the National Coordinating Center. First, it must provide technical assistance for the development, evaluation, and improvement of grantee transition programs. Secondly, it should assist in developing the standards and measurable competencies for the awarding of a meaningful credential by the comprehensive postsecondary transition programs. In establishing a meaningful credential, the Coordinating Center needs to work with the transition programs in identifying the necessary programmatic components for these programs, how student progress should be evaluated, and what should determine student eligibility. The law also calls for the creation of a National Center for Information and Technical Support for Postsecondary Students with Disabilities (National Center). As of 2011, no award for the creation of such a center had been made. The law envisions the National Center as providing technical assistance to two primary constituencies: students and their families and institutions of higher education. The law indicates that the National Center would provide information and technical assistance to individuals with a disability and their families both while they are still receiving secondary education and while they are participating in some type of postsecondary education or transition program. Included in this is a mandate for support to Individualized Education Program Teams, who are mandated to provide transition planning for students in secondary education by the Individuals with Disabilities Education Act. The law calls for the National Center to provide assistance to administrators, staff, and faculty of institutes of higher education to better recruit and retain students with a disability. Specifically, the law encourages the center to collect and disseminate proven
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best-practices models for transitioning and supporting students with a disability in postsecondary education. It also calls on the National Center to provide training modules and tutorials for administrators, staff, and faculty on the best practices of supporting and retaining students with disabilities in postsecondary education. The reauthorization of the Higher Education and Opportunities Act of 2008 does provide numerous other changes in the law, in addition to those that directly address students with an intellectual disability. The variety of changes are too numerous to elaborate on all of them here. The most important changes are summarized briefly below. One major area of changes in the HEOA of 2008 revolves around greater transparency and reporting requirements on college costs. There are several components of the law that create new requirements for institutes of higher learning to calculate, publish, and report to the department of education accurate estimates of the net cost of attending school at their institution. In addition to the financial aid changes specific to individuals with an intellectual disability described above, the law makes several other important changes to federal financial aid law. Concerning the Pell Grant, the maximum award is increased and it is made available to students year-round (in the past, it was not available for summer sessions) in order to encourage a shorter time to degree in more schools. A major change in student eligibility for financial aid is a provision that permits institutes of higher education to register students who are also dually (or concurrently) enrolled in secondary education. The provision may also provide significant new opportunities for students with an intellectual disability. In addition, the law calls for simplifying and streamlining the federal financial aid process, including fewer a more understandable questions on the Free Application for Federal Student Aid (FAFSA).
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dramatically new opportunities for individuals with an intellectual disability to participate in higher education. As of October of 2008, the U.S. Department of Education had funded 27 model “comprehensive transition and postsecondary programs for individuals with an intellectual disability” at both 2- and 4-year institutes of higher education throughout the United States. In addition, the Institute for Community Inclusion at the University of Massachusetts Boston received funding to establish the National Coordinating Center to support the 27 initial model programs and any other programs that might develop. The mandate for the National Coordinating Center involves the development of national standards for the provisions of education via the comprehensive transition programs, qualitative and quantitative measures for student outcomes on program strengths, and the criteria necessary for establishing a meaningful credential. It can be expected that as these requirements are defined and solidified, more institutions of higher education will become involved in the provision of educational services to individuals with an intellectual disability. The law calls for the creation of a National Center for Information and Technical Support for Postsecondary Students with Disabilities. As of 2011, no grant had been awarded to create such a center. It is expected that the Department of Education will create such a center and that this center will become an important advocate to individuals with an intellectual disability, and their families, seeking to continue their education beyond secondary school. Then mandate for the National Center also calls for it to be a resource for Independent Education Program Teams doing IDEA-mandated transition planning for students in secondary schools. Such a national resource of information and technical assistance should greatly increase the incidence of postsecondary education being part of the formal IEP transition plans for students with an intellectual disability.
Future Directions The Higher Education Opportunity Act (HEOA) of 2008 (PL 110–135) was signed into law by President George W. Bush on August 14, 2008. This reauthorization of the original 1965 law opened up
See Also ▶ Comprehensive Transition Program ▶ Individual Education Plan
Healthcare Transition for Individuals with Autism
▶ Individualized Transition Plan (ITP) ▶ Individuals with Disabilities Education Act (IDEA) ▶ Transition Planning ▶ Transitional Living
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distribution of health may be considered unnecessary, avoidable, unjust, and unfair. Outside of the USA, such disparities are typically referred to as health inequalities or inequities.
References and Reading References and Reading Grigal, M., & Hart, D. (2010). Think college: Postsecondary education options for students with intellectual disabilities. Baltimore: Paul H. Brookes. Higher Education Opportunity Act of 2008, Pub. L. No, 110–31, USC. Higher Education Resource Hub. (n.d.). Higher Education Act of November 8, 1965; Retrieved May 29, 2011, from http://www.highered.org/resources/HEA.htm TG Research and Analytical Services. (2005). Higher Education Act: Forty years of opportunity. Retrieved May 29, 2011, from http://www.tgslc.org/: http://www.tgslc. org/pdf/HEA_History.pdf U.S. Department of Education Office of Civil Rights. (n.d.). Students with Disabilities Preparing for Postsecondary Education: Know Your Rights and Responsibilities. Retrieved July 23, 2010, from ED.Gov: http://www2.ed.gov/about/offices/list/ocr/transition. html
Health Disparities Eric Emerson Centre for Disability Research, Lancaster University, Lancaster, LA, UK Centre for Disability Research and Policy, University of Sydney, Lidcombe, NSW, Australia
Synonyms Health inequalities; Health inequity
Definition Differences in health status between different population groups. Some health disparities may be attributable to biological variations or free choice. Others may be attributable to environmental conditions beyond the control of individuals concerned. In these instances, the uneven
Graham, H. (2007). Unequal lives: Health and socioeconomic inequalities. Maidenhead: McGraw Hill. Healthy People 2020. Retrieved from http://www. healthypeople.gov/2020/default.aspx World Health Organization (2008). Closing the gap in a generation: Health equity through action on the social determinants of health. Final report of the Commission on the Social Determinants of Health. Geneva: Author. World Health Organization and the World Bank. (2011). World report on disability. Geneva: World Health Organization.
Health Inequalities ▶ Health Disparities
Health Inequity ▶ Health Disparities
Healthcare Transition for Individuals with Autism Dorothea A. Iannuzzi Division of Academic Pediatrics, Autism Intervention Research Network on Physical Health (AIR-P), Autism Treatment Network (ATN), Mass General Hospital for Children, Boston, MA, USA
Definition Healthcare transition for individuals with ASD is defined as the transition from pediatric to adult healthcare systems.
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Historical Background The access to and provision of quality healthcare for individuals with disabilities including autism spectrum disorder (ASD) has been identified as a significant public health issue (Bruder et al. 2012). During the period of transition from pediatric care to adult healthcare for young adults with ASD, the gap in access to high-quality patient-centered care widens further (Cooley 2013; Lotstein et al. 2008). Transition age is generally defined as occurring between the ages of 14 and 17 years, up to the age of 22 years (AAP et al. 2011). In addition, compared to youth with other disabilities, youth and young adults with ASD are more likely to require specialty care for common comorbidities such as epilepsy, gastrointestinal problems including inflammatory bowel disease (IBD), and anxiety and depression, as well as respiratory, food, and skin allergies (AAP et al. 2011). Many obstacles can interfere with a smooth healthcare transition for TAY with ASD. The possible roadblocks include the attitudes of stakeholders; age limits in pediatric practices; complexity of health conditions; lack of insurance reimbursement for management of medical complexity often associated with transition age youth (TAY) with ASD; and, most importantly, the scarcity of competent educated healthcare providers prepared to meet the needs of this ever-growing patient demographic (Oswald et al. 2013). Individuals with ASD have higher rates of healthcare utilization (Croen 2006; Kogan et al. 2008; Warfield and Gulley 2006; Liptak 2006), an increased burden of unmet healthcare needs (Newacheck and Kim 2005), and decreased satisfaction with the medical care received (Leslie and Martin 2007; Souders 2002). These data further support the imperative for improvement in access to quality healthcare services for individuals with ASD across the life span and especially during the period of transition from pediatric to adult healthcare (Iannuzzi et al. 2018). Many medical comorbidities can first present during the adolescent years, adding to the challenges of meeting the medical needs of this patient population (Bauman 2010). In addition, access to developmentally appropriate quality healthcare for TAY with ASD is
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extremely limited due to a critical shortage of primary care providers, including physicians and advanced practice nurses, willing to accept TAY with ASD into their adult primary care practices (Warfield et al. 2015). Studies addressing the scarcity of primary care providers have suggested that the lack of providers for this population may be a result of the lack of content, including an experiential component, focused on the healthcare needs of individuals with ASD and other developmental disabilities within health professional education programs (Bruder et al. 2012; Patel and O’Hare 2010).
Current Knowledge Many TAY with ASD present with complex medical, behavioral, and sensory processing challenges. For example, some studies have described a prevalence of up to 80% for sleep disorders in individuals with ASD, as compared to the rate of 30% for individuals without ASD (Bauman 2010; Malow et al. 2012). Seizure disorders can occur in up to 35% of individuals with ASD, and about 60% of patients with ASD have abnormal electroencephalograms (EEGs) (Bauman 2010). The true prevalence of gastrointestinal disorders (GI) in individuals with ASD is not known; however, current estimates range anywhere from 9% to 70% (Buie et al. 2010). Individuals with ASD can also have a hypersensitivity to auditory, visual, and tactile stimuli. These sensory challenges can be especially difficult to manage in a medical setting, where an individual may be exposed to novel smells, sounds, lighting, and medical staff that are unfamiliar (Scarpinato et al. 2010). Many individuals with ASD, including TAY, can have difficulties in overly stimulating environments like the environments found in most healthcare settings (Aylott 2010). These stimuli may result in hyper-arousal and maladaptive behavior, which can often be minimized by accommodating an individual’s sensory profile and any specific sensory sensitivity they may have. Having some understanding and appreciation of the significance of these sensory processing challenges before a medical procedure or physical exam can greatly reduce the
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level of distress for the patient, caregiver, and providers. A healthcare professional may misinterpret maladaptive behaviors and not understand that these maladaptive behaviors can be a manifestation of pain. This misunderstanding can lead to serious errors in assessment and treatment (Iannuzzi et al. 2014). Assessment of discomfort can be particularly difficult in nonverbal patients with autism; pain is often communicated through maladaptive behavior (Kopecky et al. 2013). It is critical for clinicians to specifically ask the individual with ASD or their parent, guardian, or caretaker about sensory sensitivities and triggers, preferred communication methods, and, most importantly, how the individual communicates that they are in distress or pain. These are questions that most clinicians are not trained to ask and can be crucial in assessment and the development of a treatment plan that is individualized to the unique comprehensive needs of an individual with ASD presenting for care. A clinician in the position of providing healthcare in either an emergent or primary care setting needs to understand that, at times, maladaptive behavior can also be a red flag, signaling the presence of an underlying medical issue. Lack of knowledge about the connection between aberrant behavior and pain is an issue that can lead to misdiagnosis and suboptimal medical care (Buie et al. 2010). Likewise, self-injurious or aggressive behavior exhibited by an individual with autism presenting for emergent care may represent an underlying medical issue. Thus, it is incumbent upon primary care and emergency clinicians to first investigate the possibility of an underlying medical condition that may explain aberrant behaviors (Iannuzzi et al. 2014). This is especially true for nonverbal individuals whose only means of communicating physical pain or distress is to exhibit maladaptive behaviors.
Future Directions The current healthcare workforce, including physicians and advanced practice nurses, is simply not equipped or prepared to meet the medical
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needs of adults with ASD (McDougle 2013). The need for a clinically competent healthcare workforce that is able to meet the healthcare needs of TAY with ASD will continue to increase as more young adults with ASD age out of pediatric healthcare practices. In order for healthcare professionals to develop competencies, a curriculum that is focused on the unique care needs of this patient population must be embedded into existing healthcare professional educational programs. Since individuals with ASD are growing older, are living longer, and develop many of the health issues and concerns of the typical aging population, their medical needs will increase in scope and severity (Drum et al. 2009; Henry et al. 2011; Smith et al. 2010). Because individuals with ASD across the life span can present in any healthcare setting, all healthcare providers, need to be prepared to meet the healthcare needs of this growing patient population (Smeltzer et al. 2014).
See Also ▶ Autism Spectrum Disorder (ASD) ▶ Healthcare Transition for Individuals with Autism ▶ Quality of Life for Transition-Age Youth with ASD
References and Reading American Academy of Pediatrics, American Academy of Family Physicians, American College of Physicians, & Transitions Clinical Report Authoring Group. (2011). Clinical report: Supporting the healthcare transition from adolescence to adulthood in the medical home. Pediatrics, 128(1), 182–200. Aylott, J. (2010). Improving access to health and social care for people with autism. Nursing Standard, 24(27), 47–56. Bauman, M. L. (2010). Medical comorbidities in autism: Challenges to diagnosis and treatment. Neurotherapeutics, 7(3), 320–327. Bruder, M. B., Kerins, G., Mazzarella, C., Sims, J., & Stein, N. (2012). Brief report: The medical care of adults with autism spectrum disorders: Identifying the needs. Journal of Autism and Developmental Disorders, 42(11), 2498–2504. Buie, T., Campbell, D., Fuchs, G., Furuta, G., Levy, J., Van de Water, J., . . . Winter, H. (2010). Evaluation,
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2324 diagnosis and treatment of gastrointestinal disorders in individuals with ASDs: A consensus report. Pediatrics, 125(Suppl 1), S1–S18. Cooley, W. C. (2013). Adolescent health care transition in transition. Journal of American Medical Association (JAMA) Pediatrics, 167(10), 897–899. Croen, L. A. (2006). A comparison of healthcare utilization costs of children with and without autism spectrum disorders in a large group model health plan. Pediatrics, 118(4), 1203–1211. Drum, C. E., Krahn, G. L., Peterson, J. J., HornerJohnson, W., & Newton, K. (2009). Health of people with disabilities: Determinants and disparities. In C. E. Drum, G. L. Krahn, & H. Bersani (Eds.), Disability and public health (pp. 125–144). Washington, DC: American Association on Intellectual and Developmental Disabilities. Henry, A. D., Long-Bellil, L., Zhang, J., & Himmelstein, J. (2011). Unmet need for disability-related health care services and employment status among adults with disabilities in the Massachusetts Medicaid program. Disability and Health Journal, 4(4), 209–218. https:// doi.org/10.1016/j.dhjo.2011.05.003. Iannuzzi, D. A., Cheng, E. R., Broder-Fingert, S., & Bauman, M. L. (2014). Brief report: Emergency department utilization by individuals with autism. Journal of Autism and Developmental Disorders, 45(4), 1096–1102. https://doi.org/10.1007/s10803-014-2251-2. Iannuzzi, D., Rismiller, P., Duty, S. M., Feency, S., Sullivan, M., Curtin, C. (2018). Journal of Autism and Developmental Disorders. https://doi.org/10.1007/ s10803-018-3846-9. Kogan, M. D., Strickland, B. B., Blumberg, S. J., Singh, G. K., Perrin, J. M., & van Dyck, P. C. (2008). A national profile of the health care experiences and family impact of autism spectrum disorder among children in the United States, 2005–2006. Pediatrics, 122(6), 1149–1158. Kopecky, K., Broder-Fingert, S., Iannuzzi, D., & Connors, S. (2013). The needs of hospitalized patients with autism spectrum disorders: A parent survey. Clinical Pediatrics, 52(7), 652–660. https://doi.org/ 10.1177/0009922813485974. Leslie, D. L., & Martin, A. (2007). Health care expenditures associated with autism spectrum disorders. Archives of Pediatrics and Adolescent Medicine, 161(4), 350–355. Liptak, G. S. (2006). Satisfaction with primary health care received by families of children with developmental disabilities. Journal of Pediatric Health Care, 20(4), 245–252. Lotstein, D. S., Inkelas, M., Hays, R. D., Halfron, N., & Brook, R. (2008). Access to care for youth with special health care needs in the transition to adulthood. Journal of Adolescent Health, 43(1), 23–29. Malow, B., Adkins, K. W., McGrew, S. G., Wang, L., Goldman, S. E., Fawkes, D., & Burnette, C. (2012). Melatonin for sleep in children with autism: A controlled clinical trial examining dose, tolerability and
Healthcare Transition for Individuals with Autism outcomes. Journal of Autism and Developmental Disorders, 42(8), 1729–1737. https://doi.org/10.1007/ s00431-011-1669-1. McDougle, C. J. (2013). Sounding a wake-up call: Improving the lives of adults with autism. Journal of the American Academy of Child and Adolescent Psychiatry, 52(6), 566–568. Montes, G., Halterman, J. S., & Magyar, C. I. (2009). Access to and satisfaction with school and community health services for US children with ASD. Pediatrics, 124(Suppl 4), S407–S413. https://doi.org/10.1542/ peds2009-1255L. Newacheck, P. W., & Kim, S. E. (2005). A national profile of health care utilization and expenditures for children with special health care needs. Archives of Pediatrics and Adolescent Medicine, 159(1), 10–17. Newschaffer, C. J., Croen, L. A., Daniels, J., et al. (2007). The epidemiology of autism spectrum disorders. Annual Review of Public Health, 28(1), 235–258. Oswald, D. P., Gilles, D. L., Cannady, M. S., Wenzel, D. B., Willis, J. H., & Bordurtha, J. N. (2013). Youth with special healthcare needs: Transition to adult health care services. Maternal and Child Health Journal, 17(10), 1744–1752. Patel, M. S., & O’Hare, K. (2010). Residency training in transition of youth with childhood-onset chronic disease. Pediatrics, 126(Suppl 3), S190–S193. Scarpinato, N., Bradley, J., Kurbjun, K., Bateman, X., Holtzer, B., & Ely, B. (2010). Caring for the child with an ASD in the acute care setting. Journal for Specialists in Pediatric Nursing, 15(3), 244–254. Smeltzer, S. C., Blunt, E., Marozsan, H., & WetzelEffinger, L. (2014). Inclusion of disability-related content in nurse practitioner curricula. Journal of the American Association of Nurse Practitioners, 27(4), 213–221. Smith, L. E., Hong, J., Seltzer, M. M., Greenberg, J. A., Almeida, D., & Bishop, S. L. (2010). Daily experiences among mothers of adolescents and adults with autism spectrum disorders. Journal of Autism and Developmental Disorders, 40(2), 167–178. https://doi. org/10.1007/s10803-009-0844-y. Souders, M. C. (2002). Caring for children and adolescents with autism who require challenging procedures. Pediatric Nursing, 28(6), 555–562. Souders, H. T. (2016). Preparing tomorrow’s doctors to care for patients with autism spectrum disorder. Intellectual and Developmental Disabilities, 54(3), 202–216. https:// doi.org/10.1352/1934-9556-54.3.202. Warfield, M. E., & Gulley, S. (2006). Unmet need and problems accessing specialty medical and related services among children with special health care needs. Maternal and Child Health Journal, 10(2), 201–216. Warfield, M. E., Crossman, M. K., Delahaye, J., Der Weerd, E., & Kuhlthau, K. A. (2015). Physician perspectives on providing primary medical care to adults with autism spectrum disorders (ASD). Journal of Autism and Developmental Disorders, 45(7), 2209–2217. https:// doi.org/10.1007/s1083-015-2386-9.
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Hearing
Hearing Sensitivity
Jennifer McCullagh Department of Communication Disorders, Southern Connecticut State University, New Haven, CT, USA
▶ Auditory Acuity
Hearing System Synonyms
▶ Auditory System
Audition
Hearing Threshold Definition Hearing is one of our five senses. The ability to hear is contingent upon the ability to perceive vibrations in air molecules that enter the auditory system through the outer ear. This energy is transmitted through the middle ear and inner ear, ending in the temporal lobe of the brain where it is ultimately perceived. The ability to hear is a critical component to the reception and comprehension of speech. Individuals with autism spectrum disorders can have impairment in their ability to hear, but more systematic research needs to be completed regarding hearing sensitivity in this population.
▶ Auditory Acuity
Heart Rate Variability (HRV) ▶ Physiological Measures of Parental Stress
Heart-Hand Syndrome ▶ Timothy Syndrome
Heller, Theodore See Also ▶ Auditory Acuity ▶ Auditory System
References and Reading Justice, L. (2006). Communication sciences and disorders: An introduction. Columbus: Pearson. Rosenhall, U., Nordin, V., Sandstrom, M., Ahlsen, G., & Gillberg, C. (1999). Autism and hearing loss. Journal of Autism and Developmental Disorders, 29(5), 349–357. Smith, D. E. P., Miller, S. D., Stewart, M., Walter, T. L., & McConnell, J. V. (1988). Conductive hearing loss in autistic, learning-disabled, and normal children. Journal of Autism and Developmental Disorders, 18, 53–65.
Alexander Westphal Division of Law and Psychiatry, Yale Child Study Center, Yale School of Medicine, New Haven, CT, USA
Name and Degrees Theodor Heller, Ph.D.
Landmark Clinical, Scientific, and Professional Contributions Heller was a founding figure in special education. He also first described a disorder he termed
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dementia infantilis, currently called childhood disintegrative disorder. In addition, he formed an institute for the treatment and education of children with special needs with international draw.
Short Biography Theodor Heller was born June 9, 1869, in Vienna, Austria. His father, Simon Heller, was the director of an institute for the blind, and Heller spent much of his childhood at the institute. During school, he worked as a reader for blind people, including the philosopher Hitschmann, and through this became interested in philosophy. He went to University of Leipzig to study under Wilhelm Wundt, an important figure in modern psychology. Heller’s area of concentration was the psychology of blindness, and his thesis, Studien zur BlindenPsychologie, was submitted in 1894. He also explored a number of other topics including the psychology of acoustic and haptic perception. During this time, he attended lectures on medicine and the brain also and became very interested in the education of children with intellectual and developmental disabilities (special education). Shortly afterward, he started an institute for handicapped children together with his father and Richard Freiherr von Krafft-Ebing (a psychiatrist and author of Psychopathia Sexualis) at a property he had inherited at Grinzing in the Döbling district of Vienna. The institute started with two children as patients, but would ultimately serve 40 children at a time, many from outside of Austria. Many of the children came from wealthy families, but Heller used the proceeds to fund places for children from less privileged backgrounds. The institute included individualized treatment as well as social and academic education. Heller was a pioneer of special education. He published works on the topic, including Grundriß der Heilpädagogik (Groundwork for Special Education) in 1904, Pädagogische Therapie (Educational Therapy) in 1914, and Die Heilpädagogik in der Gegenwart und Zukunft (Special Education in the Present and Future) in 1922. Heller also wrote about children who
Heller, Theodore
underwent a sudden and catastrophic regression of their adaptive function, including sociability and language, coupled with the onset of stereotyped behaviors (all defining characteristics of autism) after a period of normal development. He termed this dementia infantilis, and its essential features are captured by the current diagnostic concept childhood disintegrative disorder, despite radical shifts in diagnostic frameworks and treatment approaches, marking the clarity of Heller’s observation. In his first publication on the topic, Über Dementia Infantilis: Verblödungsprozess im Kindesalter (On Dementia Infantilis: Mental Regression in Childhood) in 1908, Heller described six cases he had seen in Vienna, referred from as far away as Turkey (Heller 1908). The children had all been developing normally until their third or fourth year of life, and then had dramatic losses, ultimately ending up with little or no language, and strange stereotypies of behavior. One of Heller’s case descriptions is excerpted below to illustrate the period of regression: At the end of his third year of life he developed states of anxiety. He didn’t want to stay in a dark room. He was afraid when he was left alone. He often cried without cause. Later his intelligence decreased rapidly. He wasn’t interested in games he had previously enjoyed and no longer understood them. He made strange movements and held strange postures. He played with his saliva. His speech became worse and worse. Although the term autism was used to describe the inward turning aspect of schizophrenia by Bleuler, a contemporary of Heller’s, around the same time that Heller was writing about these cases, it was not until many years later that Kanner and Asperger would use the term to describe the combination of social disabilities and repetitive behaviors. However, Heller’s vivid descriptions of dementia infantilis are without question also descriptions of autism. Heller led an extremely productive life. He founded organizations (Society for Special Education, Society for Pediatric Research, Austrian Society for Special Education), contributed significantly to other organizations (International Society for Special Education), and served as an editor (Journal of
Hendrick Hudson Central School District v. Rowley
Child Research). Heller gave his last public speech during March of 1938. Later that month, as a Jew, he was removed from his position as head of the institute by the Nazis. His temporary replacement was a butcher. In May, Heller attempted suicide. He died on December 12, 1938, as a result. The following year, his wife and daughter were taken to Riga and killed by the Nazis. His institute was disbanded in 1941.
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Hemispheric Lateralization ▶ Dominance, Cerebral
Hemispheric Specialization ▶ Dominance, Cerebral
See Also ▶ Childhood Disintegrative Disorder
References and Reading Heller, T. (1908). Über dementia infantilis: Verblödungsprozeß im kindesalter. Zeitschrift für die Erforschung und Behandlung des Jugendlichen Schwachsinns, 2, 17–28. http://de.wikipedia.org/wiki/Theodor_Heller Lotz, D. (2000). Theodor Heller (1869–1938). In M. Buchka, R. Grimm, & F. Klein (Eds.), Lebensbilder bedeutender Heilpädagoginnen und Heilpädagogen im 20. Jahrhundert (pp. 111–128). München/Basel: E. Reinhardt. Westphal, A., Schelinski, S., Volkmar, F., & Pelphrey, K. (2013). Revisiting regression in autism: heller’s dementia infantilis. Journal of Autism and Developmental Disorders, 43, 265–271.
HELSNF1 ▶ CHD8
Hemifacial Microsomia ▶ Goldenhar Syndrome
Hemispheric Dominance ▶ Dominance, Cerebral
Hendrick Hudson Central School District v. Rowley (Provision of What Is an Appropriate Program) Heather McKay Quinnipiac University School of Law, Hamden, CT, USA
Definition Bd. of Educ. of Hendrick Hudson Cent. Sch. Dist. v. Rowley In General In this case, the United States Supreme Court declared that the “free appropriate public education” (FAPE) requirement in the Education for All Handicapped Children Act (EAHCA) – the Individuals with Disabilities Education Act’s (IDEA) predecessor statute – required school districts merely to provide all disabled students with educational benefits rather than to maximize each disabled student’s potential. The plaintiffs, parents of a hearing-impaired student, brought suit against the defendant school district alleging that its refusal to provide their daughter with a sign language interpreter constituted a denial of FAPE. Noting the student’s successful completion of her grade level, the Court sided with the school district by finding the FAPE requirement satisfied regardless of whether she may have performed better with access to a sign language interpreter.
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In reaching this conclusion, the Court found that the Act neither expressly defined FAPE, nor imposed upon the states any substantive standard prescribing the level of education to be accorded to disabled children. The Court thus prescribed a flexible two-part inquiry for examining educational programs, whereby courts should ask: (1) whether the state complied with the procedures set forth in the IDEA and (2) whether the student’s individualized educational program (IEP) was reasonably calculated to enable the child to receive educational benefits. Pursuant to this decision, a disabled student’s education must merely provide the student with some meaningful benefit rather than providing the best education possible. Thus, school districts must provide only a “basic floor of opportunity” to children with disabilities, even when it is arguable that students could perform better if granted additional services. Implications for ASD Students Autism spectrum disorders were added to the IDEA’s list of disabilities in 1991. Since then, however, much debate surrounds the issue of defining FAPE for ASD students. The low threshold announced in Rowley made it difficult for parents to prove that their ASD student’s IEP is inadequate. The preferred early intervention programs for ASD students, such as applied behavioral analysis (ABA), are very costly and require trained personnel. District programs are oftentimes school-based and do not provide the student with the one-on-one attention that some experts believe is necessary for ASD treatment. Some critics argue that the “equal floor of opportunity” requirement is too vague, particularly in light of the broad spectrum of autism disorders and the wide variety of treatment options for ASD. Litigation Strategies It is important to note that there is much room for disagreement with respect to defining appropriate educational programs for ASD students. These disagreements pose problems for parents and professionals attempting to prove that an ASD student’s IEP is inadequate under the IDEA. In addition, parents must be aware before challenging their child’s IEP that IDEA litigation is expensive. Although victorious parents may sometimes
Heritage Language Use for Intervention in Autism
collect attorneys’ fees, they are unlikely to recover such fees if they reach a settlement agreement before trial. Since 2007, though, parents have a recognized right to bring suit under the IDEA directly, without legal representation, which helps reduce the costs associated with such litigation.
See Also ▶ Dispute Resolution Procedures ▶ Individuals with Disabilities Education Act (IDEA)
References and Reading Bates, M. W. (1994). Free appropriate public education under the individuals with disabilities education act: Requirements, issues, and suggestions. Brigham Young University Education and Law Journal, 1, 215–222. Bd. of Ed. of Hendrick Hudson Cent. Sch. Dist. v. Rowley, 458 U.S. 176 (1982). Breslin, M. A. (2009). No Child left behind and the inherent conflict with the individuals with disabilities education act: Leaving special education students further behind. Albany Government Law Review, 2, 653–676. Caruso, D. (2010). Autism in the U.S.: Social movement and legal change. American Journal of Law & Medicine, 36, 483–539. Hinson, C. (2009). A supreme paradox: Autism spectrum disorder and Rowley misapplication of judicial relic to an unprecedented social epidemic. Florida A & M University Law Review, 5, 87–105. Holland, C. D. (2010). Autism, insurance, and the IDEA: Providing a comprehensive legal framework. Cornell Law Review, 95, 1253–1282. Individuals with Disabilities Education Act (IDEA). 20 U.S.C. §§ 1400, et seq. (2012). Kemper, K. A. (2006). Construction and application of individuals with disabilities act. American Law Reports Federal 2d, 13, 321.
Heritage Language Use for Intervention in Autism Nataly Lim University of Texas at Austin, Austin, TX, USA
Definition Heritage languages are those languages that are specific to an ethnic or racial group. For instance,
Heritage Language Use for Intervention in Autism
the heritage language of a Chinese person might be Mandarin. An individual’s heritage language may be different from the majority language (i.e., the language that is spoken by most people in a region), like in the case of a Chinese person living in the United States. For individuals with autism spectrum disorder (ASD) whose families might speak a heritage language that is different from the majority language, heritage language usage during intervention might look like (a) delivering the entire intervention in the heritage language, (b) providing verbalizations in the heritage language, or (c) teaching using heritage and majority languages.
Historical Background Because a core characteristic of ASD is the impairment of language abilities, some parents and practitioners believed that exposure to more than one language could exacerbate the already impaired language development of children with ASD (Hampton et al. 2017; Kay-Raining Bird et al. 2012; Kremer-Sadlik 2005; Ohashi et al. 2012). As Lim et al. (2018) pointed out, the arguments against dual-language exposure mirror those that surrounded the use of augmentative and alternative communication (AAC), which is now widely used to help children with ASD communicate. Studies that have compared the language abilities of bilingual and monolingual children with ASD have found no detrimental effects of bilingual exposure (Dai et al. 2018; Gonzalez-Barrero and Nadig 2017; Lund et al. 2017; Zhou et al. 2017). In particular, Dai et al. (2018) compared the expressive and receptive language abilities of bilingually exposed to monolingually exposed children with ASD before they received any intervention. Results demonstrated that there were no detrimental effects of bilingual exposure, even when cognitive functioning and socioeconomic status were controlled for. Additionally, some studies even suggest that bilingual children with ASD might have an advantage over monolingual children with ASD in terms of vocabulary size (Gonzalez-Barrero and Nadig 2017) and early social communication skills like gesturing (Valicenti-McDermott et al. 2013; Zhou
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et al. 2017). Overall, it would seem that learning more than one language does not negatively impact the language development of children with ASD.
Current Knowledge Because research regarding the effects of duallanguage exposure in children with ASD is relatively new, few studies have investigated the effects heritage language use in interventions for individuals with ASD. Seung et al. (2006) reported on a case study whereby a KoreanAmerican preschooler with ASD received speech-language intervention entirely in Korean, then bilingually in Korean and English, and finally entirely in English. Results indicated that conducting speech-language intervention in Korean, the participant’s heritage language, helped to facilitate the participant’s acquisition of English, the majority language. Specifically, authors noted that the participant acquired new English words as his Korean vocabulary increased. In terms of providing verbalizations in heritage languages, preference assessments suggest that children with ASD may prefer listening to verbalizations such as praise in their primary home language, which may be the heritage or majority language depending on the household (Aguilar et al. 2016, 2017). Bilingually exposed children with ASD’s language preferences may also depend on task difficulty levels such that they prefer their primary home language as tasks become more challenging (Aguilar et al. 2016). Additionally, the language that verbalizations are delivered in may also affect children with ASD’s engagement in play behaviors (Lim and Charlop 2018). Specifically, children with ASD may demonstrate more play behaviors when play partners deliver verbalizations like instructions, comments, and praise in their heritage languages. A recent meta-analysis of studies comparing the effects of teaching children with neurodevelopmental disorders in the heritage language versus the majority language identified five studies that included participants with ASD (Lim et al. 2019). One study did not disaggregate the data of
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participants with ASD from those with intellectual disability (Durán and Heiry 1986). Thus, the specific effects of providing verbal prompts in English versus Spanish on the vocational skills of adults with ASD are unclear. Of the remaining four studies, one study found that conducting discrete trial training in the participant’s heritage language, Spanish, led to increased accuracy and decreased instances of challenging behaviors as compared to the majority language (Lang et al. 2011). The other three studies found no effects of language of instruction in the context of functional communication training (Dalmau et al. 2011), tact training (León 2016), and pivotal response training (Vaughn 2014). Functional communication training in Spanish and English were equally effective at decreasing challenging behaviors and increasing task completion and requests for reinforcement (Dalmau et al. 2011). Conducting tact training in English and bilingually (i.e., English and Spanish) had similar effects on correct responding (León 2016). Pivotal response training in Spanish and English had comparable effects on participants’ conversation skills (Vaughn 2014). In sum, the current literature indicates that (a) delivering speech-language intervention in the heritage language of a child with ASD might facilitate the acquisition of the majority language, (b) children with ASD from bilingual households might demonstrate preferences for their primary home language and engage in more play behaviors when verbalizations are provided in their heritage languages, and (c) heritage and majority language-based treatments may be equally effective.
Future Directions In general, more studies investigating the effects of using heritage languages during interventions for children with ASD are needed. With preference assessments indicating that bilingual children with ASD might prefer their primary home languages, researchers may want to pay special attention to reporting the amount of exposure that participants have with heritage and majority languages within their homes. A challenge for
Heritage Language Use for Intervention in Autism
those conducting research in this area may be finding interventionists who are fluent in participants’ heritage languages. Because heritage languages are most often spoken within the context of family, researchers may want to look at training parents or siblings of children with ASD to deliver interventions in their heritage languages. Nonetheless, if the family member is not fluent in the majority language, then the same challenge of finding a trainer who speaks the heritage language will be present. Thus, another area for future research may be investigating how training can be conducted in the presence of language barriers.
References and Reading Aguilar, J. M., White, P. J., Fragale, C., & Chan, J. M. (2016). Preference for language of instruction of an English language learner with autism. Developmental Neurorehabilitation, 19, 207–210. https://doi.org/10. 3109/17518423.2015.1044133. Aguilar, J. M., Chan, J. M., White, P. J., & Fragale, C. (2017). Assessment of the language preferences of five children with autism from Spanish-speaking homes. Journal of Behavioral Education, 1–14. https://doi.org/10.1007/s10864-017-9280-9. Dai, Y. G., Burke, J. D., Naigles, L., Eigsti, I.-M., & Fein, D. A. (2018). Language abilities in monolingual- and bilingualexposed children with autism or other developmental disorders. Research in Autism Spectrum Disorders, 55, 38–49. https://doi.org/10.1016/j.rasd.2018.08.001. Dalmau, Y. C. P., Wacker, D. P., Harding, J. W., Berg, W. K., Schieltz, K. M., Lee, J. F., . . . Kramer, A. R. (2011). A preliminary evaluation of functional communication training effectiveness and language preference when Spanish and English are manipulated. Journal of Behavioral Education, 20, 233–251. https://doi.org/10. 1007/s10864-011-9131-z Durán, E., & Heiry, T. J. (1986). Comparison of Spanish only, Spanish and English, and English only cues with handicapped students. Reading Improvement, 23, 138–141. Gonzalez-Barrero, A. M., & Nadig, A. (2017). Verbal fluency in bilingual children with autism spectrum disorders. Linguistic Approaches to Bilingualism, 7, 460–475. https://doi.org/10.1075/lab.15023.gon. Hampton, S., Rabagliati, H., Sorace, A., & FletcherWatson, S. (2017). Autism and bilingualism: A qualitative interview study of parents’ perspectives and experiences. Journal of Speech, Language, and Hearing Research, 60, 435–446. https://doi.org/10. 1044/2016_JSLHR-L-15-0348. Kay-Raining Bird, E., Lamond, E., & Holden, J. (2012). Survey of bilingualism in autism spectrum disorders.
Higher Education Opportunity Act of 2008 (HEOA) International Journal of Language & Communication Disorders, 47, 52–64. https://doi.org/10.1111/j.14606984.2011.00071.x. Kremer-Sadlik, T. (2005). To be or not to be bilingual: Autistic children from multilingual families. In J. Cohen, K. McAlister, K. Rolstad, & J. MacSwan (Eds.), Proceedings of the 4th international symposium on bilingualism (pp. 1225–1234). Somerville: Cascadilla Press. Lang, R., Rispoli, M., Sigafoos, J., Lancioni, G., Andrews, A., & Ortega, L. (2011). Effects of language of instruction on response accuracy and challenging behavior in a child with autism. Journal of Behavioral Education, 20, 252–259. https://doi.org/10.1007/s10864-0119130-0. León, A. L. (2016). Evaluating the effects of tact training in two languages on the acquisition of tacts and untrained listener responses (Master’s thesis). Available from ProQuest Dissertations & Theses Global. (Order No. 10289910). Lim, N., & Charlop, M. H. (2018). Effects of English versus heritage language on play in bilingually exposed children with autism spectrum disorder. Behavioral Interventions, 33, 339–351. https://doi.org/10.1002/ bin.1644. Lim, N., O’Reilly, M. F., Sigafoos, J., & Lancioni, G. E. (2018). Understanding the linguistic needs of diverse individuals with autism spectrum disorder: Some comments on the research literature and suggestions for clinicians. Journal of Autism and Developmental Disorders, 48, 2890–2895. https://doi.org/10.1007/ s10803-018-3532-y. Lim, N., O’Reilly, M. F., Sigafoos, J., Ledbetter-Cho, K., & Lancioni, G. E. (2019). Should heritage languages be incorporated into interventions for bilingual individuals with neurodevelopmental disorders? A systematic review. Journal of Autism and Developmental Disorders, 49, 887–912. https://doi.org/10.1007/s10803018-3790-8. Lund, E. M., Kohlmeier, T. L., & Durán, L. K. (2017). Comparative language development in bilingual and monolingual children with autism spectrum disorder: A systematic review. Journal of Early Intervention, 39, 106–124. https://doi.org/10.1177/1053815117690871. Ohashi, J. K., Mirenda, P., Marinova-Todd, S., Hambly, C., Fombonne, E., Szatmari, P., . . . Thompson, A. (2012). Comparing early language development in monolingual- and bilingual- exposed young children with autism spectrum disorders. Research in Autism Spectrum Disorders, 6, 890–897. https://doi.org/10. 1016/j.rasd.2011.12.002 Seung, H., Siddiqi, S., & Elder, J. H. (2006). Intervention outcomes of a bilingual child with autism. Journal of Medical Speech-Language Pathology, 14, 53–63. Valicenti-McDermott, M., Tarshis, N., Schouls, M., Galdston, M., Hottinger, K., Seijo, R., . . . Shinnar, S. (2013). Language differences between monolingual English and bilingual English-Spanish young children with autism spectrum disorders. Journal of Child Neurology, 28, 945–948. https://doi.org/10.1177/ 0883073812453204
2331 Vaughn, J. L. (2014). An evaluation of English versus Spanish language choice during conversation training intervention for children with autism (Doctoral dissertation). Available from ProQuest Dissertations & Theses Global. (Order No. 3612044). Zhou, V., Munson, J. A., Greenson, J., Hou, Y., Rogers, S., & Estes, A. M. (2017). An exploratory longitudinal study of social and language outcomes in children with autism in bilingual home environments. Autism: The International Journal Of Research And Practice, 23, 394–404. https://doi.org/10.1177/1362361317743251.
HFDT ▶ Human Figure Drawing Tests
Higher Education Opportunity Act of 2008 (HEOA) Ernst O. VanBergeijk Vocational Independence Program, New York Institute of Technology, Central Islip, NY, USA Threshold Program, Lesley University, Cambridge, MA, USA
Definition In 2008, Congress reauthorized the Higher Education Act as the Higher Education Opportunity Act (HEOA). This reauthorization brought about major changes to the eligibility requirements under Title IV which is the title that governs the Federal Student Aid (FSA). Now students with intellectual disabilities (ID) and developmental disabilities (DD) including some students on the autism spectrum may have an opportunity to attend postsecondary education as a result of these amendments and receive some limited forms of Federal Student Aid. This piece of legislation creates an avenue through which more students with autism spectrum disorders and other developmental disabilities can attend college. The Higher Education Opportunity Act (HEOA) allows Institutions of Higher Education
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(IHE) that already are approved to distribute Federal Student Aid under Title IV to apply to the US Department of Education (DOE) for approval of a comprehensive transition and postsecondary program (CTP). Colleges and universities would need to design a curriculum and advising structure specifically geared toward meeting the needs of students with intellectual disabilities. At least 51% of the students with ID’s time would need to be spent with nondisabled peers. If the US DOE approved a college’s CTP, then the student with an intellectual disability would be limited to only some forms of Federal Student Aid, namely, Federal Pell Grants, Federal Supplemental Educational Opportunity Grants (FSEOG), and Federal Work-Study monies. Although the student with ID enrolled in a US DOE-approved CTP does not have to pay back these grants, it does limit their ability to pay the high costs of postsecondary education. The bulk of Federal Student Aid is in the form of guaranteed student loans that are both subsidized and unsubsidized by the government. An eligible student with an intellectual disability (ID) is defined in Section 760 of the HEOA (with slight modifications) and includes a student (A) with mental retardation or significant cognitive impairment and (B) who is/was eligible for FAPE under IDEA including students who were private and/or homeschooled. The student must be enrolled in an approved CTP and must meet all of the general student eligibility requirements under Section 668.32 except: Does not have to be enrolled for the purpose of obtaining a degree or certificate Is not required to have a high school diploma or have passed an ability-to-benefit test Must maintain satisfactory academic progress under school’s policy for students in the CTP (Bergeron et al. 2010. The student must also have a documentation demonstrating that he or she has an intellectual disability or significant cognitive impairment which can also include individuals with autism spectrum disorders (VanBergeijk and Cavanagh 2012).
Higher Education Opportunity Act of 2008 (HEOA)
An eligible CTP must be offered by a college that already has an established financial aid program for its general student body. It also must be specifically designed to support students with intellectual disabilities (ID) and include an advising and curriculum structure. The CTP must require students with ID to participate in courses and activities with students without disabilities. These activities can include vocational internships in addition to academic coursework (VanBergeijk and Cavanagh 2012). Students with ID and/or significant cognitive impairments including those students on the autism spectrum can complete the Free Application for Federal Student Aid (FAFSA) and receive aid provided they are intending to enroll in a US DOEapproved CTP. The FAFSA form helps the college determine how much aid a student enrolled in a CTP is entitled to by calculating the Expected Family Contribution (EFC) (Finkel et al. 2010).
Historical Background The Higher Education Opportunity Act can trace its roots to the Higher Education Act of 1965 (P.L. 89–329). This act authorized most federal student financial aid programs, including the Federal Supplemental Educational Opportunity Grant program and the guaranteed student loan program (FinAid 2016) (known as Title IV). At the time President Lyndon B. Johnson signed the act into law, the proportion of grants to loans is the mirror opposite of what they are today. In the 1960s, about 2/3 of the Federal Student Aid came in the form of grants. Now that proportion is inverted with 2/3 of today’s Federal Student Aid being given to students in the form of loans. Prior to the passage of HEOA, only students who were enrolled full time in a degree-bearing program were eligible for Federal Student Aid. Full-time students were able to complete the Free Application for Federal Student Aid (FAFSA). The student was eligible for both subsidized and unsubsidized guaranteed student loans as well as grants including Federal Pell Grants, Federal Supplemental Educational Opportunity Grants (FSEOG), and Federal Work-Study program funds. The Congress was amending the
Higher Education Opportunity Act of 2008 (HEOA)
act in the midst of the worst economic recession since the Great Depression. A driving force behind the recession was the provision of risky mortgage and business loans. Consequently, the Congress decided not to allow students with ID to be eligible. The thought was that the students with IDs’ debit to income ratio would be too high for them to be able to pay off the debt. Therefore, this population of students was perceived as being yet another risky loan prospect. Interestingly, Wehman (2001) found that students with disabilities who did complete a college degree were employed at a rate that was not statistically different from their nondisabled peers. Many individuals on the autism spectrum have the capacity to complete college level work. It is their impairment in executive functioning, independent living, and social skills that make it difficult for them to complete a degree – not their intellectual ability. This blanket policy of excluding this population needs further examination.
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colleges of universities may have created these programs for students with ID. What we know from current research is that postsecondary transition programs do make a difference in the employment outcomes of students with intellectual disabilities. Moore and Schelling (2015) found that nine out of ten students who graduated from a postsecondary program were employed within 2 years of their study. This is in contrast to the finding from the National Longitudinal Transition Study-2 that only about half of the students with intellectual disabilities were employed (as cited in Moore and Schelling 2015). Wehman et al. (2013) used postsecondary vocational training as an intervention in a randomized clinical trial. His team’s finding was that 87.5% of the students on the autism spectrum who participated in the postsecondary vocational training were employed at the end of the study, whereas the control group, which consisted of students working with their high school and their state office of Vocational Rehabilitation Services, had an employment rate of 6.5%.
Current Knowledge Although the HEOA was passed in 2008, it was not until 2011 that the US DOE began taking applications from Institutions of Higher Education (IHE) to have their comprehensive transition and postsecondary programs (CTP) approved to distribute Federal Student Aid. Despite the fact that there are over 7600 IHE that are authorized to disperse Federal Student Aid under Title IV, only about colleges have a US DOE-approved CTP. This represents that less than of colleges and universities are serving this population through the CTP structure. The reasons for the slow adoption of the CTP model by our nation’s Institutions of Higher Education are unclear. The law was enacted with little fanfare considering this provides students with ID an entrée into postsecondary education. The actual implementation of the law took a number of years and appeared to be not well coordinated. There were high administrative costs incurred by the universities to undertake the creation of a CTP with little economic incentive to commit the organization’s resources. Perhaps, if the legislation had also included the provision of student loans to students with ID enrolled in a CTP, then more
Future Directions In order for the CTP model to gain traction, three things need to occur. First, research into the efficacy of this model in general needs to be conducted. We need to examine not only if these models improve the employment outcomes of individuals with ID/DD and autism but also whether or not these individuals are making a living wage, are able to live independently, and are engaged in their communities with meaningful connections such as friendships and affiliations with organizations including civic, recreational, and religious organizations. More sophisticated research should examine the structural differences in the CTP that promote positive outcomes with this population. Given the broad nature of the requirements of a CTP outlined by HEOA, there will be a great deal of variance in the programs’ offerings and structure. Some CTP will have a residential component. Others will simply stand as a day program where the students commute daily to campus. The academic offerings will also vary widely with some colleges allowing
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students with ID to simply audit credit-bearing courses. Others will encourage students to take coursework for credit. Another possible structure is that the CTP offer vocational training and preemployment skills readiness courses. With the passage of the Workforce Innovation and Opportunity Act (WIOA), colleges and universities need to learn that a substantial amount of government funding is available for programs and services directed to transitional age youth with disabilities. Institutions of Higher Education are eligible to be providers of preemployment services. The law not only specifically designated IHEs as potential partners but also mentions CTP as being potentially eligible for funding as well. Only when universities realize this potential funding stream is available will the CTP model gain traction. The final change that needs to occur before the CTP model will become more widespread is amending the HEOA to allow students with ID/DD to obtain student loans. The rationale for not allowing this population of students to receive the federal student loans is that their ability to repay those loans is perceived as being low. The unintended consequence of this feature of the HEOA is that a number of families are turning to private lenders for continuing education loans in order to finance their sons’ and daughters’ attendance at a CTP. These loans are much more expensive for families to maintain and are more likely to result in a default in the loan than the government-provided subsidized and unsubsidized loans at lower rates of interest.
See Also ▶ Free Appropriate Public Education ▶ Individuals with Disabilities Education Act (IDEA) ▶ Workforce Innovations Opportunity Act
References and Reading Bergeron, D., Murray, B., & Shanley, J. L. (2010). Comprehensive transition and postsecondary (CTP) program: The process and lessons learned. Washington, DC: U.S. Department of Education: State of the Art Conference.
High-Functioning Autism (HFA) FinAid (2016). The history of Federal Student Aid. Retrieved from http://www.finaid.org/educators/his tory.phtml. Accessed 10 Oct 2016. Finkel, J., Anderson, H., & Shanley, J. L. (2010). Eligibility of students with intellectual disabilities to receive Federal Student Aid: The FAFSA and more! Washington, DC: U.S. Department of Education: State of the Art Conference. Higher Education Opportunities Act of 2008. (P.L. 110–315). Moore, E. J., & Schelling, A. (2015). Postsecondary inclusion for individuals with intellectual disabilities and its effects on employment. Journal of Intellectual Disabilities, 19(2), 130–148. The Workforce Innovation and Opportunity Act (WIOA) (H.R. 803). VanBergeijk, E.O., & Cavanagh, P.K. (2012, May/June). Federal Student Aid for students with intellectual disabilities: A well-kept secret? Parenting Special Needs Magazine, pp. 64–65. http://www.mydigitalpu blication.com/publication/?i¼111307&l¼&m¼& p¼8&id¼5779 Wehman, P. H. (2001). Life beyond the classroom: Transition strategies for young people with disabilities (3rd ed.). Baltimore: Paul H. Brookes Publishing. Wehman, P. H., et al. (2013). Competitive employment for youth with autism spectrum disorders: Early results from a randomized clinical trial. Journal of Autism and Developmental Disorders, 44, 487–500. https://doi. org/10.1007/s10803-013-1892-x.t
High-Functioning Autism (HFA) Joshua J. Diehl1,4, Karen Tang2,4 and Brynn Thomas3 1 Autism Services, LOGAN Community Resources, Inc., South Bend, IN, USA 2 Center for Autism and the Developing Brain, Weill Cornell Medicine, White Plains, NY, USA 3 The Neurodevelopmental Disabilities Laboratory, Laboratory for Understanding Neurodevelopment (FUN Lab), Northwestern, and the University of Notre Dame, Chicago, IL, USA 4 Department of Psychology, University of Notre Dame, Notre Dame, IN, USA
Definition High-functioning autism is a term used to refer to a subset of individuals on the autism spectrum who have cognitive and/or linguistic abilities
High-Functioning Autism (HFA)
that are in the average to above-average range for their age. It is common for the acronym “HFA” to be used to describe individuals in this range of functioning.
Historical Background In the first characterizations of the disorder, both Kanner and Asperger noted average to aboveaverage intellectual capabilities in the individuals they observed, despite their significant social deficits. Kanner argued explicitly in several papers that social deficits were separable from intellectual disability and that most of the children he encountered were of average to above average intelligence. Interestingly, Asperger used the “special gifts” of the children he observed to advocate for their protection from the Nazi eugenics movement (Feinstein 2010). Historically, testing IQ in children on the autism spectrum was difficult (Alpern 1967; Alpern and Kimberlin 1970). Although many of the children had discernible strengths (and sometimes savant skills) in areas of cognitive function, language and social deficits often interfered with standardized administration of tests. Moreover, even individuals on the autism spectrum who did not exhibit specific savant skills often showed pronounced relative strengths and weaknesses from subtest to subtest that made generating an overall IQ score difficult. In one of the earliest instances of the use of “high-functioning” as a term in reference to individuals with ASD in a research study, DeMeyer and colleagues suggested that children who currently were defined as “high-functioning psychotic children” should be reclassified as children with “highfunctioning infantile autism” (DeMyer et al. 1971). They used the term “high-autistic children” to refer to a group of children with IQs within two standard deviations of the mean or higher. The term “high-functioning autism” was used infrequently until the late 1980s, when Asperger syndrome grew in popularity as a distinct diagnosis. At that time, “High-functioning autism” was used to describe the subgroup of individuals with autism who had average or above average intelligence or language abilities to compare them to
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individuals with Asperger syndrome (e.g., DeLong and Dwyer 1988). The use of the acronym “HFA” also began at around this time (e.g., Szatmari et al. 1989). Recently, the use of the term “highfunctioning” has become a common qualifier in research studies and for clinical diagnoses. In research, its use is common in part because a significant portion of published research is conducted on this subset of individuals with autism spectrum disorder (ASD). Individuals with average to above average IQs/language abilities are overrepresented in research because of the behavioral challenges in conducting research on individuals with ASD and intellectual disability. The term HFA is sometimes used in clinical evaluations, even though it is not currently an established diagnostic category. In DSM-5 criteria for ASD, there is an item that categorizes “Severity Level,” but these categories do not include the term HFA. The categories are “Level 1 – Requiring Support,” “Level 2 – Requiring Substantial Support,” and “Level 3 – Requiring Very Substantial Support.” Each diagnostic criterion receives its own Severity Level score. It is likely that most individuals with an HFA label would fall under “Level 1,” although it is possible that somebody who is considered high-functioning could still require substantial support in one or more areas. Deficits at Level 1 are described as causing noticeable impairments without supports in place, whereas impairments in the higher levels (2 and 3) are noticeable even with appropriate supports in place.
Current Knowledge “High-functioning” is a qualifier that is commonly used to describe a subgroup of individuals on the autism spectrum, but currently there is no recognized definition or criteria for inclusion in this group, either clinically or in research. The term high-functioning has been used: (a) to differentiate this subgroup of individuals on the autism spectrum from a group with intellectual disability (i.e., “low-functioning”), (b) to differentiate it from a group with an Asperger syndrome
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diagnosis, and (c) to compare it a typically developing comparison group or to specify an individual’s level of cognitive and/or language ability in the context of a research study. In research, inclusion criteria can vary from study to study. Minimally, group membership is defined as having either an intelligence test or language test standard score above 70 (or within two standard deviations of the mean or above, if the test uses a different statistic). Some studies use more stringent criteria, such as requiring both language and cognitive standard scores to be above 70. Other studies will require one or both language/cognitive scores to be above 80, which would exclude individuals who have scores in the “borderline” range of intellectual functioning (standard 70–80). Still other studies will use criteria that exclude individuals with standard scores below 85 (i.e., one standard deviation below the mean) on standardized language and/or cognitive tests. These stringent criteria include only individuals that are at or above the typical range of functioning, as defined by standardized testing. Even though the term “functioning” is included in the label, adaptive functioning level is generally not considered when using the terminology. “Functioning” is generally used to refer to cognitive and or linguistic functioning. This is not consistent with the conceptualization of intellectual disability; the intellectual disability definition includes both cognitive and adaptive functioning in the criteria.
High-Functioning Autism Versus Asperger Syndrome The increased frequency of the use of the term HFA has been due in part to the addition of Asperger syndrome as a diagnostic category. In the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM-IV), individuals with Asperger’s Disorder meet the criteria for social impairment and restricted/repetitive behavior necessary for a diagnosis of Autistic Disorder but are not required to meet requirements for impairments in communication and do not show
High-Functioning Autism (HFA)
delays in early language milestones. In addition, individuals with an Asperger syndrome diagnosis must have shown no delays in cognitive development. It was known, however, that a subgroup of individuals with the autistic disorder diagnosis also showed no delays in cognitive development, which necessitated comparisons between the two groups to understand categorical differences. There has been a considerable amount of research comparing individuals with the diagnosis of Asperger syndrome to individuals with HFA in order to determine if there are categorical differences between the autism and Asperger syndrome diagnoses. Some of the factors that have been proposed as differences between individuals with Asperger syndrome and HFA include (but are not limited to) presence/absence of delays in reaching language developmental milestones, motor skills/clumsiness, types of restricted/repetitive behavior displayed, and speaking style (including prosody), among other characteristics. Research has shown that DSM-IV criteria do not yield meaningful and categorical differences between Asperger syndrome and HFA. As a result, Asperger syndrome was removed from the newest version of the DSM, although this change generated considerable controversy. One argument against the change is that the problem in differentiating the groups might be an artifact of imprecise diagnoses, rather than the absence of categorical differences. It is likely, however, that, even with the elimination of Asperger syndrome as a diagnosis in DSM-5, the use of the “high-functioning” qualifier (e.g., “high-functioning ASD”) will continue. In the past several years, research studies have begun to refer to children with Asperger syndrome and HFA together as a group of individuals with “high-functioning ASD.”
Future Directions For clarity, it will be important to set clear criteria for what constitutes high-functioning autism, for both research and clinical purposes. To date, it has been difficult to make meaningful comparisons across research studies because of the wide
High-Functioning Autism (HFA)
variety of ways group membership has been defined in research studies. For research studies, criteria should include both language level and cognitive level when defining a group as “highfunctioning.” Moreover, groups defined as “highfunctioning” should strongly consider excluding individuals with cognitive or language abilities below one standard deviation from the mean and above the cutoffs for intellectual disability or language impairment (i.e., individuals with IQ and Language standard scores between 70 and 85). These more stringent criteria are more consistent with the idea that the group is “high-functioning.” Moreover, these criteria create a more homogenous group that is more easily matched with a typically developing comparison group, both on mean and standard deviation of scores. Second, it will be important to consider adaptive functioning, in addition to cognitive and language level, when defining using the term “highfunctioning.” The use of adaptive functioning in the high-functioning criteria will provide a definition that is more consistent with the definition of Intellectual Disability, which includes both intellectual and adaptive functioning. Moreover, the inclusion of adaptive functioning in the definition is a more valid representation of level of functioning, because individuals with ASD can have significant discrepancies between cognitive and adaptive functioning, and language/IQ scores may not be representative of level of functioning for some individuals. In clinical settings, clear criteria would be important, but only if it is determined that there are clinically relevant benefits of using a “highfunctioning” qualifier. It is important to investigate whether there are specific intervention recommendations that can be tailored to individuals with this qualifier. Finally, it should also be investigated whether the addition of the “high-functioning” qualifier lessens the stigma of the diagnosis, and if the qualifier changes the way that parents, peers, and/or professionals interact with the child or the family. Research on other disabilities has shown that adding levels of ability in a label increases the stigma associated with individuals on the “lower” end of ability. It is possible that the high-
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functioning qualifier reinforces stigma associated with portions of the population who are not considered high-functioning.
See Also ▶ Adaptive Behavior ▶ Asperger Syndrome ▶ DSM-IV ▶ Intelligence Norms ▶ Intelligence Quotient ▶ Language Tests ▶ Pervasive Developmental Disorder Not Otherwise Specified ▶ Psychological Assessment ▶ Spectrum/Continuum of Autism
References and Reading Alpern, G. D. (1967). Measurement of “untestable” autistic children. Journal of Abnormal Psychology, 72(6), 478–486. Alpern, G. D., & Kimberlin, C. C. (1970). Short intelligence test ranging from infancy levels through childhood levels for use with the retarded. American Journal of Mental Deficiency, 75, 65–71. Asperger, H. (1944). Die ‘autistischen psychopathen’ im kindesalter. Archiv für Psychiatrie und Nervenkrankheiten, 117, 76–136. Baron-Cohen, S. (2009). The short life of a diagnosis. New York Times, p. A35. DeLong, G. R., & Dwyer, J. T. (1988). Correlation of family history with specific autistic subgroups: Asperger syndrome and bipolar affective disease. Journal of Autism and Developmental Disorders, 18(4), 593–600. DeMyer, M. K., Churchill, D. W., Pontius, W., & Gilkey, K. M. (1971). A comparison of five diagnostic systems for childhood schizophrenia and infantile autism. Journal of Autism and Childhood Schizophrenia, 1, 175–189. DeMyer, M. K., Barton, S., Alpern, G. D., Kimberlin, C., Allen, J., Yang, E., et al. (1974). The measured intelligence of autistic children. Journal of Autism and Developmental Disorders, 4(1), 42–60. Diehl, J. J., Wolf, J., Herlihy, L., & Moller, A. C. (2011). Seeing red: Are colors a window into implicit societal conceptions about the autism spectrum? Disability Studies Quarterly, 31(3). http://dsq-sds.org/article/ view/1676/1595. Feinstein, A. (2010). A history of autism: Conversations with pioneers. Hoboken: Wiley-Blackwell.
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2338 Frith, U. (1991). Autism and Asperger syndrome. Cambridge: Cambridge University Press. Ghaziuddin, M., & Gerstein, L. (1996). Pedantic speaking style differentiates Asperger syndrome from highfunctioning autism. Journal of Autism and Developmental Disorders, 26, 585–595. Howlin, P., & Goode, S. (1998). Outcome in adult life for people with autism and Asperger’s syndrome. In F. R. Volkmar (Ed.), Autism and pervasive developmental disorders (pp. 209–241). New York: Cambridge University Press. Kanner, L. (1943). Autistic disturbances of affective contact. The Nervous Child, 2, 217–250. Miller, J. N., & Ozonoff, S. (1997). Did Asperger’s cases have Asperger disorder? A research note. Journal of Child Psychology and Psychiatry, 38, 247–251. Miller, J. N., & Ozonoff, S. (2000). The external validity of Asperger disorder: Lack of evidence from the domain of neuropsychology. Journal of Abnormal Psychology, 109, 227–238. Szatmari, P. (2000). Perspectives on the classification of Asperger syndrome. In A. Klin & F. R. Volkmar (Eds.), Asperger syndrome (pp. 403–417). New York: The Guilford Press. Szatmari, P., Bartolucci, G., & Bremner, R. (1989). Asperger’s syndrome and autism: Comparison of early history and outcome. Developmental Medicine and Child Neurology, 31(6), 709–720. Volkmar, F. R., Lord, C., Bailey, A., Schultz, R. T., & Klin, A. (2004). Autism and pervasive developmental disorders. Journal of Child Psychology and Psychiatry, 45, 135–170. Wing, L. (1981). Asperger’s syndrome – A clinical account. Psychological Medicine, 11, 115–129.
High-Functioning Autism Diagnostic Interview ▶ Asperger Syndrome Diagnostic Interview
High-Probability Requests Mary Jane Weiss and Samantha Russo Institute for Behavioral Studies, Endicott College, Beverly, MA, USA
High-Functioning Autism Diagnostic Interview
nonaversive intervention commonly used to treat noncompliance by reducing escapemotivated behavior. It is also associated with and has been historically referred to as interspersed requests, pretask requests, and behavioral momentum. It is considered an antecedent intervention. The basic procedure involves the teacher presenting three to five “high-probability requests,” which are easy and quick responses already mastered by the child and highly likely that the child will emit. The teacher presents these requests quickly, one after the other, providing brief verbal praise after each one. Immediately after receiving praise for the last of these highprobability requests, the teacher immediately presents the request to which the child has historically not complied. Reinforcement can also be added to the high-probability sequence to effectively increase compliance with low-p instructions (Wilder et al. 2015). The use of the high-p sequence often prevents the noncompliance. Responding is facilitated (and momentum is built) through the high-p requests. Theoretically, the effect of the high-p sequence stems from the abative effects of an abolishing operation (AO). It reduces the value of reinforcement for noncompliance to the lowprobability requests. The value of escape from demands is lowered. In application, behavior analysts must ensure that the high-p response request intervention is not done after the occurrence of a problem behavior. Such use could inadvertently strengthen escape-motivated noncompliance. In addition, attention must be paid to the quality of reinforcement delivered for compliance, especially if behaviors are severe (e.g., Mace and Belfiore 1990). Finally, the number of high-probability requests can be gradually reduced as compliance with low-probability requests improves.
Definition
See Also
High-probability request sequence is an intervention used to increase compliance. It is a
▶ Behavioral Momentum ▶ Establishing Operations
Hippotherapy
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References and Reading
References and Reading
Mace, F. C., & Belfiore, P. (1990). Behavioral momentum in the treatment of escape-motivated stereotypy. Journal of Applied Behavior Analysis, 23, 507–514. Wilder, D. A., Majdalany, L., Sturkie, L., & Smeltz, L. (2015). Further evaluation of the high-probability instructional sequence with and without programmed reinforcement. Journal of Applied Behavior Analysis, 48(3), 511–522.
Andersen, P., Morris, R., Amaral, D., Bliss, T., & O’Keefe, J. (2007). The hippocampus book. New York: Oxford University Press. Raymond, G., Bauman, M. L., & Kemper, T. L. (1989). The hippocampus in autism: Golgi analysis. Acta Neuropathologica, 91, 117–119.
Hippotherapy High-Throughput Sequencing ▶ Next-Generation Sequencing
K. Mark Derby, Anjali Barretto and Tim MacLaughlin Department of Special Education, Gonzaga University, Spokane, WA, USA
Definition
Hippocampus Ilanit Gordon Child Study Center, Yale University, New Haven, CT, USA
Definition The hippocampus is a bilateral incurved brain structure, a coiled elongated ridge located beneath the neocortex in the basal medial surface of the temporal lobes. Comprising a major component of the limbic system, the hippocampus is phylogenetically considered an ancient brain structure. It was first coined hippocampus by the sixteenth-century Italian anatomist Julius Caesar Aranzi who likened its shape to that of a seahorse (in Greek hippo ¼ horse, kampe ¼ curve). The hippocampus is mostly implicated in processes of memory formation and consolidation as it is critical for the formation of new factual and autobiographical memories. Hippocampal damage can result in anterograde amnesia: the loss of ability to form new memories. The hippocampus is also known for its involvement in the coding of spatial information, navigation, and orientation.
Hippotherapy is a treatment that uses the multidimensional movement of the horse. The term hippotherapy comes from the Greek word “hippos” which means horse. Specially trained physical, occupational, and speech therapists use this treatment for clients who have movement dysfunction and social deficits. The central tenant of the intervention is to use the movement the client experiences while on horseback to improve physical capabilities while developing a therapeutic relationship (All et al. 1999; Bass et al. 2009).
Historical Background Pet ownership has been shown to be beneficial for individuals with a wide range of diagnoses (Voelker 1995). Pet or animal therapy is based on the assumption that interactions with animals can cause both physiological changes and psychological benefits (All et al. 1999). For example, Lehrman and Ross (2001) reported an increase in verbal sounds made by a 9-year-old girl with visual impairments when pet therapy was employed. In addition, children diagnosed with autism have been shown to make improvements in communication when animal-assisted therapy has been employed (Blue 1986). Social skills have
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been taught to children with autism via the use of animal therapy (Law and Scott 1995; Turner 2005). Horseback riding has been shown to benefit children with communication disorders (Health and McKinney 1989). There have also been a few anecdotal reports that horseback riding and hippotherapy can have recreational and social benefits. Bass et al. (2009) evaluated the effects of horseback riding on the social functioning of children with autism. Utilizing a wait-list control design, those authors were able to demonstrate the efficacy of the interventions for increasing children’s sensory seeking, sensory sensitivity, and social motivation. In addition, an inverse relationship was observed for problem behaviors such as inattentiveness and distractibility. Its use for children with autism spectrum disorders (ASD) is in its infancy, but the preliminary data are promising (Bass et al. 2009).
Rationale or Underlying Theory At this time, the underlying theory for why hippotherapy has been beneficial appears to rely on both physiological and environmental factors (Macaulay and Gutierrez 2005). Hippotherapy is believed to help an individual develop an awareness of body movement, weight distribution, improved hand-eye coordination, improved speech, a wider tactile experience, and a wider experience of sounds (Macaulay and Gutierrez 2005). In a descriptive evaluation of therapist perceptions in Germany and Britain, Debuse et al. (2005) found that most practitioners of hippotherapy believed that hippotherapy improved the rejuvenation of muscle tone, improved postural control, and resulted in psychological benefits. However, these findings relied on the self-report of practitioners. Environmentally, the positive outcomes could be hypothesized to occur based on applied behavior analysis literature. For example, Carr and Durand (1985) demonstrated that through differential reinforcement, severe behaviors are reduced and a covariant increase in communicative behavior occurrences. Similarly, Derby et al. (1997) reported that if the child is provided an alternate
Hippotherapy
form of communication to obtain identified reinforcers (e.g., increased horse movement), appropriate social behavior increases. Thus, increased social behavior could be the function of a reinforcer/behavior contingency. Within the context of hippotherapy, the contingency would be between horse movement and social behavior.
Goals and Objectives Physiologically, through the movement of the horse, the goal of hippotherapy is to help an individual develop an awareness of body movement, weight distribution, improved hand-eye coordination, improved speech, a wider tactile experience, and a wider experience of sounds (Macaulay and Gutierrez 2005). It is also used as a treatment tool by speech pathologists to increase the social motivation (Taylor et al. 2009), sensory seeking and sensitivity (Bass et al. 2009), and social behavior (Nelson et al. 2006).
Treatment Participants Hippotherapy has been used as a therapy technique and is primarily used for persons with physical impairments (Benda et al. 2003) and speech impairments (Bass et al. 2009; Taylor et al. 2009).
Treatment Procedures Occupational therapists, speech-language pathologists, and physical therapists typically perform the practice of hippotherapy. Even though every specialty derives its effectiveness from the unique movement of the horses, each enters the therapeutic environment with their own unique goals. Physical therapists target functional skills such as sitting, standing, and walking. Conversely, speech-language therapists use the therapy to remediate student communication. Each therapy modality relies on the movement of the horse to improve client function. Typical therapy sessions may last between 45 and 60 min. The therapy session is often broken down into distinct
Hippotherapy
segments, which may include putting on protective equipment such as a helmet, mounting the equine, engaging in activities while with horse, and dismounting the horse. For a safe and effective experience, a minimum of four trained professionals are required to complete a therapeutic session. A trained horse leader is always present who is responsible for prompting all horse movements. Two side-walkers are placed on either side of the equine to ensure client stability and safety. A lead therapist is assigned who is responsible for all therapeutic activities. Once mounting the equine activities is achieved, the procedures used focus on the individual needs of the client. The client may be seated on the equine a number of ways including sitting forward or sideways. Therapy is focused on the use of activities that are meaningful to the client and also meet treatment goals. For example, Benda et al. (2003) evaluate the effects of 8 min of a steady walking movement (4 min clockwise followed by 4 min counterclockwise) versus a stationary board control condition with children with spastic cerebral palsy. These authors found that following as little as 8 min of therapy, significant improvement in symmetry of muscle movements occurred. As an adjunctive speech intervention, Nelson et al. (2006) utilized a functional communication approach to increase the vocalizations of three children with autism spectrum disorder. In the Nelson et al. investigation, the children were required to sign or say “please” to prompt the horse handlers to start, stop, turn, and change the stride of the horse. The researchers found that the children’s use of the functional communication and other social vocal responses increased. Nelson et al. hypothesized that the horse functioned as a reinforcer for appropriate communications. Similarly, Bass et al. (2009) exposed children with autism to 12, 1-h treatment sessions of hippotherapy across specific activities. Specifically, the therapist prompted the children to engage in a number of horse-related activities which included mounting/dismounting, riding skills, movement games (i.e., Simon says, catch and throw, etc.), and horse grooming. These investigators found that the children who participated in therapy exhibited increased sensory seeking, sensory sensitivity,
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and social motivation. Regardless of the specific professionals conducting the therapy (i.e., occupational therapy, speech therapy, physical therapy), sessions are individualized in order to obtain functional goals for the client.
Efficacy Information Overall, the literature found supports the efficacy of hippotherapy is in its infancy for increased social functioning (Bass et al. 2009). Conversely, the use of the horse as a therapeutic tool to increase posture, balance, and overall functioning of persons with motor dysfunction is better documented. For example, Benda et al. (2003) reported that hippotherapy could increase the symmetry of muscle activity when compared to sitting astride on a barrel using surface electromyography as an outcome measure. Positive effects have also been reported and have produced improved posture and balance for persons with cerebral palsy (Zadnikar and Kastrin 2011).
Outcome Measurement Outcome measures vary depending on the focus of therapy. If therapy is focused on improving physical attributes, outcome measures range from the therapist using researcher-made objective scoring scales, weight barring, and standardized measures such as the Gross Motor Function Measure (Russel et al. 2000) or the Pediatric Evaluation of Disability Inventory Scale (Feldman et al. 1990). In addition, surface electromyography has been used to measure muscle activity (Benda et al. 2003). If therapy is focused on social skill and motivation, measures typically involve the use of questionnaires and direct observational data collection systems. Bass, Duchowny, and Llabre documented sensory outcomes using the Sensory Profile (Dunn 1999) and increase social functioning and motivation using the Social Responsiveness Scale (Constantino 2002). Conversely, Nelson et al. (2006) measured increased social behaviors via in vivo measurement of child behaviors and vocalizations.
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Qualifications of Treatment Providers Licensed occupational therapists, physical therapists, and speech-language pathologists typically practice hippotherapy. A majority of the professionals that practice hippotherapy do not have a formal certificate beyond the qualifications for one of these professions. However, the American Hippotherapy Association has established a certificate process. Persons with these credentials need to have practiced their profession for 3 years, have 100 h of hippotherapy practice, and passed a multiple-choice examination.
References and Reading All, A. C., Loving, G. L., & Crane, L. L. (1999). Animals, horse riding, and implications for rehabilitation therapy. Journal of Rehabilitation, 65(3), 49–57. Bass, M. M., Duchowny, C. A., & Llabre, M. M. (2009). The ethics of therapeutic horseback riding on social functioning in children with autism. Journal of Autism and Developmental Disorders, 39, 1261–1267. Benda, W., McGibbon, W. H., Grant, K., & Davis, M. (2003). Improvement in muscle symmetry in children with cerebral palsy after equine-assisted therapy (hippotherapy). Journal of Alterative and Complimentary Medicine, 9, 817–875. Blue, G. F. (1986). The value of pets in children’s lives. Childhood Education, 62, 85–90. Carr, E., & Durand, V. M. (1985). Reducing behavior problems through functional communication training. Journal of Applied Behavior Analysis, 18, 111–126. Constantino, J. N. (2002). The social responsiveness scale. Los Angeles: Western Psychological Services. Debuse, D., Chandler, C., & Gibb, C. (2005). An exploration of German and British physiotherapists views on the effects of hippotherapy and their measurement. Physiotherapy Theory and Practice, 21, 219–242. Derby, K. M., Wacker, D. P., Berg, W., DeRaad, A., Ulrich, S., Asmus, J., et al. (1997). The long-term effects of functional communication training in home settings. Journal of Applied Behavior Analysis, 30, 507–531. Dunn, W. (1999). The sensory profile manual. San Antonio: The Psychological Corporation. Feldman, A. B., Haley, S. M., & Coryell, J. (1990). Concurrent and construct validity of the pediatric evaluation of disability inventory. Physical Therapy, 70, 602–610. Health, T. D., & McKinney, P. C. (1989). Potential benefits of companion animals for self-care children. Childhood Education, 7, 311–314. Law, S., & Scott, S. (1995). Tips for practitioners: Pet care – A vehicle for learning. Focus on Autistic Behavior, 10(2), 17–18.
Histaprin [OTC] Lehrman, J., & Ross, D. P. (2001). Therapeutic riding for a student with multiple disabilities and visual impairment: A case study. Journal of Visual Impairment and Blindness, 95, 108–109. Macaulay, B. L., & Gutierrez, K. M. (2005). The effectiveness of hippotherapy for children with language learning disabilities. Communication Disorders Quarterly, 25, 205–217. Nelson, K., Derby, K. M., Barretto, A., & Axtell, J. (2006). The use of equine therapy to increase social communicative behavior in children with autism. In D. Holmes (Chair) Collaboration in Autism Intervention. Paper presented at the annual conference of the Association for Behavior. Russel, D. J., Avery, L. M., Rosenbaum, P. L., Raina, P. S., Walter, S. D., & Palisano, R. J. (2000). Improved scaling of the gross motor function measure for children with cerebral palsy: Evidence of reliability and validity. Physical Therapy, 80, 873–885. Taylor, R. R., Kielhofner, G., Smith, C., Cahill, S. M., Ciaka, M. D., & Gehman, M. (2009). Volitional change in children with autism: A single case design study of the impact of hippotherapy on motivation. Occupational Therapy in Mental Health, 25, 192–200. Voelker, R. (1995). Puppy love can be therapeutic, too. Journal of the American Medical Association, 279, 1897–1899. Zadnikar, M., & Kastrin, A. (2011). Effects of hippotherapy and therapeutic horseback riding on postural control or balance in children with cerebral palsy: A meta-analysis. Developmental Medicine and Child Neurology, 53(8), 684–691.
Histaprin [OTC] ▶ Diphenhydramine
Histidinemia Jeffrey P. Brosco Department of Pediatrics, Miller School of Medicine, University of Miami, Mailman Center for Child Development, Miami, FL, USA
Definition Histidinemia is an essential amino acid during infancy. In the 1960s, disorders of histidine metabolism were thought to be associated with
History of Autism as a Diagnostic Concept
speech delay and intellectual impairment. There were also multiple case reports noting the co-occurrence of histidinemia and various disorders, including autism. Using newborn screening and dietary management of phenylketonuria as a model, New York and Massachusetts implemented newborn screening programs for histidinemia. By the late 1970s, histidinemia was understood as a benign metabolic variant, and enthusiasm for state-sponsored newborn screening for histidinemia has become a cautionary tale.
History of Autism as a Diagnostic Concept John Donvan1 and Caren Zucker2 1 Washington, DC, USA 2 New Jersey, USA
Historical Background Among the many questions that surround autism, one likely never to get a solid, uncontroverted answer is the following: How far back in human history does autism go?
It is obvious that at some stage in our species’ development, some person was first to display the behaviors we today call “autistic.” To date, however, we have no way to identify when or where that happened on the timeline of human evolution. There are no direct fossils for behavior. Despite that, it seems reasonable and intuitive to assume that the autism’s defining behaviors, relative to our own time, are nothing new – and that a thousand years ago, there were people acting in ways which, if they were observed today, would prompt a modern-day diagnosis of autistic spectrum disorder (ASD). That such individuals were not diagnosed during their own lifetimes, over most of those ten centuries, owes to a simple fact that autism, as a diagnostic concept, achieved recognition only remarkably recently, coming into existence, in the minds of medical observers, only around the time that World War II was getting started.
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Before that, people exhibiting such behaviors, if they were pronounced enough, risked getting pinned with some other label inevitably suggesting either mental illness or intellectual disability. The sorts of hurtful-sounding terms used in the past – lunatic, psychotic, demented, idiot, imbecile, retarded, among many others – were actual clinical categories in their day, before lay society twisted them into slurs. They also had consequences. Starting in the nineteenth century, especially in the more “advanced” societies of Europe and North America, people so labelled were increasingly pushed out of mainstream society and shunted off to supposed “asylums” or “training schools.” Initially launched with therapeutic and educational aspirations, these institutions all too often decayed into soul-destroying human warehouses, overcrowded, underfunded, and inhumane. Lost within their populations, where their cluster of “odd” behaviors were not recognized as distinctively meaningful in any way, were many people with today’s ASD. They were lost, in part, because no one had ever heard of autism.
Current Knowledge In 1943, a child psychiatrist named Leo Kanner published a groundbreaking description of 11 children, in which he made the case in vivid detail that their short histories merited a new kind of diagnosis. These eight boys and three girls were clearly not typical in their development – to the extent, Kanner wrote that “several. . .were introduced to us as idiots or imbeciles.” Following the usual pattern, most of the children, none older than 11, had already been sent off to institutions, some given diagnoses of “feeblemindedness” or schizophrenia. To Kanner’s eyes, however, there was something seriously wrong with these assessments. The example of the boy he designated “case 1” demonstrates why. “Donald T” was 5 years old in 1938, the first time his concerned parents brought him from Mississippi to be seen by Kanner at his clinic at Johns Hopkins University. In describing Donald, Kanner relied on his own and his staff’s
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observations, then and over the next few years, while also quoting liberally from a lengthy description of his behaviors written by Donald’s father: “It was observed at an early time that he got happiest when left alone. . .He paid no attention to persons around him.” He had never cried for his mother and never noticed his father’s arrivals home from work. Yet inanimate objects fascinated him, and he “developed a mania for spinning blocks and pans and other round objects.” His speech was unusual too. Donald was using words by 13 months, but he reversed the pronouns “you” and “I” and spoke with a flat intonation. When asked a question, his answer was to recite the question, verbatim, followed by silence. Otherwise, his utterances consisted mainly of the same small set of phrases, such as “I could put a little comma or semicolon,” which he had once heard someone else say and then taken to speaking aloud in a ritualistic manner. Finally, Kanner noted, Donald hated change and cherished repetition. Most of his actions were repetitions carried out in exactly the same way in which they had been performed originally. If he spun a block, he must always start with the same face uppermost. When he threaded buttons, he arranged them in a certain sequence that had no pattern to it but happened to be the order used by the father when he first had shown them to Donald. There were also innumerable verbal rituals recurring all day long. . .[which his mother had to comply with] or else he squealed, cried, and strained every muscle in his neck in tension.
But there was something else: Donald was clearly gifted in some areas. His memory was prodigious, and he was very quick at absorbing information. At the age of 1 year ‘he could hum and sing many tunes accurately.’ Before he was 2 years old, he had ‘an unusual memory for faces and names, knew the names of a great number of houses’ in his home town. . . He knew the pictures of the presidents and knew most of the pictures of his ancestors and kinfolks on both sides of the house. He quickly learned the whole alphabet ‘backward as well as forward’ and to count to 100.
To Kanner, such talents ruled out intellectual disability as an explanation. Schizophrenia made no sense either, since its symptoms typically first manifest in adolescence and include
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hallucinations, which were no part of Donald’s presentation. At the same time, Kanner did discern a few overarching patterns in Donald’s behaviors and in those of the other ten children he described, which he judged significant. One was their distinct relationship to, and use, of language. Another was an “obsessive need for sameness.” A third was what Kanner referred to as “autistic aloneness.” Critically, too, Kanner concluded that these traits were “innate,” “inborn,” and present “from the beginning of life.” He declared these as the key aspects of the syndrome, when he published his findings, in 1943, in a paper entitled “Autistic Disturbances of Affective Contact.” Kanner’s choice of that adjective – autistic – proved consequential. It was not his invention. By 1943, it was already in use, and familiar to psychiatrists – along with the noun form autism – but in a context significantly different from the one Kanner was introducing. Till that time, adjective and noun alike had served to label exclusively a transient symptom in adults with schizophrenia: their tendency at times to disengage from contact with their external surroundings and the people in them. Such patients entered “a world of their own. . .encased with their desires and their wishes,” wrote the Swiss psychiatrist Eugen Bleuler in 1911. Indeed, Bleuler himself came up with the term. “This detachment from reality and absolute predominance of the inner life, he wrote, “we call autism.” He based it on the Greek word αutóB, which means “self.” Kanner did not attempt to anoint his syndrome “autism” in 1943, when he used the noun form of the word only once in the 34 pages of his journal article. Instead, he made liberal use of Bleuler’s adjective “autistic” as a descriptor – a way to get across some aspects of what his children’s behaviors resembled. But, as Kanner saw it, the comparison went only so far, as he made clear that this rough resemblance to schizophrenic autism – what he referred to as “extreme aloneness” – was but part of the total story. “The total picture,” he pointed out, included additional behaviors such as “obsessiveness, stereotypy and echolalia,” which fell outside the boundaries of autism as the term’s coiner defined it. Certainly, Kanner’s
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contemporaries grasped these nuances, and for years afterwards, when requiring a name for the syndrome Kanner had described, chose to call it, not “autism” but “Kanner’s syndrome.” Notably, a similar borrowing of Bleuler’s concept took place, at nearly the same time, in Vienna, where a pediatrician named Hans Asperger had published work in German in 1938 and 1944, describing a number of Austrian boys struggling significantly in the domain of interpersonal relationships. Their challenges, as Asperger described them, resulted from a syndrome of obliviousness to social cues, inflexibility in behavior, and a tendency to develop obsessive interests in narrowly limited subjects. Overall, Asperger’s boys differed in significant ways from Kanner’s group, exhibiting greater capability for independence, creative output, and intellectual performance (above average in some cases) than the children Kanner wrote about. In short, Asperger highlighted children who were likely to struggle socially throughout life but were less likely to be institutionalized. And where Kanner considered his syndrome to be “rare enough,” Asperger saw his as “not at all” rare. Like Kanner, however, Asperger used Bleuler’s autism as a reference point, and in a similar way, to suggest a rough resemblance to some aspects of schizophrenia’s social detachment. Again like Kanner, he did not call his syndrome “autism” (though, confusingly, the definitive English translation of his 1944 paper has him doing so, in more than 20 instances) but employed the adjective in describing his boys as “autistic psychopaths” and in the title of his major paper on the subject “Autistic Psychopathy in Childhood.” It should be noted that the term “psychopathy” in German best translates to English as “personality disorder,” without the negative connotations the word carries in English. That both men made the same word choice has been put down by some to mere coincidence and by others to the normal cross-pollination of ideas that occur in science and medicine. In fact, Kanner employed a former colleague of Asperger’s, a Jew fleeing Vienna ahead of Nazi persecution whom also Jewish Kanner helped reach the United States
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and with whom he could well have shared insights. As it happened, however, for the next 40 years, Asperger’s writing about boys he called “autistic” went largely overlooked outside the German-speaking world, while Kanner’s proved definitive everywhere else. Why does autism’s etymological lineage merit focus at all? Because in it lie the origins of a major and enduring obstacle to studying, treating, and even talking about the disorder, one that remains operative to this day, namely, that from the beginning, well-intentioned voices have disagreed vigorously about what autism actually is, how to describe it, and where to draw its boundaries. This started with the terminology, a problem the astute British child psychiatrist and autism researcher Michael Rutter pointed to in 1978, when he lamented: “To begin with, there was the unfortunate choice of name.” As Rutter explained, this caused immediate confusion, owing to the unintended implication that these children had schizophrenia after all. Kanner played an unintentional part in that. In 1944, in an effort at clarity, he began referring to his syndrome as “early infantile autism.” He did this to reemphasize that the “extreme aloneness” – the “autism” which comprised but one part of the picture – was there from the start, from infancy. Notably, this was his first use of the noun form in this manner, and, with hindsight, it marks the moment where his descriptions of autistic-like behaviors began shaping up into a new “-ism” of their own. In the short term, however, calling his condition autism only muddied the waters for the many who knew the term solely by connection to schizophrenia. A few years after Kanner’s initial paper, a remarkable sort of diagnostic melee ensued from the confusion. On the one hand, other researchers and clinicians began bringing forth new reports of children under their care similar in presentation to Kanner’s but under diagnoses they packaged according to slightly different parameters and under disparate names – for example, “symbiotic psychosis,” “prepubertal schizophrenia,” “infantile psychosis,” and “atypical child.” Again, Rutter put his finger on the problem: “It is by no means clear that all these authors are talking about
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the same condition.” Then, too, it was by no means clear they weren’t. On the other hand, and at the same time, there were many clinicians who positively embraced Kanner’s syndrome but who interpreted the diagnostic criteria far more loosely than he ever did. By the mid-1950s, Kanner was complaining about autism diagnoses being rendered merely for “one or another isolated symptom,” inflating the number of cases out of all proportion to reality. “Almost overnight,” he later said of this early period, “the country seemed to be populated by a multitude of autistic children.” Such diagnostic disorder of course played havoc with the lives of people being given these labels, who in this period were entirely children. It also confounded attempts to conduct scientific research on autism. How, for example, were epidemiologists to determine a prevalence rate of autism when there was such profound disagreement on what constituted its necessary and sufficient symptoms? This was exactly the issue faced by a Londonbased psychology graduate student named Victor Lotter, who in 1964 attempted the first-ever prevalence study of autism among the population of boys and girls aged eight to ten living in Middlesex County, England – a total of 78,000 children. When Lotter turned to the medical literature to construct a checklist of behaviors that were flags for autism, he discovered a definitional din: numerous competing portraits of the condition which, taken together, yielded little clarity but instead established – as he said with chagrin – “the lack of agreed behavioural criteria.” Pressing on, however, Lotter improvised by combining some of Kanner’s criteria with several from a list known as Creak’s nine points. With these as his starting point, Lotter in 1966 reported a prevalence rate of autism of 1 in 2500 among his cohort in Middlesex. But even as he did so, the intellectually honest student sent up a critical warning that his finding should not be considered dispositive. “‘True’ prevalence,” Lotter wrote near the end of his report, “may not be a useful concept in the case of a syndrome (or syndromes) so poorly defined.” Despite that, to this day, advocacy groups cite
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Lotter’s 1 in 2500 result as representing a solid fact: what the global prevalence rate of autism “used to be.” Efforts to standardize criteria for the diagnosis would preoccupy stakeholders for years to come. Continuing frustration over the issue appeared regularly in the scholarly literature, literally for decades. The following are some examples: • In 1968, Michael Rutter was writing, not for the last time, about the “hopelessly confused state of affairs.” • In 1978, Eric Schopler, of the University of North Carolina, was arguing that scientists working on autism were not understanding one another’s research “because criteria for diagnosis are different.” • In 1988, Yale’s Fred Volkmar and Donald Cohen were voicing frustration over the “long and controversial history of the concepts surrounding autism.” • In 1998, Yale’s Ami Klin, joined by Volkmar, was underscoring “the confusion and the plethora of diagnostic concepts.” Schopler’s comment, in particular, illustrates but one ramification of this diagnostic confusion: disputes over criteria tended to confound research. Perhaps more insidious than mismatched symptom lists – for the sake of people getting diagnosed – was the clash over basic concepts of autism – the term used by Volkmar, Cohen, and Klin. A syndrome so prone to definitional malleability (largely because it offered no clear biomarkers but has been based on the interpretation of behaviors) also lent itself to rather disparate theories concerning its essential nature. Even as they came up short as universal explanations for autism, many of these represented coherent and insightful ideas while doing little harm. For a time, for example, one theory held that the core deficit, at least in more classic cases of autism, was a failure in the facility of language and that all other symptoms, including social challenges, flowed from that. Other ideas placed compromised cognitive functions at the center, with much attention given to the part played by
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“theory of mind” and “weak central coherence.” Still others went down more biological paths, seeking to understand whether sensory issues, and even metabolic ones, were at the heart of the disorder. And then there was the so-called psychogenic model of autism, which caused enormous harm to autistic individuals and their families. Better known as the “refrigerator mother theory” and dominant from the late 1940s at least into the mid-1970s, this concept of autism held that the disorder was a child’s defensive reaction to insufficient maternal love. Treatment, such as it existed, centered on intensive psychotherapy of the mother (and sometimes the father as well). Reflecting the sway held by traditional psychoanalytic thinking at mid-century, especially in American psychiatry, the psychogenic concept stole decades of meaningful psychological and biological research attention to the topic of autism, bringing enormous pain to families and no practical therapy or support for autistic people themselves. The only good thing that can be said about the dominance of the “refrigerator mother” theory is that it finally lost currency, roughly around the time that steps were taken to codify a valid, science-and-data-based autism diagnosis in the American Psychiatric Association’s Diagnostic and Statistical Manual of Mental Disorders (DSM). In itself, however, this effort turned into yet another endless deliberation, spanning four decades (the late 1970s to the present), during which the successive working groups of experts recruited to validate autism’s definition from one DSM edition to the next repeatedly moved the boundary lines – sometimes jarringly. To some extent, the zigzags reflected the fact that fresh data on autism was coming in all the time, shedding new light on the disorder, and legitimizing revisions. But each update (and they tended to be significant) reflected something else: a negotiation. By design, the expert working groups represented diverse points of view, whose members brought to the table their own distinctive frameworks for thinking about autism. These were shaped by their training (psychiatry or
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psychology), by the professional milieus in which they worked (numerous regions of the world were represented), and especially by each person’s own interactions with autistic people either as researcher or clinician (itself an important distinction, though many of the recruited experts were both). Inevitably, though, diversity meant disagreement, and consensus rarely meant unanimity. To a degree that might surprise the lay observer, the evolving definition of autism – at least as it appeared in successive editions of the DSM – reflected a debate and a vote and an outcome influenced by which people in the room possessed the better powers of persuasion. A case in point: The radical overhaul that autism’s diagnostic criteria underwent between 1980 and 1987. DSM-III, which appeared in 1980, marked the first time autism was addressed in any DSM (3 years after it was listed in the World Health Organization’s International Classification of Diseases). DSM-III used a name that closely tracked what Kanner came up with 36 years earlier – “infantile autism” – placing it under the category “pervasive developmental disorders,” with the diagnostic code number 299.00. At a mere 70 words long, its tightly written criteria comprised three categories of behavior – lack of responsiveness to other people, deficits in communicative skills, and “bizarre responses to. . .the environment” – with a few sample behaviors listed for each category: for example, echolalia, in the communication category, and resistance to change, in the “bizarre responses” category.” No guidance was given on how to weigh these various behaviors against one another in deciding on a diagnosis. The picture was dramatically different 7 years later, with the publication of an updated DSM-IIIR (“R” for “revision”). Gone for good in 1987 was “infantile autism,” which was seen to cloud recognition that autism was lifelong. Also gone were a further four closely pervasive developmental disorders – for example, “infantile autism residual state,” which was supposed to account for individuals whose autistic behaviors lost some of their intensity over time. Now, DSM-III-R gave the
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299.00 designation to an overhauled construct it dubbed “autistic disorder.” Designed to be both less restrictive than the old “infantile autism” and more reflective of developmental concepts (Szatmari 1992), it was framed around a diagnostic checklist that ran to more than 600 words. Again, symptomatic behaviors were grouped around three themes. This time, the categories were social interaction, communication (verbal and nonverbal), and a “restricted repertoire of activities and interest.” Also, a more deliberative diagnostic scheme was put in place. Now 16 specific behaviors were listed – such as “gross impairment in ability to make peer friendships” and “marked distress over changes in trivial aspects of environment, e.g., when a vase is moved from usual position.” These 16 behaviors were sorted among the 3 categories, and a rule was specified: diagnosis required a match with at least 8 of the 16 behaviors; at least 2 had to belong to the social interaction group; and at least 1 had to come from the other two groups. This more operational approach to diagnosis was but one of that edition’s innovations in autism diagnosis. The other, even more sweeping one, was the creation of a controversial new “sister” diagnosis: “299.80, Pervasive Development Disorder Not Otherwise Specified.” The inelegant nomenclature belied a well-intentioned purpose – to provide a diagnostic landing spot to individuals who were near misses for the 299.00 diagnosis. These were defined as people displaying “qualitative impairment in the development of reciprocal interaction and of verbal and nonverbal communication skills,” but not to the extent, or in such combinations, that satisfied the checklist. Because this relatively vague definition made allowance for even quite mild symptoms, some would come to think of PDDNOS as a kind of “autismlite” – especially compared to the “classic” autism Kanner had described. But this misunderstood the breadth and depth of impairment a diagnosis of PDDNOS might represent for many people who were given this diagnosis. In the push and pull among experts, 1987’s DSM revision also represented a period of
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ascendency for one particularly compelling viewpoint on how to make sense of a phenomenon obvious to everyone: that autistic traits were remarkably variable in how differently they presented themselves in different people. This is the observation that gives rise to the oft-repeated aphorism, “when you’ve met one person with autism, you’ve met one person with autism.” In practice, the matter of how to interpret this heterogeneity has been an enduring source of tension in understanding what the word “autism” properly represents. One view holds that these differences likely indicate distinct and separate underlying syndromes – different autisms, so to speak – which should be delineated as such by splitting them off from one another. Both Kanner and Asperger, for example, stood among the so-called splitters, as both insisted adamantly that, despite some overlap, each had described a full syndrome distinct from the other’s. The splitter viewpoint was dominant in the 1980 DSM, where autistic traits were covered across those five different diagnoses. But in the 1987 DSM, the “splitter” position lost ground to a competing perspective: that in nearly all cases, nearly all manifestations of autistic traits resulted from the same core mechanism or mechanisms, which happened to affect different people to different degrees. Those holding this view were often called the “lumpers,” for preferring to see past the differences and for preferring to” lump” together, to the maximum extent, the vast array of people displaying any measure of autistic behavior. Prominent in this group was the British psychiatrist Lorna Wing, who played a singular role in crystalizing the concept she called the autism “spectrum,” the profoundly influential proposition that all autistic behavior – though representing a huge range of intensities, from profound to extremely mild, while operating independently of intelligence – is nevertheless rooted in the same syndrome of impairments, which she identified as a “triad of impairments.” This model, originally based on epidemiological studies Wing carried out with Judith Gould in the mid-1970s London, was not a dramatic departure from
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previous formulations, specifying three areas of challenge: in social interaction, in the use of reciprocal language, and in what they called “social imagination,” where the ability to pretend play is an example of the last item. Where she did break ground, however, was in the inclusiveness of her concept, which resisted the “splitters’” tendency to want to draw sharp lines between, say, those who were called “higher” and “lower” functioning. Such distinctions, Wing wrote disapprovingly, have the “effect of excluding those who do not fit neatly into the categories.” It should come as no surprise that Wing served on the working group that developed the PDDNOS diagnosis in 1987, which was an excellent example of a “lumper”-type category and took a major step toward a “spectrum”-type conceptualization of autism. Wing, the mother of a daughter with autism, was motivated in part by a desire to justify support services for a far broader range of people than narrow diagnosis would allow. In time, the implementation of her ideas led to just that outcome. But that is not to say that the diagnostic push and pull ever ended. Indeed, in 1994, it moved back toward “splitting” mode, slicing up the pervasive developmental disorders once again to include, in addition to autistic disorder, some of the previously “not otherwise specified” PDD conditions: Rett’s disorder, childhood disintegrative disorder, and, half a century after it was first described by its namesake, Asperger’s disorder. It was Lorna Wing who had rediscovered Asperger’s work in the late 1970s and then popularized it in the 1980s. Wing’s involvement proved ironic, however, because her intention was never to see Asperger’s “autistic psychopathy” recognized as a standalone diagnosis. Rather, she brought attention to the Austrian’s work for the opposite purpose: she had wanted to suggest just how far the spectrum concept could be stretched, given that Asperger’s children were so different from Kanner’s, even as both employed “autistic” to describe some of their behaviors. The elevation of Asperger’s in 1994’s DSM was an outcome Wing neither desired nor anticipated, always
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insisting there “no clear boundaries separating it from other autistic disorders (Wing 2005).” Yet acknowledgment of Asperger’s syndrome by the DSM that year (and even earlier in the ICD) proved pivotal across multiple fronts. It quickly caught on with clinicians, becoming an increasingly assigned diagnosis, often employed, Wing herself believed, to spare parents the discomfort of hearing the word “autism.” The diagnosis also began to be given to adults – people who had grown up facing social challenges without the benefit of a diagnostic perspective in which to frame their difficulties. Finally, recognition of Asperger’s as “a form of autism” – a phrase the media often used – had important cultural impact, to some degree destigmatizing autism in general by association with often highly intelligent and articulate self-advocates who positively embraced their own diagnoses as central to their identities. Plays, books, and movies began to appear, centering sympathetically on the stories of people who more or less matched the diagnosis, while websites began appearing designed for, and later by, self-described “Aspies,” who expressed pride in the ways they were different from the population at large. But then, in 2013, a new DSM appeared, and this one no longer included Asperger’s syndrome. Once again demonstrating the extreme malleability of such constructs, the team assigned to define the pervasive developmental disorders listened to the researchers and clinicians who had long complained that Asperger’s was actually a problematic concept, resistant to stringent criteria, and understood differently in different clinical settings – or, as one team member puts it, the diagnosis had proven “confusing and not terribly useful.” Asperger’s was dropped and so, too, was PDDNOS and all the other specified developmental disorders that had been in use for the previous two decades. Indeed, in vindication of Lorna Wing’s 35-year-old argument that autism represented a spectrum, a new formulation appeared in the latest DSM, using that term “299.00 Autistic Spectrum Disorder” (ASD).
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History of Autism as a Diagnostic Concept
Future Directions
References and Reading
ASD represented the apotheosis of “lumper” thinking, by aiming to eliminate all reference to sub-syndromes. In addition, the criteria aimed to be inclusive of nearly all individuals previously assigned any of the now-vanished diagnoses, as well as some who had earlier qualified for no diagnosis at all. As a result of this development, the number of individuals empowered to claim “I have autism” or “I’m autistic,” by reference to how professionals in the field defined the term, reached an all-time high and is estimated to account for roughly 1 percent of the human race. At least, for now. It is by no means clear that the all-encompassing spectrum will remain the last word in how autism is perceived and addressed. The lack of certainty about the underlying drivers of autistic behaviors remains as persistent as ever, while the interplay of subjective and even competitive perspectives in the diagnostic process remains as problematic as ever. It is entirely possible that genetic and other biomedical research may, ultimately, discover the existence of multiple pathways to the same still rather amorphous grouping of the behaviors that are used to identify autism. This could once again revive interest in delineating multiple syndromes “hidden” within the spectrum, each with its own instigator, internal mechanisms, and treatments. If that were that to happen, the ever-evolving debate about what we all mean when we say “autism” would, without a doubt, enter yet another new round.
Asperger, H. (1944). Die Autistischen Psychopathen im Kindesalter. Archiv fur Psychiatrie und Nervenkrankheiten, 117, 76–136. Bettelheim, B. (1967). The empty fortress: Infantile autism and the birth of the self. New York: Free Press. Bleuler, E. (1950). Dementia praecox, or the group of schizophrenias (trans: J. Zinkin). New York: International Universities. Creak, M. (1961). Schizophrenic syndrome in childhood: Progress report of a working party (April 1961). British Medical Journal, 2, 889. Donvan, J., & Zucker, C. (2016). In a different key: The story of autism. New York: Crown Publishers/Random House. Kanner, L. (1943). Autistic disturbances of affective contact. Nervous Child, 2, 217–250. Kanner, L. (1944). Early infantile autism. The Journal of Pediatrics, 25(3), 211–217. Kanner, L. (1965). Infantile autism and the schizophrenias. Behavioral Science, 10(4), 412–420. Lotter, V. (1966). Epidemiology of autistic conditions in young children. Social Psychiatry, 1(3), 163–173. Rutter, M. (1968). Concepts of autism: A review of research. Journal of Child Psychology and Psychiatry, 9(1), 1. Rutter, M. (1978). Diagnosis and definition of childhood autism. Journal of Autism and Childhood Schizophrenia, 8(2), 139–161. Schopler, E. (1978). On confusion in the diagnosis of autism. Journal of Autism and Childhood Schizophrenia, 8(2), 137–138. Szatmari, P. (1992). A review of the DSM-III-R criteria for autistic disorder. Journal of Autism and Developmental Disorders, 29(4), 507. Volkmar, F. R., & Cohen, D. J. (1988). Classification and diagnosis of childhood autism. In E. Schopler & G. Mesibov (Eds.), Diagnosis and assessment in autism (p. 72). New York: Plenum Press. Volkmar, F. R., & Klin, A. (1998). Asperger syndrome and nonverbal learning disabilities. In E. Schopler, G. Mesibov, L. Kunce (Eds.) Asperger Syndrome or High-Functioning Autism? (pp 107–121). New York: Plenum Press. Wing, L. (1981a). Language, social, and cognitive impairments in autism and severe mental retardation. Journal of Autism and Developmental Disorders, 11(1), 31–44. Wing, L. (1981b). Asperger’s syndrome: A clinical account. Psychological Medicine, 11(1), 115–129. Wing, L. (2000). Past and future of research on Asperger syndrome. In A. Klin, R. R. Volkmar, & S. S. Sparrow (Eds.), Asperger syndrome. New York: Guilford Press. Wing, L. (2005). Problems of categorical classification systems. In F. Volkmar, R. Paul, A. Klin, & D. Cohen (Eds.), Handbook of autism and pervasive developmental disorders, diagnosis, development neurobiology, and behavior. Hoboken: Wiley.
See Also ▶ Asperger, Hans ▶ Broader Autism Phenotype ▶ DSM-5 ▶ DSM-III ▶ DSM-III-R ▶ DSM-IV ▶ Kanner, Leo
Hoarding in Youth with Autism Spectrum Disorders
Hoarding in Youth with Autism Spectrum Disorders Brian A. Zaboski1 and Eric A. Storch2,3 1 School of Special Education, School Psychology, and Early Childhood Studies, University of Florida, Gainesville, FL, USA 2 Department of Pediatrics and Psychiatry, University of South Florida, St. Petersburg, FL, USA 3 Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA
Definition Before publication of the DSM-5, hoarding was conceptualized as a symptom of obsessivecompulsive disorder (OCD). However, research findings has led to the reclassification of pathological hoarding, known as hoarding disorder (HD), into a separate diagnosis from OCD with primary diagnostic criteria encompassing (a) a persistent difficulty discarding possessions, (b) distress associated with discarding items, (c) the accumulation of clutter, and (d) clinically significant distress/impairment (American Psychiatric Association 2013). The age of onset of compulsive hoarding ranges from 11 to 15 years old, but symptoms may not become clinically significant until adulthood (Tolin et al. 2010). The overall prevalence rate of HD ranges from about 1.5 to 5.8%, though most research has studied adults (Nordsletten and Mataix-Cols 2012; Timpano et al. 2011). Hoarding behaviors may be common among youth with autism spectrum disorders (ASD) for three reasons. First, executive functioning deficits (e.g., in planning, attention, task completion, organization) are associated with both childhood (and adult) HD and ASD and could contribute to hoarding behaviors. High rates of HD have been found with ADHD (Fullana et al. 2013; Hacker et al. 2016), which is comorbid in 28.2–30.6% of cases with ASD (Leyfer et al. 2006; Simonoff
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et al. 2008). Executive functioning difficulties may be associated with impulsive collecting/acquisition, difficulty organizing and discarding possessions, and planning to throw away objects in the future (Storch 2011a). Second, the repetitive and restricted interests associated with ASD may influence incidence of hoarding behaviors (Storch et al. 2016). For instance, a child with ASD, who has a strong interest in geology, may attempt to acquire rocks and related items and have difficulty discarding those items. Third, the overlap between ASD and anxiety disorders in children and adolescents may increase the risk or severity of HD. Anxiety disorders are often maintained by safety cues (e.g., stuffed animals, music, caregivers) that children or adolescents use to mitigate the anxiety they experience from aversive stimuli (Storch et al. 2016). As a result, children and adolescents may develop personal attachments to these objects that lead to strong emotional reactions when having to discard or separate from them.
Assessment In children and adolescents, behaviors meeting criteria for HD must first be differentiated from developmentally appropriate behaviors (i.e., hobbies, collections) as well as symptoms associated with ASD (e.g., the collection of objects due to restricted interests or repetitive behaviors). To aid in this differentiation, Storch et al. (2011b) note that children who hoard often collect objects such as boxes, paper clips, school papers, stickers, items from nature (i.e., rocks), or erasers. In addition, children and adolescents who hoard may develop strong emotional attachments to objects by assimilating them into their personal identities (Plimpton et al. 2009). These attachments are often strong enough to cause significant distress about hurting or damaging specific items, thereby preventing children or adolescents from discarding them. Another distinguishing feature is that among children and adolescents who hoard, removing or throwing away hoarded objects typically leads to negative emotional/behavioral reactions (Plimpton et al. 2009).
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To assist with diagnoses and treatment, a multimodal, multi-method assessment that identifies hoarding symptoms and comorbidities is recommended for children with hoarding behaviors (Frost and Hristova 2011). An interview with a trained clinician who has experience in working with children with ASD is the cornerstone of assessment. Currently, the only developmentally appropriate measure is the Child Saving Inventory (CSI; Storch et al. 2011a), which is a 23-item, parent-rated scale assessing four domains: discarding, clutter, acquisition, and distress/ impairment. Beyond this, broadband measures that include items that assess hoarding may be helpful (e.g., Achenbach 1991) as well as the Children’s Yale-Brown Obsessive-Compulsive Scale (CY-BOCS; Scahill et al. 1997), which includes a single item assessing hoarding symptom presence, as well as overall severity.
Treatment Few studies have been conducted with children and adolescents experiencing comorbid ASD and HD. Although not among children with ASD and HD, case studies have reported success in childhood HD when using a parent-focused behavioral intervention involving contingency management. For instance, Ale et al. (2014) demonstrated that parental involvement played an integral role in treatment to generalize skills to multiple settings, to increase treatment gains, and to provide encouragement. In his case study on a 10-year-old with OCD and hoarding behaviors, McKay (2016) first utilized an assessment battery containing the CY-BOCS and conducted a functional assessment to provide more detail about the patient’s hoarding behaviors. When the functional assessment revealed hoarding that was narrowly focused on objects related to academics (e.g., schoolwork from years ago), he implemented cognitivebehavioral therapy with exposure and built a fear hierarchy to help the patient systematically address her hoarding behaviors. Similar to Ale et al. (2014), McKay (2016) also highlighted the need for parent-provider collaboration and reward systems to boost motivation and treatment compliance. Preliminary data are also available from a
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study focused on cognitive-behavioral therapy (CBT) for anxiety among youth with ASD. Storch et al. (2016) conducted exposure-based CBT with 26 youth with ASD and a comorbid anxiety disorder over 16, 90-minute sessions. Although the treatment was focused on anxiety broadly, core hoarding symptoms across several domains (e.g., distress, clutter, difficulty discarding, and acquiring items) decreased following intervention.
Future Directions There are several areas highlighted for future work. First, large epidemiological studies may elucidate the role of genetics and heritability in ASD and HD. As a result, more accurate prevalence rates will narrow comorbidity estimates and provide researchers and practitioners with more accurate base rates for their diagnostic decisions. Second, treatment protocols for HD in youth are needed, as well as how to adapt such interventions to be individualized to working with youth with ASD. Third, further development and evaluation of rating scales to supplement the CSI will improve assessment practices and increase diagnostic accuracy. Finally, the literature could benefit from studies investigating shared etiology in HD and ASD that could provide a developmental/ prospective course of such symptoms.
See Also ▶ Anxiety ▶ Anxiety Disorders ▶ Behavior Therapy ▶ Child Psychotherapy ▶ Cognitive Behavioral Therapy (CBT) ▶ Diagnosis and Classification ▶ Diagnostic Process ▶ DSM-5 and Autism Spectrum Disorder ▶ General Anxiety ▶ Onset ▶ Prevalence ▶ Psychotherapy ▶ Repetitive Behavior ▶ Stereotypic Behavior ▶ Study Design Impact on Prevalence
Holliday Willey, Liane
References and Reading Achenbach, T. M. (1991). Manual for the child behavior checklist/4–18 and 1991 profile. Burlington: Department of Psychiatry, University of Vermont. Ale, C. M., Arnold, E. B., Whiteside, S. P., & Storch, E. A. (2014). Family-based behavioral treatment of pediatric compulsive hoarding: A case example. Clinical Case Studies, 13(1), 9–21. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: Author. Frost, R. O., & Hristova, V. (2011). Assessment of hoarding. Journal of Clinical Psychology, 67(5), 456–466. Fullana, M. A., Vilagut, G., Mataix-Cols, D., Adroher, N. D., Bruffaerts, R., Bunting, B., . . . & Alonso, J. (2013). Is ADHD in childhood associated with lifetime hoarding symptoms? An epidemiological study. Depression and Anxiety, 30(8), 741–748. Hacker, L. E., Park, J. M., Timpano, K. R., Cavitt, M. A., Alvaro, J. L., Lewin, A. B., . . . & Storch, E. A. (2016). Hoarding in children with ADHD. Journal of Attention Disorders, 20(7), 617–626. Leyfer, O. T., Folstein, S. E., Bacalman, S., Davis, N. O., Dinh, E., Morgan, J., . . . & Lainhart, J. E. (2006). Comorbid psychiatric disorders in children with autism: Interview development and rates of disorders. Journal of Autism and Developmental Disorders, 36(7), 849–861. McKay, D. (2016). Cognitive-behavioral treatment of hoarding in youth: A case illustration. Journal of Clinical Psychology, 72(11), 1209–1218. Nordsletten, A. E., & Mataix-Cols, D. (2012). Hoarding versus collecting: Where does pathology diverge from play? Clinical Psychology Review, 32(3), 165–176. Plimpton, E. H., Frost, R. O., Abbey, B. C., & Dorer, W. (2009). Compulsive hoarding in children: Six case studies. International Journal of Cognitive Therapy, 2(1), 88–104. Scahill, L., Riddle, M. A., McSwiggin-Hardin, M., Ort, S. I., King, R. A., Goodman, W. K., . . . Leckman, J. F. (1997). Children's Yale-Brown obsessive compulsive scale: Reliability and validity. Journal of the American Academy of Child & Adolescent Psychiatry, 36(6), 844–852. Simonoff, E., Pickles, A., Charman, T., Chandler, S., Loucas, T., & Baird, G. (2008). Psychiatric disorders in children with autism spectrum disorders: Prevalence, comorbidity, and associated factors in a populationderived sample. Journal of the American Academy of Child & Adolescent Psychiatry, 47(8), 921–929. Storch, E. A., Lack, C. W., Merlo, L. J., Geffken, G. R., Jacob, M. L., Murphy, T. K., & Goodman, W. K. (2007). Clinical features of children and adolescents with obsessive-compulsive disorder and hoarding symptoms. Comprehensive Psychiatry, 48(4), 313–318. Storch, E. A., Muroff, J., Lewin, A. B., Geller, D., Ross, A., McCarthy, K., . . . Steketee, G. (2011a). Development and preliminary psychometric evaluation of the
2353 Children's saving inventory. Child Psychiatry & Human Development, 42(2), 166–182. Storch, E. A., Rahman, O., Park, J. M., Reid, J., Murphy, T. K., & Lewin, A. B. (2011b). Compulsive hoarding in children. Journal of Clinical Psychology, 67(5), 507–516. Storch, E. A., Nadeau, J. M., Johnco, C., Timpano, K., McBride, N., Mutch, P. J., . . . & Murphy, T. K. (2016). Hoarding in youth with autism spectrum disorders and anxiety: Incidence, clinical correlates, and behavioral treatment response. Journal of Autism and Developmental Disorders, 46(5), 1602–1612. Timpano, K. R., Exner, C., Glaesmer, H., Rief, W., Keshaviah, A., Brähler, E., & Wilhelm, S. (2011). The epidemiology of the proposed DSM-5 hoarding disorder: Exploration of the acquisition specifier, associated features, and distress. Journal of Clinical Psychiatry, 72(6), 780–786. Tolin, D. F., Meunier, S. A., Frost, R. O., & Steketee, G. (2010). Course of compulsive hoarding and its relationship to life events. Depression and Anxiety, 27(9), 829–838.
Holliday Willey, Liane Adam Feinstein Autism Cymru and Looking Up, London, UK
Short Biography Liane Holliday Willey, Ed.D. (born 1959), is a best-selling an American author, educator and public speaker. She was diagnosed with Asperger’s syndrome in 1996. She coined the term “Aspie” in her 1999 autobiography, Pretending to be Normal: Living with Asperger Syndrome. Dr. Willey has authored several other internationally best-selling books, all published by Jessica Kingsley Publishers, including Asperger Syndrome in Adolescence: Living with the Ups, the Downs and Things in Between and Asperger Syndrome in the Family: Redefining Normal. Her latest book is Safety Skills for Asperger Women: How to Save a Perfectly Good Female Life (2011). Dr. Willey has contributed to many additional books and journals and is currently the senior editor for Autism Spectrum Quarterly. In addition to her numerous interviews on national and international television and radio, her life story was an
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inspiration for the film Normal Folk, currently in preproduction, and the feature film Adam, as well as the focus of the video Asperger Syndrome: Crossing the Bridge with Dr. Tony Attwood. Dr. Willey believes the Asperger’s syndrome community is a wonderfully rich world that is populated by some of the most interesting and incredible individuals on the planet and she is exceptionally proud to be a part of it. She is the founder of the Asperger Society of Michigan.
References and Reading Willey, L. H. (1999). Pretending to be normal: Living with Asperger’s syndrome. London: Jessica Kingsley. Willey, L. H. (2001). Asperger syndrome in the family: Redefining normal. London: Jessica Kingsley. Willey, L. H. (2003). Asperger syndrome in the adolescent years: Living with the ups, the downs and things in between. London: Jessica Kingsley. Willey, L. H. (2010). Unwrapping the mysteries of Asperger’s: The search for truth and discovery of solutions – Guide for girls and women with Asperger’s syndrome. AuthorHouse. Willey, L. H. (2011). Safety skills for Asperger women: How to save a perfectly good female life. London: Jessica Kingsley.
Home Living Skills ▶ Daily Living Skills
Home Schooling in Autism Noah Remnick Ezra Stiles College, Yale University, New Haven, CT, USA
Definition The 1990 Individuals with Disabilities Education Act (IDEA) promotes students being placed in the least restrictive environment for educational purposes, and that has encouraged the widespread
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placement of students with autism spectrum disorder (ASD) in brick-and-mortar schools – public and private, general education and special education, and integrated and specialized. The heterogeneity of the ASD population complicates this task because while certain core communications deficits affect most individuals with ASD, the levels of intensity and interference vary widely, as do the particular constellations of symptoms that any person manifests. The characteristics of students with ASD, and the lack of preparedness of schools to deal with them, have had a negative impact on the experiences of many of these students in school settings, with problems encompassing poor academic performance, bullying, and strained social relationships. For many ASD students, homeschooling – where there are fewer conflicts and greater individualization – could provide a more appropriate and successful setting for learning. Homeschooling is an option available to all students, and families choose this alternative educational setting for a variety of reasons, including religious, professional, financial, medical, social, and philosophical. Homeschooling programs can be taught by parents, by joining in cooperatives with other families, by computer, or by hiring professional pedagogues. In the case of students with ASD, those who function at a higher intellectual level can usually avail themselves of the full range of homeschool implementation choices. But students with more severe manifestations of ASD generally require trained, experienced clinicians, special education teachers, and therapists to carry out an effective individualized home-based education plan that fosters meaningful academic, social, and behavioral progress.
Historical Background The Centers for Disease Control and Prevention (CDC) reports that 1 in 68 children in the United States has been diagnosed with ASD (Baio 2014), and all of those children are entitled to a Free and Appropriate Public Education (FAPE), as dictated by the 1990 Individuals with Disabilities Education Act. Prior to 1975, school districts could
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decide that a child with a disability was too difficult, or too expensive, to educate. In 1970, for example, government statistics show that only 1 in 5 children with disabilities attended public schools, with school districts regularly refusing with impunity to embrace children who were, among other disorders, deaf, blind, and mentally retarded. Most children with special needs were educated in private schools paid for by their families, or they were institutionalized (United States Department of Education, Office of Special Education and Rehabilitative Services, pamphlet date unknown). In 1975, however, Congress passed the Education for All Handicapped Children Act, enshrining the rights of all children to a government-funded education, no matter their circumstances. The law was expanded and updated in 1990 with the passage of IDEA and further amended in 2004. Today, the vast majority of students with disabilities, including those with ASD, are taught in brick-and-mortar schools (Fleury et al. 2014; Chen and Schwartz 2012). The schools are public and private, special education, and mainstream. Some ASD students learn alongside typically developing peers, in some cases with assistance from special educators or paraprofessionals for all or part of their days. IDEA demands that students be allowed to learn in the least restrictive environment, a rule intended to counteract the forced warehousing of students with disabilities that happened in decades past. The process for deciding the appropriate placement for any student is complex. What seems restrictive for one person might be more liberating and productive for another. For some students, even those with high IQs, being in a traditional school involves a level of sensory overstimulation, conformity to extensive rules, and continuous social interaction that could impede their ability to make meaningful educational progress. Indeed, current methods for dealing with ASD students show limited long-term success. People with ASD rank third from the bottom in college enrollment among 11 disability categories. Meanwhile, only 37 % of young adults with ASD are employed, and most of them only in parttime jobs without benefits (Fleury et al. 2014). Ironically, those with ASD who are not
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intellectually compromised have triple the chance of being unemployed and unengaged in leisure activities with their peers, compared with young adults who have ASD coupled with intellectual disabilities (Fleury et al. 2014). This statistic is especially disconcerting because the overwhelming majority of people diagnosed with ASD has milder manifestations of the disorder (Van Bergeijk et al. 2008) and presumably should be able to attain greater success in the classroom and beyond. In general, minimal research has been done on this population as it moves past young adulthood; still, while these shards of data are limited, they offer a bleak window onto the prospects for ASD adults.
Current Knowledge Academic Barriers to School Success for ASD Students The characteristics that define a person as having ASD render school a treacherous system to navigate. In all its iterations, autism is primarily a communication disorder that undermines a person’s capacity to interact with others appropriately (Van Bergeijk et al. 2008). People with ASD have, to varying degrees, a compromised ability to observe, imitate, and apply new skills – all essential components of acquiring knowledge. They typically exhibit rigid and repetitive behaviors that interfere with learning and dealing with other people, whether fellow students, teachers, or school staff. Their auditory and visual processing speed is often slow and difficult to manage (Fleury et al. 2014; Van Bergeijk et al. 2008). Their conversation skills are severely constrained – at the most affected edge of the spectrum in basic expressive and receptive language and even in quality of speech production, and at the higher functioning and Asperger syndrome (AS) edge, in their dexterity in reciprocating and empathizing with another in conversation (Fleury et al. 2014; Van Bergeijk et al. 2008; Volkmar and Klin 2001). The list of scholastic roadblocks runs on. While many ASD students have good decoding skills in reading, they often have poor
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comprehension, in part because of their difficulty in assuming the perspective of another (Fleury et al. 2014). In writing, poor fine motor skills interfere with the physical ability to produce words on paper or to type, a process further frustrated by weak visual motor speed and coordination. In terms of content, trouble with empathy and perspective gets in the way of composing creatively and with clarity (Fleury et al. 2014). And one of the most complex skills needed to succeed in a school environment is the organization born of executive function processes that most people with ASD lack (Rosenthal et al. 2013). Over the last few decades, schools have relied more upon team learning and projects, a looming obstacle for ASD students compromised in their pragmatic and social language abilities, and their facility at reading social cues well enough to join forces for a group endeavor (Durkin and ContiRamsden 2007). Moreover, as focus shifts from individual students with ASD to the group, executive functioning deficits can seriously undermine their ability to retain information and contribute meaningfully to the collaborative work (Rosenthal et al. 2013). People with ASD also have a high incidence of other psychiatric and neurological conditions that exacerbate the autism itself and can inhibit learning. Common comorbid disorders for people with ASD include attention deficit hyperactivity disorder (ADHD), anxiety, depression, grapho-motor deficits, seizures, and intellectual disabilities (Van Bergeijk et al. 2008). Indeed, Van Bergeijk et al. (2008) note that 65 % of people with ASD have anxiety and/or depression (p. 1361), conditions that on their own would impede any student’s ability to cope in the fast-paced academic and social world in a school and that can overwhelm a person already reeling under the weight that ASD deficits impose. Imagine for a minute the ASD student in a school building bustling with children racing to class, returning from a football game, or practicing for the annual musical. The aroma of tacos and pizza wafts from the cafeteria – a crowded room abuzz with laughter, conversation, and song. In addition to all the intellectual and organizational
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skills he lacks, the ASD student is also sensitive to sudden noise and movement, keenly aware of sights, sounds, and smells he cannot control. These deficits are obstacles to every facet of learning in a school filled with other pupils, whether those students share one’s disabilities or are free of them. Since every person with ASD has a different cluster of symptoms, behaviors, challenges, strengths, and compensations, schools are stymied in their efforts to create comfortable environments for all. The heterogeneity of the population makes it impossible for schools to develop an easy, “off-the-rack” approach to deal with ASD students. Furthermore, behavioral problems can deluge the child and staff, interfering with the learning not only of the individual with ASD but of the entire class as well. Given the staggering demands, there are generally insufficient academic and behavioral support services to foster reliable success for ASD students in a school setting (Van Bergeijk et al. 2008; Catherine Lord, personal communication, June 25, 2014). One significant measure of the subpar school options being offered to students with ASD is the rise in litigation by dissatisfied parents seeking reimbursement for nonpublic school programs for their children. Parents of children with ASD are 10 times more likely than parents of children with other disabilities to sue school districts to force fulfillment of FAPE, and they account for about a third of all cases filed overall (Zirkel 2001). This disproportionality is due in part to the complexity and severity of ASD but also speaks to the failure of school districts to offer the proper comprehensive services and environment necessary to educating a student with the disorder. Social Barriers to School Success for ASD Students By their nature, schools and classrooms are not just centers of scholarship; they are communal organizations that depend on the kind of compromise and cooperation that eludes most people with ASD because the interfering symptoms and behaviors are the root expression of the neurological condition itself. Poor social skills are a hallmark of people all along the autism spectrum
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(Volkmar and Klin 2001; Durkin and ContiRamsden 2007; Van Bergeijk et al. 2008). When someone has poor social competence and lagging academic skills, the two deficiencies feed off one another in a way that produces negative outcomes in both spheres (Welsh et al. 2001). There is a “chicken-and-egg” debate about which is the primary determinant of the other: some research suggests that academic failure leads to students being ostracized socially, while other research points to low social status as a source of low academic achievement. Whether or not a definitive cause and effect can be established, it is clear that over time, the two influence one another and are mutually predictive. Welsh et al.’s (2001) longitudinal study found that “academic competence exerts a significant influence over social competence consistently,” with the negative correlate being true as well (pp. 477–478). This finding bodes ill for students with ASD studying in traditional schools, where managing social relationships with teachers, fellow students, and staff is enmeshed in all daily activities. The need while in school to respect boundaries, to empathize with others, to be flexible, and to read social cues thwarts people with ASD, who lack such emotional intelligence when it comes to dealing with peers and with adults who work in schools. Even the most academically capable students with ASD, namely, those with Asperger syndrome, suffer from poor social skills, sensory issues, and pragmatic language deficits (e.g., lack of reciprocal conversation and perseverative topics) that compromise their overall performance and well-being in school (Church et al. 2000). The inability to communicate appropriately through language circumscribes a child’s ability to form close relationships. When combined with impaired social cue reading and inconsistent self-regulation, ASD students are limited in their ability to access the friendships theoretically available in a school setting (Durkin and ContiRamsden 2007). High functioning students with autism, although unburdened by comorbid intellectual disabilities, can be so rigid and rulesbound that they often, for example, tattle on their classmates, causing further social alienation (Church et al. 2000). As one parent lamented
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about his son, “He doesn’t have weak social skills, his social skills are non-existent!” (Church et al. 2000, p. 18). The opportunity to socialize with peers, to learn from their example how to behave appropriately, is often cited as a critical potential benefit for ASD students in attending schools. Reality, however, does not commonly live up to such expectations, according to the literature and interviews with experts in the field. For ASD students in specialized schools, peers often model maladaptive behaviors – such as tantrums and flapping – that should not be imitated but frequently are. For those in mainstream schools, acceptance is not easily forthcoming. In school, where one sits, the foods one eats, the clothes one wears, all send myriad silent signals that most ASD students cannot interpret. “The greatest lesson of childhood is not reading, writing or arithmetic,” said Dr. Orrin Devinsky, a neurologist and the director of NYU’s Comprehensive Epilepsy and Seizure Center, who has many patients with ASD since seizures are a common comorbid condition with autism (O. Devinsky, personal communication, June 26, 2012). “For most of human evolution, we acquired language without teachers, just older individuals who spoke. Socialization is the key of childhood. The lesson. But for ASD kids, they are with other ASD kids and the lesson of socialization is a bizarre and foreign one – the role models are skewed to watching kids with self-injurious behaviors, tantrums, lack of eye contact, etc. For those who get some mainstream time, there are great benefits (more normal social peers) but also cases where kids can be insensitive and cruel.” Media stereotypes of people with autism coupled with ignorance reinforce the social sand traps that the disorder itself sets. Typical children generally reveal unfavorable attitudes toward peers with autism when they are shown pictures and film clips of them. Although some research has shown that these negative associations improve when the children are given explanations for the aberrant behavior – including the lack of eye contact and repetitive motions, such as arm flapping – other research does not (Campbell et al. 2005). This negative stereotyping not only influences students but also affects the views of their
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parents, of teachers in the classroom, and of the general school staff. In some cases, students engaged as peer mentors can help bring an ASD classmate into the social fold. This intervention tends to work best when the mentors are so-called high-status children, whose leadership skills and popularity influence classroom social dynamics. But the research indicates that acceptance of the ASD student can be fleeting because it occurs due to a desire by the other students to please the mentors, not a genuine change in their attitudes. The mentors themselves often do not change their attitudes either, nor do they always act out of altruism; rather, they take on the role generally because they perceive that being an advocate for a disabled peer will enhance their own leadership ranking (Campbell et al. 2005). Few typical students are willing or able to become a genuine protector to their ASD classmates. Most students simply ignore them. But some seize on the ASD students as easy prey for ostracizing and maltreatment. Having low status and diminished social skills leaves many students with ASD particularly vulnerable to bullying in school, as targets are often awkward, marginalized young people with poor peer relationships – characteristics common to ASD students (Cappadocia et al. 2011; Chen and Schwartz 2012). Parents, students, and teachers report high levels of victimization of students with ASD by fellow classmates. About 40 % of students with ASD report being bullied, according to Dr. Fred Volkmar, director of the Yale Child Study Center; for those with a comorbid condition, that number rises to roughly 75 % (F. Volkmar, personal communication, June 29, 2014). Research by Cappadocia et al. in 2011 put the percentage of ASD students who are victimized as possibly higher, with 77 % of parents interviewed reporting that their children with ASD had been bullied at school in the previous month (Cappadocia et al. 2011). Such victimization has serious negative consequences for the target from academic, behavioral, social, physical, and psychological perspectives (Chen and Schwartz 2012; Sterzing et al. 2012). School bullying is difficult to root out, however, because it involves a complex web of social interactions, rather than a singular act of cruelty
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(Cappadocia et al. 2011). Bullies are generally adept at choosing moments of attack that evade adult detection, and students with ASD often lack the verbal skills to report their own victimization. Even when they are able to identify an attacker, ASD students – and, indeed many typically developing students who are targets – overwhelmingly fail to report being abused because they are ashamed, fear retribution, or are too socially oblivious to realize they have been humiliated or shunned (Campbell et al. 2005; Chen and Schwartz 2012). Moreover, ASD students generally do not have the self-advocacy skills to confront their tormentors or a safety net of peers to look out for them and comfort them when they are victimized (van Roekel et al. 2010). So instead of learning from typical peer models how to socialize appropriately, ASD students generally pull back from social interactions, or worse: Sometimes they imitate the bullying behaviors and target others – either out of anger, or ignorance (Chen and Schwartz 2012). Indeed, many people with ASD are emotionally labile and even abusive (Church et al. 2000), characteristics that make them especially susceptible to the invidious lure of bullying others. Even when ASD students avoid the perils of bullying – as both perpetrators and, more often, victims – the structure of the school environment underscores that they are different from their peers. They labor under labels that regularly remind them – and their classmates – that they stand apart. They are the ones with special educators or paraprofessionals shadowing them in the classrooms and on the playing fields, the ones who get extra time on tests, or have augmentative communication devices, the ones who get pulled out of class for therapy. Throughout the school day, the emphasis is on their differences, not their similarities – a dagger to the self-esteem of those special education students intellectually and emotionally aware enough to take note (Ensign 2000).
Future Directions There is scant peer-reviewed or scholarly research and literature on homeschooling students with
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ASD. There are some books on the subject, but they are almost entirely how-to manuals, with some incorporating personal stories. None contains any systematic research. As such, in order to hypothesize how a similar home learning environment might work for students with ASD, extrapolating from the professional literature that examines the homeschooling experiences of less compromised students, for example, those with learning disabilities or ADHD, is required. In a 9-year longitudinal study of homeschooled special education students, Ensign (2000) revealed that learning in a home environment freed such students of the stigmas and stereotypes that inhibited their education and socialization in traditional schools – mainstream and specialized. Ensign noted research that showed “the culture of school orchestrated labeling and disabling” students with special needs (p. 149), a plight common to ASD students educated in schools, while being at home minimized or eliminated those stressors. Duvall led two different investigations into the homeschool experiences of children with other interfering disorders – one in 1997 of students with learning disabilities and one in 2004 of students with ADHD (Duvall et al. 1997, 2004). Both studies showed that students with special needs flourished in the calmer, more attentive home environment and that they engaged significantly more with their work than they had when they were in school, making greater reading and math gains than their peers with similar issues who attended the public schools. Although small in size, both studies indicated that homeschooling provides an advantage to students who need more intensive instruction. In her study, Ensign (2000) found that the success emanated from the fact that parents were able to create an educational milieu at home that emphasized the strengths of special students, rather than their deficits, and that encouraged excellence based on individual aptitude and interests. Unbound by the dogma of most state rules and regulations, the parents (or their surrogate teachers) could give extra time for areas of struggle and encourage acceleration in areas of promise (Ensign 2000). Dr. Devinsky said he has observed
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that for ASD students with heightened sensitivities and circumscribed capabilities, a homeschool program often offers the only appropriate environment for fostering progress and forestalling regression. “The greatest benefits of homeschooling are removing a child from a chaotic environment where their mind may be hyperstimulated and so overwhelmed they shut down or explode as their major behavioral patterns,” he noted (O. Devinsky, personal communication, June 26, 2014). Furthermore, he stated that homeschooling students with ASD “removes them from negative peer behaviors and models (they don’t learn to spit or hit their heads) and can remove them from a chaotic environment to a more nurturing one, and can allow a more relaxed and individually targeted program” (O. Devinsky, personal communication, June 26, 2014). Creating a homeschool program for typically developing students is cumbersome; for ASD students with the need for highly specialized therapies and instruction, doing so is especially costly – financially and in the burden it places on the student’s family. It puts added pressure on the parents to earn enough money to cover the significant expenses such programs impose and burdens them with managing its details (F. Volkmar, personal communication, June 29, 2014). Since IDEA guarantees a free and appropriate public education to all children, when the school district fails to provide an ASD student with an education that is appropriate to his or her unique needs, parents often construct a program on their own and then litigate to receive reimbursement to fulfill the promise of “free” embedded in FAPE. Not all families have the intellectual, emotional, and financial resources to sue their school districts. The process can be long and success is not assured. The responsibility to ascertain what their ASD student needs educationally – and to arrange, monitor, and fund the program – falls on the shoulders of parents already exhausted by the responsibility of caring for a challenging child. “Most families do not have situations in which students could learn at home – they do not have enough space and often do not have someone at home who can coordinate various educational activities,” reported Dr. Catherine Lord, Director
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of the Center for Autism and the Developing Brain in New York, and one of the creators of the Autism Diagnostic Observation Schedule (ADOS), a leading assessment tool for diagnosing ASD (C. Lord, personal communication, June 25, 2014). But faced with inadequate programs in their local schools, parents of students with ASD are increasingly crafting individualized homeschool programs for their children outside of the public or private school system, and they are increasingly suing for reimbursement of the substantial costs (Zerkel 2011). For many students, the barriers to learning and socializing in brick-and-mortar schools can be overcome, and they can achieve success. But the literature that does exist and the experiences of experts in the field suggest that many students could do much better academically, behaviorally, socially, and psychologically in an individualized homeschool setting. At home there are no learning disabilities, there is only learning. As such, this approach should be studied in a comprehensive and methodical way to measure its potential for improved academic, post-academic, and social outcomes for students with ASD.
See Also ▶ Bullying ▶ Communication Disorder/Communication Impairment ▶ Comorbidity ▶ Free Appropriate Public Education ▶ Home-Based Programs ▶ Individual Education Plan ▶ Sensory Impairment in Autism ▶ Social Behaviors and Social Impairment
References and Reading Baio, J. (2014, March 28). Prevalence of autism spectrum disorder among children aged 8 Years — Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2010. Centers for Disease Control and Prevention. Retrieved July 2, 2014, from http:// www.cdc.gov/mmwr/preview/mmwrhtml/ss6302a1. htm?s_cid=ss6302a1_w.
Home Schooling in Autism Campbell, J. M., Ferguson, J. E., Herzinger, C. V., Jackson, J. N., & Marino, C. (2005). Peers’ attitudes toward autism differ across sociometric groups: An exploratory investigation. Journal of Developmental and Physical Disabilities, 17, 281–298. Cappadocia, M. C., Weiss, J. A., & Pepler, D. (2011). Bullying experiences among children and youth with autism spectrum disorders. Journal of Autism and Developmental Disorders, 42, 266–277. Chen, P. Y., & Schwartz, I. S. (2012). Bullying and victimization experiences of students with autism spectrum disorders in elementary schools. Focus on Autism and Other Developmental Disabilities, 27, 200–212. Church, C., Alisanski, A., & Amanullah, S. (2000). The social, behavioral, and academic experiences of children with asperger syndrome. Focus on Autism and Other Developmental Disabilities, 15, 12–20. Durkin, K., & Conti-Ramsden, G. (2007). Language, social behavior, and the quality of friendships in adolescents with and without a history of specific language impairment. Child Development, 78, 1441–1457. Duvall, S. F., Ward, D. L., Delquadri, J. C., & Greenwood, C. R. (1997). An exploratory study of home school instructional environments and their effects on the basic skills of students with learning disabilities. Education and Treatment of Children, 20, 150–172. Duvall, S. F., Delquadri, J. C., & Ward, D. L. (2004). A preliminary investigation of the effectiveness of homeschool instructional environments for students with attention-deficit/hyperactivity disorder. School Psychology Review, 33, 140–158. Ensign, J. (2000). Defying the stereotypes of special education: Home school students. Peabody Journal of Education, 75, 147–158. Fleury, V. P., Hedges, S., Hume, K., Browder, D. M., Thompson, J. L., Fallin, K., El Zein, F., Klein Reutebuch, C., & Vaughn, S. (2014). Addressing the academic needs of adolescents with autism spectrum disorder in secondary education. Remedial and Special Education, 35, 68–80. Giangreco, M. F., Broer, S. M., & Edelman, S. W. (2001). Teacher engagement with students with disabilities: Differences between paraprofessional service delivery models. Journal of the Association for Persons with Severe Handicaps, 26, 75–86. Koegel, R. L., Fredeen, R., Kim, S., Danial, J., Rubenstein, D., & Koegel, L. (2012). Using perseverative interests to improve interactions between adolescents with autism and their typical peers in school settings. Journal of Positive Behavior Interventions, 14, 133–141. Pennington, R. C., & Delano, M. E. (2012). Writing instruction for students with autism spectrum disorders: A review of literature. Focus on Autism and Other Developmental Disabilities, 27, 158–167. Prater, M. A. (2003). Learning disabilities in children’s and adolescent literature: How are characters portrayed? Learning Disability Quarterly, 26, 47–62. Rogers, S. (2000). Interventions that facilitate socialization in children with autism. Journal of Autism and Developmental Disorders, 30, 399–409.
Home-Based Programs Rosenthal, M., Wallace, G. L., Lawson, R., Wills, M. C., Dixon, E., Yerys, B. E., & Kenworthy, L. (2013). Impairments in real-world executive function increase from childhood to adolescence in autism spectrum disorders. Neuropsychology, 27, 13–18. Sterzing, P. R., Shattuck, P. T., Narendorf, S. C., Wagner, M., & Cooper, B. P. (2012). Bullying involvement and autism spectrum disorders: Prevalence and correlates of bullying involvement among adolescents with an autism spectrum disorder. Archives of Pediatric and Adolescent Medicine, 166, 1058–1064. Van Bergeijk, E., Klin, A., & Volkmar, F. (2008). Supporting more able students on the autism spectrum: College and beyond. Journal of Autism and Developmental Disorders, 38, 1359–1370. Van Roekel, E., Scholte, R. H. J., & Didden, R. (2010). Bullying among adolescents with autism spectrum disorders: Prevalence and perception. Journal of Autism and Developmental Disorders, 40, 63–73. doi: 10.1007/s10803-009-0832-2. Volkmar, F. R., & Klin, A. (2001). Asperger syndrome and higher functioning autism: Same or different? International Review of Research in Mental Retardation, 23, 83–110. Welsh, M., Park, R. D., Widaman, K., & O’Neil, R. (2001). Linkages between children’s social and academic competence: A longitudinal analysis. Journal of School Psychology, 39, 463–481. Zirkel, P. A. (2001). Autism litigation under the IDEA: A new meaning of “disproportionality”? Journal of Special Education Leadership, 24, 92–103.
Home-Based Programs Svein Eikeseth Department of Behavioral Science, Oslo and Akershus University College, Lillestrøm, Norway
Definition Home programs to provide treatment for young children with autism were pioneered by O. Ivar Lovaas in the 1970s and 1980s. A home program focuses on remediating the children’s delays in communication and social and emotional skills and emphasizes the integration of these children with typical peers in mainstream community settings. Principles derived from laboratory and applied research on learning psychology are used to teach developmentally appropriate and functional skills.
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As described by Green and colleagues, a home program has the following characteristics: 1. Treatment is individualized and comprehensive, addressing all skill domains. 2. Typical developmental sequences derived from data from developmental psychology guide the selection of intervention goals and short-term objectives. 3. Multiple behavior analytic procedures are used to teach new skills and to reduce interfering behavior (e.g., differential reinforcement, prompting, discrete-trial teaching, natural environment teaching, activityembedded trials, task analysis, and others). 4. One or more professionals with advanced training in applied behavior analysis and advanced training in behavioral intervention with young children with autism direct and supervise the intervention. 5. Parents serve as active co-therapists for their children. 6. Intervention is initially delivered in one-toone sessions, with gradual transitions to small-group and large-group formats as a child progresses. 7. Intervention begins in the home and is carried over into other environments (e.g., community settings), with gradual, systematic transitions to preschool, kindergarten, and elementary school classrooms when children develop the skills required to learn in those settings. 8. Intervention involves intensive, year-round teaching, including 20–40 h of structured sessions per week plus informal instruction and practice throughout most of the children’s other waking hours. 9. In most cases, a child receives two or more years of intervention. 10. Most children start intervention in the preschool years. Home programs are usually associated with early intervention programs, especially Early and Intensive Behavioral Intervention (EIBI) for children with autism. The use of home programs to deliver EIBI originates from the Lovaas
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approach (the UCLA Model) and the UCLA Young Autism Project.
Historical Background Home programs have roots in applied behavior analytic (ABA) interventions that were first introduced for children with autism in the early 1960s. Home programs originated a decade later as a result of finding from the first long-term follow-up study of ABA interventions for children with autism (Lovaas et al. 1973). Twenty children with autism participated. After 12–14 months of behavioral treatment, stereotyped behaviors including echolalia decreased, and appropriate speech, appropriate play, and social nonverbal behaviors. Some of the children were discharged to a state hospital while others remained with their parents, who had received parent training. Follow-up assessments one to four years after the end of treatment showed that children whose parents were trained to carry out the behavioral treatment continued to improve, while children who were institutionalized regressed. After a brief reinstatement of the behavioral treatment, however, the institutionalized children recouped some of their original gains. From the 1973 follow-up study, Lovaas made several important observations that led him to develop the home program. First, the youngest children in the 1973 study appeared to make the greatest gains; hence, he focused on providing treatment to toddlers and preschoolers with autism and regarded home as the most developmentally appropriate setting for children. Second, treatment effects in the 1973 study were situation specific. As a result, treatment was moved away from a hospital or clinic setting and into the children’s everyday environments (including the home). Third, some parents became skilled teachers of their children and became the best allies in helping accelerate and maintain treatment gains. Provision of treatment at home maximized opportunities for parental involvement.
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Following the 1973 study, a home program became an integral part of EIBI services, especially in the UCLA Young Autism Project. In 1987, Lovaas (1987) reported an evaluation of a group of 19 children with autism who participated in a home-based EIBI program for 40 h per week for a minimum of 2 years. This report indicated that, after treatment, the average IQ of children in home-based EIBI programs was more than 30 points higher than 40 children in comparison groups and that the children were much more likely to be included in general education settings. Subsequent reports also showed that children in home-based EIBI made large gains relative to comparison groups (e.g., Cohen et al. 2006; Sallows and Graupner 2005; Smith et al. 2000). Since Lovaas’s (1987) report, home programs have become increasingly accepted as an intervention for young children with autism. They are a common feature of EIBI services that emphasize ABA teaching methods. They are also a component of other treatment models such as the Developmental, Individual Differences, Relationship-Based Approach (DIR).
Rationale or Underlying Theory In a home program, an underlying rationale is that the treatment environment should differ as little as possible from the typical learning environment in order to help the child with autism transfer skills to the typical environment and function more successfully in that setting. Also, active involvement of the parents and other family members is a crucial component of treatment success.
Goals and Objectives A home program is usually set up for delivering an EIBI curriculum such as the curriculum described in the sections on the UCLA Young Autism Project and Lovaas approach. The curriculum is individualized based on each child’s needs and stages of development that typically developing children go through.
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Treatment Participants The home program was developed primarily in EIBI models for children with autism who have mild to moderate intellectual disability and are under the age of 42 months at treatment onset. However, it has also been evaluated with children with more severe intellectual disability. Several studies have examined relations between intake variables and outcome measures. Meta-analyses have indicated that the most robust finding to date is a strong positive correlation between treatment intensity and outcome.
Treatment Procedures Typically, 30 to 40 h per week of one-to-one instruction is provided in the child’s home by the parents and trained therapists. If the child is younger than 3 years of age, 20 to 30 h per week of instruction is more common. Initially, most of the teaching is carried out in a discrete trial format, which is a highly structured teaching procedure used to establish a number of important skills, including cognitive skills, communication, play, social, and self-help skills. Also, time is devoted to play and other structured activities, in which the child has opportunities to use skills acquired during discrete trial teaching (e.g., labeling toys or taking turns with the tutor during a game) and learning new skills in situations where they naturally occur (e.g., learning names of new toys, acquiring motor skills while playing soccer, or practicing dressing before going out for community trips). After the child has acquired important communication, play, and social skills, play dates with typical peers become a priority for the home program. The tutor facilitates mastered activities for the child and peer (e.g., conversation, pretend play with toys, or turn-taking games) and encourages the peer to engage the child. After the child is able to sit still and attend to a teacher during small group instruction, and has learned prerequisite communication, preacademic (e.g., cutting, pasting, drawing, doing puzzles)
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play, and social skills, the child is mainstreamed in a regular preschool. At that time, there is a gradual decrease in number of one-to-one hours at home and a gradual increase in time spent in school. Trained tutors accompany the child to school functioning as a classroom aide.
Efficacy Information Since the original study by Lovaas (1987), there have been a number of other studies on homebased EIBI programs. Recently, a number of effect-size studies have been published, and several report large effect sizes for IQ and adaptive functioning. While researchers generally agree that the results of studies have generally been positive, there remains controversy about the scientific quality of the studies, and many have called for additional research with improved research designs. In addition, direct comparisons between home programs and center-based or school-based programs have not been conducted. Thus, it remains unclear to what extent the provision of treatment at home contributes to overall outcomes.
Outcome Measurement The most common outcome measures used are IQ, language, and adaptive functioning. The Bayley Scales of Infant Development and the Wechsler Intelligence Scales (WIPPSI or WISC) are the most common IQ tests. The most widely used instrument to assess language is the Reynell Developmental Language Scales, but due to their severe delays in communication, many participants fail to achieve basal on the Reynell at intake. To assess adaptive behavior, the Vineland Adaptive Behavior Scales is used. Some studies have also assessed other domains, such as maladaptive behavior (using the maladaptive scale from Vineland Adaptive Behavior Scales, the Personality Inventory for
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Children, and the Child Behavior Checklist), school performance (using the Woodcock-Johnson III Tests of Achievement), and changes in diagnosis (using the Autism Diagnostic Interview-Revised (ADI-R).
Qualifications of Treatment Providers There are currently no established programs to certify professionals to become competent homeprogram therapists or home-program supervisors. Practitioners of the home program indicate that, to become a competent therapist, individuals should obtain a bachelor’s degree in psychology, education, or social work, with coursework applied behavior analysis, developmental disorders, and child development. In addition, they require therapists to work at least three months hands-on with a child in an apprenticeship with a trained therapist. At the end of this training, the therapist must be able to apply the behavioral principles correctly during one-to-one teaching. To become a supervisor, Lovaas and colleagues have required the practitioner to be an experienced home-program therapist and hold a master’s degree that includes graduate courses in applied behavior analysis, developmental disorders, and child development. In addition, the supervisors must learn how to conduct staff training, develop and individualize the child’s curriculum, and work with families. These skills can be learned through working in an apprenticeship with a competent home-program supervisor, and by studying available teaching manuals, evidenced-based parent training programs, and typical development. Finally, the supervisor should be supervised by a Ph.D. level clinical psychologist specializing in the home program.
See Also ▶ Early Intensive Behavioral Intervention (EIBI) ▶ Early Intervention ▶ Lovaas, O. Ivar ▶ Psychological Assessment ▶ UCLA Young Autism Project
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References and Reading Bibby, P., Eikeseth, S., Martin, N. T., Mudford, O. C., & Reeves, D. (2002). Progress and outcomes for children with autism receiving parent-managed intensive interventions. Research in Developmental Disabilities, 23, 81–104. Cohen, H., Amerine-Dickens, M., & Smith, T. (2006). Early intensive behavioral treatment: Replication of the UCLA model in a community setting. Developmental and Behavioral Paediatrics, 27, 145–155. Cooper, J. O., Heron, T. E., & Heward, W. L. (2007). Applied behavior analysis (2nd ed.). New Jersey: Prentice Hall. Davis, B. J., Smith, T., & Donahoe, P. (2002). Evaluating supervisors in the UCLA treatment model for children with autism: Validation of an assessment procedure. Behavior Therapy, 33, 601–614. Eikeseth, S. (2009). Outcome of comprehensive psychoeducational interventions for young children with autism. Research in Developmental Disabilities, 30, 158–178. Eikeseth, S. (2010). Examination of qualifications required of an EIBI professional. European Journal of Behavior Analysis, 11, 239–246. Eikeseth, S. (2011). Intensive early intervention. In J. L. Matson & P. Sturmey (Eds.), International handbook of autism and pervasive developmental disorders (pp. 321–338). New York: Springer. Eldevik, S., Hastings, R. P., Hughes, J. K., Jahr, E., Eikeseth, S., & Cross, S. (2009). Meta-analysis of early intensive behavioral intervention for children with autism. Journal of Clinical Child and Adolescent Psychology, 38, 439–450. Eldevik, S., Hastings, R. P., Hughes, J. K., Jahr, E., Eikeseth, S., & Cross, S. (2010). Analyzing outcome for children with autism receiving early intensive behavioral intervention: Secondary analysis of data from 457 children. The American Journal on Intellectual and Developmental Disabilities, 34, 16–34. Ferster, C. B., & DeMyer, M. K. (1961). The development of performances in autistic children in an automatically controlled environment. Journal of Chronic Diseases, 13, 312–345. Green, G., Brennan, L. C., & Fein, D. (2002). Intensive behavioral treatment for a toddler at high risk for autism. Behavior Modification, 26, 69–102. Hayward, D. W., Eikeseth, S., Gale, C., & Morgan, S. (2009). Assessing progress during treatment for young children with autism receiving intensive behavioural interventions. Autism, 13, 613–633. Leaf, R., & McEachin, J. (1999). A work in progress: Behavior management strategies and a curriculum for intensive behavioral treatment of autism. New York: DRL Books, L. L. C. Lovaas, O. I. (1987). Behavioral treatment and normal educational and intellectual functioning in young autistic children. Journal of Consulting and Clinical Psychology, 55, 3–9.
Homework/Assignments, Modifying Lovaas, O. I. (2003). Teaching individuals with developmental delays: Basic intervention techniques. Austin: Pro-Ed. Lovaas, O. I., & Smith, T. (1989). A comprehensive behavioral theory of autistic children: Paradigm for research and treatment. Journal of Behavior Therapy and Experimental Psychiatry, 20, 17–29. Lovaas, O. I., Koegel, R. L., Simmons, J. Q., & Long, J. (1973). Some generalization and follow-up measures on autistic children in behavior therapy. Journal of Applied Behavior Analysis, 6, 131–166. Lovaas, O. I., Ackerman, A., Alexander, D., Firestone, P., Perkins, M., & Young, D. B. (1981). Teaching developmentally disabled children: The ME Book. Austin: Pro-Ed. Maurice, C. (Ed.), Green, G., & Luce, S. (Co-Eds.). (1996). Behavioral intervention for young children with autism: A manual for parents and professionals. Austin: Pro-Ed. Maurice, C., Green, G., & Foxx, R. M. (Eds.). (2001). Making a difference: Behavioral intervention for autism. Austin: Pro-Ed. McEachin, J. J., Smith, T., & Lovaas, O. I. (1993). Longterm outcome for children with autism who received early intensive behavioral treatment. American Journal on Mental Retardation, 97, 359–372. Perry, A., Cummings, A., Dunn Geier, J., Freeman, N. L., Hughes, S., LaRose, L., et al. (2008, epub). Effectiveness of Intensive Behavioral Intervention in a large, community-based program. Research on Autism Spectrum Disorders. Reed, P., Osborne, L. A., & Corness, M. (2007a). The realworld effectiveness of early teaching interventions for children with autism spectrum disorder. Exceptional Children, 73, 417–433. Reed, P., Osborne, L. A., & Corness, M. (2007b). Brief report: Relative effectiveness of different home-based behavioral approaches to early teaching intervention. Journal of Autism and Developmental Disorders, 37, 1815–1821. Reichow, B. (2011). Overview of meta-analysis on early intensive behavioral intervention for young children with autism spectrum disorders. Journal of Autism and Developmental Disorders. https://doi.org/10. 1007/s10803-011-1218-9. Remington, B., Hastings, R. P., Kovshoff, H., degli Espinosa, F., Jahr, W., Brown, T., et al. (2007). A field effectiveness study of early intensive behavioral intervention: Outcomes for children with autism and their parents after two years. American Journal on Mental Retardation, 112, 418–438. Sallows, G. O., & Graupner, T. D. (2005). Intensive behavioral treatment for children with autism: Four-year outcome and predictors. American Journal on Mental Retardation, 110, 417–438. Smith, T., Eikeseth, S., Klevstrand, M., & Lovaas, O. I. (1997). Intensive behavioral treatment for preschoolers with severe mental retardation and pervasive developmental disorder. American Journal on Mental Retardation, 102, 238–249.
2365 Smith, T., Groen, A. D., & Wynn, J. W. (2000). Randomized trial of intensive early intervention for children with pervasive developmental disorder. American Journal on Mental Retardation, 105, 269–285. Wolf, M. M., Risley, T., & Mees, H. (1964). Application of operant conditioning procedures to the behavior problems of an autistic child. Behaviour Research and Therapy, 1, 305–312.
Homework/Assignments, Modifications ▶ Homework/Assignments, Modifying
H Homework/Assignments, Modifying Kara Hume University of North Carolina, Chapel Hill, NC, USA
Synonyms Homework/assignments, modifications
Definition Changes in the content, instructional level, or performance criteria provided for students with autism. Modifications to homework or in-class assignments are implemented to allow active participation in classroom activities while also ensuring that IEP goals are addressed and successfully accomplished. Common modifications for individuals with ASD include the use of picture symbols and/or visual supports such as social stories; audio and video recordings in place of/in addition to text; shorter or alternate assignments such as outlines or projects in place of lengthy essays or written reports; accessing technology such as calculators, personal digital assistants (PDA), or spell check programs during homework/assignments;
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incorporating motivational procedures; alternative assessments; and adjusted grading standards.
See Also ▶ Environmental Engineering/Modifications ▶ Individual Education Plan ▶ Pictorial Cues/Visual Supports (CR)
References and Reading Adams, L., Gouvousis, A., VanLue, M., & Waldron, C. (2004). Social story intervention: Improving communication skills in a child with autism spectrum disorder. Focus on Autism & Other Developmental Disabilities, 19, 87–94. Koegel, R., Tran, Q., Mossman, A., & Koegel, L. (2006). Incorporating motivational procedures to improve homework performance. In R. L. Koegel & L. K. Koegel (Eds.), Pivotal response treatments for autism. Baltimore: Brookes. Myles, B., Ferguson, H., & Hagiwara, T. (2007). Using a personal digital assistant to improve the recording of homework assignments by an adolescent with Asperger syndrome. Focus on Autism & Other Developmental Disabilities, 22, 96–99. Newman, L. (2007). Secondary school experiences of students with autism. National Center for Special Education Research Facts from NLTS2. U.S. Department of Education NCSER, 2007–3005.
Horizant ▶ Gabapentin
Horizant
Hug Machine Temple Grandin Department of Animal Sciences, Colorado State University, Fort Collins, CO, USA
Definition I developed the hug machine, also called the squeeze machine, to calm my anxiety when I was a teenager. I am a person with autism, and when I entered puberty, I was in a constant state of anxiety and panic attacks. When I visited my aunt’s ranch at the age of 15, I observed cattle being handled in a squeeze chute for their vaccination. A squeeze chute is a narrow metal stall that holds the cattle tightly between two metal side panels. Some of the cattle appeared to relax when pressure was applied by the squeeze sides. After I saw this, I tried the squeeze chute and the deep pressure calmed my anxiety. Then I built a squeeze chute-type device that I could get in (Grandin 2006; Grandin and Scariano 1986). It has padded sides that apply pressure on both sides of the body. I got in the squeeze machine on my hands and knees, and the sides applied pressure to the sides of my body. The effect was to have even, deep pressure over a large area of my body. Another feature of the squeeze machine was that I could control the amount of pressure that it applied. This is a really important feature. Even though the pressure was calming, it sometimes becomes overwhelming. When it became too intense, I could release it. At any time, I could stop the pressure.
Hormone ▶ Oxytocin
HRB ▶ Halstead-Reitan Neuropsychological Test Battery
Sensory Difference Sensory differences in autism are highly variable. Practical experience and anecdotal evidence both show that some individuals are deep pressure seekers and others are not (Bestbier and Williams 2017). I was a deep pressure seeker. When I was a child, I liked my bed covers tucked in very tight, and I would get under the sofa cushions and ask
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my sister to sit on top. The hug machine and other methods for applying deep pressure may have positive effects on some individuals and no effect on others. Further research is definitely needed. Subjects should be chosen to participate in studies depending on whether or not they are a deep pressure seeker. The hug machine and other deep pressure methods are most likely to have a positive effect on individuals who are “deep pressure seekers.” Research on Deep Pressure Our first studies on the effects of deep touch pressure assessed the reaction of college students to the hug machine (Grandin 1992). Many students were calmed and relaxed, but a few students felt claustrophobic. It was clear that some people reacted positively and others did not. Edelson et al. (1999) reported that 20 min in the hug machine reduced galvanic skin response in some individuals with autism. Other researches with weighted vests and weighted blankets have reported positive results in some individuals (Fertel-Daly et al. 2001; Mullen et al. 2008). Occupational therapists have found from clinical experiences that deep pressure is most effective if it is applied for 20 min and then taken off. When I used the squeeze machine, I found that the calming effect peaked at 20 min. People working with aggressive dogs have also found that deep pressure over wide areas of the body has a calming effect (Williams and Borchelt 2003). Further studies with dogs showed that a pressure garment reduced heart rate in dogs that were separated from other dogs (King et al. 2014). Studies on sensory-type interventions have been hindered by the vast heterogeneity of individuals with autism. Sensory reactions and sensory oversensitivity are highly variable. In conclusion, the hug machine or other methods for applying deep pressure may be useful for some individuals and not useful for others. When large numbers of studies are analyzed, the effectiveness of deep pressure is not validated (Losinski et al. 2016). The problem is that deep pressure is only effective for a subgroup of individuals with autism. When all the individuals with an autism label are mixed
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together, the pressure responders would be mixed with the pressure non-responders. Deep pressure may be most effective for individuals who actively seek it. Studies are needed that separate the subtypes of sensory problems in autism. The disadvantage of the hug machine is that it is expensive. A smaller sitting design has been developed (Lo and Huang, 2018). Occupational therapists have developed many simpler methods that are easy and economical to use on young children, such as weighted vests, rolling up in gym mats, or laying in tied-together inner tubes (Ayres 1979). The hug machine would be most useful with teenagers and adults because the other methods do not apply sufficient pressure.
References and Reading Ayres, J. A. (1979). Sensory integration and the child. Los Angeles: Western Psychological Services. Bestbier, L., & William, T. I. (2017). The immediate effects of deep pressure on young people with autism and severe intellectual difficulties – Demonstrating individual differences, Occupational Therapy International 2017–7534972. Edelson, S. M., Edelson, M. G., Kerr, D. C., & Grandin, T. (1999). Behavioral and physiological effects of deep pressure on children with autism: A pilot study evaluating the efficacy of Grandin’s hug machine. American Journal of Occupational Therapy, 53, 145–152. Fertel-Daly, D., Bedell, G., & Hinojosa, J. (2001). Effects of a weight vest on attention to task and self-stimulatory behaviors in preschoolers with developmental disorders. American Journal of Occupational Therapy, 55, 626–640. Grandin, T. (1992). Calming effects of deep tough pressure in patients with autistic disorders, college students and animals. Journal of Child and Adolescent Psychopharmacology, 2, 63–72. Grandin, T. (2006). Thinking in pictures. New York: Vintage. Grandin, T., & Scariano, M. (1986). Emergence labeled autistic. Novato: Arena Press. King, C., Buffington, L., Smith, T. J., & Grandin, T. (2014). The effect of pressure wrap (Thundershirt) on the heartrate and behavior of canines diagnosed with anxiety disorder. Journal of Veterinary Behavior: Clinical Applications and Research, 9, 215–221. Lo, J. S., & Huang, S. L. (2018). Creative design of sitting hug machine in the treatment of students with autism. MATEC Web of Conferences, 213, 01009. Losinski, M., Sanders, S. A., & Wiseman, N. M. (2016). Examining the use of deep touch pressure to improve
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2368 educational performance of students with disabilities. Research and Practice for Persons with Severe Disabilities, 4, 3–18. Mullen, B., Champagne, T., Krishnamurty, S., Dickson, D., & Gao, R. (2008). Exploring the safety and therapeutic effects of deep pressure stimulation using a weighted blanket. Occupational Therapy in Mental Health, 24, 65–89. Williams, N. G., & Borchelt, P. L. (2003). Full body restraint and rapid stimulus exposure as a treatment for dogs, with defensive aggressive behavior: Three core studies. International Journal of Comparative Psychology, 16, 226–236.
Human Figure Drawing Tests Evon Batey Lee Pediatrics, Kennedy Center/Vanderbilt University, Nashville, TN, USA
Synonyms DAP:IQ; Draw-a-person intellectual ability test for children, adolescents, and adults; HFDT
Description The Draw-A-Person Intellectual Ability Test for Children, Adolescents, and Adults (Reynolds and Hickman 2004) provides one of the most recently standardized scoring systems for calculating an IQ estimate based on human figure drawings. In addition to updated norms, the authors have expanded the age range beyond childhood to include adolescents and adults. The DAP:IQ uses a simple administration procedure and “provides an estimate of cognitive development that is at least an estimate of the lower bound of the person’s ability and does so with a task and scoring criteria that have less cultural specificity than most intelligence tests, verbal or nonverbal” (Examiner’s manual, p. vi). The DAP:IQ manual states that this test is designed for ages 4 through 89 years and may be administered to individuals or groups. Most individuals complete the drawing in 5 min or less, and
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the combination of both administration and scoring is estimated to take 10–12 min. Very few materials are required: the administration/scoring form, the drawing form, two sharpened pencils with erasers, and the Examiner’s manual which includes technical information, normative data, and scoring examples. The DAP:IQ is purported to be appropriate for a wide range of backgrounds and ability levels. While the authors are careful not to say it is “culture-free,” they describe it as “culture-reduced.” However, because it is a drawing task that relies on visual-motor skills, it is obviously not appropriate for individuals with severe motor or visual impairments. Because the DAP:IQ was devised as a measure of cognitive ability, the authors recommend that examiners have formal training in assessment and knowledge about current theories of cognitive development and neuropsychology. Guidance about the testing environment includes the usual recommendations for freedom from noise and distractions but adds the unique caveat that there should be no large pictures or posters containing human figures in the room that an individual could copy while taking the DAP:IQ. Task instructions are fairly brief but do require receptive language skills: I want you to draw a picture of yourself. Be sure to draw your whole body, not just your head, and draw how you look from the front, not from the side. Do not draw a cartoon or stick figure. Draw the very best picture of yourself that you can. Take your time and work carefully. Go ahead (Examiner’s manual, p. 5). After the figure is drawn, the examiner assigns points for 23 scoring elements. Scores for each element range from 0 to 3 points. Raw score totals may be converted to a variety of scores: IQ scores (M ¼ 100, SD ¼ 15), T-scores (M ¼ 50; SD ¼ 10), z-scores, stanines, percentile ranks, age equivalents, or grade equivalents. The authors used a continuous norming procedure to determine the optimal breakdown of age groupings for the normative tables, with the size of the age groupings ranging from 6-month intervals at 4 through 8 years to 15-year intervals at 75 through 89 years. The administration/scoring form appears to be well designed and easy to use. The first page
Human Figure Drawing Tests
includes space for demographic information, test scores, description of testing conditions, and both individual and group directions. The second and third pages provide written and pictorial guidelines for the scoring criteria for each of the 23 scoring elements.
Historical Background The DAP:IQ follows a long tradition of using human figure drawings as a means of obtaining information about mental or emotional development. Florence Goodenough (1926) is recognized as the first person to develop a drawing test to measure intelligence in children (Jolley 2010). For her Draw-A-Man Test, children were asked to draw their best picture of a man. This drawing was then scored based on 51 details that pertained to the number of body parts, their proportion, and their attachment to the figure. The points were summed, and the total score was converted to a mental age from which a ratio IQ was calculated (Harris and Pinder 1974). In emphasizing the Draw-A-Man Test’s long history, Anastasi (1988) reported that it was “used without change from its original standardization in 1926 until 1963.” It was then revised by Dale B. Harris and published as the GoodenoughHarris Drawing Test (1963). The age range was extended to include young adolescents, and three drawings were required rather than just one (i.e., children were asked to draw a picture of a man, a woman, and themselves). Scoring criteria were expanded, and separate norm tables were developed for boys and girls, though the self-drawing was not scored. A few years after Harris’ revision, Koppitz (1968) developed another HFDT based on drawings by nearly 2,000 children between 5 and 12 years of age. Children were only asked to draw one figure, and this was scored according to 30 developmental items derived from the Goodenough-Harris test. Items were grouped by expected, common, not unusual, and exceptional details, and the total score could be converted to an IQ score. In contrast to the previously described HFDTs, Koppitz (1968, 1984) viewed
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her drawing test as both a developmental test and a projective test. After another 20 years, Naglieri (1988) published the Draw-a-Person: A Quantitative Scoring System (DAP) which was reported to update and improve the prior scoring systems and expand the norm group. Consistent with the Harris revision, children drew three figures, but the self-drawing was scored, and the three scores were averaged. Rather than having separate norms for boys and girls, Naglieri presented only one conversion table for all three drawings. Consequently, Jolley (2010) characterized the Naglieri DAP as a shorter and simpler alternative to the Goodenough and Goodenough-Harris tests. When the DAP:IQ appeared in 2004, the manual states that no norming on HFDTs had been done since the late 1980s. Like the original DrawA-Man Test, examinees are asked to draw only one figure – but this time it is a self-drawing. Reynolds and Hickman (2004) emphasized the conceptual aspects of the drawing to estimate IQ. Their stated goals for the DAP:IQ were to provide updated norms, expand the age range to also include adults, and “reduce the influence of motor skills on the scoring of figure drawings” (Examiner’s manual, p. 2).
Psychometric Data The DAP:IQ was normed on a sample of 3,290 individuals from 46 states. The sample was decreased to 2,295 to closely match the U.S. Bureau of the Census (2001) demographic characteristics in terms of geographic area, gender, race, Hispanic origin, family income, educational attainment of parents, and disability status. A continuous norming procedure was used to develop standard scores for the DAP:IQ. Consequently, the size of the age groupings differs across the life span, and it should be noted that there is wide variability in the number of participants included within the specific age groupings (range: N ¼ 194 at age 5 years to N ¼ 14 at 75–89 years). Chapter 5 of the Examiner’s manual provides information about the reliability of the DAP:IQ
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test scores in terms of content sampling, time sampling, and interscorer reliability. With regard to content sampling, internal consistency reliability of the scoring elements for the entire normative sample was calculated using Cronbach’s coefficient alpha. The median alpha reliability was 0.82, with the lowest being 0.74 at age 4. The standard errors of measurement for all 22 age intervals are 4–5 standard score points. Coefficient alphas were also calculated for selected subgroups by gender, racial/ethnic origin, and self-identified handedness, with all of the coefficients falling above 0.73. Evidence for test-retest reliability was based on one small study of 45 individuals ranging from 6 through 57 years of age. They were tested and then retested after a 1-week interval, and the standard scores obtained from the two sessions correlated at 0.84. Interscorer reliability was established by two studies. In the first study, two PRO-ED staff members independently scored 31 drawings completed by individuals 11–75 years of age. In the second study, two PhD graduate students each scored 148 protocols for children from 6 to 11 years. The IQs obtained by these examiners correlated very highly – .95 for the first study and .91 for the second. In an independent study, Williams et al. (2006) supported the reliability results reported in the DAP:IQ manual. They administered the DAP:IQ to a sample of college students aged 19–29 years who were primarily Caucasian and female. Using scores from 110 undergraduate and graduate students to examine internal consistency, they obtained alpha coefficients of 0.82 – which matched the values reported in the DAP:IQ manual. With a smaller subsample of 31 students, the interscorer reliability coefficient was 0.83 which is somewhat lower than the correlation reported in the manual. The intrascorer reliability in this study was 0.92, but this calculation was not reported in the manual, so it is not possible to make a comparison. Evidence for the validity of the DAP:IQ is provided from several different vantage points in the manual. The authors state that the DAP:IQ is “offered as a measure that estimates overall cognitive ability as represented in the drawing or construction of the human figure” (Examiner’s
Human Figure Drawing Tests
manual, p. 24). They support the validity of the DAP:IQ by citing historical evidence of the use of human figure drawings by clinicians and researchers for over 100 years. The scoring elements of this current test were generated based on knowledge of the literature “but without reference to any other scoring criteria” (Examiner’s manual, p. 25). However, it is unclear how one could be familiar with the previous tests and not be influenced by their scoring systems. After the DAP:IQ scoring criteria were developed, Reynolds and Hickman had them reviewed by PhD-level psychologists, special educators, and measurement specialists affiliated with the PROED research department. Two studies comparing DAP:IQ scores with scores from the Koppitz for children 4–11 years and one study comparing scores with the Goodenough-Harris for children 6–11 years yielded strong correlations of 0.85 to 0.86. The DAP:IQ was also studied in relation to other frequently administered tests of intelligence, aptitude, and achievement. For example, a correlation of 0.46 was obtained between the DAP:IQ scores and the Full Scale IQ scores from the Wechsler Intelligence Scale for Children – Third Edition (WISC-III) administered to a sample of 211 children between 6 and 11 years. Because the correlation was higher between the DAP:IQ and the performance IQ (0.49) than the verbal IQ (0.33), this was interpreted as supporting the contention that the DAP:IQ is more closely associated with nonverbal as compared to verbal skills. Correlations between the DAP:IQ and other measures (e.g., Detroit Test of Learning Aptitude-Primary: Second Edition, Woodcock Johnson Revised: Tests of Achievement, and Wechsler Individual Achievement Test) were consistent with the WISC-III results in yielding moderate correlations.
Clinical Uses Despite the time and effort devoted by the authors to design and validate this measure, questions remain about when it is appropriate to administer and for what purpose. The authors
Hyperkinetic Disorders
state that the DAP:IQ is designed to “improve the pervasive practice of evaluating human figure drawings as a measure of cognitive ability” (Examiner’s manual, p. v) and further suggest that it is appropriate for large screening efforts, in part because it lacks significant language demands and cultural bias. However, because carefully constructed brief IQ measures that assess a wider array of skills have been developed (e.g., Kaufman Brief Intelligence Test – Second Edition, Reynolds Intellectual Screening Test, and Wechsler Abbreviated Scale of Intelligence) and because the DAP:IQ correlates only moderately with more comprehensive measures of intelligence, aptitude, and achievement, it does not seem advisable to use the DAP:IQ to assess cognitive ability or to make placement decisions. It still may be useful to clinicians who utilize drawings as a means of building rapport or facilitating conversation.
2371 Koppitz, E. M. (1968). Psychological evaluation of children’s human figure drawings. New York: Grune & Stratton. Koppitz, E. M. (1984). Psychological evaluation of human figure drawings by middle school pupils. New York: Grune & Stratton. Naglieri, J. A. (1988). Draw-a-person: Quantitative scoring system. San Antonio: The Psychological Corporation. Reynolds, C. R., & Hickman, J. A. (2004). Draw-a-person intellectual ability test for children, adolescents, and adults: Examiner’s manual. Austin: PRO-ED. Reynolds, C. R., & Kamphaus, R. W. (2003). Reynolds intellectual screening test (RIST). Odessa: Psychological Assessment Resources. Wechsler, D. (1999). Wechsler abbreviated scale of intelligence (WASI). San Antonio: Harcourt Assessment. Williams, T. O., Jr., Fall, A. M., Eaves, R. C., & WoodsGroves, S. (2006). The reliability of scores for the draw-a-person intellectual ability test for children, adolescents, and adults. Journal of Psychoeducational Assessment, 24, 137–144.
Human or Animal Motion References and Reading Abell, S. C., Wood, W., & Liebman, S. J. (2001). Children’s human figure drawings as measures of intelligence: The comparative validity of three scoring systems. Journal of Psychoeducational Assessment, 19, 204–215. Anastasi, A. (1988). Psychological testing (6th ed.). New York: McMillan. Cox, M. V. (1993). Children’s drawings of the human figure. Hove: Lawrence Erlbaum. Goodenough, F. L. (1926). Measurement of intelligence by drawings. Yonkers-on-Hudson: World Book. Goodenough, F. L., & Harris, D. B. (1963). The Goodenough-Harris drawing test. New York: Harcourt, Brace, and World. Harris, D. B., & Pinder, G. D. (1974). The GoodenoughHarris drawing test as a measure of mental maturity of youths. Rockville: U.S. Department of Health, Education and Welfare. Hiltonsmith, R. W. (2010). Review of the draw-a-person intellectual ability test for children, adolescents, and adults. In K. F. Geisinger, R. A. Spies, J. F. Carlson, & B. S. Plake (Eds.), The seventeenth mental measurements yearbook. Lincoln: University of Nebraska Press. Jolley, R. P. (2010). Children and pictures: Drawing and understanding. Chichester: Wiley-Blackwell. Kaufman, A. S., & Kaufman, N. L. (2004). Kaufman brief intelligence test-second edition (KBIT-2). Circle Pines: AGS Publishing.
▶ Biological Motion
Hydramine [OTC] [DSC] ▶ Diphenhydramine
Hydrocortisone ▶ Cortisol, Serum
Hyperactivity Syndrome ▶ Serotonin Syndrome
Hyperkinetic Disorders ▶ Attention Deficit/Hyperactivity Disorder
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Hyperlexia Adam Naples1 and James C. McPartland2 1 Yale Child Study Center, Yale University, New Haven, CT, USA 2 School of Medicine, Child Study Center, Yale University, New Haven, CT, USA
Synonyms Reading comprehension disorder
Short Description or Definition “Hyperlexia” is a term used, most often, to describe skilled word reading, that is, decoding print to speech, in the absence of concomitant reading comprehension ability. Hyperlexia is not a formal diagnosis; thus, the term is used inconsistently to describe a heterogeneous group of individuals. These descriptions most often refer to individuals whose reading decoding skills exceed their reading comprehension skills, usually assessed via a discrepancy in standard scores. However, other descriptions include individuals with ASD who exhibit precocious reading ability across both decoding and comprehension. Consequently, hyperlexia can refer to individuals with above average reading comprehension and even further above average reading decoding skills as well as individuals with poor reading decoding skills and even poorer reading comprehension skills, for example, individuals who can successfully read aloud but may have no functional language and profound intellectual disability. There is no “gold standard” for defining hyperlexia. Nevertheless, the common depiction of an individual with hyperlexia is a child with an autism spectrum disorder, who begins reading words at a very early age without explicit instruction and without any apparent comprehension of the text.
Categorization The current characterization of hyperlexia, and the operational definition most often used, is an
Hyperlexia
individual with an autism spectrum disorder who exhibits precocious word-reading skills. As these individuals develop, their comprehension skills almost invariably begin to even out with their decoding skills because, once mastered, word decoding does not get harder. Despite this operational characterization, the field does not have a single cohesive model for why hyperlexia emerges. Prior hyperlexia research has focused on characterizing comprehension deficits in these individuals, concluding that, although advanced, their worddecoding skills are no different from those of typically developing readers (Aram 1997) or that these skills simply represent preserved “cognitive modules” specialized for processing words (O’Conner and Hermelin 1994).
Epidemiology There are very few papers that attempt to characterize the prevalence of hyperlexia. The most recent and thorough estimate comes from a sample of 80 children with developmental delays (Grigorenko et al. 2002). The paper employed a discrepancy criterion wherein children were categorized as hyperlexic if their reading-decoding scores were two or more standard deviations higher than their IQ scores. Using these criteria, 12 of the 80 children were characterized as hyperlexic. However, as this is only one study with a relatively small sample, further research is required to determine if these results are comparable across different age ranges and populations.
Natural History, Prognostic Factors, and Outcomes The term “hyperlexia” was first used in the late 1960s by Silberberg and Silberberg (Sliberberg 1967) to describe children in the classroom who were able to read printed words but failed to adequately comprehend text. This description distinguished between word decoding, the ability to decode a printed word to speech, and reading comprehension, the ability to garner meaning from printed text. It is important to note both features of this description: (1) Hyperlexia was
Hyperlexia
not characterized as a strength; these children were identified because of their reading comprehension difficulties. (2) The reading difficulties these children exhibited were not associated with decoding the printed word. As the subsequent 40 years of research showed, many children who struggle with reading do so at the word-decoding level, and these difficulties can, and often do, lead to difficulties in reading comprehension, but what was described in the hyperlexia literature was a reading difficulty at the level of comprehension in the presence of otherwise skilled decoding. Subsequent hyperlexia research, primarily driven by small samples and case studies, described a heterogeneous group of children. Typically, these children have autism spectrum disorders (ASDs), are early readers, and exhibit difficulty with reading comprehension. Characterizations in the literature broadly range from individuals with ASDs who would otherwise not be expected to read due to age, or severe language or cognitive disability, and individuals with ASDs who exhibit a disparity between their ability to decode and comprehend text but would otherwise be expected to be able to learn to read (Graziani et al. 1983; Nation 1999; Sliberberg 1967). Importantly, the published literature is composed of primarily case reports and small samples from fewer than 100 papers in the last four decades; thus, it is difficult to make strong conclusions regarding the characterization of these individuals. Successful reading relies on the development of spoken language. Because many individuals with ASD exhibit language delays or dysfunction, it would be expected that they would show similar difficulties with reading, and many do. However, some individuals with ASD and hyperlexia successfully read words despite profound language impairments. Printed words are symbols that represent the sounds of spoken words. Thus, reading develops upon the scaffold of spoken language. This dependence on spoken language highlights the inconsistency of precocious word decoding in children with ASDs, as language and communicative impairments are part of the diagnostic criteria of ASD. Furthermore, it is not the case that children without language delays exhibit hyperlexia; quite the contrary, there are children
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with profound language impairments who nonetheless begin to read with no apparent instruction. It is unknown what leads to the development of successful reading in the presence of language dysfunction in some individuals with ASD. Hyperlexia in individuals with language impairments is inconsistent with the developmental trajectory of typical reading, and further research is required to elucidate our understanding of the phenomenon. It has been proposed that hyperlexia is the result of a circumscribed interest in letters and words. This explanation is in line with the clinical presentation of circumscribed interests in ASDs and the obsession with words and letters that some individuals with hyperlexia exhibit. Nevertheless, successful word decoding requires mastery of the speech sounds of the language; this mastery requires attention to spoken language. Thus, theories of hyperlexia as the result of a circumscribed interest must reconcile the mastery of speech sounds, a very social domain, with the clinical expression of ASDs, where social information is often avoided or ignored.
Clinical Expression and Pathophysiology The clinical expression of hyperlexia is one of heterogeneity. However, many standardized measures for reading have allowed insight into the reading mechanism of these children. First and foremost is that these individuals are decoding and not memorizing words. It had been hypothesized that these children have a savant-like memory for words wherein they memorized entire words as pictures and then named them; however, research has shown this not to be the case. Several studies have shown that these individuals are able to successfully read pseudowords, for example, pronounceable but novel words like “loink” or “ugcrity” (Cardoso-Martins and Da Silva 2008; Newman et al. 2007). Furthermore, tests of memory do not show these individuals to perform better than their typically developing peers. Secondly, while these individuals do exhibit comprehension difficulties, these difficulties are in line with what would be predicted by their IQ and oral language skills; thus, it is not that their reading comprehension performance is unexpectedly low,
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but rather, their word-decoding skills are unexpectedly high (Nation 1999; Nation et al. 2006; Nation and Norbury 2005; Pennington et al. 1987). Importantly, as these individuals get older, their age adjusted word-decoding standard scores will get lower and lower. This does not reflect a regression; instead, it reflects that worddecoding skills reach a ceiling of difficulty. As individuals get older, their peers are attaining mastery in word decoding. For both the caretaker and clinician, it is important to appreciate just how scant the hyperlexia literature is, lest they be guided by out of date, underpowered, and unreplicated research. While the quality of research in hyperlexia is high, it is based on a very small group of children. Since the term was coined in 1967, there are fewer than 100 peer-reviewed articles focusing on hyperlexia. Of these articles, there are multiple review papers, case studies, and instances where the same individual is included in the sample of multiple papers. The latter issue is understandable as children are often seen over multiple occasions by the same clinicians or recruited for multiple studies at the same institution. However, it highlights just how little research there is to base clinical practice on. Finally, there are several papers with provocative titles, claiming that hyperlexia is a marker for positive outcomes in ASDs, or how to accurately define and diagnose hyperlexia, when there is very little data to base these claims on.
Evaluation and Differential Diagnosis Again, as there is no formal diagnosis of hyperlexia, there is no standardized assessment procedure. However, it can be very useful to have a clear characterization of reading skills in children and individuals in academic settings, especially if they are having reading difficulties. In addition to a clinical characterization for autism spectrum disorders including both cognitive and language abilities, a comprehensive assessment of written language proficiency is warranted. This assessment would include, depending on the age and functioning of the individual, measures of reading decoding, single-word reading, reading
Hyperlexia
comprehension, reading fluency, phonological processing, and concepts of print. The expected profile from this assessment for an individual with hyperlexia would be one of very strong single-worddecoding skills contrasted by relatively poor reading comprehension skills. Given that individuals with ASDs exhibit a range of communicative and cognitive abilities, it is important that the assessments used during evaluation are chosen to provide an accurate estimate of skill; the particular assessments will vary based on the participant’s age and cognitive ability, as well as their native language.
Treatment Treatment recommendations for reading comprehension difficulties in individuals with hyperlexia combine best practices in reading comprehension interventions, for example, oral language training (Clarke et al. 2010; Snowling and Hulme 2011), and awareness of the specific needs of individuals with ASDs, for example, difficulty with inference, figurative language, and executive functioning. Specifically, direct instruction in reading comprehension strategies includes those used before, during, and after reading a text, including activating background knowledge, previewing vocabulary and content, comprehension monitoring (with embedded stimulus cues), and reviewing text. The use of graphic organizers matched to text types, for example, Venn diagram for cause and effect, is often important for organizing information and providing a visual support for language-based tasks. Finally, for many students with ASDs, a focus on the development of nonfiction reading comprehension and written expression will be important, as these are the text types that will constitute the majority of their academic classes moving forward.
See Also ▶ Academic Skills ▶ Academic Supports ▶ Achievement Testing ▶ Case Study ▶ Ceiling Effect
Hyperresponsiveness
▶ Decoding Skills ▶ Diagnostic Instruments in Autistic Spectrum Disorders ▶ Dyslexia ▶ Educational Testing ▶ Floor Effect ▶ High-Functioning Autism (HFA) ▶ Intellectual Disability ▶ Language ▶ Language Tests ▶ Literacy ▶ Phonemic Fluency ▶ Phonetics ▶ Phonological Deficits ▶ Phonological Development ▶ Phonological Disorders ▶ Phonology
2375 O’Conner, N., & Hermelin, B. (1994). 2 Autistic savant readers. Journal of Autism and Developmental Disorders, 24(4), 501–515. Pennington, B. F., Johnson, C., & Welsh, M. C. (1987). Unexpected reading precocity in a normal preschooler: Implications for hyperlexia. Brain and Language, 30(1), 165–180. Sliberberg, S. (1967). Hyperlexia–specific word recognition skills in young children. Exceptional Children, 34(1), 41–42. Snowling, M. J., & Hulme, C. (2011). Evidence-based interventions for reading and language difficulties: Creating a virtuous circle. The British Journal of Educational Psychology, 81(Pt 1), 1–23.
Hyperresponsiveness Winifred Schultz-Krohn Department of Occupational Therapy, San José State University, San José, CA, USA
References and Reading Aram, D. (1997). Hyperlexia: Reading without meaning in young children. Topics in Language Disorders, 17(3), 1–13. Cardoso-Martins, C., & Da Silva, J. (2008). Cognitive and language correlates of hyperlexia: Evidence from children with autism spectrum disorders. Reading and Writing, 23, 1–17. Clarke, P. J., Snowling, M. J., Truelove, E., & Hulme, C. (2010). Ameliorating children’s readingcomprehension difficulties: A randomized controlled trial. Psychological Science, 21(8), 1106–1116. Graziani, L. J., Brodsky, K., Mason, J. C., & Zager, R. P. (1983). Variability in IQ scores and prognosis of children with hyperlexia. Journal of the American Academy of Child Psychiatry, 22(5), 441–443. Grigorenko, E. L., Klin, A., Pauls, D. L., Senft, R., Hooper, C., & Volkmar, F. (2002). A descriptive study of hyperlexia in a clinically referred sample of children with developmental delays. Journal of Autism and Developmental Disorders, 32(1), 3–12. Nation, K. (1999). Reading skills in hyperlexia: A developmental perspective. Psychological Bulletin, 125(3), 338–355. Nation, K., & Norbury, C. (2005). Why reading comprehension fails: Insights from developmental disorders. Topics in Language Disorders, 25(1), 21–32. Nation, K., Clarke, P., Wright, B., & Williams, C. (2006). Patterns of reading ability in children with autism spectrum disorder. Journal of Autism and Developmental Disorders, 36(7), 911–919. Newman, T., Macomber, D., Naples, A., Babitz, T., Volkmar, F., & Grigorenko, E. L. (2007). Hyperlexia in children with autism spectrum disorders. Journal of Autism and Developmental Disorders, 37(4), 760–774.
Definition This term, used in the context of sensory processing, refers to the exaggerated responses demonstrated by a person following a sensory experience. The term overresponsiveness is also used in the literature. The nervous system is designed to receive and register incoming sensory information in a graded manner where stronger sensory information is registered at a more intense level. Individuals exhibiting hyperresponsiveness or overresponsiveness demonstrate a heightened state of arousal when seemingly non-noxious or subtle sensory stimuli are presented. An individual may be hyperresponsive to only one form of sensory information such as auditory or tactile or an individual may be hyperresponsive to several types of sensory experiences. An individual with hyperresponsiveness may actively avoid situations that include a specific form of sensory stimulus such as avoiding noisy rooms or an individual may demonstrate unusual behaviors when encountering overwhelming sensory experiences such as grimacing, crying, or becoming agitated as the sensory experience becomes increasingly intense. The hallmark of hyperresponsiveness is an overreaction to a seemingly
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subtle or non-noxious sensory experience. Occupational therapy intervention addresses this hyperresponsiveness from several perspectives to allow the individual more opportunities to engage in everyday activities.
References and Reading Lane, S. J., Miller, L. J., & Hanft, B. E. (2000). Toward a consensus in terminology in sensory integration theory and practice: Part 2: Sensory integration patterns of function and dysfunction. Sensory Integration Special Interest Section Quarterly, 23, 1–3. Miller, L. J., Anzalone, M. E., Lane, S. J., Cermak, S. A., & Osten, E. T. (2007). Concept evolution in sensory integration: A proposed nosology for diagnosis. American Journal of Occupational Therapy, 61, 135–140. Smith Roley, S., Mailloux, Z., Miller-Kuhaneck, H., & Glennon, T. (2007). Understanding Ayres Sensory Integration ®, OT Practice September 24 (17), CE-1-CE-8. Wilbarger, P., & Wilbarger, J. (1991). Sensory defensiveness in children aged 2–12: An intervention guide for parents and other caretakers. Santa Barbara: Avanti Educational Programs.
Hyperserotonemia Carolyn A. Doyle1 and Christopher J. McDougle2,3 1 Indiana University School of Medicine, Indianapolis, IN, USA 2 Lurie Center for Autism, Massachusetts General Hospital, Lexington, MA, USA 3 Nancy Lurie Marks Professorship in the Field of Autism, Harvard Medical School, Boston, MA, USA
Definition Serotonin [5-hydroxytryptamine (5-HT)] is a neurotransmitter with effects in the central nervous system and some peripheral organs. It plays a key role as a growth factor in the immature mammalian brain, directing proliferation and maturation (Whitaker-Azmitia 1993). As a result, serotonin has long been implicated in the pathophysiology of autistic disorder (autism), with the first report of
Hyperserotonemia
elevated whole-blood serotonin (WBS) levels in patients with autism published in 1961 by Schain and Freedman. WBS levels have been a consistent finding in individuals with autism in a number of research studies, although it is unclear what role serotonin exactly plays, if any, in the pathophysiology of autism (McDougle et al. 2003). The degree of elevated serotonin, referred to as “hyperserotonemia,” has been defined using various cutoffs. One 1987 paper by Anderson et al. compared the WBS levels of 40 children with autism to that of 87 normal children. The 95th percentile of WBS in the normal group was used to define “hyperserotonemia” (WBS greater than 200 ng/ml), and 38% of autistic individuals were found to be hyperserotonemic. The causal reason for this increase is unknown as these subjects did not appear to represent any identifiable subgroup. Other studies have attempted to study WBS or platelet 5-HT levels in children with autism with regard to age, pubertal development, ethnicity, cognitive impairment, or familial WBS or platelet 5-HT levels, with mixed results (McBride et al. 1998; Croonenberghs et al. 2000; Leboyer et al. 1999; Piven et al. 1991). Overall, many but not all investigations have found elevated WBS levels in prepubertal autistic subjects compared to controls, whereas most normal subjects show an agerelated decline of WBS (McDougle et al. 2003). Studies that attempt to probe 5-HT function via endocrine challenges in patients with autism have suggested that 5-HT responsivity may be reduced in autistic subjects compared to controls, particularly in adults. These findings have led researchers to propose that an abnormal maturational process in the 5-HT system may contribute to the pathophysiology of autism. Studies of urinary excretion of 5-HIAA, whole-blood tryptophan concentrations, and baseline levels of cerebrospinal fluid (CSF) 5-HIAA have not found significant differences between autistic subjects and controls. Genetic studies of the 5-HT system in autism have also yielded inconsistent results. It is unclear if WBS levels will prove to be a useful measure in the search for genetic susceptibility to autism, but the concept of elevated WBS as a recurring phenomenon in autism continues to intrigue researchers.
Hypo-arousal
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See Also
Definition
▶ Serotonin
Arousal refers to the physiological state of readiness or general state of excitation of one’s nervous system. Arousal states lie on a continuum from low to high, and the ability to maintain optimal arousal levels is often required for adaptive interaction with one’s environment. Hypo-arousal refers to an arousal state that lies of the low end of this continuum. Behaviorally, hypo-arousal may be observed as under-responsiveness to stimuli and one’s environment, for example, as lethargy, inattention, apathy, or boredom. Theories of hypo-arousal in autism originated in the 1960s, linked directly to the idea of autism-specific deficits in the arousal system per se, specifically implicating the reticular activating system (Rimland 1964). Although general arousal levels have since been found to be normal in persons with autism, there is empirical support for under-arousal in response to specific sensory stimuli in many persons with autism. For example, laboratory data has shown physiological underresponsiveness to auditory, tactile, visual, vestibular, and olfactory stimuli compared to typically developing peers matched on chronological age (Ben-Sasson et al. 2009). However, the evidence base for hypo-arousal and under-responsiveness in autism is limited, due to the paucity and methodological limitations of the available studies (Rogers and Ozonoff 2005). Moreover, results are often inconsistent across studies, possibly reflecting a genuine heterogeneity in patterns of sensory responsivity in individuals with autism.
References and Reading Anderson, G. M., Freedman, D. X., et al. (1987). Whole blood serotonin in autistic and normal subjects. Journal of Child Psychology and Psychiatry, 28(6), 885–900. Croonenberghs, J., Delmeire, L., et al. (2000). Peripheral markers of serotonergic and noradrenergic function in post-pubertal, caucasian males with autistic disorder. Neuropsychopharmacology, 22(3), 275–283. Leboyer, M., Philippe, A., et al. (1999). Whole blood serotonin and plasma beta-endorphin in autistic probands and their first-degree relatives. Biological Psychiatry, 45(2), 158–163. McBride, P. A., Anderson, G. M., et al. (1998). Effects of diagnosis, race, and puberty on platelet serotonin levels in autism and mental retardation. Journal of the American Academy of Child and Adolescent Psychiatry, 37(7), 767–776. McDougle, C. J., Posey, D. J., & Potenza, M. N. (2003). Neurobiology of serotonin function in autism. In E. Hollander (Ed.), Autism spectrum disorders (pp. 199–220). New York: Marcel Dekker. Piven, J., Tsai, G. C., et al. (1991). Platelet serotonin, a possible marker for familial autism. Journal of Autism and Developmental Disorders, 21(1), 51–59. Schain, R. J., & Freedman, D. X. (1961). Studies on 5-hydroxyindole metabolism in autistic and other mentally retarded children. The Journal of Pediatrics, 58, 315–320. Whitaker-Azmitia, P. M. (1993). The role of serotonin and serotonin receptors in development of the mammalian nervous system. In I. S. Zagon & P. J. McLaughlin (Eds.), Receptors in the developing nervous system (Neurotransmitters) (Vol. 2, pp. 43–53). London: Chapman and Hall.
See Also
Hypo-arousal Sandra Hodgetts Pediatrics, University of Alberta, Edmonton, AB, Canada
▶ Hyporesponsiveness ▶ Low Registration ▶ Sensation-Seeking ▶ Sensory Impairment in Autism
References and Reading Synonyms Under-arousal
Ben-Sasson, A., Hen, L., Fluss, R., Cermak, S. A., EngelYeger, B., & Gal, E. (2009). A meta-analysis of sensory modulation symptoms in individuals with autism
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2378 spectrum disorders. Journal of Autism and Developmental Disorders, 39, 1–11. Huebner, R. A., & Dunn, W. (2001). Introduction and basic concepts. In R. A. Huebner (Ed.), Autism: A sensorimotor approach to management (pp. 3–40). Austin: Pro-ed. Rimland, B. (1964). Infantile autism. New York: Appleton. Rogers, S. J., & Ozonoff, S. (2005). Annotation: What do we know about sensory dysfunction in autism? A critical review of the empirical evidence. Journal of Child Psychology and Psychiatry, 46(12), 1255–1268.
Hypogenesis (Partial Formation) ▶ Agenesis of Corpus Callosum
Hypoplasia (Underdevelopment) ▶ Agenesis of Corpus Callosum
Hypogenesis (Partial Formation)
carton and spilling the milk by overfilling the cup. Individuals with hyporesponsiveness may appear disinterested in surrounding events due to a limited ability to respond effectively to those events. For individuals with hyporesponsiveness, occupational therapy services are provided to enhance the safety and ability of the individual to engage in everyday activities with better accuracy and efficiency.
References and Reading Lane, S. J., Miller, L. J., & Hanft, B. E. (2000). Toward a consensus in terminology in sensory integration theory and practice: Part 2: Sensory integration patterns of function and dysfunction. Sensory Integration Special Interest Section Quarterly, 23, 1–3. Miller, L. J., Anzalone, M. E., Lane, S. J., Cermak, S. A., & Osten, E. T. (2007). Concept evolution in sensory integration: A proposed nosology for diagnosis. American Journal of Occupational Therapy, 61, 135–140. Smith Roley, S, Mailloux, Z, Miller-Kuhaneck, H & Glennon, T. (2007). Understanding Ayres Sensory Integration ®, OT Practice September 24 (17), CE-1-CE-8.
Hyporesponsiveness Winifred Schultz-Krohn Department of Occupational Therapy, San José State University, San José, CA, USA
Definition Hyporesponsiveness: This term, used in the context of sensory processing, refers to the limited responses demonstrated by a person following a sensory experience. The term underresponsiveness is also used in the literature. The nervous system is designed to receive and register incoming sensory information in a graded manner where stronger sensory information is registered at a more intense level. For individuals who have hyporesponsiveness or underresponsiveness, they may have difficulties detecting novel or pertinent sensory information such as watching a quickly approaching car when attempting to cross a street or controlling the speed of pouring milk from a
Hypothalamic-PituitaryAdrenal Axis Dorie Glover Psychiatry and Biobehavioral Sciences, University of California at Los Angeles, Los Angeles, CA, USA
Definition The hypothalamic-pituitary-adrenal axis (HPAA) is a chemical messenger system that generates and regulates glucocorticoids (GC). Cortisol, the principal GC in humans, is central for the maintenance of homeostasis, including basic body temperature, autonomic stability, sleep/wake cycles, feeding and thirst, as well as the functioning of higherorder systems (e.g., learning and memory). As such, the HPAA facilitates neurobiological response to all significant internal signals and external stimuli from the environment. It does so
Hypothalamic-Pituitary-Adrenal Axis
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through the sequential release of neurosubstances in the brain structures of the hypothalamus, pituitary, and adrenal glands.
Historical Background Widely examined in animal and human research, the hypothalamic-pituitary-adrenal axis (HPAA) is best known as the regulator of the “fight-flight” response to threat, diverting energy resources needed for protective actions and promoting a subsequent return to homeostasis when the threat is no longer present. Such a system exists in all vertebrates (Lovejoy 2005). Thus, the HPAA has been a central focus of basic animal research on fear conditioning, general effects of environmental stress on the developing brain, “learned helplessness” models of depression, as well as human studies of anxiety and mood disorders more generally (Friedman et al. 1996). In humans, the perception and interpretation of stimuli occurs within the limbic system, consisting
Cerebrum
mainly of the hippocampal formation, amygdala, and entorhinal cortex. When stimuli are identified as potential perturbations to the basic state of equilibrium (i.e., physical or emotional “stressors”), the Sympathetic Adrenomedullary (SAM) system and the HPAA are triggered. The SAM system responds to threat with immediate release of norepinephrine and epinephrine through the sympathetic ganglia of the spinal cord, and the adrenal glands, which sit atop the kidneys. Release of these catecholamines facilitates processes that increase the heart rate, blood pressure, and blood glucose levels in muscles and vital organs in order to allow the body to carry out defensive actions (Fig. 1). In contrast, the HPAA responds in a much slower fashion (occurring over several minutes). The hypothalamus releases corticotrophinreleasing hormone (CRH/alternatively “factor” or CRF) to the anterior pituitary. The pituitary response to CRH is to release adrenocorticotropic hormone (ACTH). In turn, ACTH stimulates
Hypothalamic Pituitary Adrenal Axis
Hypothalamus
Hypothalamus: Corticotrophin-releasing hormone (CRF)
Pineal gland Pituitary gland Brain stem Spinal cord
Cerebellum
Pituitary: Adrenocorticotropic hormone (ACTH)
Sympathetic Adrenomedullary System Negative Feedback Loop
Adrenal Glands: Glucocorticoids (Cortisol)
Sympathetic Ganglia
Adrenal Medulla (center)
Adrenal Cortex (outer)
Epinephrine Norepinephrine (Adrenaline) Together, the HPAA and SAM systems are responsible for elevations in catecholamine and cortisol levels in response to acute stress and the HPAA system ensures a return to resting levels through the negative feedback system.
Hypothalamic-Pituitary-Adrenal Axis, Fig. 1 Hypothalamic-Pituitary Adrenal axis
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release of cortisol from the adrenal glands. Cortisol feeds back to the hypothalamus to inhibit further release of CRH. Thus, the HPAA represents a “negative feedback” loop by which cortisol release is triggered and subsequently inhibited. Verbal reports and behavioral observations from family, caretakers, and service providers confirm frequent disruptive behaviors in children diagnosed with autism spectrum disorder (ASD). These may include unexplained fear or anxiety, agitation, tantrums, aggression, and resistance to seemingly innocuous directives and situations. Such behaviors are consistent with the HPAA regulated actions of “fight or flight” and are often precipitated by efforts to engage the child in social interactions, elicit communication, or thwart preferred stereotypic movements, activities and routines-all of which can be interpreted as a potential threat to a child with ASD. However, the HPAA has not been viewed as etiologically relevant to core ASD abnormalities (i.e., social reciprocity, communication and restricted range of behaviors, interests and activities) including (a) repetitive stereotypic behaviors (e.g., hand-flapping and other vestibular movements or lining up objects); (b) reduced or inappropriate affect (including no responsive to common appetitive and aversive stimuli or a fearful response to certain innocuous sensory stimuli); and (c) preoccupation with a single toy or topic. The absence of a theoretical model linking the HPAA to ASD may account for the limited number of studies examining HPAA functioning in ASD groups. There is some direct evidence for abnormalities specific to the limbic system which triggers HPAA activation (e.g., the size of the amygdala). There are also a few studies showing abnormal levels (higher and lower) of peripheral salivary, urinary or plasma cortisol, and plasma ACTH (Hamza et al. 2010), with greater peak elevations and slow recovery in cortisol reactivity to threat in ASD versus controls (Spratt et al. 2011). The “fractional autism triad” proposed by Happe and Ronald (2008), suggests the three symptom domains used to diagnose ASD may not share the same etiology. Hypothesized
Hypothalamic-Pituitary-Adrenal Axis
etiological mechanisms of ASD to date do not account equally well for established knowledge of ASD, including the strong genetic component, male predominance of the disorder, early onset (detectable by the age of two), and frequent overlap with epilepsy and other neurodevelopmental comorbidities. Data indicate none of the symptom domains are primary, universal across all ASD cases, or specific to ASD and not other developmental disorders. With such diversity of symptom patterns, it is perhaps not surprising that HPAA findings in ASD have largely been equivocal.
Current Knowledge The basic functioning of the HPAA is now known to be considerably more complex than early “fight-flight” models indicated and this complexity may also lead to equivocal findings in ASD. Under normal circumstances, cortisol inhibitory feedback allows for a return to homeostasis after threatening stimuli are no longer present. However, when threat is very intense, frequent, or chronic, the effects of the HPAA on target cells and organs may be prolonged. Cortisol and the catecholamines interact with cell receptors to produce short-term changes in cell processes, as well as changes in gene expression that may have longlasting consequences for cell function. Over time, these effects can lead to brain changes (structural atrophy, suppressed neuorgenesis, and synaptic and dendritic remodeling), and ultimately, poor health outcomes across multiple systems, including cardiovascular, metabolic, and immune systems (McEwen and Gianaros 2011). Such central neurobiological changes may lead to HPAA dysregulation that is opposite to expected patterns. A counterintuitive low level of urinary cortisol was first reported in men diagnosed with Posttraumatic Stress Disorder (PTSD) (Yehuda et al. 1990). PTSD is, by definition, a fear-based psychiatric condition. Diagnostic requirements include (a) exposure to a traumatic event involving perceived threat of death or harm to physical integrity of the self or another person and (b) an acute emotional reaction of fear, helplessness, or horror. Brain
Hypothalamic-Pituitary-Adrenal Axis
imaging studies confirm distinct neurological areas are activated in PTSD patients who show heightened peripheral SAM system reactivity (e.g., increased heart rate) versus those showing blunted reactivity (Lanius et al. 2010) with an explanatory proposal that there are two forms of PTSD. These and subsequent studies in other psychiatric populations (e.g., Major Depressive Disorder: Ahrens et al. 2008; Panic Disorder: Petrowski et al. 2010) and otherwise healthy individuals experiencing chronic stress (e.g., Juster et al. 2011) demonstrate the bidirectional nature of HPAA output at rest and/or in response to new threat. The general direction of HPAA dysregulation (hyper- vs. hypo responsive) is thought to depend upon characteristics of the threat (e.g., the intensity, frequency, and chronicity), the timing of assessment relative to the threat (immediate or minutes/hours versus days or months), or to the developmental stage in which the threat occurs (McEwen 2010). Also, the directional impact of cortisol on other neurotransmitters and systems appears to depend upon the perceived anticipatory immediacy of new and recurring threat (Sapolsky et al. 2000). Of note, these are environmental, not genetic factors. The experiential nature of threat effects on HPAA reactivity is demonstrated in a study of monozygotic twins discordant for bullying (one with and one without a victimization history) (Ouellet-Morin et al. 2011). Bullied twins showed a blunted cortisol response to a psychosocial stress test, whereas their non-bullied siblings had a normal cortisol rise. There was no evidence that these group differences were due to genetic makeup, family environment, preexisting or concomitant individual factors, and the perception of stress or the level of subjective emotional responding to the stress test. Given the heterogeneity of HPAA findings among non-ASD populations and the considerable heterogeneity of symptoms in ASD patients specifically, assessment of HPAA functioning by currently available measurement methods from peripheral urine, saliva, or blood may not yield clear data. Nonetheless, connections between the hypothalamus and pituitary that are proximal to
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and interact with those of the HPAA system (see Gardner and Shoback 2007) have been directly implicated in affiliative behavior relevant for ASD. The hypothalamus is a cone-shaped brain structure that sits below the thalamus, projecting downward into the pituitary (infundibular) stalk which ends at the roundish-shaped pituitary gland (Afifi and Bergman 1998). CRH is secreted in the paraventricular nucleus (PVN) and the arcuate nucleus (AN) of the hypothalamus. CRH is transported to the anterior pituitary via a system of blood vessels called the hypophyseal portal system, where it stimulates release of ACTH. The PVN is also the primary originator of oxytocin (OXY) and one of two hypothalamic nucleus sources of arginine vasopressin (AVP) (known alternatively as vasopressin, argipressin, or antidiuretic hormone (ADH)). [The primary source of AVP is thought to be the supraoptic nucleus.] (Fig. 2). Cell bodies of OXY and AVP neurons have direct distal axon terminals in the posterior pituitary, and as such, are not neurosecreted via blood vessels like CRH and other hormones traveling into the anterior pituitary. Despite this difference, mutual origination from the PVN and excitatory and inhibitory cross-talk between CRH, OXY and AVP suggest these may be more interrelated than previously thought. For example, recent data indicate AVP potentiates CRF stimulation of ACTH and OXY inhibits release of CRF (van den Burg and Neumann 2011). The role of OXY and AVP in affiliative behavior was recently reviewed by Insel (2010). Like CRH, these neuropeptides originate in the hypothalamus and are carried to the pituitary to facilitate further downstream changes. Stimuli perceived as significant may not only elicit agonistic or avoidance behavior (i.e., fight/flight) and thus CRH, but also approach and affiliation behaviors (“tend and befriend”), which are associated with OXY and AVP (Taylor et al. 2000). Building on this concept, Insel reviews evidence for the social effects of oxytocin and vasopressin within and across species, emphasizing complex interactions among, neuromodulators and species-specific behaviors, including mating, pair-bonding (monogamous or promiscuous),
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Hypothalamic-Pituitary-Adrenal Axis
Hypothalamic-PituitaryAdrenal Axis, Fig. 2 Relevant Hypothalamic Nuclei
Relevant Hypothalamic Nuclei PVN Secretes... • CRH • Oxytocin (OXY) • Arginine Vasopressin (AVP) • Dopaminergic neuron cell bodies (DA)
Paraventricular nucleus (PVN) SN Secretes AVP
Suptraoptic Arcuate nucleus nucleus (SN) (AN)
AN Secretes CRH, DA
Pituitarygland
parental involvement (maternal and paternal), and social structure (solitary or interactive). An interaction between oxytocin and dopaminergic neurotransmission has also been proposed to control social behaviors relevant for ASD (Baskerville and Douglas 2010). Dopaminergic (DA) neuron cell bodies, although not exclusive to this region, are found in the PVN and AN. DA cells project to the anterior pituitary via the tubero-infundibular tract. A recent study of the distribution and morphology of catecholamine elements in the hypothalamus showed them to be almost exclusively dopaminergic synapses (Dudas et al. 2010). As with bidirectional dysregulation of cortisol in HPAA, behavioral effects of OXY and AVP do not appear to be directly related to levels per se. Instead, receptor location and expression patterns are suggested to be more strongly related to downstream effects (Insel 2010). Current clinical research measurement techniques from peripheral sources (e.g., urine, saliva, and blood) clearly limit the study of central mechanisms occurring within the brain. Nonetheless, the profound social and behavioral deficits seen in ASD may arise from abnormalities in these neuroanatomical areas and corresponding neuroendocrine systems.
Future Directions Pharmacological and behavioral interventions are known to be effective in dampening the negative sequelae of disruptive behavioral symptoms. Yet, these are likely to be secondary to core domains that temporally precede and act as antecedents to HPAA actions. Whereas the HPAA is likely to play a central role in behavioral symptoms in response to perceived threat, the most parsimonious interpretation of evidence to date is that HPAA abnormalities are secondary consequences of ASD etiology arising from abnormalities in proximal or interrelated areas of the brain.
References and Reading Afifi, A. K., & Bergman, R. A. (1998). Functional neuroanatomy. New York: McGraw-Hill. Ahrens, T., Deuschle, M., Krumm, B., van der Pompe, G., den Boer, J. A., & Lederbogen, F. (2008). Pituitaryadrenal and sympathetic nervous system responses to stress in women remitted from recurrent major depression. Psychosomatic Medicine, 70(4), 461–467. Baskerville, T. A., & Douglas, A. J. (2010). Dopamine and oxytocin interactions underlying behaviors: Potential contributions to behavioral disorders. CNS Neuroscience & Therapeutics, 16, e92–e113.
Hypotonia Dudas, B., Baker, M., Rotoli, G., Girgnol, G., Bohn, M. C., & Merchenthaler, I. (2010). Distribution and morphology of the catecholaminergic neural elements in the human hypothalamus. Neuroscience, 171, 187–195. Friedman, M. J., Charney, D. S., & Deutch, A. Y. (Eds.). (1996). Neurobiological and clinical consequences of stress: From normal adaptation to post-traumatic stress disorder. Philadelphia: Lippencott-Raven. Gardner, D. G., & Shoback, D. (Eds.). (2007). Greenspan’s basic and clinical endocrinology. New York: McGrawHill Companies. Green, R. L., & Ostrander, R. L. (2009). Neuroanatomy for students of behavioral disorders. New York: W. W. Norton & Company. Hamza, R. T., Hewedi, D. H., & Ismail, M. A. (2010). Basal and adrenocorticotropic hormone stimulated plasma cortisol levels among Egyptian autistic children: Relation to disease severity. Italian Journal of Pediatrics, 36(71), 1–6. Happe, F., & Ronald, A. (2008). The fractionable autism triad. A review of evidence from behavioural, genetic, cognitive, and neural research. Neuropsychological Review, 18, 287–304. Insel, T. R. (2010). The challenge of translation in social neuroscience: A review of oxytocin, vasopressin and affiliative behavior. Neuron, 65, 768–779. Juster, R. P., Marin, M. F., Sindi, S., Nair, N. P., Ng, Y. K., Pruessner, J. C., et al. (2011). A clinical allostatic load index is associated with burnout symptoms and hypocortisolemic profiles in healthy workers. Psychoneuroendocrinology, 36(6), 797–805. Lanius, R. A., Vermetten, E., Loewenstein, R. J., Brand, B., Schmahl, C., Bremner, J. D., et al. (2010). Emotion modulation in PTSD: Clinical and neurobiological evidence for a dissociative subtype. American Journal of Psychiatry, 167, 640–647. Lovejoy, D. A. (2005). Neuroendocrinology: An integrated approach. Chichester: Wiley. McEwen, B. S. (2010). Stress, sex, and neural adaptation to a changing environment: Mechanisms of neuronal remodeling. Annuls of the New York Academy of Science, 1204(Suppl), E38–E59. McEwen, B. S., & Gianaros, P. J. (2011). Stress and allostasis-induced brain plasticity. Annual Review of Medicine, 62, 431–445. Ouellet-Morin, I., Danese, A., Bowes, L., Shakoor, S., Ambler, A., Pariante, C. M., et al. (2011). Discordant monozygotic twin design shows blunted cortisol reactivity among bullied children. Journal of the American Academy of Child and Adolescent Psychiatry, 50(6), 574–582. Petrowski, K., Herold, U., Joraschky, P., Wittchen, H. U., & Kirschbaum, C. (2010). A striking pattern of cortisol non-responsiveness to psychosocial stress in patients with panic disorder with concurrent normal cortisol awakening responses. Psychoneuroendocrinology, 35(3), 414–421. Sapolsky, R. M., Romero, L. M., & Munck, A. U. (2000). How do glucocorticoids influence stress responses? Integrating permissive, suppressive, stimulatory and preparative actions. Endocrine Reviews, 21(1), 55–89.
2383 Spratt, E. G., Nicholas, J. S., Brady, K. T., Carpenter, L. A., Hatcher, C. R., Meekins, K. A., et al. (2011). Enhanced cortisol response to stress in children in autism. Journal of Autism and Developmental Disorders. https://doi. org/10.1007/s10803-011-1214-0. Online 22 Mar 2011. Taylor, S. E., Klein, L. C., Lewis, B. P., Gruenewald, T. L., Gurung, R. A., & Updegraff, J. A. (2000). Biobehavioral responses to stress in females: Tend-and-befriend, not fight-or-flight. Psychological Review, 107, 411–429. van den Burg, E. H., & Neumann, I. D. (2011). Bridging the gap between GPCR activation and behaviour: Oxytocin and prolactin signalling in the hypothalamus. Journal of Molecular Neuroscience, 43, 200–208. Yehuda, R., Southwick, S. M., Nussbaum, G., Waahby, V., Giller, E. L., & Mason, J. W. (1990). Low urinary cortisol excretion in patients with post-traumatic stress disorder. Journal of Nervous and Mental Disorders, 178, 366–369.
H Hypotonia Wouter Staal Neuroscience, Radboud University Nijmegen Medical Centre Karakter, Nijmegen, The Netherlands
Definition A state of reduced muscle tension or resistance to movement in a muscle. Hypotonia is not a disorder on its own but a sign or symptom of many different disorders. In infants, hypotonia may be accompanied by delay in motor skills, drooling and speech difficulties, and hyperflexibility of the joints and tendons.
See Also ▶ Inborn Errors of Metabolism
References and Reading Harris, S. R. (2008). Congenital hypotonia: Clinical and developmental assessment [Review]. Developmental Medicine and Child Neurology, 50(12), 889–892. Lisi, E. C., & Cohn, R. D. (2011). Genetic evaluation of the pediatric patient with hypotonia: Perspective from a hypotonia specialty clinic and review of the literature. Developmental Medicine and Child Neurology, 53(7), 586–599.
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IBSE ▶ Behavior Summarized Evaluation-Revised (BSE-R)
ICD 10 Research Diagnostic Guidelines Michael B. First Department of Psychiatry, Columbia University, New York State Psychiatric Institute, New York, NY, USA
Definition The International Classification of Mental and Behavioral Disorders – 10th Edition is used by over 70% of the psychiatrists in the world. Although the DSM-IV and ICD-10 definitions for the majority of disorders included in both classifications are quite similar, they are rarely identical, and for a substantial minority of disorders, there are significant differences. Unlike the DSM-IV definitions of mental disorder which are meant to be used in all settings (i.e., clinical, research, administrative, educational), there are actually three versions of the ICD-10 mental disorders section. The “official” version, the one approved by the World Health Assembly and primarily used by administrators,
includes brief glossary definitions for each of the mental disorders. The ICD-10 Clinical Descriptions and Diagnostic Guidelines (CDDG), commonly known as the “blue book” (because of the color of its cover), is “intended for general clinical, educational, and service use” (ICD-10 CDDG, p. 1) and provides descriptions of the main clinical features for each disorder, accompanied in most cases by diagnostic guidelines, “indicating the number and balance of symptoms usually required before a confident diagnosis can be made” (CDDG, p. 1). The ICD10 Diagnostic Criteria for Research (DCR), commonly known as the “green book,” is intended primarily for researchers and contains specific diagnostic criteria based on the definitions in the ICD-10 CDDG. These diagnostic criteria were designed to be “deliberately restrictive: their use allows the selection of groups of individuals whose symptoms and other characteristics resemble each other in clearly stated ways” (DCR, p. 1). A comparison of the diagnostic guidelines and diagnostic criteria for research for a particular ICD-10 disorder usually reveals much greater precision and specificity in the DCR in terms of required minimum and maximum durations and required numbers of symptom features. For example, Table 1 shows the ICD-10 diagnostic guidelines for childhood autism, and Table 2 shows the corresponding diagnostic criteria for research. Although the diagnostic guidelines, like the diagnostic criteria for research, require qualitative impairments in both reciprocal social interaction
© Springer Nature Switzerland AG 2021 F. R. Volkmar (ed.), Encyclopedia of Autism Spectrum Disorders, https://doi.org/10.1007/978-3-319-91280-6
2386 ICD 10 Research Diagnostic Guidelines, Table 1 ICD10 Diagnostic guidelines for childhood autism (ICD-10 CDDG, pp. 253–254) Reprinted by permission of World Health Organization Usually there is no prior period of unequivocally normal development but, if there is, abnormalities become apparent before the age of 3 years. There are always quantitative impairments in reciprocal social interaction. These take the form of an inadequate appreciation of socio-emotional cues, as shown by a lack of response to other people’s emotions and/or a lack of modulation of behavior according to social contexts; poor use of social signals and a weak integration of social, emotional, and communicative behaviors, and especially, a lack of socioemotional reciprocity. Similarly, quantitative impairments in communication are universal. These take the form of a lack of social usage of whatever language skills are present; impairment in make-believe and social imitative play; poor synchrony and lack of reciprocity in conversational interchange, poor flexibility in language expression and a relative lack of creativity and fantasy in thought processes; lack of emotional response to other people’s verbal and nonverbal overtures; impaired use of variations in cadence or emphasis to reflect communicative modulation; and a similar lack of accompanying gesture to provide emphasis or aid meaning in spoken communication. The condition is also characterized by restricted, repetitive, and stereotyped patterns of behavior, interests, and activities. These take the form of a tendency to impose rigidity and routine on a wide range of aspects of day to day functioning; this usually applies to novel activities as well as to familiar habits and play patterns. In early childhood particularly, there may be specific attachment to unusual, typically non-soft objects. The children may insist on the performance of particular routines in rituals of a nonfunctional character; there may be stereotyped preoccupations with interests such as dates, routes, or timetables; often, there are motor stereotypies; a specific interest in nonfunctional elements of objects (such as their smell or feel) is common; and there may be a resistance to changes in routine or in details of the personal environment (such as the movement of ornaments or furniture in the family home).
and communication, and by the presence of restricted, repetitive, and stereotyped patterns of behavior, interests, and activities, there is no indication about how many of the exemplary symptoms are required for the diagnosis; this judgment is left up to the clinician. In contrast, the diagnostic criteria for research specify that at least six autistic symptoms must be present, with at least two symptoms indicative of a qualitative abnormality in reciprocal social interaction, at least one
ICD 10 Research Diagnostic Guidelines ICD 10 Research Diagnostic Guidelines, Table 2 ICD10 Diagnostic Criteria for Research for F84.0 childhood autism (ICD-10 DCR, 147–149) A. Abnormal or impaired development is evident before the age of 3 years in at least one of the following areas: (1) Receptive or expressive language as used to social communication (2) The development of selective attachments or of reciprocal social interactions (3) Functional or symbolic play B. A total of at least six symptoms from (1), (2), and (3) must be present, with at least two from (1) and at least one from each of (2) and (3): (1) Qualitative abnormalities in reciprocal social interaction are manifest in at least two of the following areas: (a) Failure adequately to use eye-to-eye gaze, facial expression, body posture, and gesture to regulate social interaction (b) Failure to develop (in a manner appropriate to mental age and despite amble opportunities) peer relationships that involve a mutual sharing of interests, activities, and emotions (c) Lack of socio-emotional reciprocity as shown by an impaired or deviant response to other people’s emotions; or lack of modulation of behavior according to social context; or a weak integration of social, emotional, and communicative behaviors (d) Lack of spontaneous seeking to share enjoyment, interests, or achievements with other people (e.g., a lack of showing, bringing, or pointing out to other people objects of interest to the individual) (2) Qualitative abnormalities in communication are manifest in at least one of the following areas: (a) A delay in, or total lack of, development of spoken language that is not accompanied by an attempt to compensate through the use of gesture or mime as an alternative mode of communication (often preceded by a lack of communicative babbling) (b) Relative failure to initiate or sustain conversational interchange (at whatever level of language skills is present), in which there is reciprocal responsiveness to the communications of the other person (c) Stereotyped and repetitive use of language or idiosyncratic use of words or phrases (d) Lack of varied spontaneous make-believe or (when young) social imitative play (3) Restricted, repetitive, and stereotyped patterns of behavior, interests, and activities are manifest in at least one of the following areas: (a) An encompassing preoccupation with one or more stereotyped and restricted patterns of interest that are abnormal in content or focus; or one or more interests that are abnormal in their intensity and circumscribed nature though not in their content or focus (continued)
ICD 10 Research Diagnostic Guidelines ICD 10 Research Diagnostic Guidelines, Table 2 (continued) (b) Apparently compulsive adherence to specific, non-functional routines or rituals (c) Stereotyped and repetitive motor mannerisms that involve either hand or finger flapping or twisting, or complex whole body movements (d) Preoccupations with part-objects or nonfunctional elements of play materials (such as their odor, the feel of the surface, or the noise or vibration that they generate) C. The clinical picture is not attributable to other varieties of pervasive developmental disorder, specific developmental disorder of receptive language (F80.2) with secondary socio-emotional problems; reactive attachment disorder (F94.1) or disinhibited attachment disorder (F94.2); mental retardation (F70-F492) with some associated emotional or behavioral disorder; schizophrenia (F20.-) of unusually early onset, and Rett’s syndrome (F84.2)
symptom indicative of a qualitative abnormality in communication, and at least one symptom indicative of restricted, repetitive, and stereotyped patterns of behavior. ICD-10 lists six specific disorders within its pervasive developmental disorders section (F84.0 Childhood autism, F84.1 Atypical autism, F84.2 Rett’s syndrome, F84.3 Other childhood disintegrative disorder, F84.4 Overactive disorder associated with mental retardation and stereotyped movements, and F84.5 Asperger’s syndrome), and one residual disorder (F84.9 Pervasive developmental disorder, unspecified) for cases that do not meet the criteria for any of the specific pervasive developmental disorders included in this section. Pervasive developmental disorders, according to the ICD-10 CDDG, are “characterized by qualitative abnormalities in reciprocal social interactions, and in patterns of communication, and by restricted, stereotyped, repetitive repertoire of interests and activities” (ICD-10, CDDG, p. 252). Because the category overactive disorder associated with mental retardation and stereotyped movements is noted in the ICD-10 to be “an ill-defined disorder with uncertain nosological validity” and because it resembles the other disorders in this section only by virtue of the presence of stereotyped movements,
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it will not be discussed any further. Notably, while DSM-IV includes criteria sets for childhood autism (called “autistic disorder” in the DSMIV), Rett’s syndrome (called Rett’s disorder), other childhood disintegrative disorder (called “childhood disintegrative disorder”), and Asperger’s syndrome (called “Asperger’s disorder”), it does not include a category for atypical autism. All cases of pervasive development disorder not meeting criteria for one of the specific disorders are classified as pervasive developmental disorder not otherwise specified, a category that subsumes the ICD-10 categories of atypical autism and pervasive developmental disorder, unspecified. Childhood autism (Tables 1 and 2) is characterized by abnormalities in all three areas (i.e., social interaction, communication, range of interests) and are evident prior to age 3. Atypical autism differs from childhood autism in terms either of age of onset or of failure to manifest symptoms from all three of the characteristic areas of abnormalities in autism. Three subtypes are provided: F84.10 (atypicality in age of onset) for cases that meet the symptomatic requirements but for which abnormal or impaired development is evident only after the age of 3 years; F84.11 (atypicality in symptomatology) for cases in which abnormal development is evident before age 3 but for which there are abnormalities in social interactions, communication, or repertoire of interests and activities but not all three; and F84.12 (atypicality in both age of onset and symptomatology) for cases that fail both the age of onset and symptom requirements for autism. The remaining specific ICD-10 pervasive developmental disorders have distinguishing features in addition to abnormalities of social interaction, communication, or repertoire of interests and activities and preempt the diagnoses of childhood autism and atypical autism, i.e., if criteria are met for one of these disorders, childhood autism and atypical autism are not also diagnosed even if criteria were to be met for them. Rett’s syndrome (see Table 3), which appears only in girls, has a very characteristic onset, course, and symptomatology. Apparently normal development for the first 5 months is followed by
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ICD 10 Research Diagnostic Guidelines
ICD 10 Research Diagnostic Guidelines, Table 3 ICD10 Diagnostic Criteria for Research for F84.2 Rett’s syndrome (ICD-10 DCR, p. 151)
ICD 10 Research Diagnostic Guidelines, Table 4 ICD10 Diagnostic Criteria for Research for F84.3 other childhood disintegrative disorder (ICD-10 DCR, pp. 151–152)
A. There is an apparently normal prenatal and perinatal period and apparently normal psychomotor development through the first 5 months and normal head circumference at birth B. There is deceleration of head growth between 5 months and 4 years and loss of acquired purposeful hand skills between 5 and 30 months of age that is associated with concurrent communication dysfunction and impaired social interaction and the appearance of poorly coordinated/unstable gait and/or trunk movements C. There is severe impairment of expressive and receptive language, together with severe psychomotor retardation D. There are stereotyped midline hand movements (such as hand wringing or “hand washing”) with an onset at or after the time when purposeful hand movements are lost
A. Development is apparently normal up to the age of at least 2 years. The presence of normal age-appropriate skills in communication, social relationships, play, and adaptive behavior at age 2 years or later is required for diagnosis B. There is a definite loss of previously acquired skills at about the time of onset of the disorder. The diagnosis requires a clinically significant loss of skills (not just a failure to use them in certain situations) in at least two of the following areas: (1) Expressive or receptive language (2) Play (3) Social skills or adaptive behavior (4) Bowel or bladder control (5) Motor skills C. Qualitatively abnormal social functioning is manifest in at least two of the following areas: (1) Qualitative abnormalities in reciprocal social interaction (of the type defined by autism) (2) Qualitative abnormalities in communication (of the type defined for autism) (3) Restricted, repetitive, and stereotyped patterns of behavior, interests, and activities, including motor stereotypies and mannerisms (4) A general loss of interest in objects and in the environment D. The disorder is not attributable to the other varieties of pervasive developmental disorder; acquired aphasia with epilepsy (F80.6); elective mutism (F94.0); Rett’s syndrome (F84.2); or schizophrenia(F20.-)
deceleration of head growth and loss of previously acquired hand skills accompanied by communication dysfunction and impaired social interaction and the appearance of unstable gait and trunk movements and hand stereotypies. Childhood disintegrative disorder (see Table 4) is distinguished by its requirement that the child have evidence of normal development up to the age of at least 2 years followed by the definite loss of previously acquired skills accompanied by qualitatively abnormal social functioning. Asperger’s syndrome (see Table 5) is characterized by the same kind of qualitative abnormalities of reciprocal social interaction and restricted stereotyped interests and activities that are typical of autism, but there is no general delay or retardation in language or cognitive development. Although a diagnosis of atypical autism with atypicality in symptomatology would also apply to such cases (i.e., abnormalities in two out of the three areas required in childhood autism), a diagnosis of atypical autism does not also apply because Asperger’s syndrome is given precedence.
Historical Background Among all the disorders in ICD-10 and DSM-IV, the criteria sets for the pervasive developmental
disorders, especially the criteria for childhood autism, are the most closely harmonized. This is a direct result of the DSM-IV field trial for autistic disorders which tested various definitions of autism for potential inclusion into DSM-IV. The multisite field trial involved 125 clinicians with varied levels of experience assessing 977 patients (454 with autism, 240 with other pervasive developmental disorders, and 283 with conditions other than pervasive developmental disorders) using a coding system which compared the DSM-III definition, the DSM-III-R definition, the 1990 draft ICD-10 definition, and a proposed DSM-IV definition derived from the results of literature reviews and data reanalyses in terms of agreement with clinician diagnosis (used as a ersatz gold standard) and relative to a diagnostic standard
ICD 10 Research Diagnostic Guidelines ICD 10 Research Diagnostic Guidelines, Table 5 ICD10 Diagnostic Criteria for Research for F84.5 Asperger’s syndrome (ICD-10 DCR, pp. 153–154) A. There is no clinically significant general delay in spoken or receptive language or cognitive development. Diagnosis requires that single words should have developed by 2 years of age or earlier and that communicative phrases be used by 3 years of age or earlier. Self-help skills, adaptive behavior, and curiosity about the environment during the first 3 years should be at a level consistent with normal intellectual development. However, motor milestones may be somewhat delayed and motor clumsiness is usual (although not a necessary diagnostic feature). Isolated special skills, often related to abnormal preoccupation, are common, but are not required for diagnosis B. There are quantitative abnormalities in reciprocal social interaction (criteria as for autism) C. The individual exhibits an usually intense, circumscribed interest or restricted, repetitive, and stereotyped patterns of behavior, interests, and activities (criteria as for autism; however, it would be less usual for these to include either motor mannerisms or preoccupations with part-objects or non-functional elements of play materials) D. The disorder is not attributable to other varieties of pervasive developmental disorder; simple schizophrenia (F20.6); schizotypal disorder (F21); obsessivecompulsive disorder (F42.-); anankastic personality disorder (F60.5); reactive and disinhibited attachment disorders of childhood (F94.1 and F94.2, respectively)
derived through latent class analysis. Based on these analyses, the draft ICD-10 diagnostic criteria had the highest agreement with clinician diagnoses and the best balance of sensitivity and specificity relative to the latent class standard (i.e., sensitivity ¼ 0.87, specificity ¼ 0.98). The ICD10 diagnostic criteria also had the highest interrater reliability (kappa ¼ 0.68) as compared to DSM-III-R (kappa ¼ 0.67) and DSM-III (kappa ¼ 0.45). Given the superiority of the ICD-10 diagnostic criteria, it was decided that the DSM-IV criteria should be based on these draft ICD-10 criteria. However, because the draft ICD-10 criteria were considered to be more detailed in terms of number of criteria than was desirable for DSM-IV, criteria were identified for elimination based on reliability and signal detection, resulting in the deletion of four criteria and a revision of the scoring algorithm. Because the ICD-10 criteria also were more
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detailed and lengthy than desired for DSM-IV, a series of additional analyses were undertaken, resulting in briefer versions of some of the ICD10 criteria.
Current Knowledge In terms of number of items and scoring rules (i.e., requiring 6 items total, at least two from the social interaction group and at least one each from the communication group and restricted interests group), the final ICD-10 and DSM-IV criteria are identical. They differ primarily in the complexity of the wording of some of the items. For example, ICD-10 criterion BI3(c) requires “stereotyped and repetitive motor mannerisms that involve either hand or finger flapping or twisting, or complex whole body movements,” whereas the corresponding DSM-IV item requires simply “stereotyped and repetitive motor mannerisms” and provides as examples “hand or finger flapping or twisting or complex whole body movements.” While these two items may superficially appear to be identical, in actuality, the ICD-10 item is more narrow in that it restricts the types of stereotyped and repetitive motor mannerisms to hand and finger flapping or twisting or complex whole-body movements, whereas DSM-IV allows the clinician to count other stereotyped body movements as well. Comparing the final ICD-10 and DSM-IV criteria sets in a group of 123 subjects randomly selected from 16 of the original field trial sites, diagnostic agreement was excellent (kappa ¼ 0.86), suggesting the research studies using either the DSM-IV or ICD-10 criteria would likely yield the same results.
See Also ▶ Comorbidity
References and Reading http://www.who.int/classifications/icd/en/
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Iceland and Autism Ingólfur Einarsson The State Diagnostic and Counseling Center, Kópavogur, Iceland
Historical Background When autism was first recognized, when program became available, important historical figures and centers for autism research and service.
Iceland is a rather small island in the North Atlantic Ocean; total size is 103,000 km2. Settlers, mainly from Norway, began to arrive in the ninth century (AD). Iceland got independent in 1944 and established a parliamentary democracy (63-members) with a directly elected president as head of state (Iceland 2018). The population grew slowly until the twentieth century (total of 78,000 in the year 1900) but has increased faster in the last 100 years and is at present around 340,000 inhabitants (Hagstofa Íslands 2017). The language spoken is called Icelandic and has its origin in the Old Norse (Iceland 2018). The term “einhverfa” is used in Icelandic for the diagnostic term “autism.” This term has been seen in the Icelandic sagas but was first seen related to a psychiatric term in 1939 (Sæmundsen and Guðmundsdóttir 2014). Special educational and intervention services started for a group of children with disabilities in Iceland in the 1970s, along with the development of deinstitutionalization (Bjarnason et al. 2016). A child psychiatric department was first established in 1970, at the National Hospital in Iceland, and around this time professionals were becoming more aware of autism diagnosis. A report of the first child recognized to be diagnosed with autism in Iceland was in 1968. Then there were two girls (2 and 10 years old) diagnosed in 1973 (Sæmundsen and Guðmundsdóttir 2014). At the child psychiatric unit, there were 57 children diagnosed with autism during 1970–1991. The educational services improved for children with disabilities following change in compulsory school act in 1974
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and with the development of special educational departments within mainstream schools and with a development of special schools. The State Diagnostic and Counselling Center (SDCC) run by the state (Ministry of Social Affairs) was established in 1986 and in 1997; the center was appointed by the ministry to establish a specialized service around the assessment and intervention for children with autism (Sæmundsen and Guðmundsdóttir 2014). All children and adolescents in Iceland with suspicion of autism were referred to SDCC during the period of more than 10 years. A systematic approach was developed to train professionals to use assessment and diagnostic tools, including ADI-R and ADOS. The methods of early intervention were explored. Two main methods have developed in Iceland, TEACCH and behavioral intervention, based on applied behavior analysis (ABA) (Hjartardóttir and Sigurjónsdóttir 2014). For the last decade due to increased awareness, autism spectrum disorders are diagnosed by other centers in the country as well. The State Diagnostic and Counselling Center still plays a major role to give support and education to parents and professionals working in the area of autism.
Legal Issues, Mandates for Service Issues related to legal recognition of autism, issues relative to eligibility for support, etc.
Primary health care is generally provided by the state in Iceland. Each region’s health-care center provides developmental surveillance until 6 years of age. If there are concerns of developmental growth or symptoms of autism for a child, it might be found during a routine visit or the child might come for extra visits. At each visit the child sees a nurse (n) and for certain visits a doctor (d) as well. The nurse or a midwife meets the child few times and as needed during the postneonatal period. The first visit at the health-care center is at 6 weeks (n+d). After this scheduled visits are at 9 weeks, (n) 3 (n+d), 5 (n), 6 (n), 8 (n), 10 (n+d), 12 (n), 18 (n+d), 30 (n), and 48 months (n). Parents are often the first to bring up the
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worries for their child, so Parent’s Evaluation of Developmental Status (PEDS) is applied for all children at 18, 30, and 48 months (Directorate of Health 2017a). The BRIGANCE screen is applied for all children at 30 and 48 months (Directorate of Health 2017b). In addition large proportion of children in Iceland start play school at a very young age, above 90% of children at 2 years of age (Þorbergsdóttir 2017). Often concerns for the child’s developments are brought up by the day care staff. The majorities of play schools are run by the local authorities around the country. Specialist services and support structures should be organized by the local authorities according to special legislations (playschool act, 2008 & regulation 2010). Further assessments are done when the parents agree to do so. The referral for further assessment is usually to the special services provided by the municipality. The local authorities usually have access to clinical psychologists, special educational professional/interventionist, and sometimes a speech and language pathologist or occupational therapist within their team. The team has some resources to do preliminary developmental assessments and screening for autism spectrum symptoms. If the child then needs further diagnostic evaluation, then a referral to a tertiary center, the State Diagnostic and Counselling Center, is made. All referrals are accepted if the child is under 6 years of age with strong suspicion of autism or global developmental delay. All referrals are also accepted for children with significantly limited participation due to autism and/or intellectual disability above 6 years of age (6–18 years). Services for adults with autism might be more fragile. There are teams within the psychiatric units (outpatients) and some private professionals that are providing somewhat limited accessible services. The directorate of health provides the healthcare professionals with some guidelines within the autism. All coding within the health-care system is done with ICD-10 (International Statistical Classification of Diseases and Health Related Problems, 10th revision) published by WHO, and the classification of autistic diagnoses is done according to ICD-10 in Iceland (Directorate of Health 2017c). Any child
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diagnosed with autism spectrum disorder is able to be supported within the disability services in Iceland (Act on the Affairs of Disabled People 1992).
Overview of Current Treatments and Centers “Snapshot” of currently prominent and active programs of intervention; its treatment and diagnosis
When the child is diagnosed, the local authorities are obligated to give proper educational services within the mainstream schools (i.e., play schools, compulsory schools). If the child is young and starting play school, parents are encouraged to get involved in choosing the method of early intervention used for their child. As mentioned above ABA and TEACCH are the most prominent methods available in Iceland and used widely at play school level. Almost all municipalities have some experience or trained personal to provide appropriate intervention for the child, but due to increase in ASD diagnoses in recent years, it has become more difficult to meet all diagnosed children with proper quantity of good quality intervention in some areas. The main reason is lack of number of appropriately educated and trained staff at many of the play schools. When the child is at compulsory school age (6–16 years of age), then these specific methods tend not to be the main focus of intervention. The compulsory school intervention is more curriculum educationally driven (special educational needs), rather than able, social, or participation driven. There are no specific centralized intervention centers in Iceland regarding autism specifically. Higher education is widely available (Iceland 2018) in the country and is run directly by the state (Ministry of Education).
Overview of Research Directions Most significant research programs Most activities in autism research in Iceland have been done by the State Diagnostic and Counselling Center. Saemundsen’s studies have
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shown the increasing prevalence in the diagnosis of autism in Iceland in the last decades (Saemundsen et al. 2013). For example, the prevalence of autism in a 1974–1983 birth cohort was 3.8 per 10.000, while it was 8.6 per 10.000 for a younger birth cohort (1984–1993) (Magnússon and Saemundsen 2001). In a study published in 2013, the prevalence of childhood autism was 33.7 per 10.000 (120 per 10.000 for the whole spectrum) in an Icelandic 1994–1998 birth cohort, by the end of 2009 (Saemundsen et al. 2013). From a phenotypic descriptions and genetic analysis, there have been some publications from Icelandic data, some copy number variations (CNV), and some genetic mutations in relation to autism, schizophrenia, and other neurodevelopmental problems (Weiss et al. 2008; Autism Spectrum Disorders Working Group of The Psychiatric Genomics Consortium 2017). There have also been some research activities in cooperation with the University of Iceland, including that children with high-functioning autism rate their quality of life themselves better than their parents (Egilson et al. 2016).
Overview of Training Ongoing training of providers (teachers, healthcare professionals, and others) The State Diagnostic and Counseling Center provides courses about autism and interventions, for professionals working in the field and for parents (SDCC website 2018).
Social Policy and Current Controversies Attitudes or social movements with regard to autistic people; debates about autism The diagnostic practices have been under some focus for few years. Due to increased awareness and observation of the importance of early intervention, there has been improvement in services in general. Many think that there might be some over diagnosis in the field of the autism spectrum disorder. Some think that a driving force is labelling to gain further access to services. This
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labelling is also driven by how the funding system is built for the municipalities providing the services. Funding is distributed according to the diagnosis of a child, and the more severe the diagnosis is some more money is provided by a special fund (Jöfnunarsjóður). A recent research exploring this issue has suggested to reduce the emphasis on diagnostic pressure (labelling) and increase early inclusive intervention within the schools in Iceland (European Agency for Special Needs and Inclusive Education 2017).
References and Reading Act on the Affairs of Disabled People, No. 59. (1992). Retrieved: https://eng.velferdarraduneyti.is/media/ acrobat-enskar_sidur/Act-on-the-Affairs-of-DisabledPeople-No-59-1992-with-subsequent-amendments.pdf. Autism Spectrum Disorders Working Group of the Psychiatric Genomics Consortium. (2017). Meta-analysis of GWAS of over 16,000 individuals with autism spectrum disorder highlights a novel locus at 10q24.32 and a significant overlap with schizophrenia. Molecular Autism, 22(8), 21. Bjarnason, D.S., Gunnþórsdóttir, H., & Jónsson Ó.P. (2016). Skóli margbreytileikans. Menntun og manngildi í kjölfar Salamanca. Háskólaútgáfan. Directorate of Health. (2017a). Guidelines on child surveillance. PEDS – Mat foreldra á þroska barna. (Parents’ Evaluation of Developmental Status). Retrieved from: https://www.landlaeknir.is/servlet/ file/store93/item32585/PEDS%20-%20Mat%20foreldra %20a%20throska%20barna.pdf. Directorate of Health. (2017b). Brigance þroskaskimun. Retrieved from: https://www.landlaeknir.is/servlet/file/ store93/item32584/Brigance%20throskaskimun.pdf. Directorate of Health. (2017c). Um flokkunarkerfi (about classification systems). Retrieved at: https://www. landlaeknir.is/tolfraedi-og-rannsoknir/flokkunarkerfi/. Egilson, S. T., Olafsdottir, L. B., et al. (2016). Quality of life of high-functioning children and youth with autism spectrum disorder and typically developing peers: Selfand proxy-reports. Autism, 21(2), 133–141. European Agency for Special Needs and Inclusive Education. (2017). Education for all in Iceland. External audit of the Icelandic system for inclusive education. Retrieved from: https://www.stjornarradid.is/media/ menntamalaraduneyti-media/media/frettatengt2016/ Final-report_External-Audit-of-the-Icelandic-System-forInclusive-Education.pdf. Hagstofa Íslands. (2017). Population development 2016. Statistical Series, 102(13). Retrieved from http://www. statice.is/publications/publication-detail?id¼58377. Hjartardóttir, S., & Sigurjónsdóttir, M. (2014). Heildstæðar aðferðir. In S. J. Jónsdóttir & E. Sæmundsen (Eds.),
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Litróf einhverfunnar (pp. 249–264). Iceland: Háskólaútgáfan. Iceland. (2018). Encyclopædia Britannica. Retrieved from http://academic.eb.com/levels/collegiate/arti cle/Iceland/106235. Magnússon, P., & Saemundsen, E. (2001). Prevalence of autism in Iceland. Journal of Autism and Developmental Disorders, 31(2), 153–163. Playschool Act. no. 90/2008 & regulation no. 584/2010. Retrieved from: https://eng.menntamalaraduneyti.is/ media/frettatengt2016/Preschool-Act-No-90-2008.pdf & https://www.reglugerd.is/reglugerdir/eftir-raduneytum/ mennta–og-menningarmalaraduneyti/nr/16592. Sæmundsen, E., & Guðmundsdóttir, A. K. (2014). Einhverfa á Íslandi. In S. J. Jónsdóttir & E. Sæmundsen (Eds.), Litróf einhverfunnar (pp. 33–55). Iceland: Háskólaútgáfan. Saemundsen, E., Magnússon, P., et al. (2013). Prevalence of autism spectrum disorders in an Icelandic birth cohort. BMJ Open, 20(6), e002748. The compulsory school act, no. 63/1974. The State Diagnosic & Counseling Center. (2018). Courses and training. Retrieved from: https://www.greining.is/ is/fraedsla-og-namskeid. Þorbergsdóttir, S.Y. (2017). Skýrsla BSRB um dagvistunarúrræði. Report on daycare services. Retrieved from: https://www.bsrb.is/static/files/Utgefid_efni/skyrsla-bs rb-um-dagvistunarurraedi-mai-2017.pdf. Weiss, L. A., Shen, Y., et al. (2008). Association between microdeletion and microduplication at 16p11.2 and autism. The New England Journal of Medicine, 14(7), 667–675.
icons has been evaluated relative to transparency and translucency. Transparency refers to how well an icon’s referent can be guessed by an unfamiliar user. Translucency refers to the semantic, linguistic, or conceptual relation of icon and its referent. For example, a photo of an object is highly iconic because it is easily recognized and has a strong visual relation with its referent. An abstract line drawing is less iconic because it is less easily recognized by an unfamiliar user and has a more abstract relation with its referent. There is some evidence that icons may be most easily learned when the icon strongly resembles its referent; this is referred to as the iconicity hypothesis. There is evidence that use of icons can be an effective communication strategy for individuals with autism.
Icon
References and Reading
Elizabeth Spencer College of Education and Human Ecology, The Ohio State University, Columbus, OH, USA
Bloomberg, K., Karlan, G. R., & Lloyd, L. L. (1990). The comparative translucency of initial lexical items represented in five graphic symbol systems and sets. Journal of Speech and Hearing Research, 33, 717–725. Bondy, A., & Frost, L. (2002). A picture’s worth: PECS and other visual communication strategies in autism. Bethesda: Woodbine House. Fuller, D. (1997). Initial study into the effects of translucency and complexity on the learning of Blissymbols by children and adults with normal cognitive abilities. Augmentative and Alternative Communication, 13, 30–39. Koul, R. K., Schlosser, R. W., & Sancibrian, S. (2001). Effects of symbol, referent, and instructional variables on the acquisition of aided and unaided symbols by individuals with autism spectrum disorders. Focus on Autism and Other Developmental Disabilities, 16, 162–169. Mirenda, P., & Erickson, K. (2000). Augmentative communication and literacy. In A. Wetherby & B. Prizant
Synonyms Symbol
Definition An icon, or symbol, is a visual representation of a word or linguistic concept. Icons are used in selection-based iconic communication systems (e.g., Picture Exchange Communication Systems, Visual Schedules). The iconicity of
See Also ▶ Iconic Systems ▶ Picture Exchange Communication System ▶ Symbol Use ▶ Visual Schedule ▶ Visual Supports
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2394 (Eds.), Autism spectrum disorders: A transaction development perspective (pp. 333–367). Baltimore: Paul H. Brookes. Mizuko, M. (1987). Transparency and ease of learning of symbols represented by Blissymbols, PCS, and Picsyms. Augmentative and Alternative Communication, 3, 129–136. Ogletree, B. T., & Harn, W. E. (2001). Augmentative and alternative communication for persons with Autism: History, issues, and unanswered questions. Focus on Autism and Other Developmental Disabilities, 16, 138–140.
Iconic Systems Elizabeth Spencer College of Education and Human Ecology, The Ohio State University, Columbus, OH, USA
Definition Iconic systems are systems of communication in which icons or symbols are used to represent words or linguistic concepts. Iconic systems are a type of augmentative communication. Iconic systems can be part of a picture exchange system (e.g., PECS) or used in augmentative communication devices. Iconic systems are hypothesized to facilitate communication by a recognition process (e.g., the user recognizes the icon as a picture of the object). In contrast, linguistic systems such as verbal speech involve a recall process (e.g., the user must recall the word that represents that object). Use of iconic systems can be an effective communication strategy for individuals with autism. The concrete and static nature of icons (e.g., a visual symbol has a specific, unchanging referent) and iconic systems (e.g., the system has a set of specific, unchanging rules) may be a reason that iconic systems are effective for individuals with autism. There is evidence that iconic systems can be useful for reducing challenging behaviors in individuals with autism particularly when implemented as part of a communication choice system.
Iconic Systems
See Also ▶ Picture Exchange Communication System ▶ Symbol Use ▶ Visual Schedule ▶ Visual Supports
References and Reading Angermeier, K., Schlosser, R. W., Luiselli, J. K., Harrington, C., & Carter, B. (2008). Effects of iconicity on requesting with the picture exchange communication system in children with autism spectrum disorder. Research in Autism Spectrum Disorders, 2, 430–446. Beukelman, D. R., & Mirenda, P. (1992). Augmentative and alternative communication: Management of severe communication disorders in children and adults. Baltimore: Paul H. Brookes. Bloomberg, K., Karlan, G. R., & Lloyd, L. L. (1990). The comparative translucency of initial lexical items represented in five graphic symbol systems and sets. Journal of Speech and Hearing Research, 33, 717–725. Bondy, A., & Frost, L. (2002). A picture’s worth: PECS and other visual communication strategies in autism. Bethesda: Woodbine House. Fuller, D. (1997). Initial study into the effects of translucency and complexity on the learning of Blissymbols by children and adults with normal cognitive abilities. Augmentative and Alternative Communication, 13, 30–39. Koegel, R. L., Frea, W. D., & Surratt, A. V. (1994). Selfmanagement as a strategy for modifying problem behavior. In E. Schopler & G. Mesibov (Eds.), Assessment and management of behavior problems in autism. New York: Plenum Press. Koul, R. K., Schlosser, R. W., & Sancibrian, S. (2001). Effects of symbol, referent, and instructional variables on the acquisition of aided and unaided symbols by individuals with autism spectrum disorders. Focus on Autism and Other Developmental Disabilities, 16, 162. Mirenda, P., & Erickson, K. (2000). Augmentative communication and literacy. In A. M. Weatherby & B. M. Prizant (Eds.), Autism spectrum disorders: A transactional developmental perspective (pp. 333–367). Baltimore: Brookes. Ogletree, B. T., & Harn, W. E. (2001). Augmentative and alternative communication for persons with autism: History, issues, and unanswered questions. Focus on Autism and Other Developmental Disabilities, 16, 138–140. Reichle, J. (1991). Implementing augmentative and alternative communication: Strategies for learners with severe disabilities. Baltimore: Paul H. Brookes. Vaughn, B., & Horner, R. (1995). Effects of concrete versus verbal choice systems on problem behavior. Augmentative and Alternative Communication, 11, 89–92.
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See Also
Identical Twins ▶ Monozygotic (MZ) Twins
▶ Linguistic Idiosyncrasies and Neologisms ▶ Metaphoric Language ▶ Pedantic Speech ▶ Pragmatic Communication ▶ Word Use
Idioglossia ▶ Idiosyncratic Language
Idiopathic Toe Walking ▶ Toe Walking
Idiosyncratic Language Brynn Thomas The Neurodevelopmental Disabilities Laboratory, Laboratory for Understanding Neurodevelopment (FUN Lab), Northwestern, and the University of Notre Dame, Chicago, IL, USA
Synonyms Idioglossia
Definition Idiosyncratic language occurs when the child uses standard words or phrases in an unusual, but meaningful way (Volden and Lord 1991). It is a broad term that can refer to a number of speech characteristics that are errors in the pragmatics of communication. A common characteristic of speech in children with Autism Spectrum Disorder (ASD), idiosyncratic language is described as stereotypical and inappropriate word use. These unusual utterances include pedantic speech, in which the child uses overly specific details.
References and Reading Volden, J., & Lord, C. (1991). Neologisms and idiosyncratic language in autistic speakers. Journal of Autism and Developmental Disorders, 21(2), 109–130.
IEP Matrix: Manuscript Solandy Forte The Center for Children with Special Needs, Glastonbury, CT, USA Milestones Behavioral Services, Inc., Milford, CT, USA
An Individualized Education Program (IEP) provides the school team with clear and concise goals and objectives to be targeted across an individual student’s school day and week. It is important for all educators (including service providers) to clearly outline, for implementers of educational and behavioral instruction, the setting(s) in which teaching opportunities need to occur based on the student’s IEP. An IEP matrix can assist the school team (including parents and caregivers) in identifying the settings where specific goals and objectives across all educational domains will be targeted for instruction in a manner that is effective and efficient in order to promote learning and generalization of skills of new and/or acquired skills. Providing the implementers of instruction with an IEP matrix will assist in expediting instruction across settings that is informed and systematic. Further, it will assist the school team in identifying any staff training needs that are specific to executing instruction within a particular setting. This may include educators engaging
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in the development and careful review of teaching protocols and lesson plans with implementer that is also paired with modeling and the delivery of feedback. In order for an IEP matrix to be effective, the school team must provide sufficient and adequate staff training to ensure that instruction is being implemented with fidelity. An IEP matrix is often a document that outlines for the implementer(s) where and when a skill will be targeted for instruction. It will specifically identify times of the day, setting, teaching format, activities, and materials necessary for ensuring that instruction occurs in a systematic fashion to promote generalization of skills day long. This may also serve as a reminder to school team members for when and where instruction needs to occur in order to ensure that teaching opportunities are happening in these settings. The content within the IEP matrix will vary depending on the identified direct instruction (e.g., individual or small group instruction) and inclusion time (i.e., time with nondisabled peers) as well as the student’s grade level. The matrix is individualized to meet the unique needs of the student with respect to the setting(s) where instruction will be delivered. Therefore, it may include information relative to the student’s accommodations, instructional and prompting strategies, and data collection procedures. It is critical that instruction continues to occur across a variety of settings that are outside of the one-on-one setting particularly after the student has acquired a new skill. An IEP matrix is a useful tool for educators to use when programming for generalization of skills as it will help to identify any resources or supports necessary for successful instruction across the school day. For a parent or caregiver, the IEP matrix provides a visual representation of where a student is receiving instruction specific to their individualized goals and objectives and also identifies the teaching methods used within each of the settings. It may also identify the implementers (e.g., related service providers, special education teacher, general education teacher, and direct-care staff) of instruction for each of the time intervals across the school day and week. The IEP matrix is intended to guide the school team in delivering instruction across settings and should be modified
IEP Signature Page
or adjusted as necessary to include special school activities, changes in implementers of the IEP, and any new instructional strategies identified to be effective. Further, the IEP matrix is intended to help promote learning of skills not restrict learning to certain settings or situations. It is a working document that allows for modifications as the student continues to progress in all areas of their educational program.
IEP Signature Page ▶ Notice of Recommended Educational Placement (NOREP)
IJA/RJA Skills ▶ Initiating/Responding to Joint Attention Skills (IJA/RJA)
ILAUGH Model Danielle Geno Kent The College of Arts and Sciences, The University of Vermont, Burlington, VT, USA
Definition The ILAUGH model is the training component of the “social thinking” intervention framework designed by Michelle Garcia Winner (2000). It is defined as the multiple skills and concepts an individual must process to have successful social interactions and engage in effective social problem solving. The ILAUGH model was created by Winner to explain to parents and professionals the many components of social thinking or what constitutes good problem solving. In addition, the framework was developed to provide an overall model of social-cognitive deficits. The key concepts of the ILAUGH model include:
ILAUGH Model
Initiation of communication Listening with your eyes and brain Abstract and Inferential Language/Communication Understanding perspective Gestalt Processing/Getting the big picture Humor and human relatedness The model was specifically designed as an assessment and intervention approach to address the complex social deficits children with highfunctioning autism, Asperger syndrome, and nonverbal learning disabilities struggle with during their day-to-day social interactions.
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“trunk’s” development (reading comprehension, playing with peers, written expression, conversational skills, working in a group). The leaves of the tree’s design are the strategies required to complete the branch, in the case of reading comprehension some skills needed are to summarize what one has read and to organize characters and events (Winner 2012). Currently, it is being developed as a research methodology, and a treatment efficacy study has been recently initiated. The ILAUGH model continues to evolve as an understanding of social cognition research expands.
Rationale or Underlying Theory Historical Background The ILAUGH model is part of Michelle Garcia Winner’s social thinking curriculum, and it investigates traits that have been identified as areas of challenge in persons with social-relatedness disorders. Winner (2000) formulated the ILAUGH model as a result of her work in a school while assessing students’ needs for social skills and specific aspects of their curriculum. From there, research was used to support the development of the six key concepts noted in the definition. Originally, the ILAUGH model was created to help students address their social communication needs in academic settings. The ILAUGH model provides an understanding of the relationship between problem solving, social interactions, and the ability to respond to specific areas of the academic curriculum (Winner and Crooke 2009). The ILAUGH model also came about as it was realized that a majority of students on the autism spectrum were weak listeners, failed to use eye contact or sustain conversations, and were challenged to understand the perspectives of another (Dunn Buron and Wolfberg 2008). Most recently, the ILAUGH model has been linked to the social thinking-social learning tree (Winner 2012) and serves primarily as the “trunk” of the tree, with the roots of the tree grounded in biological underpinnings of social thinking (joint attention, executive function, central coherence, theory of mind, emotional regulation and reciprocity) and the branches of the tree representing skills that emerge from the
The key concepts of the ILAUGH model are grounded in research in the area of social relatedness. A summary of each concept and associated research is described below. I – Initiation of Communication (Krantz and McClannahan 1993). Children with ASD have significant challenges in initiating conversations in stressful situations or when information is not easily understood. Even individuals on the autism spectrum with higher cognitive and linguistic abilities have difficulty with communication, in sharp contrast to the interest they display when talking about a favorite topic of interest. L – Listening with Your Eyes and Brain (Jones and Carr 2004; Kunce and Mesibov 1998; Mundy and Crowson 1997). Many individuals with ASD struggle with auditory comprehension. From a social perspective, however, listening requires much more than our ears; it includes listening with our eyes, which requires a mastery of joint attention. Once children begin to learn that we listen with our eyes, they can be shown the value of nonverbal cues. This can then expand into teaching children to listen with their whole body. A – Abstract and Inferential Language/Communication (Minshew et al. 1992). Most of our communication is not intended for literal comprehension and, instead, is littered with metaphors and idioms – an area of particular
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challenge for individuals with ASD. Accurate message interpretation requires an understanding of literal and figurative language. U – Understanding Perspectives (Baron-Cohen et al. 1997b; Flavell 2004). To understand the perspectives of others, children must have a developed theory of mind (ToM). Whereas typically developing children acquire ToM between the ages of 4 and 6, children with ASD are often delayed in this acquisition, not fully developing ToM until the age of 10. Perspective taking or ToM considers the following abilities: actively considering and adjusting to the thoughts and emotions of one’s self as well as the person(s) with whom one is communicating; actively considering and then comparing and contrasting beliefs (e.g., religious, political, and cultural) of one’s self as well as the person(s) with whom one is communicating; actively considering and then adjusting one’s message given the prior knowledge or experiences of one’s communicative partner (s); and actively considering and then adjusting given the motives and intentions of one’s self and/or their communicative partner. Winner (2004a) argues that there is a range of perspective taking skills across the autism spectrum. G – Gestalt Processing/Getting the Bigger Picture (Fullerton et al. 1996; Shah and Frith 1993). Information is conveyed through conceptual processing, not just factual processing. When engaged in a discussion, conversationalists are constantly assessing the root of the conversation instead of assessing each piece as a separate entity. Conceptual processing is a key component in the understanding of academic and social information. Children who have difficulties with conceptual processing often have difficulties with organizational skills, written expression, and time management while being tangential in their expressive skills. H – Humor and Human Relatedness (Greenspan and Wieder 2003; Gutstein 2001; Prizant et al. 2006a, b). Although many children on the spectrum have a good sense of humor, they struggle with anxiety secondary to missing
ILAUGH Model
social cues that allow them to understand how to have successful interactions with others. Some children on the spectrum have inappropriate humor, and this must also be addressed during intervention. To support human relatedness, it is important to teach students how to relate to their own emotions as well as that of others while helping them understand and experience the happiness or joy in mutual sharing. Winner (2002) created the following table to establish the rationale behind the ILAUGH framework and how social-cognitive deficits impact social interaction and classroom functioning.
Key concept I – poor initiation of communication or action
L – listening with eyes and brain
How it affects social interactions Student does not initiate appropriate social interactions
Student does not observe other’s social cues Student does not process the meaning of their message
A – abstract and inferential
Student doesn’t infer meaning from social cues and has difficulty deciphering meaning from spoken language
How it affects classroom performance Student does not ask for help Student sits and does nothing when others are doing something In class group work, student may not participate or only knows how to direct others; weak negotiator Student does not observe other’s social cues in the classroom Student does not process the meaning of the message in the academic environment Student is limited in the ability to infer meaning from books and teacher’s lectures Student literally interprets all materials (continued)
ILAUGH Model
Key concept U– understanding perspective
G – gestalt processing; getting the big picture
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How it affects social interactions Student has difficulty recognizing and incorporating other person’s perspectives into selfregulation in social relationships
Student has tangential language
Student fails to track the underlying concept of a verbal interaction
H – humor
Student usually has a great sense of humor, but may miss the subtleties of humor
Student may not understand if they are being laughed at or laughed with
How it affects classroom performance Student has difficulty understanding the perspective of characters in literature Student has difficulty regulating classroom behavior in consideration of others’ needs Student attends to details but misses the underlying concept of assignments Writing can be tangential; the point is often missed Student has difficulty staying with the concept of group work and cooperative learning Student responds well to a teacher who has a more relaxed, humorous style, but is still able to follow a fairly structured routine Student may use inappropriate humor in the class as an attempt to engage others
Goals and Objectives The ILAUGH model is designed to help professionals/families make sense of the challenges that
are seen by those students with social-learning challenges and also to provide direction for professionals to build on a child’s strengths and needs in intervention. For children with socialrelatedness disorders, goals include concepts that are within the realm of social cognition and apply directly to each of the key components of the ILAUGH model. The ILAUGH model was designed to explain ways to succeed at social interaction and engage in successful problem solving. A primary goal of the ILAUGH model is to teach children specific language-based concepts that can be used to explain social expectations and related social emotions and thoughts (Winner and Crooke 2009).
Treatment Participants Participants should be those with social thinking challenges, including individuals with and without diagnostic labels, primarily those with autism spectrum disorders. In practice, Winner implements the model for children and adolescents with high-functioning autism, Asperger syndrome and those with nonverbal learning disabilities.
Treatment Procedures The ILAUGH framework offers different components to use in the assessment of a child suspected of having deficits in social cognition. Winner (2002)suggests that there are different ways to assess a child using the ILAUGH framework: (a) observe the student with their peers, across different aspects of their environment, (b) relate with the student without facilitating their social success, (c) use informal assessment tools, (d) administer carefully considered standardized measures, and (e) interview teachers and parents about a student’s daily functioning. As the ILAUGH model is part of Winner’s social cognition program, there are steps that interventionists take during the process. They must focus on making abstract things concrete,
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provide a scaffold for language support, foster self-esteem and self-awareness, program in a progressive manner, and provide structured opportunities for generalization along with ongoing practice (Krasney et al. 2003; Winner and Crooke 2009). To address the components in the ILAUGH model, Winner (2000, 2002, 2005, 2007, 2008) recognized the need to create concrete means of teaching abstract social concepts, so she developed language-based concepts titled social thinking vocabulary (Winner and Crooke 2009). Social thinking vocabulary includes terms such as “thinking with your eyes,” expected/unexpected behavior, smart guess/wacky guess, and social fake (Winner and Crooke 2009). Other topics included during social cognition intervention include addressing what good social skills are and recognizing that social rules change with age (Winner and Crooke 2009).
Efficacy Information There is no research currently available on the efficacy of ILAUGH as a treatment, and/or factors affecting treatment. However, there is research to support the efficacy of the conceptual frameworks used to develop the ILAUGH model (as described above). The skills that children develop to support their social skills interactions are the same skills that are needed for a child to work successfully as part of a group in the classroom (Winner 2002). Social skills deficits have been correlated with academic performance deficits, and thus, by improving social skills, academic skills can also improve (Winner 2004b). Strahan (2003) found that among other things, social skills were a significant predictor of GPA among college-level students. Crooke, Hendrix, and Rachman (2008) examined the effectiveness of teaching social thinking concepts to students with Asperger syndrome and high-functioning autism (HFA). They found that teaching social concepts resulted in a decrease of unexpected nonverbal/verbal social behaviors.
ILAUGH Model
Outcome Measurement Currently there are no identified outcome measurement tools.
Qualifications of Treatment Providers There are no suggested criteria for treatment providers; however, Winner states that assessments of individuals with ASD should be conducted by qualified treatment providers who can evaluate a child’s social, communicative, academic, and play needs.
See Also ▶ Social Cognition
References and Reading Baron-Cohen, S. (1995). Mindblindness. An essay on autism and theory of mind. Massachusetts: Bradford Book/MIT Press. Baron-Cohen, S. (2000). Theory of mind and autism: A fifteen year review. In S. Baron-Cohen, H. Tager-Flusberg, & D. Cohen (Eds.), Understanding other minds: Perspectives from developmental cognitive neuroscience. New York: Oxford University Press. Baron-Cohen, S., Baldwin, D. A., & Crowson, M. (1997a). Do children with autism use the speaker’s direction of gaze strategy to crack the code of language? Child Development, 68, 48–57. Baron-Cohen, S., Jolliffe, T., Mortimore, C., & Robertson, M. (1997b). Another advanced test of theory of mind: Evidence from very high functioning adults with Autism and Asperger Syndrome. Journal of Child Psychiatry and Psychology, 38(7), 813–822. Crooke, P., Hendrix, R., & Rachman, J. (2008). Measuring the effectiveness of teaching social thinking to children with autism spectrum disorder. Journal of Autism & Developmental Disorders, 38(3), 581–591. Dunn Buron, K., & Wolfberg, P. J. (Eds.). (2008). Learners on the autism spectrum: Preparing highly qualified educators. Shawnee Mission: Autism Asperger Publishing. Flavell, J. (2004). Theory-of-mind development: Retrospect and prospect. Merrill Palmer Quarterly, 50(3), 274–290.
Imagination Fullerton, A., Stratton, J., Coyne, P., & Gray, C. (1996). Higher functioning adolescents and young adults with autism. Austin: Pro-ED. Greenspan, S., & Wieder, S. (2003). Engaging autism: The floortime approach to helping children relate, communicate and think. Jackson: Perseus Books. Gutstein, S. E. (2001). Autism/Aspergers: Solving the relationship puzzle. Arlington: Future Horizons. Jones, E., & Carr, E. G. (2004). Joint attention in children with autism: Theory and intervention. Focus on Autism and Other Developmental Disabilities, 19(1), 13–26. Krantz, P., & McClannahan, L. (1993). Teaching children with autism to initiate to peers: Effects of a scriptfading procedure. Journal of Applied Behavior Analysis, 26, 121–132. Krasney, L., Williams, B., Provencal, S., & Ozonoff, S. (2003). Social skills interventions for the autism spectrum: Essential ingredients and a model curriculum. Child & Adolescent Psychiatric Clinics, 12, 107–122. Kunce, L., & Mesibov, G. (1998). Educational approaches to high-functioning autism and Asperger syndrome (Chapter 11). In E. Schopler, G. Mesibov, & L. Kunce (Eds.), Asperger syndrome or high-functioning autism? New York: Plenum Press. Minshew, N., Goldstein, G., Muenz, L., & Payton, J. (1992). Neuropsychological functioning in nonmentally retarded autistic individuals. Journal of Clinical and Experimental Neuropsychology, 14(5), 749–761. Mundy, P., & Crowson, M. (1997). Joint attention and early social communication: Implications for research on intervention with autism. Journal of Autism and Developmental Disorders, 27(6), 653–676. Prizant, B., Wetherby, A., & Rydell, P. (2000). Communication intervention issues for children with autism spectrum disorders. In A. Wetherby & B. Prizant (Eds.), Autism spectrum disorders: A transactional developmental perspective (Vol. 9). Baltimore: Paul H. Brooks. Prizant, B., Wetherby, A., Rubin, E., Laurent, A., & Rydell, P. (2006a). The SCERTS™ model. A comprehensive educational approach for children with autism spectrum disorders (Program planning and intervention) (Vol. II). Baltimore: Paul H. Brookes. Prizant, B., Wetherby, A., Rubin, E., Laurent, A., & Rydell, P. (2006b). The SCERTS™ model. A comprehensive educational approach for children with autism spectrum disorders (Assessment) (Vol. 1). Baltimore: Paul H. Brookes. Shah, A., & Frith, U. (1993). Why do autistic individuals show superior performance on the block design task? Journal of Child Psychology and Psychiatry, 34(8), 1351–1364. Winner, M. (2000). Inside out: What makes the person with social cognitive deficits tick? San Jose: Think Social Publishing.
2401 Winner, M. (2002). Assessment of social skills for students with Asperger syndrome and high-functioning autism. Assessment for Effective Intervention, 27, 73–80. Winner, M. (2005). Think social! A social thinking curriculum for school age students. San Jose: Think Social Publishing. Winner, M. (2007). Thinking about you thinking about me (2nd ed.). San Jose: Think Social Publishing. Winner, M. (2008). The first step of communication, teaching thinking strategies. Autism Asperger’s Digest Magazine, May–June 2008 issue. Winner, M. (2012). Social thinking. Winner, M., & Crooke, P. (2009). Social Thinking ®: A developmental treatment approach for students with social learning/social pragmatic challenges. Perspectives on Language Learning and Education, 16(2), 62–69.
Illusion des sosies ▶ Capgras Syndrome
Imagination Maria Fusaro and Sally J. Rogers Department of Psychiatry and Behavioral Sciences, UC Davis M.I.N.D. Institute, Sacramento, CA, USA
Synonyms Creativity; Generativity; Pretense
Definition The process of mentally representing concepts that are not immediately, perceptually evident. These concepts include those in the realm of fantasy as well as those in the realm of the nonpresent reality (e.g., the recent past and the historical past). Imagination skills are evident in symbolic play, perspective taking, problem solving, and other behaviors that require the flexible manipulation of mental representations (see Harris 2000).
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In ASD research, imagination in the context of pretend play has received significant attention. Deficits in imaginative play have been useful as a diagnostic marker for ASD, although the interpretation of these deficits has been controversial. One prominent theoretical view holds that the lack of imaginative play indicates a symbolic deficit. From a Piagetian framework, pretend play requires operating from mental representations of previous experiences (Piaget 1962). Leslie (1987) argued that pretense requires the ability to simultaneously hold two representations in mind: the primary (veridical) representation and the imagined representation. This symbolic ability was proposed to be lacking in ASD. This perspective has been challenged by a robust finding of an autism-specific deficit only in the spontaneous generation of pretend play, not in the generation of pretend play in response to elicitation (Lewis and Boucher 1988, 1995). Children with autism generate fewer ideas for pretend acts, with and without props, than controls (Jarrold et al. 1996). However, they are no different from well-matched peers when prompted verbally to perform a pretend act, such as an object substitution, by an adult (Lewis and Boucher 1988). Further, they appear to understand the imagined consequences of an adult’s novel, pretend actions (Kavanaugh and Harris 1994). Taken together, children with autism appear able to form and manipulate symbols when suggested by another person but demonstrate a production problem when left to generate pretend acts on their own. An interpretation of these findings is that the pretend play deficit reflects a performance deficit stemming from an autism-specific impairment in generating ideas for play (Jarrold 1997; Jarrold et al. 1996; Lewis and Boucher 1995). Impairments in generativity may reflect an executive functioning problem that extends beyond the play context, to explain, for example, impairments generating word lists and original ideas for drawings (Lewis and Boucher 1991, 1995). Craig and Baron-Cohen (1999) report diminished performance among school-age children with autism, relative to controls, on relevant tasks that involve generating multiple drawings
Imagination
from a standard baseline of parallel lines and squiggles, as well as generating ideas for improving a toy, and uses for novel 3-D objects. Additional studies are needed to further understand the interplay of social and cognitive factors underlying diminished imaginative behaviors in autism, in spontaneous pretend play, and in other contexts.
See Also ▶ Play ▶ Pretend Play ▶ Symbolic Play
References and Reading Craig, J., & Baron-Cohen, S. (1999). Creativity and imagination in autism and Asperger syndrome. Journal of Autism and Developmental Disorders, 29, 319–326. Harris, P. L. (2000). The work of the imagination. Oxford: Blackwell. Jarrold, C. (1997). Pretend play in autism: Executive explanations. In J. Russell (Ed.), Autism as an executive disorder (pp. 101–133). Oxford: Oxford University Press. Jarrold, C., Boucher, J., & Smith, P. K. (1993). Symbolic play in autism: A review. Journal of Autism and Developmental Disorders, 23, 281–307. Jarrold, C., Boucher, J., & Smith, P. K. (1996). Generativity deficits in pretend play in autism. British Journal of Developmental Psychology, 14, 275–300. Kavanaugh, R. D., & Harris, P. L. (1994). Imagining the outcome of pretend transformations: Assessing the competence of normal children and children with autism. Developmental Psychology, 30, 847–854. Leslie, A. M. (1987). Pretence and representation: The origins of ‘theory of mind’. Psychological Review, 94, 412–426. Lewis, V., & Boucher, J. (1988). Spontaneous, instructed and elicited play in relatively able autistic children. British Journal of Developmental Psychology, 6, 325–339. Lewis, V., & Boucher, J. (1991). Skill, content and generative strategies in autistic children’s drawings. British Journal of Developmental Psychology, 9, 393–416. Lewis, V., & Boucher, J. (1995). Generativity in the play of young people with autism. Journal of Autism and Developmental Disorders, 25, 105–121. Piaget, J. (1962). Play, dreams and imitation in childhood. London: Routledge & Kegan Paul.
Imitation
Imipramine Lawrence David Scahill Nursing and Child Psychiatry, Yale Child Study Center, Yale University School of Nursing, New Haven, CT, USA Marcus Autism Center, Children’s Healthcare of Atlanta, Atlanta, GA, USA Department of Pediatrics, Emory University, Atlanta, GA, USA
Synonyms Tofranil
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also carry some risk of altering the electrical conduction in the heart. They are well known to be fatal on overdose due to their potential for causing cardiac arrhythmia. Because of their known toxicity at higher doses, treatment with tricyclic antidepressants requires blood-level monitoring and electrocardiogram monitoring as well. Finally, the tricyclic antidepressants are also vulnerable to drug-drug interaction. For example, some medications such as SSRIs or certain antibiotics may interfere with the breakdown of tricyclic antidepressant medications. The interference of metabolism of the tricyclic can cause a sharp increase in the blood levels of the tricyclic antidepressants and increase the vulnerability to toxic effects. The tricyclic medications have not been well studied in children or adults with autism.
Indications Imipramine is a tricyclic antidepressant. The term tricyclic refers to the three-ring structure of this class of antidepressant medications. These medications are not used as commonly as in the past as they have been largely replaced by the SSRIs. Imipramine has been used to treat both depression and anxiety.
References and Reading Martin, A., Scahil, L., & Christopher, K. (2010). Pediatric psychopharmacology: Principles and practice (2nd ed.). New York: Oxford University Press. McDougle, C., & Posey, D. J. (2011). Assessment and treatment of autistic and other pervasive developmental disorders. In A. Martin, L. Scahill, & C. Kratochvil (Eds.), Pediatric psychopharmacology: Principles and practice (pp. 547–560). New York: Oxford University Press.
Mechanisms of Action While desipramine has highly selective norepinephrine reuptake inhibitor properties, and clomipramine is well known for its more selective serotonin reuptake inhibiting properties, imipramine is intermediate with both norepinephrine and serotonin reuptake inhibiting properties.
Imitation Kathy Lawton Special Education and Nisonger Center, The Ohio State University, Columbus, OH, USA
Definition Clinical Use (Including Side Effects) The tricyclic antidepressants have several adverse effects in common including dry mouth, urinary retention, constipation, nausea, increased heart rate, dizziness, and, at higher doses, confusion. The tricyclic antidepressants
Imitation is considered to be an integral component of young children’s social development. The ability to imitate helps lay the foundation for quality social interactions. Empirical findings exploring early imitative abilities in children have found them to relate to an array of
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early childhood skills including: social engagement, nonverbal communication, language development, social understanding, and cognitive skills (Young et al. 2011; Rose et al. 2009; Olineck and Poulin-Dubois 2009; Strid et al. 2006).
Historical Background Although empirical evidence supports the notion that individuals with ASD, as a group, imitate others with less accuracy and less frequency than their typically developing peers (Vivanti and Hamilton 2014), studies reporting on imitation abilities in individuals with ASD report heterogeneity across individuals (Rogers and Dawson 2010; Salowitz et al. 2012; Vanvuchelen et al. 2011; Vivanti et al. 2011). These differences in imitation abilities seem to be related to differences in social and communicative abilities in this population (Young et al. 2011). A number of studies have provided evidence suggesting a relationship between imitation and language in children with ASD (Ingersoll and Meyer 2011; McDuffie et al. 2007; Toth et al. 2007). In addition, a relationship also appears to exist between imitation and joint attention (Ingersoll and Schreibman 2006; Rogers et al. 2003), play (Vivanti et al. 2013), social reciprocity (McDuffie et al. 2007; Young et al. 2011), and severity of autism symptomatology (Ingersoll and Meyer 2011; Rogers et al. 2003). While some studies suggest that imitation is a core deficit of autism spectrum disorder (Rogers and Pennington 1991; Rogers 1999), others contend that there is incomplete evidence to suggest that impairments in imitation are unique and specific to ASD (Williams 2008). As such, although there is empirical evidence to support that children with ASD exhibit deficits in their imitation capabilities, there is not a consensus in the field regarding this matter (e.g., Toth et al. 2006). Regardless of whether or not imitation is a core deficit of autism spectrum disorder, its relationship to other critical early childhood skills distinguish it as a worthy target of early intervention.
Imitation
Current Knowledge Intervening on Imitation Due to its prominent role in the development of social and communication skill, there has been much work done on the development of interventions aimed at improving imitation skills in children with ASD. These interventions report promising findings suggesting improvements in object imitation (Ingersoll and Gergans 2007; Ingersoll and Schreibman 2006), motor imitation (Ingersoll et al. 2006), gesture imitation (Ingersoll et al. 2007), and overall imitation (Ozonoff and Cathcart 1998; Rogers et al. 2005). In addition to improvements in several aspects of imitative abilities, interventions targeting imitation have also been shown to have positive effects on various related skills including imitative language (Ingersoll et al. 2006), expressive language (Young et al. 2011), appropriate play schemes (Ingersoll and Schreibman 2006), joint attention (Ingersoll et al. 2011), combined gestures (Ingersoll et al. 2007), spontaneous gestures (Ingersoll et al. 2007), spontaneous language (Rogers et al. 2005), social communication behaviors (Rogers et al. 2005), as well as fine motor, gross motor, and cognitive performance (Dawson et al. 2010). Imitation is also used as a pivotal component in early interventions that aim to effect change in core deficits of children with autism. One such example is an intervention that targets joint attention, symbolic play, engagement, and regulation (JASPER) in which adults frequently imitate young children’s play acts in order to promote social communication (Kasari and Lawton 2010). Imitation is conceptualized as an important adult strategy for helping children achieve better social communication.
Future Directions Work in the field still aims to determine the centrality of imitation as a deficit in individuals with ASD, and the ways in which imitation profiles differ between individuals on the spectrum. In addition, there continues to be work done to determine which imitation interventions are most
Imitation
successful in improving imitation skills of young children with ASD, as well as the ways in which these imitation interventions affect other important social communication skills.
References and Reading Dawson, G., Rogers, S., Munson, J., Smith, M., Winter, J., Greenson, J., . . . Varley, J. (2010). Randomized, controlled trial of an intervention for toddlers with autism: The Early Start Denver Model. Pediatrics, 125(1), e17–e23. Ingersoll, B., & Gergans, S. (2007). The effect of a parentimplemented imitation intervention on spontaneous imitation skills in young children with autism. Research in Developmental Disabilities, 28(2), 163–175. Ingersoll, B., & Meyer, K. (2011). Examination of correlates of different imitative functions in young children with autism spectrum disorders. Research in Autism Spectrum Disorders, 5(3), 1078–1085. Ingersoll, B., & Schreibman, L. (2006). Teaching reciprocal imitation skills to young children with autism using a naturalistic behavioral approach: Effects on language, pretend play, and joint attention. Journal of Autism and Developmental Disorders, 36(4), 487. Ingersoll, B., Lewis, E., & Kroman, E. (2007). Teaching the imitation and spontaneous use of descriptive gestures in young children with autism using a naturalistic behavioral intervention. Journal of Autism and Developmental Disorders, 37(8), 1446–1456. Kasari, C., & Lawton, K. (2010). New directions in behavioral treatment of autism spectrum disorders. Current Opinion in Neurology, 23(2), 137. McDuffie, A., Turner, L., Stone, W., Yoder, P., Wolery, M., & Ulman, T. (2007). Developmental correlates of different types of motor imitation in young children with autism spectrum disorders. Journal of Autism and Developmental Disorders, 37(3), 401–412. Mumford, K. H. (2014). The relationship between vocabulary and gesture development in early childhood and infancy. Doctoral dissertation, University of Birmingham. Ozonoff, S., & Cathcart, K. (1998). Effectiveness of a home program intervention for young children with autism. Journal of Autism and Developmental Disorders, 28(1), 25–32. Rogers, S. J. (1999). An examination of the imitation deficit in autism. Rogers, S. J., & Dawson, G. (2010). Early Start Denver Model for young children with autism: Promoting language, learning, and engagement. New York: Guilford Press. Rogers, S. J., & Pennington, B. F. (1991). A theoretical approach to the deficits in infantile autism. Development and Psychopathology, 3(2), 137–162. Rogers, S. J., Hepburn, S. L., Stackhouse, T., & Wehner, E. (2003). Imitation performance in toddlers with
2405 autism and those with other developmental disorders. Journal of Child Psychology and Psychiatry, 44(5), 763–781. Rogers, S. J., Cook, I., & Meryl, A. (2005). Imitation and play in autism. In Handbook of autism and pervasive developmental disorders (Vol. 1, pp. 382–405). Rose, S. A., Feldman, J. F., & Jankowski, J. J. (2009). A cognitive approach to the development of early language. Child Development, 80(1), 134–150. Salowitz, N. M., Eccarius, P., Karst, J., Carson, A., Schohl, K., Stevens, S., . . . Scheidt, R. A. (2012). Brief report: Visuo-spatial guidance of movement during gesture imitation and mirror drawing in children with autism spectrum disorders. Journal of Autism and Developmental Disorders, 43(4), 985–995. Strid, K., Tjus, T., Smith, L., Meltzoff, A. N., & Heimann, M. (2006). Infant recall memory and communication predicts later cognitive development. Infant Behavior and Development, 29(4), 545–553. Toth, K., Munson, J. N., Meltzoff, A., & Dawson, G. (2006). Early predictors of communication development in young children with autism spectrum disorder: Joint attention, imitation, and toy play. Journal of Autism and Developmental Disorders, 36(8), 993–1005. Toth, K., Dawson, G., Meltzoff, A. N., Greenson, J., & Fein, D. (2007). Early social, imitation, play, and language abilities of young non-autistic siblings of children with autism. Journal of Autism and Developmental Disorders, 37(1), 145–157. Vanvuchelen, M., Roeyers, H., & De Weerdt, W. (2011). Imitation assessment and its utility to the diagnosis of autism: Evidence from consecutive clinical preschool referrals for suspected. Journal of Autism and Developmental Disorders, 41(4), 484–496. Vivanti, G., & Hamilton, A. (2014). Imitation in autism spectrum disorders. In Handbook of autism and pervasive developmental disorders (4th ed.). Vivanti, G., McCormick, C., Young, G. S., Abucayan, F., Hatt, N., Nadig, A., . . . Rogers, S. J. (2011). Intact and impaired mechanisms of action understanding in autism. Developmental Psychology, 47(3), 841. Vivanti, G., Dissanayake, C., Zierhut, C., Rogers, S. J., & Victorian ASELCC Team. (2013). Brief report: Predictors of outcomes in the Early Start Denver Model delivered in a group setting. Journal of Autism and Developmental Disorders, 43(7), 1717–1724. Whalen, C., Schreibman, L., & Ingersoll, B. (2006). The collateral effects of joint attention training on social initiations, positive affect, imitation, and spontaneous speech for young children with autism. Journal of Autism and Developmental Disorders, 36(5), 655–664. Williams, J. H. (2008). Self–other relations in social development and autism: Multiple roles for mirror neurons and other brain bases. Autism Research, 1(2), 73–90. Young, G. S., Rogers, S. J., Hutman, T., Rozga, A., Sigman, M., & Ozonoff, S. (2011). Imitation from 12 to 24 months in autism and typical development: A longitudinal Rasch analysis. Developmental Psychology, 47(6), 1565.
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Imitation of Intransitive Actions
Historical Background
Imitation of Intransitive Actions ▶ Body Movements, Imitation of
Typically, studies of immigrant status and autism spectrum disorders (ASDs) have examined whether the occurrence of ASDs is the same for children born to a mother or father who was born in another country (immigrant) as compared to parent-child pairs who were born in the same country (nonimmigrant).
Imitation of Nonmeaningful Gestures Current Knowledge ▶ Body Movements, Imitation of
Imitative Speech ▶ Echolalia
Immediate Echolalia ▶ Echolalia
Immigrant Status (Epidemiological Studies) Catherine E. Rice National Center on Birth Defects and Developmental Disabilities, Centers for Disease Control and Prevention, Atlanta, GA, USA
Definition An immigrant is a person who leaves his or her country of origin to reside in another country. A refugee is a specific type of immigrant who leaves his or her country of origin due to safety threats or out of fear of persecution.
Most epidemiologic studies of ASDs have been conducted in Europe, North America, and areas of Asia; there is limited information on the occurrence and characteristics of ASDs in other areas of the world. A few studies have raised speculation about increased occurrence of ASDs among children who had at least one parent who immigrated to the country where the child was born (Gardener et al. 2009 for a review). These studies look at the occurrence of ASDs among subgroups within the country being studied and have not included comparisons of ASDs across countries. For example, a few studies in the UK found that Caribbean-born mothers and fathers residing in the UK were more likely to have a child with an ASD than UK-born parents (Goodman and Richards 1995; Keen et al. 2010; Wing 1980). In addition, mothers born in Africa and Asia were more likely to have children with ASDs in the UK; however, European-born immigrants to the UK showed no association (Keen et al. 2010). Studies from Sweden report increased prevalence of ASDs in the group where either parent was not Swedish-born (Gillberg and Gillberg 1996). They also found increased frequency of ASDs among immigrant groups only in urban, but not rural areas (Gillberg et al. 1987). Increased occurrence of ASDs among children born in Sweden was reported for mothers born outside of Sweden (Hultman et al. 2002), among mothers born in Uganda (Gillberg et al. 1995), and if either parent was of Somali descent (BarnevikOlsson et al. 2010) as compared to Swedish-born parents. Concern about increased prevalence of ASDs among US-born children with parents of Somali descent has also been raised in the USA
Immigrant Status (Epidemiological Studies)
(Minnesota Department of Health [MN] 2009). Among children with ASDs born in Denmark, increased association was found for offspring of mothers born outside of Europe or parents born in different countries (Lauritsen et al. 2005) with one study finding an association for mothers, but not fathers, born outside Denmark (Maimburg and Vaeth 2006). Increased frequency of ASDs in New South Wales was noted for children of mothers born outside of Australia, specifically from Asia (Williams et al. 2008). There has also been some indication that the level of impairment may be more severe among parental immigrant groups (Dealberto 2011). Contrary to the hypothesis that parental immigration increases the chances of having a child with an ASD in the new country of residence, a US study indicated that mothers who immigrated from Mexico to California were less likely to have a child with an ASD than Hispanic mothers born in the United States (Croen et al. 2002). While many of these studies are suggestive of an association between parental immigration status and having a child with an ASD, small sample sizes limit firm conclusions. There are many challenges in evaluating immigrant status including the potential variation and bias in the way immigration status may be documented. This could impact the ability to classify a child as having an ASD among immigrant families. This could also decrease the validity of data on parental birth location and limit the available data on the total population of immigrants by specific countries of origin or ethnicities needed to make reliable prevalence estimates (MN 2009; Fombonne 2003). In addition, there are many reasons why an individual or family may immigrate to another country. Grouping all immigrants together as a homogeneous group may mask some important differences among subgroups. For example, there may be different sociodemographic, environmental, and possibly biological profiles among groups who immigrate for higher education or sponsored employment from those who immigrate based on refugee status or who are undocumented (i.e., not legally registered in the new country of residence). The larger sociopolitical context is also relevant. For example, administrative prevalence
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estimates of ASDs among Hispanic children in California vary based on immigration policy changes (Fountain and Bearman 2011). Several theories have been proposed to account for a difference in the occurrence of ASDs based on parental immigrant status. These explanations include cultural differences in how the features of ASDs are identified and viewed by parents and professionals among different ethnic groups, which may result in differential identification of ASDs among some groups. In addition, there may be disparities in how children are identified. However, contrary to the findings that minority groups tend to be underidentified with ASDs (CDC 2009; Kogan et al. 2009), higher frequency among select immigrant groups indicates the opposite pattern. For some immigrant groups, it could be that having unique status such as a member of a political refugee group enhances identification of ASDs through linkages to service systems available to this population (MN 2009). While several identification mechanisms have been proposed, it is important to consider whether there is a true etiologic difference caused by biologic factors, exposures, or a combination of the two across immigrant and nonimmigrant groups. For example, differences in familial relatedness and differential genetic risk for ASDs or other developmental disabilities have been noted, but not well studied (Barnevik-Olsson et al. 2010; Morrow et al. 2008; Ropers 2010). Another possibility is that there is differential migration of people with ASD-like social deficits, resulting in an increased likelihood of their children having an ASD compared to the general population (Fombonne 2005; Gillberg et al. 1995). Hypotheses of differential environmental experiences among immigrant and nonimmigrant groups have also been raised as possible routes to increased risk for ASDs among these groups. These include increased maternal stress (Gardener et al. 2009), exposure to new infectious agents (Gillberg et al. 1995) or environmental neurotoxicants such as pesticides (Fountain and Bearman 2011), and differential vitamin D deficiency among groups with skin or clothing that reduces exposure (Dealberto 2011; Fernell et al. 2010).
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Future Directions Multiple studies suggest an increased occurrence of ASDs among children with at least one immigrant parent as compared to nonimmigrant parents. Additional studies with larger samples in different locations would be helpful both to confirm and better understand this possible association. In addition, data on the prevalence of ASDs in the countries of origin (e.g., in Somalia or areas of the Caribbean) would provide useful comparison data. Further attention to the heterogeneity of immigrant populations and whether specific characteristics apply to a group (e.g., refugee status) would also be helpful. In addition, attention to the quality of the data to include case-finding methods that attempt to identify all individuals in a defined geographic area by relevant characteristics (age group, race/ethnicity, parent birth place, etc.) is important. Also needed is a reliable source for the population denominator (base population of people within that immigrant status group). Finally, more careful examination of biologic and exposure differences between immigrant and nonimmigrant groups in association with status of having an ASD could be very useful in identifying unique risk factors for ASDs among these populations.
See Also ▶ Epidemiology ▶ Incidence ▶ Obstetrical Complications/Risk Factors ▶ Prevalence ▶ Study Design Impact on Prevalence
References and Reading Barnevik-Olsson, M., Gillberg, C., & Fernell, E. (2010). Prevalence of autism in children of Somali origin living in Stockholm: Brief report of an at-risk population. Developmental Medicine and Child Neurology, 52(12), 1167–1168. https://doi.org/10.1111/j.14698749.2010.03812.x. Epub 2010 Oct 21. Centers for Disease Control and Prevention. (2009). Prevalence of autism spectrum disorders – Autism and
Immigrant Status (Epidemiological Studies) developmental disabilities monitoring network, united states, 2006. Morbidity and Mortality Weekly Report Surveillance Summaries, 58(10), 1–20. Croen, L., Grether, J., & Selvin, S. (2002). Descriptive epidemiology of autism in a California population: Who is at risk? Journal of Autism and Developmental Disorders, 32(3), 217–224. Dealberto, M. J. (2011). Prevalence of autism according to maternal immigrant status and ethnic origin. Acta Psychiatrica Scandinavica, 123(5), 339–348. https:// doi.org/10.1111/j.1600-0447.2010.01662.x. Epub 2011 Jan 11. Durkin, M. S., Maenner, M. J., Meaney, F. J., Levy, S. E., DiGuiseppi, C., Nicholas, J. S., et al. (2010). Socioeconomic inequality in the prevalence of autism spectrum disorder: Evidence from a U.S. cross-sectional study. PLoS One, 5(7), e11551. Fernell, E., Barnevik-Olsson, M., Bågenholm, G., Gillberg, C., Gustafsson, S., & Sääf, M. (2010). Serum levels of 25-hydroxyvitamin D in mothers of Swedish and of Somali origin who have children with and without autism. Acta Paediatrica, 99(5), 743–747. Epub 2010 Mar 5. Fombonne, E. (2003). Epidemiological surveys of autism and other pervasive developmental disorders: An update. Journal of Autism and Developmental Disorders, 33, 365–382. Fountain, C., & Bearman, P. (2011). Risk as social context: Immigration policy and autism in California. Social Forum, 26(2), 215–240. Gardener, H., Spiegelman, D., & Buka, S. L. (2009). Prenatal risk factors for autism: Comprehensive metaanalysis. British Journal of Psychiatry, 195(1), 7–14. Review. Gillberg, I. C., & Gillberg, C. (1996). Autism in immigrants: A population-based study from Swedish rural and urban areas. Journal of Intellectual Disability Research, 40(Pt 1), 24–31. Gillberg, C., Steffenburg, S., Börjesson, B., & Andersson, L. (1987). Infantile autism in children of immigrant parents. A population-based study from Göteborg, Sweden. British Journal of Psychiatry, 150, 856–858. Gillberg, C., Schaumann, H., & Gillberg, I. C. (1995). Autism in immigrants: Children born in Sweden to mothers born in Uganda. Journal of Intellectual Disability Research, 39(Pt 2), 141–144. Goodman, R., & Richards, H. (1995). Child and adolescent psychiatric presentations of second-generation AfroCaribbeans in Britain. The British Journal of Psychiatry, 167(3), 362–369. Hultman, C. M., Sparén, P., & Cnattingius, S. (2002). Perinatal risk factors for infantile autism. Epidemiology, 13(4), 417–423. Keen, D. V., Reid, F. D., & Arnone, D. (2010). Autism, ethnicity and maternal immigration. The British Journal of Psychiatry, 196(4), 274–281. Kogan, M., Blumberg, S., Schieve, L., Boyle, C., Perrin, J., et al. (2009). Prevalence of parent-reported diagnosis of
Impact of Race, Ethnicity, and Gender on Social and Behavioral Ratings Within the ADOS autism spectrum disorder among children in the U.S., 2007. Pediatrics, 124(5), 1395–1403. Lauritsen, M. B., Pedersen, C. B., & Mortensen, P. B. (2005). Effects of familial risk factors and place of birth on the risk of autism: A nationwide registerbased study. Journal of Child Psychology and Psychiatry, 46(9), 963–971. Maimburg, R. D., & Vaeth, M. (2006). Perinatal risk factors and infantile autism. Acta Psychiatrica Scandinavica, 114(4), 257–264. Minnesota Department of Health. (2009). Autism spectrum disorders among preschool children participating in the Minneapolis Public Schools early childhood special education programs. http://www.health.state.mn.us/ ommh/projects/autism/index.cfm Morrow, E. M., Yoo, S. Y., Flavell, S. W., Kim, T. K., Lin, Y., Hill, R. S., et al. (2008). Identifying autism loci and genes by tracing recent shared ancestry. Science, 321(5886), 218–223. Ropers, H. H. (2010). Genetics of early onset cognitive impairment. Annual Review of Genomics and Human Genetics, 11, 161–187. Williams, K., Helmer, M., Duncan, G. W., Peat, J. K., & Mellis, C. M. (2008). Perinatal and maternal risk factors for autism spectrum disorders in New South Wales, Australia. Child: Care, Health and Development, 34(2), 249–256. Wing, L. (1980). Childhood autism and social class: A question of selection. The British Journal of Psychiatry, 137(410), 417.
Impact of Race, Ethnicity, and Gender on Social and Behavioral Ratings Within the ADOS Ashley J. Harrison and Yvette F. Bean Department of Educational Psychology, University of Georgia, Athens, GA, USA
Definition At present, diagnosing autism spectrum disorder (ASD) relies on the identification of symptoms by comparing clinician-observed and parentreported behaviors to typical and atypical exemplars. Historically these examples reflect behaviors from the most highly researched groups – White, Western, and males (Hilton et al. 2010). Using reference behaviors based on
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majority groups discounts the dynamic nature of cultural and social behavior and can inadvertently lead to culture-based biases in ASD measures assessing social constructs. Research has documented cross-cultural variability in a range of behaviors central to ASD diagnosis. Distinct values about eye contact and varied interpretation guidelines among cultures (i.e., individualist and collectivist countries) result in different gaze topography. The extent of sustained eye contact and regularity of eye contact between adults and children also varies between racial groups in the USA (Fugita et al. 1974). Similarly, cross-cultural differences exist in the amount and type of play, particularly in child and adult dyads (Lancy 2007). Both internationally and among distinct US racial and gender groups, play differences reflect cultural variability in sociocultural gender roles (Lai et al. 2015) and in valuation of attributes like self-assertion (Kochman 1981). Culture also impacts emotion expression and recognition. Cross-cultural variability in emotion recognition (Elfenbein et al. 2007) is attributable to using unique facial expressions to represent basic emotions across cultures and different facial viewing patterns (i.e., holistic patterns in collectivistic cultures and analytic patterns in individualistic cultures; Masuda and Nisbett 2001). Culture can also impact how different facial features are “weighted” during face processing (Yuki et al. 2007). Culture also impacts nonverbal facial cues used to express emotions, referred to as “nonverbal accents.” Thus, people demonstrate an in-group effect where they recognize emotions from their own culture more accurately (Marsh 2003). Different levels of expressivity between racial groups and gender in the USA (Vrana and Rollock 2002) might reflect distinct values placed on spontaneous expression of feelings (McClure 2000). Aspects of language with cultural underpinnings, such as pragmatic use, nonverbal communication, and speech patterns are embedded in both social and repetitive/restrictive ASD symptom clusters. Cultures weight verbal and nonverbal messages differently depending on their reliance on context (Knapp et al. 2013) and have differing styles for proximity, volume, and other nonverbal conversational cues (Collett
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Impact of Race, Ethnicity, and Gender on Social and Behavioral Ratings Within the ADOS
1971). Cultural variability in pragmatic language impacts important aspects of conversing (i.e., turn-taking, interrupting, humor), as well as frequency of conversation between children and adults (Carter et al. 2005). Cultural variability is also documented in pronoun usage among bilingual individuals (Carter et al. 2005) and grammatical patterns between different US racial groups depending on the English dialect spoken at home (Hall and Freedle 1975). A failure to account for known variability in these behaviors fundamental to ASD diagnostic assessment could result in meaningful under- or over-pathologizing of behavioral deviations from classic exemplars. One study examined a subset of items from one widely used observation-based ASD diagnostic tool to determine if racial, ethnic, or gender measurement biases existed. After holding overall symptom levels constant, results revealed modest biases in items assessing eye contact, stereotyped language use, and echolalia for racial and ethnic minority groups (Harrison et al. 2017). Although the amount of bias in the sample was small, results highlight the diminished accuracy that could arise through a reliance on narrow, culturally bound definitions of what constitutes typical behaviors and arbitrary clinical thresholds. The tendency to rely on one presentation of ASD is linked to inaccurate identification of autistic females (Kirkovski et al. 2013). Cultural variability in caregiver perception of behavior and personal values can also influence endorsement of problems and, thus, diagnostic outcomes. Taken together, these cultural factors could impact ASD prevalence. Increasing clinician awareness of cultural differences in child and parent behaviors and utilizing rigorous transadaptation strategies for assessments are critical strategies for buffering diagnostic measurement bias.
See Also ▶ Clinical Assessment ▶ Culture and Autism ▶ Diagnostic Process ▶ Measurement Error ▶ Test Bias
References and Reading Carter, J. A., Lees, J. A., Murira, G. M., Gona, J., Neville, B. G., & Newton, C. R. (2005). Issues in the development of cross-cultural assessments of speech and language for children. International Journal of Language & Communication Disorders, 40(4), 385–401. https://doi.org/10.1080/136828 20500057301. Collett, P. (1971). Training Englishmen in the non-verbal behaviour of Arabs. International Journal of Psychology, 6(3), 209–215. https://doi.org/10.1080/ 00207597108246684. Elfenbein, H. A., Beaupré, M., Lévesque, M., & Hess, U. (2007). Toward a dialect theory: Cultural differences in the expression and recognition of posed facial expressions. Emotion, 7(1), 131. https://doi.org/ 10.1037/1528-3542.7.1.131. Fugita, S. S., Wexley, K. N., & Hillery, J. M. (1974). Black-White differences in nonverbal behavior in an interview setting. Journal of Applied Social Psychology, 4(4), 343–350. https://doi.org/10.1111/j. 1559-1816.1974.tb02606.x. Hall, W. S., & Freedle, R. O. (1975). Culture and language: The black American experience. Washington, DC: Hemisphere Publishing. Harrison, A. J., Long, K. A., Tommet, D. C., & Jones, R. N. (2017). Examining the role of race, ethnicity, and gender on social and behavioral ratings within the Autism Diagnostic Observation Schedule. Journal of Autism and Developmental Disorders, 47(9), 2770–2782. https://doi.org/10.1007/s10803-0173176-3. Hilton, C. L., Fitzgerald, R. T., Jackson, K. M., Maxim, R. A., Bosworth, C. C., Shattuck, P. T., . . . Constantino, J. N. (2010). Brief report: Underrepresentation of African Americans in autism genetic research: A rationale for inclusion of subjects representing diverse family structures. Journal of Autism and Developmental Disorders, 40(5), 633–639. https://doi.org/10.1007/s10803-009-0905-2 Kirkovski, M., Enticott, P. G., & Fitzgerald, P. B. (2013). A review of the role of female gender in autism spectrum disorders. Journal of Autism and Developmental Disorders, 43(11), 2584–2603. https://doi.org/10. 1007/s10803-013-1811-1. Knapp, M. L., Hall, J. A., & Horgan, T. G. (2013). Nonverbal communication in human interaction. Boston, MA: Wadsworth Cengage Learning. Kochman, T. (1981). Black and white styles in conflict. Chicago: University of Chicago Press. Lai, M.-C., Lombardo, M., Auyeung, B., Chakrabarti, B., & Baron-Cohen, S. (2015). Sex/gender differences and Autism. Journal of the American Academy of Child & Adolescent Psychiatry, 54(1), 11–24. Lancy, D. F. (2007). Accounting for variability in mother– child play. American Anthropologist, 109(2), 273–284. https://doi.org/10.1525/aa.2007.109.2.273.
Inborn Errors of Metabolism Marsh, A. (2003). Nonverbal “accents” cultural differences in facial expressions of emotion. Psychological Science, 14(4), 373–376. Masuda, T., & Nisbett, R. E. (2001). Attending holistically versus analytically: Comparing the context sensitivity of Japanese and Americans. Journal of Personality and Social Psychology, 8(5), 922–934. https://doi.org/10. 1037//0022-35I4.81.5.922. McClure, E. B. (2000). A meta-analytic review of sex differences in facial expression processing and their development in infants, children, and adolescents. Psychological Bulletin, 126(3), 424. https://doi.org/ 10.1037/0033-2909.126.3.424. Vrana, S. R., & Rollock, D. (2002). The role of ethnicity, gender, emotional content, and contextual differences in physiological, expressive, and selfreported emotional responses to imagery. Cognition & Emotion, 16(1), 165–192. https://doi.org/10.1080/ 0269993014300018. Yuki, M., Maddux, W. W., & Masuda, T. (2007). Are the windows to the soul the same in the East and West? Cultural differences in using the eyes and mouth as cues to recognize emotions in Japan and the United States. Journal of Experimental Social Psychology, 43(2), 303–311. https://doi.org/10.1016/j.jesp.2006.02.004.
Inactive ▶ Passive Group
Inborn Errors of Metabolism Jessica L. Roesser Department of Pediatrics (SMD), University of Rochester, School of Medicine and Dentistry, Rochester, NY, USA
Synonyms Congenital metabolic diseases; Inherited metabolic diseases
Definition Inborn errors of metabolism are a very large group of genetic or inherited disorders, often due to singlegene defects, that impact the typical function of specific and identified biochemical processes. It is
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estimated that they occur in 70/10,000 live births. Metabolism is the act of synthesizing or breaking down compounds and involves biochemical action. When there is a congenital absence of an enzyme or other biochemical impediments to breakdown of cellular by-products or creation of necessary molecules, the intended cellular functions cannot take place resulting in illness or cellular damage. Accumulation of toxic substance(s) may occur. An individual may be unable to create an essential compound. One example of an inborn error of metabolism is phenylketonuria or PKU. This is an autosomal recessive genetic disorder characterized by the absence of an enzyme (phenylalanine hydroxylase) that forms the amino acid phenylalanine ingested through the diet into tyrosine. If untreated, phenylalanine accumulates as a toxic compound and can cause brain damage. Prior to newborn screening programs to identify this disorder and allow for implementation of a special dietary treatment, this was one cause of symptoms of autism and intellectual disability. Inborn errors of metabolism may affect biochemical systems that are responsible for synthesis and/or removal of amino acids, cholesterol and bile acids, creatine, fatty aldehydes, lysosomes, organic acids, peroxisomes, pyrimidines, urea cycle intermediates, and vitamins/cofactors and glucose homeostasis and transport, among others. This class of disorders is often categorized into disorders that impact amino acid, carbohydrate, or organic acid metabolism in addition to lysosomal storage diseases. Newborn screening with mass spectroscopy makes it possible to identify many of these disorders early in infancy. At this time, research studies indicate that evaluation for inborn errors of metabolism in children with nonsyndromic autism is not indicated (Campistol et al. 2016). Measured metabolites suggesting metabolic dysfunction that are not associated with a known inborn error of metabolism could be causative of the symptoms of ASD or may be secondary to either treatments used for ASD or to associated medical conditions. Mutations of genes in the maternally inherited mitochondrial genome appear to cause increased oxidative stress and changes in energy metabolism in the cell. It has been suggested that there is an increased rate of mitochondrial disorders among individuals with ASD.
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See Also ▶ Medical Evaluation in Autism ▶ Metabolic Testing
References and Reading Baieli, S., Pavone, L., Meli, C., Fiumara, A., & Coleman, M. (2003). Autism and phenylketonuria. Journal of Autism and Developmental Disorders, 33(2), 201–204. Campistol, J., Díez-Juan, M., Callejón, L., Fernandez-De Miguel, A., Casado, M., Garcia Cazorla, A., Lozano, R., & Artuch, R. (2016). Inborn error metabolic screening in individuals with nonsyndromic autism spectrum disorders. Dev Med Child Neurol, 58(8):842–7. https:// doi.org/10.1111/dmcn.13114. Epub 2016 Mar 31. Myers, S. M., & Johnson, C. P. (2007). Management of children with autism spectrum disorders. Pediatrics, 120, 1162–1182. Siddiqui, M. F., Elwell, C., & Johnson, M. H. (2016). Mitochondrial dysfunction in autism spectrum disorders. Autism Open Access, 6(5), pii: 1000190.
Incapacity
conditions. More specifically, incidence is expressed as the number of new conditions measured over a period of time divided by a denominator. When this denominator is the number of persons “at risk” (free of the condition being studied) at the beginning of the specified period of time, this measure of incidence is called the incidence proportion, the cumulative risk, or the cumulative incidence. Cumulative incidence is often expressed as a percentage. When the denominator is the amount of “person-time” at risk, the measure of incidence is called the incidence density, incidence density rate, incidence rate, or person-time incidence rate. Person-time is a single number which represents the amount of time an entire population was at risk over the prespecified period of time.
See Also ▶ Prevalence
Incapacity References and Reading ▶ Disability
Incidence
Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern epidemiology (3rd ed.). Philadelphia: Lippincott Williams & Wilkins. Szklo, M., & Nieto, J. (2006). Epidemiology: Beyond the basics (2nd ed.). Boston: Jones and Bartlett.
Lisa Croen Autism Research Program, Kaiser Permanente Division of Research, Oakland, CA, USA
Incidence Density ▶ Incidence
Synonyms Cumulative incidence; Cumulative risk; Incidence density; Incidence rate
Incidence Rate ▶ Incidence
Definition Broadly, incidence refers to newly occurring health events. In the context of public health, incidence may be used to describe new cases of disease, as opposed to chronic or preexisting
Incidental Emotional Processing ▶ Visual Responses to Implicit Emotional Faces
Incidental Teaching
Incidental Teaching Andrea McDuffie MIND Institute University of California-Davis, Sacramento, CA, USA
Definition Incidental teaching is a naturalistic language intervention approach which targets the acquisition of spoken language within the context of naturally occurring adult-child interactions, including play. Incidental teaching requires that the child’s environment be arranged to attract the child to interact with toys and activities. Child learning can then be structured and sequenced to take advantage of the child’s interests and motivation within these playbased activities. Incidental teaching focuses on child-directed activities, that is, following the child’s lead to encourage child motivation to initiate an interaction by requesting help from the adult. Because it is situated within the natural environment, incidental teaching supports the generalization of targeted skills. Teachers/clinicians and parents can use arrangement of the natural environmental to indirectly prompt child initiation of teaching episodes which are based upon predetermined intervention targets or educational objectives. Once the child shows an interest in the activity, by gesturing or requesting, the parent or clinician can prompt an elaborated version of the child’s initiation. Natural consequences (in the form of access to desired materials, assistance, or adult attention) are provided consistently to reinforce the child’s production of an elaborated communicative attempt. Incidental teaching differs from traditional discrete trial behavioral approaches in that teaching trials are child initiated and the reward for correct language production is directly related to the child’s target behavior.
Historical Background The motivation for the development of a naturalistic language teaching paradigm emerged from
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the work of Don Baer at the University of Kansas in the 1970s. Baer’s work highlighted the lack of skill generalization that could result from the implementation of highly structured, massed trials using behavior modification techniques. These operant techniques resulted in language behaviors that were under strict stimulus control and failed to generalize to spontaneous use in uncontrolled contexts. Incidental teaching was first introduced by Hart and Risley (1968, 1974, 1975) to teach labels, adjectives, and compound sentences to disadvantaged children within a preschool classroom in which the environment was carefully arranged to motive child initiations. In addition, incidental teaching provides many of the stimulus conditions necessary to support generalization. It is conducted on numerous occasions throughout the day within the setting conditions that maintain natural language use. Additionally, reinforcement contingencies are likely to be less discriminable under incidental teaching, than in one-to-one teaching, leading to enhanced generalization. Because the child controls the instances in which incidental teaching occurs (also known as “teachable moments”) and specifies the reinforcers, incidental teaching constitutes “loose training” (training that involves many exemplars in an environment that is not tightly controlled).
Rationale or Underlying Theory The rationale for the effectiveness of incidental teaching arises from both developmental and behavioral theories. According to behavioral theories, incidental teaching should be effective in teaching language targets in natural contexts because it incorporates the behavioral learning principles of imitation, modeling, shaping, loose teaching, multiple exemplars, and contingent reinforcement. According to developmental theories, incidental teaching should be effective in teaching language targets because it follows the child’s lead and teaches to the child’s interests and motivations. In addition, there is a close correspondence between the child’s focus of attention to an event and the way this event is linguistically encoded by the adult. The selection of developmentally
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progressive teaching targets is closely matched to the child’s current abilities, and the child is able to observe that his communicative attempts have an effect on the environment by gaining him access to attention, assistance, and child-selected reinforcers.
Goals and Objectives The goal of incidental teaching is to increase language skills within the natural environment by means of adult-child interactions that resemble those in typical development. The incidental teaching approach is child directed and uses natural consequences to reinforce child communicative attempts.
Incidental Teaching
child engagement. Incidental teaching differs from naturally occurring language learning by typically developing children in that language targets are preselected by the adult and a sequence of least to most prompts is used. Adapted versions of incidental teaching have introduced the use of time delay procedures and the mand-model technique. Time delay is a procedure in which a brief pause is introduced between a stimulus and a prompt in order to elicit a previously learned response from the child. In the mand-model procedure, non-yes-no questions as well as imitative prompts are used to increase the number of teaching episodes that can be implemented while using an incidental teaching approach. Another modification of incidental teaching is the training of parents to act as their child’s interventionist.
Treatment Participants Efficacy Information Over the years, incidental teaching has been used successfully with many groups of children who are language delayed. The earliest participants in studies of incidental teaching were children who were economically disadvantaged. The approach was then extended to children with intellectual impairments. More recently, incidental teaching strategies have been examined for use with children with autism (e.g., the Walden Toddler Program). The application of incidental teaching to children with autism was motivated by problems with prompt dependence and generalization that might emerge for children with autism following language teaching based on discrete trial training.
Many published studies over the last 40 years have demonstrated the efficacy of various versions of incidental teaching procedures in teaching language skills to children with autism.
Outcome Measurement Outcome measures for studies utilizing an incidental teaching approach typically consist of spontaneous and imitative spoken responses by the child. However, some studies of incidental teaching have also measured children’s receptive identification of targeted vocabulary.
Treatment Procedures Qualifications of Treatment Providers Incidental teaching involves: (1) arranging the environment to increase the likelihood of child initiations (which set the occasion for teaching episodes), (2) selecting developmentally appropriate language targets for the child, (3) responding to child initiations with requests for elaborated language that resemble targeted forms, and (4) reinforcing child communication bids with attention and access to desired materials. Incidental teaching episodes are brief to maintain
Incidental teaching has been found to be effective when used by teachers, clinicians, and parents.
See Also ▶ Environmental Engineering/Modifications ▶ Milieu Teaching ▶ Natural Language Paradigm
Inclusion
References and Reading Charlop-Christy, M. H., & Carpenter, M. H. (2000). Modified incidental teaching sessions: A procedure for parents to increase spontaneous speech in their children with autism. Journal of Positive Behavior Interventions, 2, 98–112. Goldstein, H. (2002). Communication intervention for children with autism: A review of treatment efficacy. Journal of Autism and Developmental Disorders, 32, 373–396. Hart, B., & Risley, T. R. (1968). Establishing use of descriptive adjectives in the spontaneous speech of disadvantaged preschool children. Journal of Applied Behavior Analysis, 1, 109–120. Hart, B., & Risley, T. R. (1974). Using preschool materials to modify the language of disadvantaged children. Journal of Applied Behavior Analysis, 7, 243–256. Hart, B., & Risley, T. R. (1975). Incidental teaching of language in the preschool. Journal of Applied Behavior Analysis, 8, 411–420. McGee, G. G., Krantz, P. J., & McClannahan, L. E. (1985). The facilitative effects of incidental teaching on preposition use by autistic children. Journal of Applied Behavior Analysis, 18, 17–31. McGee, G., Morrier, M., & Daly, T. (1999). An incidental teaching approach to early intervention for toddlers with autism. Journal of the Association for Persons with Severe Handicaps, 24, 133–146. Stokes, T., & Baer, D. (1977). An implicit technology of generalization. Journal of Applied Behavior Analysis, 10, 349–367. Warren, S., & Kaiser, A. (1986). Incidental language teaching: A critical review. Journal of Speech and Hearing Disorders, 51, 291–299.
Inclusion Joan Lieber Counseling, Higher Education and Special Education, University of Maryland, College Park, MD, USA
Definition Inclusion means that children with autism (ASD) learn alongside typically developing children in general education classrooms. For young children with ASD, those classrooms may be communitybased child care programs, Head Start, or prekindergarten programs. For school-aged children,
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those programs are in their local elementary, middle, or high schools. When children with ASD are included, they have the opportunity to develop relationships and learn from and model the behavior of their typically developing peers (Fein and Dunn 2007). When children with ASD are fully included, they not only have the opportunity to learn academic and functional skills, they become accepted members of their community (Allen and Cowdery 2012).
Historical Background The term “inclusion” began to appear in the 1990s (Odom and Diamond 1998). Before then, children who received instruction in general education classrooms were mainstreamed. Children who were mainstreamed were in general education classrooms part time. They visited the general education classroom for selected lessons and activities, but their primary placement was still in a special education classroom. The special education teacher was responsible for their educational progress (Mastropieri and Scruggs 2000). That changed in the 1990s with inclusion. Today, children who are included are now placed in general education classrooms, and the general education teacher has that responsibility. The change from segregated placements to mainstreaming to inclusion came about through a series of laws and court cases with the most prominent being IDEA (Individuals with Disabilities Education Act), passed originally as PL 94–142: The Education for All Handicapped Children Act. This law and its subsequent amendments contain a number of provisions, which have provided the basis for educating children with ASD in inclusive environments. Among those provisions are zero reject and least restrictive environment. Zero reject sets forth the basic principle that children with disabilities cannot be excluded from receiving a public education. Least restrictive environment states that, to the maximum extent possible, children with disabilities should be educated with their peers who do not have a disability. Another law, the Americans with Disabilities Act (ADA), has also had an impact on
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children being served in inclusive environments. The ADA has had a particular impact on young children because ADA stipulates that children cannot be excluded from child care programs unless their presence affects the health or safety of other children (Grisham-Brown et al. 2005). There are additional arguments that support the education of children with disabilities in inclusive environments that include moral and rational reasons (Bailey et al. 1998). From a moral perspective, children with disabilities have the same right as other children to participate in programs and activities. Further, it is just the right thing to do. From a rational perspective, children with disabilities benefit when they interact with typically developing children because they can learn new skills from them. Typically developing children benefit as well because they learn about differences among people and may become more accepting of people who are different (Bailey et al. 1998).
Current Knowledge According to Harrower and Dunlap (2001), there have been relatively few studies which have investigated inclusion as the independent variable, that is, few which ask the question if children with ASD perform better socially and academically in inclusive classrooms than they do in segregated classrooms. However, as Harrower and Dunlap suggested, the better question to ask is how can children with ASD receive the help they need to be successful in an inclusive classroom? Inclusion cannot be accomplished successfully if the adults who work with the children with ASD in their classroom are not prepared for that challenge. The Role of Teachers in Inclusive Classrooms There are a number of ways that general education and special education teachers can work together to provide instruction for children with ASD. One approach is coteaching (Friend and Cook 2007). In coteaching, a general education teacher and a special education teacher are classroom equals. They plan together and make decisions about
Inclusion
how learning can be optimized for all the children in the classroom. The general education teacher is the curriculum expert; the special education teacher knows how to differentiate instruction so that children with ASD can learn as well. Coteachers can use a number of approaches to ensure that all children are engaged during instruction. They include one teaching and one observing, station teaching, parallel teaching, alternative teaching, teaming, and one teaching and one assisting (Friend and Cook 2007). In all the approaches, it is important to be sure that the special education teacher does not get relegated to the role of a classroom assistant who is only there to help out. Paraeducators Another approach to fostering inclusion for children with ASD is to assign a paraeducator to them in the classroom. This approach is used frequently with children with ASD because they often have intensive needs. The paraeducator works under the supervision of the special education teacher who plans the specific instruction. The paraeducator’s role might include providing alternative instruction within the general education classroom, modifying and simplifying the response that the child with ASD is expected to provide, providing reinforcement when the child with ASD makes an appropriate response, and collecting data on child progress (Friend and Cook 2007). It is important that the teachers in the classroom ensure that the paraeducator does not become a “Velcro” aide. If the paraeducator is always attached to the child with ASD, the benefits of being in an inclusive classroom disappear because the child does not have the opportunity to learn from other children. General Approaches to Inclusion Regardless of the age of the children with ASD, their inclusion into general education classrooms and activities can be facilitated if the curriculum incorporates elements of Universal Design for Learning (UDL). A UDL curriculum focuses on creating learning environments and adopting practices that give children a variety of formats for responding, demonstrating what they know, and expressing ideas, feelings, and preferences
Inclusion
(Center for Applied Special Technology [CAST] 2004). The three key principles of UDL are multiple means of representation, multiple means of engagement, and multiple means of expression (CAST 2004). Teachers who use multiple means of representation provide their instruction using a variety of formats for children who may need actual objects, pictures, or text to understand the content that the teacher presents. When a curriculum is designed using UDL principles, children are given the opportunity to use multiple means of expression for their responses. Some children may make written responses or respond using language. Others may use gestures or PECS or other assistive devices to demonstrate what they know. A curriculum that includes multiple means of engagement has activities and lessons that are interesting to all children; they are engaged and interested in participating and learning (Blackhurst et al. 1999; CAST 2004; Orkwis 2003). Having a curriculum that incorporates elements of UDL is particularly valuable for children with ASD who are included in general education classrooms. Inclusion in Early Childhood Classrooms The importance of including young children with disabilities in all early childhood classrooms has been supported by two major early childhood organizations, the Division for Early Childhood (DEC) and the National Association for the Education of Young Children (NAEYC). These organizations published a joint statement on inclusion in 2009. This position statement argues that children can be included successfully if they have access, can participate, and are provided with supports. Access comes through the design of the program and the environment; participation occurs when teachers use a variety of approaches including routine-based teaching and explicit interventions; and supports happen when the administration, teachers, related service providers, and families collaborate to ensure children’s success (DEC/NAEYC 2009). More recently, in 2015, the U.S. Department of Health and Human Services and the U.S. Department of Education issued a Policy Statement on Inclusion of Children with Disabilities in Early Childhood Programs.
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Included in this policy statement is: (1) the expectation that inclusion in early childhood programs be of high quality; (2) that there are legal and scientific reasons for inclusion; and (3) identification of free resources for States, LEAS, schools, and early childhood programs to support high quality inclusion (HHS/ED 2015, p. 1). Young children with ASD are eligible to receive services as soon as they are identified. Children before the age of three are typically served in their homes, but they may receive services in other natural environments like a child care center. Once they turn three, however, most attend programs with other children and may be included in a variety of programs. Children with disabilities have been included in Head Start since 1972 with the passage of PL 92–424. Beginning in 1972, Head Start programs have been required to ensure that 10% of the children they serve have disabilities (Grisham-Brown et al. 2005). Although many Head Start programs serve children with more mild disabilities such as speech/language disabilities, some children with ASD are included. Young children with ASD may also attend state-funded prekindergarten programs and community-based child care programs. In order for young children with ASD to learn and thrive in inclusive programs, the first step is to ensure that that the program is of high quality. According to Sandall and Schwartz (2008), a high-quality program provides a consistent schedule that is divided into time segments that are appropriate for young children, has a combination of large group and small group activities as well as time for children to play and work alone, has activities that teachers lead and those that are child-directed, allows children time for outdoor play and routines such as toileting and eating, has both quiet times and active times, and finally, maximizes the time that teachers teach and minimizes the time that children wait. Having a high-quality program is only the beginning. In order for children with ASD to learn both academically and socially, the teachers need to be ready to make modifications to the curriculum. In their Building Blocks model, Sandall and Schwartz (2008) described eight curriculum modifications: environmental support,
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materials adaptation, activity simplification, child preferences, special equipment, adult support, peer support, and invisible support. Because young children with ASD are typically delayed in their language and social development, a number of these modifications can be particularly useful as teachers include them in the ongoing classroom routine. Barton et al. (2011) described how curriculum modifications can help a child with ASD participate successfully in circle time, a large group activity that is standard in most early childhood classrooms. Circle time is a teacher-led activity that is used to teach early literacy activities through book reading, time concepts through reviewing the daily routine and calendar, as well as music and movement activities. Barton et al. suggested that circle time can be particularly challenging for children with ASD because during circle time, children are required to attend to a teacher and follow teacher directions, to be next to other children, and to use language to communicate. However, it can be a valuable activity because it provides an opportunity for children with ASD to address skills that they have most difficulty mastering. Barton et al. provided a number of examples of how the curriculum modifications can be used for children with ASD during the circle time activity. If children have difficulty making the transition from another activity to the circle, the teacher can use child preferences and allow the children to bring a favorite toy with them to the circle. Children can hold on to that toy throughout the circle activity. Once children are seated at the circle, the teacher can reduce the distractions that come from the shelves of toys that surround the circle area by covering those shelves with fabric that drop down during circle time. The teacher can adapt the materials used during circle by creating a daily schedule that incorporates words and pictures to symbolize parts of the daily schedule. In another adaptation, as the teacher reads a book, the children can have their own smaller version of the book to follow along with. Peers can be used to support the children with ASD in a variety of ways. Children with ASD can sit next to a favorite peer, the peer can go
Inclusion
first when the teacher asks a question so that the children with ASD have a language model to follow, and the peer can even prompt the children with ASD to make a response. These curriculum modifications can be used throughout the daily routine for other large group as well as small group activities. In addition to curriculum modification, children’s learning is enhanced in inclusive classrooms when teachers embed children’s learning objectives within the context of ongoing classroom activities (Sandall and Schwartz 2008). If a child with ASD has an IEP objective to enhance expressive language by making requests that objective can be targeted during many activities throughout the day. During snack time, the child can be asked to request juice, cookies, and fruit. And children are naturally reinforced when given the food they asked for. Inclusion for School-Aged Children Sandall and Schwartz (2008) described a general approach to adapting instruction that can be used across curricula in early childhood classrooms to promote effective inclusion for young children with disabilities. Harrower and Dunlap (2001) similarly discussed general instructional strategies that have been shown to be effective in improving social and academic outcomes and that can be implemented in inclusive settings for schoolaged children. A number of the approaches that Harrower and Dunlap (2001) described involve peers. One approach is peer tutoring in which a typical peer provides assistance, instruction, and feedback to a child with ASD. Another approach, that involves larger groupings, is cooperative learning. In cooperative learning, small groups of children work together to learn information. Small groups allow children with ASD to participate more frequently than they could in a large group, teacher-led activity. More frequent participation leads to higher levels of engagement with the material. Cooperative learning also allows the children with ASD to engage in social interactions with their peers while learning academic content. Handleman et al. (2005) listed other
Inclusion
peer-focused interventions that have been used successfully in inclusive classrooms. These include having a peer buddy system for all children in the classroom and instituting social skills groups. A second group of approaches that Harrower and Dunlap (2001) identified are self-management strategies. According to Harrower and Dunlap, these strategies help children with ASD become more independent which allows them to operate more effectively in an inclusive classroom. As children become more independent, teachers can spend more of their time providing instruction to the class. Harrower and Dunlap (2001) provided additional strategy categories including antecedent procedures and delayed contingencies. These approaches are designed to help children with ASD become less dependent on adults so that they can operate more effectively in inclusive classrooms where there are fewer adults and more children. Because general education teachers are responsible for the instructional content in inclusive classrooms, they can foster the participation of children with ASD by ensuring that the content is meaningful to all of the children, by having a range of materials to teach the content, and by using a variety of lesson formats (Kluth 2010). These methods incorporate elements of UDL and make the content accessible to a wide range of learners.
Future Directions Although children with ASD have been included in general education classrooms for over 20 years, there are a number of areas that need further investigation. These areas include: • Effective strategies for middle and high school students with ASD in inclusive settings • How best to train general education teachers to provide instruction for children with ASD • Strategies to enhance collaboration between general and special educators who work with children with ASD
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See Also ▶ Integration ▶ Least Restrictive Environment (LRE) ▶ Natural Environment
References and Reading Allen, K. E., & Cowdery, G. E. (2012). The exceptional child: Inclusion in early childhood education (7th ed.). Belmont: Wadsworth. Bailey, D. B., McWilliam, R. A., Buysse, V., & Wesley, P. W. (1998). Inclusion in the context of competing values in early childhood education. Early Childhood Research Quarterly, 13(1), 27–47. Barton, E. E., Reichow, B., Wolery, M., & Chen, C. (2011). We can all participate! Adapting circle time for children with autism. Young Exceptional Children, 14(2), 2–21. Blackhurst, E., Carnine, D., Cohen, L., Kameenui, E., Langone, J., Palley, D., et al. (1999, Fall). Research connections in special education: Universal design. Retrieved 7 June 2011, from http://www.hoagiesgifted. org/eric/osep/recon5/rc5cov.html Center for Applied Special Technology. (2004). Universal design for learning. Retrieved 7 June 2011, from http://www.cast.org/udl DEC/NAEYC. (2009). Early childhood inclusion: A joint position statement of the division for early childhood (DEC) and the National Association for the education of young children (NAEYC). Chapel Hill: The University of North Carolina, FPG Child Development Institute. Fein, D., & Dunn, M. A. (2007). Autism in your classroom: A general educator’s guide to students with autism spectrum disorders. Bethesda: Woodbine House. Friend, M., & Cook, L. (2007). Interactions: Collaboration skills for school professionals (5th ed.). Boston: Pearson. Grisham-Brown, J., Hemmeter, M. L., & Pretti-Frontczak, K. (2005). Blended practices for teaching young children in inclusive settings. Baltimore: Brookes. Handleman, J. S., Harris, S. L., & Martins, M. P. (2005). Helping children with autism enter the mainstream. In F. R. Volkmar, R. Paul, A. Klin, & D. Cohen (Eds.), Handbook of autism and pervasive developmental disorders (Vol. II, pp. 1029–1042). Hoboken: Wiley. Harrower, J. K., & Dunlap, G. (2001). Including children with autism in general education classrooms: A review of effective strategies. Behavior Modification, 25(5), 762–784. Kluth, P. (2010). You’re going to love this kid! (2nd ed.). Baltimore: Brookes. Mastropieri, M. A., & Scruggs, T. E. (2000). The inclusive classroom: Strategies for effective instruction. Upper Saddle River: Merrill.
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Inclusion of Adolescents with ASD in Community Sporting Clubs
Odom, S. L., & Diamond, K. E. (1998). Inclusion of young children with special needs in early childhood education: The research base. Early Childhood Research Quarterly, 13(1), 3–25. Orkwis, R. (2003). Universally designed instruction. ERIC/OSEP Digest (ERIC document reproduction service No. ED 475386). Sandall, S. R., & Schwartz, I. S. (2008). Building blocks for teaching preschoolers with special needs (2nd ed.). Baltimore: Brookes. U.S. Department of Health and Human Services and U.S. Department of Education. (2015). Policy statement on inclusion of children with disabilities in early childhood programs.
Inclusion of Adolescents with ASD in Community Sporting Clubs Kate O’. Brien Mary Immaculate College, Limerick, Ireland
Synonyms Community sport; Exercise; Social inclusion
Definition 1.0% to 2.64% of children are estimated to have a diagnosis of autism spectrum disorder (ASD) with an increasing number of individuals receiving a diagnosis of ASD (ASD; Baio et al. 2018; BaronCohen et al. 2009; Boilson et al. 2016; Kim et al. 2011; Wing and Potter 2002). Given this increase, there is a growing demand on diagnostic and intervention services (Shattuck and Grosse 2007; Wise et al. 2010), and parents of children with ASD report that they are not receiving the disability services that they need (Jones et al. 2017). Furthermore families with a child with a diagnosis of ASD report feelings of social isolation and feeling cut off from the wider society (Byrne et al. 2018; Woodgate et al. 2008). Given families with a child with ASD often face long wait lists for intervention in public disability services and report not receiving needed
services (Jones et al. 2017; Rivard et al. 2014), exercise interventions outside the clinic could be a worthwhile alternative. Moreover there is research to suggest that providing social skill promotional activities in a child’s natural setting appears to be more effective than traditional clinical settings (Bellini and Peters 2008). It is necessary to distinguish structured exercise from physical activity. While there are many definitions of exercise and physical activity, Caspersen et al.’s (1985) interpretation shall be employed for the purpose of this definition. Physical activity is defined in a broad term as any movement of the body, created by the body, that results in energy expenditure, (p. 126), which can be interpreted as movements that are required in everyday life such as climbing stairs, cleaning, etc. Caspersen et al. (1985) define exercise as a purposeful, planned activity that is repetitive in nature and has set goals pertaining to the improvement or maintenance of physical fitness (p. 128). Therefore, structured exercise has a clear start and finish time. Given individuals with autism spectrum disorder (ASD) have greater difficulties coping with unstructured time than their typically developing peers, there is benefit to be sought from structured exercise (Van Bourgondien et al. 2003). The inclusion of disabilities within exercise is neither a new nor novel idea as Block and Obrusnikova (2007) reviewed 20 years of studies highlighting the success of inclusion of children with disabilities in a supported physical education environment. Furthermore, there is converging evidence to suggest that the benefits of structured exercise in children with ASD extend beyond general physical health and have a positive impact on their social development. Ohrberg (2013) described structured exercise in the lives of children with ASD as essential, emphasizing that it allows children to develop interpersonal skills through engaging in fun activities with peers in a very normal way (p. 53). There is converging evidence to suggest that structured exercise and inclusive physical education classes improve social interaction and promote social development in students with ASD (Foti et al. 2014; Pan et al. 2011). Structured cycling, swimming, and martial arts programs
Inclusion of Adolescents with ASD in Community Sporting Clubs
have reported improvements in self-efficacy and social interaction and a reduction in antisocial behaviors in children with ASD (Movahedi et al. 2013; Pan 2010; Todd et al. 2010). However, the core characteristics of autism spectrum disorder (ASD), which include impairments in social communication and interaction, and the presentation of restricted, repetitive behaviors (American Psychiatric Association [APA] 2013) may conflict with the expectations of exercise (Jones 2003; Obrusnikova and Cavalier 2011; Pan 2014; Srinivasan et al. 2014). Such a conflict may explain why children with a diagnosis of ASD are not meeting the recommended exercise guidelines (Pan and Frey 2006). Furthermore, children with ASD are reported to be less active during breaktime and engage in few physical activities for shorter periods of time when compared with their typically developing peers (Bandini et al. 2013; Healy et al. 2013; Hilton et al. 2008; Pan 2008). Moreover, parents and teachers report being unaware of the many inclusive structured exercise programs for children with ASD (Srinivasan et al. 2014). The social model of disability guides the rationale and success of inclusive sport programs. The social model welcomes diversity, including and valuing each adolescent and providing a liberating perspective on disability, relocating the disability into the structures of society and outside of the individual. The restrictions that people with impairments face in sport can be readily observed, and challenged, through the social model, from individual attitudinal and institutional prejudices to inaccessible sporting facilities, exclusionary policies, or unusable transport systems (Oliver 2004). Mike Oliver (2004) argued that the social model has the power to “transform consciousness” (p. 42) by connecting personal experience to professional practice. Despite these benefits, sport is an area which is still governed by the medical model of disability (Townsend et al. 2016). The medical model of disability would suggest that people are disabled by their impairments or differences. The medical model looks at what is “wrong” with the person and not what the person needs, and it can create low expectations which can lead to people losing
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independence, choice, and control in their own lives. In light of the medical model of disability, the provision of inclusive opportunities still has a low priority in community recreation and sport in many countries (Storey 2004) causing individuals with additional needs to take part in sporting activities much less frequently than other people with disability and those who do not have a disability (Darcy and Dowse 2013). A lack of coaching expertise specific to disability and adaptions has been identified as a barrier for the participation of adolescents with additional needs in sport (D. B. Jones 2003; Kozub and Porretta 1998; Tsai and Fung 2009). Coaching is a vital aspect of athletic development and success, and as such, coaches play an important role in facilitating, encouraging, and guiding participation (Erickson et al. 2008; Martin and Whalen 2014). Coaches report very little inclusion of information specific to disability in the accredited training courses for their specific sport (Wareham et al. 2018). Moreover, coaches report the necessity of familiarization with the biological and physiological effects of their athletes’ impairment(s), highlighting that it was left to them to source this information. There is some evidence to suggest that factors such as insufficient knowledge of, or experience with, disability are contributing factors to the reluctance by many coaches to make the transition to coaching athletes with additional needs (Martin and Whalen 2014). There is a need to develop localized theoretical frameworks to inform initiatives going forward (Edwards 2015). Research suggests there is an absence of participant experiences and perspectives of inclusive sport (Levermore 2010; Rich et al. 2015) and in particular hearing the voices and viewpoints of people with disabilities (Wickman 2015). Future research should focus on how adolescents with autism spectrum disorder (ASD) can be integrated and included within community sporting clubs with the potential focus sport to be rowing. Such research should obtain the perspective of the main stakeholders regarding the facilitation of an ASD inclusive community club.
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Inclusion of Adolescents with ASD in Community Sporting Clubs
These stakeholders would be community club coaches, parents, adolescents with ASD, and typically developing adolescents involved in the club. Ohrberg (2013) advises on the development of a handbook to facilitate an autism-aware culture in sport. Therefore the input from the stakeholders would potentially be compiled to produce a handbook to facilitate the participation of adolescents with ASD (i.e., identifying potential barriers to participation and sport-specific coaching content to aid understanding of ASD and the subsequent and necessary adaptions to coaching style and training program). This handbook would be introduced and piloted with local clubs and adjusted based on subsequent feedback. This handbook would then be introduced into the national coaching training course as an independent module. The handbook would be edited one further time based on national feedback before being submitted to national sporting body. The proposed handbook would inform a sport-specific framework which encompasses the perspective of all relevant stakeholders, including the adolescent with ASD.
References and Reading American Psychiatric Association [APA]. (2013). Diagnostic and statistical manual of mental disorders (DSM-5 ®). Philadelphia: American Psychiatric Pub. Baio, J., Wiggins, L., Christensen, D. L., Maenner, M. J., Daniels, J., Warren, Z., et al. (2018). Prevalence of autism spectrum disorder among children aged 8 years—Autism and developmental disabilities monitoring network, 11 sites, United States, 2014. MMWR Surveillance Summaries, 67(6), 1. Bandini, L. G., Gleason, J., Curtin, C., Lividini, K., Anderson, S. E., Cermak, S. A., et al. (2013). Comparison of physical activity between children with autism spectrum disorders and typically developing children. Autism, 17(1), 44–54. Baron-Cohen, S., Scott, F. J., Allison, C., Williams, J., Bolton, P., Matthews, F. E., & Brayne, C. (2009). Prevalence of autism-spectrum conditions: UK schoolbased population study. The British Journal of Psychiatry, 194(6), 500–509. Bellini, S., & Peters, J. K. (2008). Social skills training for youth with autism spectrum disorders. Child and Adolescent Psychiatric Clinics of North America, 17(4), 857–873.
Block, M. E., & Obrusnikova, I. (2007). Inclusion in physical education: A review of the literature from 19952005. Adapted Physical Activity Quarterly, 24(2), 103–124. Boilson, A. M., Staines, A., Ramirez, A., Posada, M., & Sweeney, M. R. (2016). Operationalisation of the European protocol for autism prevalence (EPAP) for autism spectrum disorder prevalence measurement in Ireland. Journal of Autism and Developmental Disorders, 46(9), 3054–3067. Byrne, G., Sarma, K. M., Hendler, J., & O’Connell, A. (2018). On the spectrum, off the beaten path. A qualitative study of Irish parents’ experiences of raising a child with autism spectrum conditions. British Journal of Learning Disabilities, 46(3), 182–192. Caspersen, C. J., Powell, K. E., & Christenson, G. M. (1985). Physical activity, exercise, and physical fitness: Definitions and distinctions for health-related research. Public Health Reports, 100(2), 126. Darcy, S., & Dowse, L. (2013). In search of a level playing field–the constraints and benefits of sport participation for people with intellectual disability. Disability & Society, 28(3), 393–407. Edwards, M. B. (2015). The role of sport in community capacity building: An examination of sport for development research and practice. Sport Management Review, 18(1), 6–19. Erickson, K., Bruner, M. W., MacDonald, D. J., & Côté, J. (2008). Gaining insight into actual and preferred sources of coaching knowledge. International Journal of Sports Science & Coaching, 3(4), 527–538. Foti, F., Mazzone, L., Menghini, D., De Peppo, L., Federico, F., Postorino, V., et al. (2014). Learning by observation in children with autism spectrum disorder. Psychological Medicine, 44(11), 2437–2447. Healy, S., Msetfi, R., & Gallagher, S. (2013). “Happy and a bit nervous”: The experiences of children with autism in physical education. British Journal of Learning Disabilities, 41(3), 222–228. Hilton, C. L., Crouch, M. C., & Israel, H. (2008). Out-ofschool participation patterns in children with highfunctioning autism spectrum disorders. American Journal of Occupational Therapy, 62(5), 554–563. Jones, D. B. (2003). “Denied from a lot of places” barriers to participation in community recreation programs encountered by children with disabilities in Maine: Perspectives of parents. Leisure/Loisir, 28(1–2), 49–69. Jones, S., Bremer, E., & Lloyd, M. (2017). Autism spectrum disorder: Family quality of life while waiting for intervention services. Quality of Life Research, 26(2), 331–342. Kim, Y. S., Leventhal, B. L., Koh, Y.-J., Fombonne, E., Laska, E., Lim, E.-C., et al. (2011). Prevalence of autism spectrum disorders in a total population sample. American Journal of Psychiatry, 168(9), 904–912. Kozub, F. M., & Porretta, D. L. (1998). Interscholastic coaches’ attitudes toward integration of adolescents with disabilities. Adapted Physical Activity Quarterly, 15(4), 328–344.
Independence Levermore, R. (2010). CSR for development through sport: Examining its potential and limitations. Third world quarterly, 31(2), 223–241. Martin, J. J., & Whalen, L. (2014). Effective practices of coaching disability sport. European Journal of Adapted Physical Activity, 7(2), 13–23. Movahedi, A., Bahrami, F., Marandi, S. M., & Abedi, A. (2013). Improvement in social dysfunction of children with autism spectrum disorder following long term Kata techniques training. Research in Autism Spectrum Disorders, 7(9), 1054–1061. Obrusnikova, I., & Cavalier, A. R. (2011). Perceived barriers and facilitators of participation in after-school physical activity by children with autism spectrum disorders. Journal of Developmental and Physical Disabilities, 23(3), 195–211. Ohrberg, N. J. (2013). Autism Spectrum disorder and youth sports: The role of the sports manager and coach. Journal of Physical Education, Recreation & Dance, 84(9), 52–56. Oliver, M. (2004). The social model in action: If I had a hammer. Implementing the social model of disability: Theory and research, 18–31. Pan, C.-Y. (2008). Objectively measured physical activity between children with autism spectrum disorders and children without disabilities during inclusive recess settings in Taiwan. Journal of Autism and Developmental Disorders, 38(7), 1292. Pan, C.-Y. (2010). Effects of water exercise swimming program on aquatic skills and social behaviors in children with autism spectrum disorders. Autism, 14(1), 9–28. Pan, C.-Y. (2014). Motor proficiency and physical fitness in adolescent males with and without autism spectrum disorders. Autism, 18(2), 156–165. Pan, C.-Y., & Frey, G. C. (2006). Physical activity patterns in youth with autism spectrum disorders. Journal of Autism and Developmental Disorders, 36(5), 597. Pan, C.-Y., Tsai, C.-L., & Hsieh, K.-W. (2011). Physical activity correlates for children with autism Spectrum disorders in middle school physical education. Research Quarterly for Exercise and Sport, 82(3), 491–498. Rich, K. A., Misener, L., & Dubeau, D. (2015). Community cup, we are a big family. Examining social inclusion and acculturation of newcomers to Canada through a participatory sport event. Social Inclusion, 3(3), 129–141. Rivard, M., Terroux, A., Parent-Boursier, C., & Mercier, C. (2014). Determinants of stress in parents of children with autism spectrum disorders. Journal of Autism and Developmental Disorders, 44(7), 1609–1620. Shattuck, P. T., & Grosse, S. D. (2007). Issues related to the diagnosis and treatment of autism spectrum disorders. Mental Retardation and Developmental Disabilities Research Reviews, 13(2), 129–135. Srinivasan, S. M., Pescatello, L. S., & Bhat, A. N. (2014). Current perspectives on physical activity and exercise recommendations for children and adolescents with
2423 autism spectrum disorders. Physical Therapy, 94(6), 875–889. Storey, K. (2004). The case against the Special Olympics. Journal of Disability Policy Studies, 15(1), 35–42. Todd, T., Reid, G., & Butler-Kisber, L. (2010). Cycling for students with ASD: Self-regulation promotes sustained physical activity. Adapted Physical Activity Quarterly, 27(3), 226–241. Townsend, R. C., Smith, B., & Cushion, C. J. (2016). Disability sports coaching: Towards a critical understanding. Sports Coaching Review, 4(2), 80–98. https:// doi.org/10.1080/21640629.2016.1157324. Tsai, E. H.-L., & Fung, L. (2009). Parents’ experiences and decisions on inclusive sport participation of their children with intellectual disabilities. Adapted Physical Activity Quarterly, 26(2), 151–171. Van Bourgondien, M. E., Reichle, N. C., & Schopler, E. (2003). Effects of a model treatment approach on adults with autism. Journal of Autism and Developmental Disorders, 33(2), 131–140. Wareham, Y., Burkett, B., Innes, P., & Lovell, G. P. (2018). Sport coaches’ education, training and professional development: The perceptions and preferences of coaches of elite athletes with disability in Australia. Sport in Society, 21(12), 2048–2067. Wickman, K. (2015). Experiences and perceptions of young adults with physical disabilities on sports. Social Inclusion, 3(3), 39–50. Wing, L., & Potter, D. (2002). The epidemiology of autistic spectrum disorders: Is the prevalence rising? Mental Retardation and Developmental Disabilities Research Reviews, 8(3), 151–161. Wise, M. D., Little, A. A., Holliman, J. B., Wise, P. H., & Wang, C. J. (2010). Can state early intervention programs meet the increased demand of children suspected of having autism spectrum disorders? Journal of Developmental & Behavioral Pediatrics, 31(6), 469–476. Woodgate, R. L., Ateah, C., & Secco, L. (2008). Living in a world of our own: The experience of parents who have a child with autism. Qualitative Health Research, 18(8), 1075–1083.
Inclusion Specialist ▶ Itinerant Teacher
Independence ▶ Self-Advocacy
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Independent Living Aurelie Welterlin Chapel Hill TEACCH Center, Carrboro, NC, USA
Synonyms
Independent Living
Independent Variable Measurement ▶ Procedural Fidelity
Index of Productive Syntax (IPSyn)
Autonomous living
Definition Independent living consists of participating in day-to-day life with full capacity to successfully choose courses of action and make decisions about where to live and how to participate in society without the oversight of another individual. Independent living requires having the skills necessary to manage life tasks including, but not limited to, household maintenance, time and money management, and self-care. Independent living is also a philosophical viewpoint within the disability culture that promotes self-determination in individuals with disabilities and equal rights to participate in and contribute to society (The National Council on Independent Living 2010).
See Also ▶ Benhaven Residential Services ▶ Living Arrangements in Adulthood ▶ Self-Determination ▶ Self-Help Skills
References and Reading National Council on Independent Living. (2010). About NCIL. Retrieved December, 2010, from http://www. ncil.org/ Stancliffe, R. J., & Lakin, K. C. (2007). Independent living. In S. L. Odom, R. H. Horner, M. E. Snell, & J. Blacher (Eds.), Handbook of developmental disabilities (pp. 429–448). New York: Guilford Press [Chapter].
Maura Moyle and Steven Long Speech Pathology and Audiology, Marquette University, Milwaukee, WI, USA
Synonyms IPSyn
Description Index of Productive Syntax (IPSyn) is a method for evaluating and quantifying the grammatical complexity of young children’s spontaneous language samples. IPSyn is based upon the grammatical categories and developmental scheme of Assigning Structural Stage (Miller 1981). Individual utterances in a sample are scored for the occurrence of 60 different syntactic forms categorized under four subscales: noun phrase, verb phrase, question/ negation, and sentence structure forms. Although IPSyn was developed for use with mainstream English speakers, it has been found to be a valid measure for children who speak African American English (Oetting et al. 2010). IPSyn scores are calculated from children’s spontaneous language samples. After a sample is elicited and recorded, a corpus is formed by transcribing 100 successive, intelligible utterances, excluding imitations, self-repetitions, and routines (stereotyped language that does not represent productive language use). Following guidelines stated in the IPSyn coding manual (Scarborough 1990), the transcript is then scored for 60 syntactic forms belonging to phrases and sentences that are categorized under four
Index of Productive Syntax (IPSyn)
subscales. The first two different occurrences of each form are credited. No additional credit is given for further occurrences in the sample. Certain early-developing structures are automatically credited if there is evidence in the transcript that the child has mastered later-developing structures in the same category. For example, if a child produces a question containing a wh-pronoun and verb (Q4), he also receives credit for intonationally marked question (Q1) and routine do/go or existence/name question (Q2). The points earned for each subscale are summed across all utterances, then added together to yield the total IPSyn score. No examiner qualifications are explicitly stated for this measure; however, the user must be familiar with English grammatical structures.
Historical Background IPSyn was developed to provide a time-efficient means for measuring the grammatical complexity of children’s utterances from the beginning of oral language through the period of early literacy. Other measures of grammatical complexity, such as Mean Length of Utterance (MLU, Brown 1973; Miller 1981) and Developmental Sentence Scoring (DSS, Lee 1974) had been and continue to be used for that purpose, but these other measures were criticized for their lack of scope and insensitivity to many important developmental language changes (Klee and Fitzgerald 1985; Klee and Sahlie 1986; Scarborough et al. 1991). IPSyn derived its grammatical categories and developmental scheme from Assigning Structural Stage (ASS, Miller 1981), a procedure generally hailed for its comprehensiveness. Furthermore, in contrast to measures such as MLU and DSS, IPSyn does not score every structure of every utterance within a corpus. Instead, it scans for a maximum of two structural types within each of its 60 syntactic forms. This approach results in an analysis that is adequately comprehensive yet not so time consuming as methods that evaluate all the structural forms produced by a child. IPSyn has been used in research on typical language development and disordered language development, including
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autism (e.g., Tager-Flusberg and Calkins 1990; Tager-Flusberg et al. 1990). It is especially valuable to researchers using designs that require subject matching by language age (Scarborough et al. 1991).
Psychometric Data No psychometric data are provided. IPSyn is not norm referenced.
Clinical Uses As a clinical resource, IPSyn can be used to quantify and document linguistic changes that occur over time in successive language samples. A small database (n ¼ 15) exists of typically developing children who were sampled at 24, 30, 36, 42, and 48 months (Scarborough 1990). Although these numbers fall short of what is needed for standardization, the available data show how IPSyn could be used for peer comparisons, if and when a larger standardization sample is gathered. Two computerized versions of IPSyn have been developed. In the version contained in Computerized Profiling (Long et al. 2006), the program performs an automatic analysis that must then be reviewed and edited by the user. Another version, designed to work with CHAT transcripts in the CHILDES system, is completely automatic (Sagae et al. 2005).
See Also ▶ Grammar ▶ Language Tests ▶ Syntax
References and Reading Brown, R. (1973). A first language. Cambridge, MA: Harvard University Press. Klee, T., & Fitzgerald, M. (1985). The relation between grammatical development and mean length of utterance in morphemes. Journal of Child Language, 12, 251–269.
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2426 Klee, T., & Sahlie, E. (1986). Review of Hixson’s DSS computer program. Child Language Teaching and Therapy, 2, 231–235. Lee, L. (1974). Developmental sentence analysis. Evanston: Northwestern University Press. Long, S. H., Fey, M. E., & Channell, R. W. (2006). Computerized profiling (PC Version 970). Milwaukee: Marq u e t t e U n i v e r s i t y. We b s i t e . h t t p : / / w w w. computerizedprofiling.org. Miller, J. F. (1981). Assessing Language Production in Children. Baltimore: University Park Press. Oetting, J. B., Newkirk, B. L., Hartfield, L. R., Wynn, C. G., Pruitt, S. L., & Garrity, A. W. (2010). Index of productive syntax for children who speak African American English. Language, Speech & Hearing Services in Schools, 41, 328–339. https://doi.org/10.1044/ 0161-1461. Sagae, K., Lavie, A., & MacWhinney, B. (2005). Automatic measurement of syntactic development in child language. In Proceedings of the 43rd annual meeting of the Association for Computational Linguistics (ACL) (pp. 197–204). Ann Arbor. Retrieved 17 Apr 2011 from http://childes.psy.cmu.edu/grasp/ac105-IPSyn.pdfk. Scarborough, H. S. (1990). Index of productive syntax. Applied PsychoLinguistics, 11, 1–22. Scarborough, H. S., Rescorla, L., Tager-Flusberg, H., Fowler, A. E., & Sudhalter, V. (1991). The relation of utterance length to grammatical complexity in normal and language-disordered groups. Applied PsychoLinguistics, 12, 23–45. Tager-Flusberg, H., & Calkins, S. (1990). Does imitation facilitate the acquisition of grammar? Evidence from a study of autistic, Down’s syndrome and normal children. Journal of Child Language, 17, 591–606. Tager-Flusberg, H., Calkins, S., Nolin, T., Baumberger, T., Anderson, J., & Chadwick-Dias, A. (1990). A longitudinal study of language acquisition in autistic and Down syndrome children. Journal of Autism and Developmental Disorders, 20, 1–21.
India and Autism Savita Malhotra1 and Ruchita Shah2 1 Fortis Hospital, Mohali, Punjab, India 2 Department of Psychiatry, Postgraduate Institute of Medical Education and Research, Chandigarh, India
Historical Background In India, the history of autism dates back to 1960s and is marked by initial sporadic efforts at clinical
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descriptions and research followed by a more sustained progress in clinical services, research, and policies. The first case report of autism appeared in an Indian journal in 1962 (Bassa 1962). There were a few publications in the 1960s and 1970s referring to infantile autism, with a steady flow after the 1990s. Before the 1980s, awareness of the disorder, both among professionals and the general public was low, that increased gradually (Daley 2004). In the next two decades, i.e., from late 1980s to early 2000s, there were major developments in the field, in terms of services, legislation, policy, and capacity building. Nongovernment organizations and parent led organizations primarily for advocacy for children and adults with autism and their families were established (e.g., Tamanna, New Delhi; Action for Autism, New Delhi). Soon, these organizations forayed into spreading awareness, providing special educational services, resource building through training of teachers, and later quality research (Barua and Daley; Daley 2004). Alongside, two major developments occurred, the enactment of the National Trust for the Welfare of Persons with Autism, Cerebral Palsy, Mental Retardation and Multiple Disabilities Act, 1999 (NTA, 1999). This Act was formulated to provide for the constitution of a trust at the national level for the welfare of persons with autism along with other disorders and disabilities. The NTA was the first time autism was recognized as a distinct condition by legislators and policy makers. This opened the doors for government-driven and government-funded initiatives for the welfare of children and adults with autism in India. In early 2000s, the Rehabilitation Council of India (RCI), an autonomous body under the Union Ministry started a 2-year Diploma in Special Education (for autism spectrum disorders) course in four centers. The DSE (ASD) course designed in 2003 was the first of its kind to develop human resource in the field of ASD in India (RCI 2014). The Central Board of Secondary Education (CBSE), an autonomous body under the Union Ministry of Human Resource Development, in 2009, introduced suitable modifications and concessions in the examination system to meet the special needs of students with disabilities such as
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extra time for completion of examination paper, facility of scribe, and exemption from second and third language courses with choice of study subjects (CBSE, year not mentioned). The Right to Education (RTE) Act, 2009 and Sarva Shiksha Abhiyan (literal translation – Campaign for Education for All), the Government of India’s flagship program for achievement of universalization of education, have a mandate for inclusive education for all children with disabilities including autism and intellectual disability (SSA, GOI). Parallel to this, the Rights of Persons with Disabilities Act (2016) that repeals the Persons with Disabilities (Equal Opportunities, Protection of Rights, and Full Participation) Act, (PDA) (1995) has included autism as one of the disabilities. The new Act provides a mandate for inclusive education, teachers’ training, vocational training, and rehabilitation. Inclusive Education for the Disabled at Secondary Stage (IEDSS) was launched in April 2009, where assistance for the inclusive education of children with disabilities including autism, as well as funds for identification and assessment, assistive devices, allowance for transport, escorts, readers, uniforms, books, and stationery have been provided. Another important development has been the recognition of child and adolescent psychiatry as an academic discipline. The Medical Council of India has made it mandatory for all the departments and institutes of psychiatry imparting post graduate degree (MD) in Psychiatry to have child and adolescent psychiatry services. There have been sustained efforts for providing specialized training in Child and Adolescent Psychiatry (CAP) to psychiatry postgraduates. A postdoctoral fellowship in Child and Adolescent Psychiatry was started in Vellore, Tamil Nadu, and National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, followed by starting of a 3-year DM course in CAP in NIMHANS and Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh. The Medical council of India recognizes DM course in Child and Adolescent Psychiatry and has framed the curricular requirements and examination pattern in India. Also, a fellowship program in Developmental Pediatrics and a DM course in Pediatric Neurology have been started at PGIMER, Chandigarh.
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Legal Issues, Mandates for Service The National Trust (NT) established under the NTA, 1999 is an autonomous organization that aims to enable and empower persons with disability (including autism) to live independently and within or as close to the community to which they belong through its various agencies and schemes. The latter include scholarships, training, employment assistance, and health insurance among others. The Trust promotes measures for the care and protection of persons with disability by appointment of guardians and trustees, facilitates realization of equal opportunities, protection of rights, and full participation of these individuals and thus shares some aims with that of PDA, 1995. It also extends support to registered nongovernmental organizations to provide needbased services (National Trust, year not mentioned). However, the main aim of the NT remains to answer the worry of parents, “What will happen to my child when I am no more?” The provisions of the Rights of Persons with Disabilities Act (2016) aim to protect the rights of these individuals as envisaged in the United Nations Convention on the Rights of Persons with Disabilities (2006), lay down specific measures to promote and facilitate inclusive education, vocational training and employment, social security, health care, insurance schemes, and rehabilitation. Chapter 3 of the Act specifically deals with education of these individuals, such as mandate for establishing adequate number of teacher training institutions, training and employing teachers who are trained in teaching children with disabilities, training of professionals and staff to support inclusive education and making necessary changes in the curriculum and examination system (Rights of Persons with Disabilities Act, 2016). Sharing the spirit of the previous two Acts, the RTE Act (2009) addresses the important right, i.e., right to full time elementary education of satisfactory and equitable quality in a formal school which satisfies certain essential norms and standards. As mentioned earlier, inclusive education for children with autism is one of the mandates. With the passage of the RTE Act, changes have
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been incorporated into the SSA approach, strategies, and norms. These children are recognized as those with special needs – CWSN. Schools are required to employ trained resource teachers for inclusive education of these children. The RCI Act and the RCI provide for the training curriculum and program of these teachers.
Overview of Current Treatments and Centers Professionals involved in care are psychiatrists, psychologists, pediatricians, speech therapists, and occupational therapists, who work either individually or in liaison with each other. Treatment and care is provided by both medical institutions and nongovernmental social organizations. In the government sector, most of the cases are seen in the departments of psychiatry and pediatrics of general hospitals and apex teaching institutes like Postgraduate Institute of Medical Education and Research (PGIMER), All India Institute of Medical Sciences (AIIMS), and National Institute of Mental Health and Neurosciences (NIMHANS), etc. Only a few of these departments (of psychiatry) run special child and adolescent clinics, while still fewer have dedicated child and adolescent psychiatric units. However, the number of departments establishing such services is steadily increasing. Still, lack of trained manpower remains a serious handicap (Malhotra and Vikas 2005). Also, there is no specialized center for autism run by the government. Few private centers which provide specialist care are out of reach of common people (Malhotra and Vikas 2005). There are several treatment and educational centers run by NGOs or parent associations and societies. Some of these have made notable contributions such as promoting greater awareness among professionals, conducting teacher and parent training through courses, workshops, and live demonstrations. Action for Autism (AFA), Academy for Severe Handicaps and Autism (ASHA) are some of the important organizations. AFA practices “open-door teaching method” in which the techniques used around the world are tested and modified to the needs of
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Indian children. However, overall, there is a huge skew with most centers located in metropolitan cities (for example, Mumbai alone has 80 registered centers, while 2–4 centers each in other states) and almost all centers are in the state capitals (complete list available at: Autism Information and Resource Centre, an initiative of the National Trust, http://www.autismresourcecenter. in/Default.aspx). Majority of the treatment protocols involve parents and are aimed at training the parents. Principles are largely drawn from TEACCH approach and Applied Behavioral Analysis (Malhotra et al. 2002, 2010). The strategies include behavioral techniques aimed at enhancing eye to eye contact, reduction of maladaptive behavior, structuring time, activities and physical environment (Malhotra et al. 2002), sensory integration, speech therapy, and picture exchange communication systems (Malhotra et al. 2010). Parent counseling and emotional support to parents have also been integral to treatment (Malhotra et al. 2002). There have been few other therapeutic interventions, e.g., ComDEALL (Karanth et al. 2010), Makaton language program, used at select centers. Few centres for complementary and alternative forms of medicine such as homeopathy and Unani also offer treatment for autistic children. Also, many families, as high as 90% opt for or combine the use of complementary and alternative medicines (Krishnamurthy 2008).
Overview of Research Directions As mentioned earlier, autism research can be dated back to 1960s and 1970s, though it progressed sluggishly before the 2000s. Malhotra and Vikas (2002), Malhotra (2006) and Vijay Sagar KJ (2011) have well-reviewed the research in autism in India in the preceding years. Since late 1980s, several descriptions and observational studies (such as Srinath et al. 1989, Kar et al. 1993, Jaydeokar et al. 1997, Malhotra et al. 2003) appeared from tertiary psychiatric centers across the country in reputed national and international journals. Research in epidemiology of ASD is sparse (Malhotra and Kate 2015) and a
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cohesive picture of the prevalence and incidence of autism spectrum disorder is missing in India (Naik 2015). Relation between social maturity and problem behaviors in children with comorbid intellectual disability have been explored (Ganaie et al. 2015). Freeth et al. 2013 conducted a crosscultural study on autistic traits in India, UK, and Malaysia. Apart from epidemiological surveys, research areas include descriptions and observational studies, interventional studies, genetic and neurobiological research, health systems research, some culturally oriented research on pathways to care, professionals and parent experiences/orientation, and psychometric validation of autism scales, development of Indian scales (such as Indian Scale for assessment of autism, NIMH, India), reviewed extensively in two publications (Naik 2015; Malhotra and Kate 2015). For example, there are studies dealing with intervention programs (e.g., parent training programs, PECS, ComDEALL) (Malhotra et al. 2002, 2010; Karanth et al. 2010). Neurobiological studies have included assessment of CSF biochemistry (Narayan et al. 1993) and urinary metabolites (Damodaran and Arumugam 2011), serum levels of transcription factor nuclear factorkappa B (Naik et al. 2011), chemical analysis of nail and hair (Priya and Geetha 2011) and BERA (Shivashankar and Satishchandra 1989). Cerebral perfusion and metabolism has been studied using PET (Anil Kumar 2012) and SPECT (Gupta and B.V. Ratnam BV. 2009). Both the studies used age-matched controls, while the former study (Anil Kumar 2012) also evaluated its association to behavior and cognitive functioning. Also, there have been efforts focusing on genetics of autism, such as studying Engrailed-2 gene (Manjunatha et al. 1989), serotonin transporter promoter variants (Guhathakurta et al. 2006), and folate metabolic pathway in Indian patients (Mohammad et al. 2009) and relation to fragile X (Sen et al. 2010; Balasubramanian et al. 2009; Suvrathan and Chattarji 2011). Socio-cultural issues including parental experiences and perspectives, met and unmet needs of families, parental stress, coping, and parenting styles (Desai et al. 2012; Divan et al. 2012;
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Tripathi 2015; Minhas et al. 2015) have been explored. Daley et al. (2014) have attempted to explore routines of adults with autism using a mixed methods approach. Besides these studies, work has been done in the areas of psychometric validation of autism scales and lately, development of indigenous scales. Recently, Indian Scale for Assessment of Autism (ISAA) has been developed as a measure of symptom severity, and there have been several studies reporting on its reliability and validity (Chakraborty et al. 2015; Mukherjee et al. 2015; Patra and Arun 2011). The ISAA has been recommended for purposes of disability certification as per the guidelines of the Government of India (GOI Notification dated 25th April, 2016). The International Clinical Epidemiology Network (INCLEN) Trust group has developed the INCLEN Diagnostic Tool for Autism Spectrum Disorder (INDT-ASD), and it has been found to have good diagnostic accuracy (Juneja et al. 2014). The tool has been recommended for use in conjunction with the ISAA (GOI Notification dated 25th April, 2016), though larger field trials are needed for ascertaining the psychometric properties and diagnostic stability of the tool (Wong and Singhal 2014). There have been a few interventional studies. Malhotra et al. (2002) offered a parent intervention package including behavioral, supportive, and educational techniques, delivered in 3–6 sessions of 45–60 min each and found that on the whole parents found this brief contact helpful. Malhotra et al. (2010) studied effects of picture exchange communication system on communication and behavioral anomalies in autism and found improvement in the target behaviors. Karanth et al. (2010) studied the efficacy of an indigenous early intervention program for children with autism spectrum disorders that specifically targets developmental issues in the areas of motor, communication, cognitive, social, and emotional skills in a small sample of 30 children. A small crosssectional study that used an autism treatment evaluation checklist pointed towards the need for training in order to improve the communication and social interaction in children with autism (Ghosh et al. 2015). Divan et al. (2015) developed
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the parent-mediated intervention for Autism Spectrum Disorder in South Asia (PASS) intervention, which is a cultural adaptation of the Preschool Autism Communication Therapy (PACT) program. Harshini and Preeti (2017) have proposed an interventional model deliverable at primary care in nonspecialist setting that focuses on parental training at the center focusing on joint attention, imitation, adaptive and social engagement skills and encouraging parents to engage the child in home-based training. Overall, in resourcelimited settings such as those in India, it is intuitive to consider parents as the instrument for therapeutic intervention. However, robust studies are needed to establish the efficacy of indigenously developed or culturally adapted intervention programs before these can be recommended on a larger scale. In 2010–2011, the Indian Council of medical Research (ICMR) announced plans to conduct extensive and in-depth research in the area of ASDs. Since then, several projects addressing early diagnosis and prevalence studies, evolution of diagnostic thresholds criteria for Indian population, standardization of multidisciplinary assessment, functional imaging studies, correlational studies with neuro-psychiatric morbidities, standardization of therapeutics, genetic studies (e.g., REELIN gene, MeCP2, β-amyloid precursor protein), biochemical markers, intervention studies and services for care, development of training modules for parents & children, development of modules of care at schools and home, special education and remediation, and speech therapy and sensory integration studies have either been launched or are in the pipeline. ICMR has also initiated collaborative projects with Autism Speaks (http://www.icmr.nic.in/). The development of INCLEN tools and PASS intervention has been the result of such international collaborations. The NIMH, Secunderabad, have started work on a project studying effectiveness of sensory integration therapy on sensory processing and adaptive behavior (NIMH; http://www. nimhindia.org/R&D%20ongoing%20projects% 2012-13.pdf). NIMHANS Bangalore is working towards developing a hospital-based Autism Registry.
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Policy and Training As mentioned earlier, the RCI started Diploma in Special Education (ASD) in four centers; the government run premier institute, National Institute for Mentally Handicapped (NIMH), Secunderabad, India, being one among these. Since then, the course curriculum has been revised every 5 years, the latest revision being in 2014 (RCI, 2014). This 2-year course is now conducted in over 20 institutes recognized by the RCI. The trained teachers can work in regular schools as resource teachers, in special educational settings, and in clinics and hospital-based departments. The RCI along with certain organizations such as Action for Autism conducts periodical workshops and seminars for awareness and training of teachers. Under the SSA program, there are detailed modules and curriculum for training of teachers in educational and behavioral methods for autism, intellectual disability, and other disabilities (Sarva Shiksha Abhiyan website). Also, the postdoctoral fellowships and DM courses shall increase the specialized manpower required in this field in the near future. With the mandates of the existing and soon to be enacted Acts, deliberately designed policies for capacity building and research, and sustained efforts across various fields such as medical research, education and rehabilitation, and legal, the stage is set for the scenario of autism services and research to change dramatically for the good in India.
References and Reading Action for Autism. http://www.autism-india.org/ Anil Kumar, B. N. (2012). PET scan in autistic spectrum disorder (M.D. thesis (2009–2012)). Department of Psychiatry, Postgraduate Institute of Medical Education and Research, Chandigarh. Autism Information and Resource Centre, An initiative of National Trust for the Welfare of Persons with Autism, Cerebral Palsy, Mental Retardation & Multiple Disabilities. Ministry of Social Justice and Empowerment, Government of India. http://www.autismresour cecenter.in/Default.aspx Balasubramanian, B. V., Bhatt, C., & Goyel, N. (2009). Genetic studies in children with intellectual disability
India and Autism and autistic spectrum of disorders. Indian Journal of Human Genetics, 15, 103–107. Barua, M., Daley, T. C. Autism. Published for Rehabilitation Council of India, Government of India, New Delhi. Available at: http://www.rehabcouncil.nic. in/writereaddata/autism.pdf. Last accessed on 11 July 2015. Bassa, D. M. (1962). A case of early infantile autism. Indian Journal of Psychiatry, 4(2), 73–76. Chakraborty, S., Thomas, P., Bhatia, T., Nimgaonkar, V. L., & Deshpande, S. N. (2015). Assessment of severity of autism using the Indian scale for assessment of autism. Indian Journal of Psychological Medicine, 37, 169–174. Central Board of Secondary Education, Ministry of Human Resource Development, Government of India (2009). Available at: http://cbseacademic.in/. Last accessed on 11 July 2015. Daley, T. C. (2004). From symptom recognition to diagnosis: Children with autism in India. Social Science and Medicine, 58(7), 1323–1335. Daley, T. C., Weisner, T., & Singhal, N. (2014). Adults with autism in India: A mixed-method approach to make meaning of daily routines. Social Science & Medicine, 116, 142–149. Damodaran, L. P., & Arumugam, G. (2011). Urinary oxidative stress markers in children with autism. Redox Report, 16(5), 216–222. Desai, M. U., Divan, G., Wertz, F. J., & Patel, V. (2012). The discovery of autism: Indian parents’ experiences of caring for their child with an autism spectrum disorder. Transcultural Psychiatry, 49(3–4), 613–637. https:// doi.org/10.1177/1363461512447139. Divan, G., Vajartkar, V., Desai, M. U., Strik-Lievers, L., & Patel, V. (2012). Challenges, coping strategies, and unmet needs of families with a child with autism spectrum disorder in Goa, India. Autism Research, 5(3), 190–200. Divan, G., Hamdani, S. U., Vajartkar, V., Minhas, A., Taylor, S., Aldred, C., et al. (2015). Adapting an evidence-based intervention for autism spectrum disorder for scaling up in resource-constrained settings: The development of the PASS intervention in South Asia. Global Health Action, 8, 27278. https://doi.org/10. 3402/gha.v8.27278. Freeth, M., Sheppard, E., Ramachandran, R., & Milne, E. (2013). A cross-cultural comparison of autistic traits in the UK, India and Malaysia. Journal of Autism and Developmental Disorders, 43(11), 2569–2583. https:// doi.org/10.1007/s10803-013-1808-9. Ganaie, S. A., Beigh, A. M., Mir, S. M., Shah, S. A., Hussain, A., Dar, A. H., et al. (2015). Social maturity and problem behaviour in children with autism spectrum disorders and intellectual disabilities. Neuropsychiatry, 5(1), 16–24. Ghosh, A., Mahajan, P. B., Mishra, B. R., Mahapatra, S. C., & Nanda, P. (2015). Exploring Health Situation of Indian Children with Autism Spectrum Disorder Using Autism Treatment Evaluation Checklist (ATEC) in an Urban Area of Odisha: A Case Study.
2431 Journal of Clinical and Diagnostic Research, 9(12), VC05–VC08. Government of India (1999). National Trust for the Welfare of Persons with Autism, Cerebral Palsy, Mental Retardation and Multiple Disabilities Act. Ministry of Social Justice and Empowerment, Government of India, New Delhi, India. Available at: http://socialjustice.nic.in/ pdf/ntact1999.pdf. Last accessed on 11 July 15. Government of India (2014) Rights of Persons with Disabilities Bill. Government of India, New Delhi. http://www. prsindia.org/uploads/media/Person%20with%20Disabil ities/The%20Right%20of%20Persons%20with%20Dis abilities%20Bill.pdf. Last accessed on 11 July 15. Guhathakurta, S., Ghosh, S., Sinha, S., Chatterjee, A., Ahmed, S., Chowdhury, S. R., et al. (2006). Serotonin transporter promoter variants: Analysis in Indian autistic and control population. Brain Research, 1092, 28–35. Gupta, S. K., & B.V. Ratnam BV. (2009). Cerebral perfusion abnormalities in children with autism and mental retardation: A segmental quantitative SPECT study. Indian Journal of Pediatrics, 46(2), 161–164. Harshini, M., & Preeti, K. (2017). Autism Behavioural Interventional Research in low–resource settings: Overcoming prevailing challenges; an Asian perspective. Asian Journal of Psychiatry. https://doi.org/10. 1016/j.ajp.2016.12.004. Indian Council of Medical Research. http://www.icmr.nic. in/ Jaydeokar, S., Bal, G., & Shah, N. (1997). Childhood disintegrative disorder. Indian Journal of Psychiatry, 39, 85. Juneja, M., Mishra, D., Russell, P. S. S., Gulati, S., Deshmukh, V., Tudu, P., et al. (2014). INCLEN diagnostic tool for autism spectrum disorder (INDT-ASD): Development and validation. Indian Pediatrics, 51(5), 359–365. Kar, N., Khanna, R., & Kar, G. C. (1993). Autistic features in children with mental retardation. Indian Journal of Psychiatry, 39(4), 304–308. Karanth, P., Shaista, S., & Srikanth, N. (2010). Efficacy of communication DEALL – An indigenous early intervention program for children with autism spectrum disorders. Indian Journal of Pediatrics, 77(9), 957–962. Krishnamurthy, V. (2008). A clinical experience of autism in India. Journal of Developmental and Behavioral Pediatrics, 29(4), 331–333. Malhotra, S. (2006). Autism: An experiment of nature. Journal of Indian Association for Child and Adolescent Mental Health, 2(1), 9–17. Malhotra, S. & Vikas, A. (2002). Pervasive developmental disorders: Indian scene. Journal of Indian Association for Child and Adolescent Mental Health, 1(3), 5. Malhotra, S., & Kate, N. (2015). Research endeavors in child ssychiatry in India 1 and 2. In S. Malhotra & S. Chakrabarti (Eds.), Developments in psychiatry in India (pp. 215–254). Springer. https://doi.org/10.1007/ 978-81-322-1674-2_12.
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2432 Malhotra, S., & Vikas, A. (2005). Pervasive Developmental Disorders: Indian Scene. Journal of Indian Association for Child and Adolescent Mental Health, 1(3), Article 5. Malhotra, S., Chakrabarti, S., & Nehra, A. (2002). Psychological interventions with parents of autistic children. Indian Journal of Psychiatry, 44(2), 108–117. Malhotra, S., Chakrabarti, S., Gupta, N., Kumar, P., & Gill, S. (2003). Pervasive developmental disorders and its subtypes: Sociodemographic and clinical profile. German Journal of Psychiatry. Malhotra, S., Rajender, G., Bhatia, M. S., & Singh, T. B. (2010). Effects of picture exchange communication system on communication and behavioural anomalies in autism. Indian Journal of Psychological Medicine, 32(2), 141–143. Manjunatha, K. R., Narayanan, H. S., Rao, B. S. S. R., Srinath, S., & Girimaji, S. (1989). Cryptogenetic investigations in autistic children: a preliminary study on the detection of fragile X chromosome. NIMHANS Journal, 7(2), 163–167. Minhas, A., Vajaratkar, V., Divan, G., Hamdani, S. U., Leadbitter, K., Taylor, C., Aldred, C., Tariq, A., Tariq, M., Cardoza, P., Green, J., Patel, V., & Rahman, A. (2015). Parents’ perspectives on care of children with autistic spectrum disorder in South Asia - Views from Pakistan and India. International Review of Psychiatry, 27(3), 247–256. https://doi.org/10.3109/ 09540261.2015.1049128. Mohammad, N. S., Jain, J. M., Chintakindi, K. P., Singh, R. P., Naik, U., & Akella, R. R. (2009). Aberrations in folate metabolic pathway and altered susceptibility to autism. Psychiatric Genetics, 19(4), 171–176. Mukherjee, S. B., Malhotra, M. K., Aneja, S., Chakraborty, S., & Deshpande, S. (2015). Diagnostic accuracy of Indian Scale for Assessment of Autism (ISAA) in children aged 2–9 years. Indian Pediatrics, 52(3), 212– 216. Naik, U. S. (2015). Autism spectrum disorder: 70 years on and the plot thickens. In S. Malhotra & S. Chakrabarti (Eds.), Developments in psychiatry in India (pp. 275–311). New Delhi: Springer. https://doi.org/ 10.1007/978-81-322-1674-2_15. Naik, U. S., Gangadharan, C., Abbagani, K., Nagalla, B., Dasari, N., & Manna, S. K. (2011). A study of nuclear transcription factor-kappa B in childhood autism. PloS One, 6(5), e19488. Narayan, M., Srinath, S., Anderson, G. M., & Meundi, D. B. (1993). Cerebrospinal fluid levels of homovanillic acid and 5-hydroxyindoleacetic acid in autism. Biological Psychiatry, 33(8-9), 630–635. National Trust. The National Trust, Ministry of Social Justice and Empowerment, Government of India. Available at: http://www.thenationaltrust.co.in/nt/ index.php?option¼com_content&task¼view&id¼94 &Itemid¼143. Last accessed on 11 July 2015. Patra, S., & Arun, P. (2011). Use of Indian scale for assessment of autism in child guidance clinic: an experience. Indian J Psychol Med., 33, 217–219.
Indicator Priya, L., & Geetha, A. (2011). Level of trace elements (copper, zinc, magnesium and selenium) and toxic elements (lead and mercury) in the hair and nail of children with autism. Biological Trace Element Research, 142(2), 148–158. Rehabilitation Council of India (2014). D.Ed. Special Education (Autism Spectrum Disorder). Syllabusnorms, regulations & course content.” Rehabilitation Council of India. (Statutory Body under Ministry of Social Justice & Empowerment), Government of India, New Delhi. Available at: http://www.rehabcouncil.nic.in/writereaddata/ dedasd.pdf. Last accessed on: 11 July 2015. Right to Education (2009/10). Ministry of Human Resource Development, Government of India. http:// mhrd.gov.in/rte Sarva Shiksha Abhiyan. Ministry of Human Resource Development, Government of India. http://ssa.nic.in/. Last accessed on 11 July 2015. Sen, B., Singh, A. S., Sinha, S., Chatterjee, A., Ahmed, S., Ghosh, S., & Usha, R. (2010). Family-based studies indicate association of Engrailed 2 gene with autism in an Indian population. Genes, Brain and Behavior, 9(2), 248–255. Shivashankar, N., & Satishchandra, P. (1989). Auditory brainstem responses in autistic children. Nimhans Journal, 7(2), 159–162. Srinath, S., Chowdhury, J., Bhide, A. V., Narayanan, H. S., & Shivaprakash. (1989). Descriptive study of infantile autism. NIMHANS Journal, 7(1), 77–81. Suvrathan, A., & Chattarji, S. (2011). Fragile X syndrome and the amygdale. Current Opinion in Neurobiology, 21(3), 509–515. Tamana. http://www.tamana.org/AutismCenter.aspx Tripathi, N. (2015). Parenting style and parents’ level of stress having Children with Autistic Spectrum Disorder (CWASD): A study based on Northern India. Journal of Neuropsychiatry, 5(1), 42–49. Vijay Sagar, K. J. (2011). Research on autism spectrum disorders in India. Andhra Pradesh Journal of Psychological Medicine, 12(1), 69–72. Wong, C. M., & Singhal, S. (2014). INDT-ASD : An autism diagnostic tool for Indian children developmental pediatrician’s perspective. Indian Pediatrics, 51(5), 355–356.
Indicator ▶ Milestone
Indiscriminate Friendliness ▶ Attachment Disorder
Individual Education Plan
Indiscriminate Sociability ▶ Attachment Disorder
Indiscriminate Social Behavior ▶ Attachment Disorder
Indiscriminately Social/ Disinhibited Attachment Disorder ▶ Attachment Disorder
Individual Appropriates ▶ Developmentally Appropriate Practice (DAP)
Individual Education Plan Erin E. Barton University of Colorado Denver, Denver, CO, USA
Definition Individual Education Program An Individualized Education Program (IEP) is provided to students with disabilities under the Individuals with Disabilities Education Improvement Act (IDEIA). Current law (IDEIA) dictates that all students who are eligible for educational supports and specialized services have an IEP. The IEP is a legal document outlining the necessary services and supports for children with disabilities (i.e., who have an educational eligibility) ages 3 years through 22 years. IEPs are created in collaboration with parents, teachers, and
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therapists working with the student. The purpose of the IEP is to outline educational goals, supports, services, and placement for individual students with disabilities and identify who is responsible for carrying out the student’s program. An IEP serves as the framework for individualized instructional programs for students with disabilities. The IEP addresses all of a student’s educational needs. It is a written program of educational services and supports developed for each student eligible for services in accordance with the criteria outlined by each state’s board of education. The IEP includes procedural safeguards for parents of students with disabilities. The procedural safeguards outline the rights of students and parents. Parents are given a copy of their procedural safeguards at each IEP meeting. The procedural safeguards are determined by each state board of education. These include (but are not limited to) free and appropriate public education for all children age 3 and older, prior written notices for all meetings and decisions, a description of instructional and placement options, and descriptions of all evaluation procedures. An IEP is required for all students older than 3 years who need specialized services. In most states, children younger than 3 who have an Individual Family Service Plan (IFSP) will transition to an IEP at age three. In some states, this transition occurs at age 5 or once the child enters kindergarten. The transition from an IFSP to an IEP always involves a reevaluation (i.e., parent and team input, informal assessments and observations, and standardized assessments). The eligibility determinations are made by the state-appointed evaluation teams based on the extent to which the disability impacts the students’ educational performance.
Historical Background IEPs are mandated by federal law, specifically the Individuals with Disabilities Act, which was recently amended in 2004. The major purpose of this law is to ensure all children with disabilities to have access to free and appropriate education
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(FAPE). The IEP outlines the services and supports needed to ensure the child receives FAPE.
Current Knowledge IEP Process For students 3 years and older, the initial IEP process begins with an evaluation of the child’s present level of development. An eligibility determination is made based on the assessment information gathered. This includes information gathered through parent interviews, informal classroom observations or assessments, and formal or standardized assessments. The state identified evaluation team determines eligibility for services based on the extent to which child’s disability negatively affects educational performance. For example, the evaluation team might determine a child’s autism is negatively affecting his ability to communicate, interact with others, access the general education curriculum, access materials in the classroom, and meet grade level expectations. The team might determine this child meets the eligibility requirements and needs specialized services. However, not all children with a medical diagnosis of autism will be eligible for educational services. For example, the evaluation team might determine a child with higher functioning autism (i.e., has above average IQ) can access the general education curriculum, materials, and have positive interactions with peers and adults with the typical classroom-wide structure and supports. Once the child is determined to be eligible for specialized services and an IEP is developed, the child’s eligibility is reevaluated every 3 years. The student’s IEP is revised each year at an annual review. IEP Components There are several components to an IEP (e.g., the educational team, eligibility category, placement, accommodations, strengths, goals, and services). These will be described in more detail. IEP Team
The student’s educational team is made up of a variety of individuals who collaborate to develop
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an educational program that will meet the student’s needs. The IEP team for students with autism should include the parents, general education teacher, special education teacher, and school district representative (e.g., the case manager, guidance counselor, administrator, or professional at the district level). Depending on the needs of the student, the IEP team also might include an autism specialist, speech/language therapist, occupational therapist, school psychologist, behavior specialist, and the student when appropriate. The special education teacher is a key member of the IEP team for students with autism (Hall 2007). Thus, special education teachers serving students with autism must have specific experience and coursework related to evidence-based practices for students with autism. Occasionally, school districts will hire autism specialists (i.e., school personnel with specific experience and training in evidence-based practices with students with autism) to support students with autism and help teachers in assessment, instructional planning and implementation, and progress monitoring of students with autism (Hall 2007). In sum, the purpose of the IEP team is to determine the extent of the student’s needs, design an instructional program, implement the program, evaluate the program, and ensure the student receives the appropriate supports and services. Thus, the role of the IEP does not begin and end at the IEP meeting – it is an ongoing effort. Effective communication among team members is essential to ensure the IEP is followed and the child is making appropriate progress toward goals and objectives. Educational Needs
The next major section of the IEP is the students’ educational needs. This section indicates the student’s eligibility category and specific information related to how the disability impacts the child’s educational performance. Eligibility categories might include autism, other health impaired, emotional delays, cognitive delays, specific language impairment, speech impairments, visual or hearing impaired, or learning disability. This section includes a statement with specific information related to how the student’s disability
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impacts his educational performance or academic achievement. Current Assessment Information
This section includes a statement about the student’s present level of development, functioning, and educational performance, including information about what skills the student has mastered and what skills are developing. This section also should include information related to the child’s current academic and behavioral repertoire, learning style, interests, and the impact of the student’s disability on academic, social, adaptive, communication, or motor skills. This can include information related to assessment results, discipline reports, grades, observational notes, and attendance information. The student’s academic and behavioral strengths also should be clearly delineated in this section. Parents and teachers should provide information about the student’s behavioral and academic strengths. This assessment information should be directly linked to instructional planning. The team will develop a list of areas of instructional priorities based on this assessment information. Successful instructional programs are directly linked to the student’s strengths, assessment results, interests, learning style, and parent priorities and concerns (Boutot and Myles 2011). Educational Needs
The next section on the IEP delineates the student’s needs, instructional priorities, and goals. The student’s needs are developed based on the assessment results, levels or area of impact of the disability, or academic performance. Annual goals and short-term objectives should be written for each area in which the student has needs. The areas of need for students with autism should include communication, social skills, and affected academic areas (e.g., reading, math). Many students with autism will need goals in fine motor, gross motor, or adaptive skills (e.g., independent functioning). The student’s needs should be directly linked to the student’s goals. Goals are the skills that the student will attain within the year or other specified amount of time. Goals should be functional, generative, measureable,
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and instructional. Functional goals are directly related to skills the student will need for independent performance within daily routines and activities. Generative goals are related to skills the child will need across settings, people, materials, and activities. Measurable goals are clearly operationalized so that the student’s progress can be clearly and efficiently monitored over time. Instructional refers to goals that will occasion opportunities for teaching throughout the student’s day. Goals also should be written in a positive manner with clear, professional language (e.g., instead of “Elisa will stop hitting peers,” the goal should read “Elisa will verbally ask a teacher for help”). Examples of annual goals for children with autism are: 1. For a preschooler: Sammy will appropriately respond (e.g., verbally or with gestures) to social initiations from a peer during classroom routines and activities. We will know he can do this when he appropriately responds to 5 peer initiations per day for 3 consecutive days. 2. For an elementary school student: Juan will follow classroom rules and directives (e.g., sit in chair, raise hand to ask questions, walk in the halls, etc.) given visual and verbal prompts. We will know he can do this when he follows 10 classrooms rules and directives for 2 days in a row. 3. For a high school student: Trevonte will ask questions of others regarding topics initiated by self or others to sustain conversation. We will know he can do this when he participates in five conversations per day for 3 consecutive days. Accommodations or Modifications
This section delineates accommodations or modifications necessary to support the student’s learning. Accommodations are changes made to the instruction or assessment to support the student and provide access to the general education curriculum without altering the content. Accommodations for students with autism might include visuals or graphic organizers, specialized seating arrangement, visual rather than written responses,
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adapted keyboards, or having material read aloud. Accommodations do not change grade level or academic expectations. Modifications refer to changes made to the assessment or general education curriculum to meet the needs of the student. Accommodations are required by law to ensure students with disabilities have an equal opportunity to learn and access the general education curriculum. Modifications are made when the expectations are not appropriate for the student. Examples of modifications for students with autism might include the following: a student works on addition, while peers work on division; a student stacks or sorts materials, while peers work on a science experiment; or a student is given a test on continents, while peers are tested on geography or countries across the world. Modifications should target the highest level possible to meet the student’s needs (i.e., closest to the general education content as possible) and only used when absolutely necessary. Related Services
The next section of the IEP describes the services necessary to support the student and supplement the services provided in the classroom. Related services are required to ensure the student is successful in the classroom. Related services for students with autism might include speech/language therapy, occupational therapy, assistive technology, transportation, and counseling. This section will describe these services in detail. The related services section includes the frequency of services (e.g., twice per week for 30 min), the location of these services (e.g., classroom, therapy room), the type of services (e.g., direct service to the individual child, group services with the child and other children with disabilities, or consultation to the teacher or parent), and the person providing these services. Indirect services (i.e., consultation and coaching) are often provided by autism specialist to students with autism in general or specialized classrooms. This might include consultation to the general education teacher about designing a predictable, visually rich environment to support the student with autism or the special education teacher about implementing an evidence-based strategy for teaching communication or social
Individual Education Plan
skills. These services should be provided directly in the classroom when appropriate to support the student with autism in accessing the classroom curriculum and interacting with peers and materials. Assistive Technology
The next section on the IEP is related to assistive technology for students with disabilities. This includes technologies used by the student to function within the classroom and community. For students with autism, this might include augmentative communication devices, computer software to enhance academic performance, or the use of personal digital assistants (PDAs) to promote independent transitioning and following schedules. Setting
The IEP also will indicate the setting(s) in which the IEP will be implemented. This is referred to as the educational placement. Placement is not based on district availability or preferences. The IEP team will determine the student’s placement based on the student’s strengths and needs. Special education law requires that all students with disabilities be educated in the least restrictive environment and with their typical peers to the maximum extent possible. The law requires the general education classroom to be considered first, and if the student is not included in the general education, the IEP should document and specify reasons why. A continuum of placements must be considered from least restrictive (general education classrooms) to more restrictive (e.g., special classroom or school). The IEP team should consider general education classroom with embedded special education services, general education classroom with pull-out to a resource or special education classroom for part of the day, special education classroom within a general education school, or specialized school. In rare cases, service will be provided in an institution or hospital. Extensive research supports the inclusion of student with disabilities in the general education classrooms. For example, inclusive classrooms provide a context for implementing evidencebased practices, occasional social interactions
Individual Education Plan
and communication among students with autism and their typical peers, which represent core skill deficits for children with autism, and research has documented more positive attitudes toward disabilities by peers when students with disabilities are included in the preschool environment (Dawson and Osterling 1997; Strain et al. 2002). If the placement is not a general education classroom, the IEP must specify the amount of time the child will participate in the general education classroom, document that opportunities for the student to interact with typically developing peers will be provided, and include a statement specifying that the least restrictive environment was considered. Positive Behavior Supports
There also is a section on the IEP that requires the team to indicate if positive behavioral supports have been considered for the student with autism. This is an important service for many students with autism, particularly students who display stereotypic behaviors (e.g., repetitive hand flapping, body rocking), or challenging behaviors (e.g., which are often due to delays in functional communication and social skills) that might interfere with learning. This section will describe the supports (e.g., preventative strategies, replacement skills, and responses to challenging behaviors) and implementation plan. Evaluation
The IEP also includes a section specifying how student progress will be measured and evaluated. Appropriately written goals and objectives will embed an evaluation process. However, this section ensures the student’s progress is intentionally and systematically monitored over year. Progress on each goal must be measured and documented.
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planning form. This form requires the IEP document the discussion about the student’s transition to adult life after school. The form will include a postsecondary vision (e.g., the student’s goals for adult life); disability-related needs; plan for supporting the student’s self-determination, academic, and adaptive skills to support postsecondary vision; and the roles of school personnel, family, student, and community providers in supporting the student. 504 Plans
Students with disabilities who require accommodations must have a written plan. If the accommodations are considered reasonable, the student can have a 504 plan rather than a full IEP. The 504 plan also is developed by the student’s educational team, but requires only minimal accommodations to access and succeed within the general curriculum. For students with autism, this might include a digital timer, longer testing time, or regular breaks during the day.
Future Directions Summary and Future Directions An Individualized Education Program (IEP) is the legal documentation of an educational plan developed with a team of professionals based on the strengths and needs of one child. IDEIA mandates that all children with special needs have a right to an IEP and free access to appropriate public education. Effective IEPs ensure that children with autism receive equitable education (the same as children without disabilities) in the least restrictive environment possible. Future research should examine components of effective IEPs and strategies for developing collaborative, effective IEPs.
Postsecondary Transition
The IEP for students with autism who are 14 years old must include a section for the student’s postschool plan or transition to adult care or services. The IEP team considers the student’s need for transition services and documents the discussion. When appropriate, the IEP will include a statement of the needs for services and a transition
See Also ▶ Free Appropriate Public Education ▶ Individualized Plan for Employment (IPE) ▶ Individualized Transition Plan (ITP) ▶ Individuals with Disabilities Education Act (IDEA)
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References and Reading Bateman, B. D., & Herr, C. (2006). Writing measurable IEP goals and objectives. Verona: Attainment Publications. Boutot, E. A., & Myles, B. S. (2011). Autism spectrum disorders. Upper Saddle River: Pearson. Dawson, G., & Osterling, J. (1997). Early intervention in autism: Effectiveness, common elements of current approaches. In M. J. Guralnick (Ed.), The effectiveness of early intervention: Second generation research (pp. 307–326). Baltimore: Brookes. Hall, L. J. (2007). Autism spectrum disorders: From theory to practice. Upper Saddle River: Pearson. Ruble, L. A., McGrew, J., Dalrymple, N., & Jung, L. A. (2010). Examining the quality of IEPs for young children with autism. Journal of Autism and Developmental Disorders, 20, 1459–1470. Strain, P., McGee, G., & Kohler, F. (2002). Inclusion of children with autism in early intervention environments. In M. J. Guralnick (Ed.), Early childhood inclusion: Focus on change (pp. 337–363). Baltimore: Paul H. Brookes.
Individualized Education Program (IEP) Team ▶ Interdisciplinary Team
Individualized Family Service Plan Erin E. Barton University of Colorado Denver, Denver, CO, USA
Definition An Individualized Family Service Plan (IFSP) is a legal documentation of the early intervention process for children with disabilities younger than 3 and their families. The IFSP guides the early intervention process in accordance with Part C of the Individuals with Disabilities Act (IDEA). Part C of IDEA is a federal grant program that supports states in operating a comprehensive system of early intervention for children younger than
Individualized Education Program (IEP) Team
3 years old with or at risk for disabilities and their families. As with the IEP (Individual Education Program), the IFSP is completed by a team of professionals (including the child’s family and caregivers); the IFSP documents services necessary to support the family and build the family’s capacity to help their child grow and develop. An IFSP is required for all children younger than 3 years who are receiving Part C early intervention services. In most states, children younger than 3 who have an IFSP will transition to an IEP at age three. In some states, this transition occurs at age 5 or once the child enters kindergarten, in which case the children have an IFSP until they are 5 years old. The transition from an IFSP to an IEP always involves a reevaluation (i.e., parent and team input, informal assessments and observations, and standardized assessments). IEP Versus IFSP IFSPs and IEPs are legal documents outlining the services needed for children with disabilities from birth to age 3 and from age 3 through 21, respectively. The IFSP and IEP are similar in that they are developed by a team for an individual child or student and include the goals, interventions, and supports necessary to support their learning. However, IFSPs are uniquely designed to revolve around the individual family’s concerns, priorities, and resources. IFSPs include child and family outcomes relating to supporting the child’s development, whereas IEPs are focused only on the student and his/her academic performance. IFSPs identify the family’s natural setting and document how and when services will be provided in the family’s natural settings (i.e., home, parks, child care). Also, IFSPs have an additional section (as compared to IEPs) where the non-Part C services required by the family are listed along with the funding sources for those services. IEPs focus on the special education and related services in schools. The final difference between IEPs and IFSPs lies within their purpose. In sum, the purpose of the IFSP is to identify and document the supports needed to strengthen the family’s capacity to promote their child’s development, whereas the IEP outlines the supports and services to be provided by professionals within schools to
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promote the student’s academic performance (McWilliam 2010; McWilliam et al. 1998).
Historical Background The Individual Family Service Plan (IFSP) is part of a system of federal and state special education laws. The federal law, the Individuals with Disabilities Education Act (IDEA), specifies the IFSP and defines the basic components that must be in an IFSP. Each state further defines the specific children and families eligible for Part C services and the IFSP components.
Current Knowledge IFSP Process For children younger than 3, the initial IFSP process begins with an evaluation of the child’s present level of development. An eligibility determination is made based on the assessment information gathered. Eligibility determinations are state-determined. Thus, although all states and eligible territories currently provide Part C services, the children and families who are eligible for these services differs across states. A few states provide Part C services to children younger than 3 who are at risk for delays and children who have delays. Eligibility determination is made based on information gathered through parent interviews (e.g., Routines Based Interview, McWilliam et al. 2009; Routines-based assessment, Woods and Lindeman 2008), informal observations or assessments in natural settings, and formal or standardized assessments. Once the child is determined to be eligible for specialized services and an IFSP is developed, the child’s eligibility is reevaluated every year. The IFSP team reviews and evaluates child and family’s progress on IFSP outcomes at least every 6 months. IFSP Components There are several components to an IFSP (e.g., the team, current assessment information, family concerns, outcomes, etc.). These will be described in more detail.
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IFSP Team
The child’s educational team is made up of a variety of individuals who collaborate to develop a plan to meet the child and family’s needs. The IFSP team for children with autism might include the parents, autism specialist, early interventionist, speech/language therapist, and service coordinator. Depending on the needs of the child and family, the IFSP team also might include a behavioral specialist, social worker, or psychologist. If the child spends more than 15 h per week in child care, the primary care provider from the child care setting should be included on the team (McWilliam 2010). The purpose of the IFSP team is to build a collaborative relationship with the family and determine the supports necessary to build the family’s capacity to promote their child’s development. For children who spend more than 15 h per week in child care, this includes supporting the primary care providers in the child care setting. As with the IEP team, role of the IFSP team does not begin and end at the IFSP meeting – it is an ongoing effort. Effective communication among team members is essential to ensure the IFSP is followed, families (or child care providers) are receiving the supports they need, and the child and family are making appropriate progress toward outcomes. One member of the team is identified as the primary service coordinator, who organizes and is responsible for ensuring implementation of the IFSP. Effective IFSP teams are guided by a transdisciplinary model of service delivery (i.e., one professional provides the weekly supports with back-up from other team members (McWilliam 2010)) and use an interagency approach (involves professionals from a several agencies). Present Levels of Development
The next section of the IFSP outlines the child’s present level of development. Each family will receive a multidisciplinary assessment of their child’s unique strengths and needs. The child’s present level of development should be assessed across five areas: motor, cognitive, communication, social emotional, and adaptive. This information should be gathered from valid and reliable assessments and authentic observations
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in the natural environments. This section should also include parent or caregiver reports about their child’s preferences, strengths, and characteristics. Family’s Resources, Priorities, and Concerns
The IFSP team also will gather information about the family’s resources, priorities, and concerns (e.g., Family Interest Survey, Cripe and Bricker 1993). This information can be gathered through informal conversations, interview, or assessments. A statement about the family’s concerns, priorities, and resources related to enhancing their child’s development is required on all IFSPs. Child Outcomes
The next section on the IFSP delineates the child outcomes. These are developed by the IFSP team (including the family) and directly related to the assessment information. Many young children with autism will need outcomes related to social or communication skills. Outcomes should be functional, generative, measureable, instructional, and participation-based. Functional goals are directly related to skills the child will need for independent performance within the family’s daily routines and activities. Generative goals are related to skills the child will use across the family’s routines and materials at home, the various community settings the family and child participate in, and people the child interacts with. Measurable goals are clearly operationalized to directly link to an intervention plan for the family and so that the child’s progress can be efficiently monitored over time. Instructional goals refer to behaviors and skills the family will have opportunities to practice with the child across their daily routines with the materials they have at home. Finally, goals for young children should be participation-based. This means they explicitly refer to the context in which the skill is needed to ensure everyone on the team understands how the goal helps the child participate in the family’s daily activities and routines
Individualized Family Service Plan
(McWilliam 2010). The criteria, procedures, and timelines for measuring progress also are included in this section. Collaboration in developing child outcomes is essential to build the family’s capacity and increase the child’s participation in the family’s routines and activities. Example child goals are: 1. Kenan will participate in meals, playtime (after bath, after breakfast), and routines (diapering, bath time, getting dressed, waking up) by looking at mom or dad and babbling. We will know he can do this when he looks at mom or dad’s eyes and babbles three times during five activities per day for 1 week. 2. Luke will participate in breakfast, lunch, midafternoon snack, and dinner by feeding himself independently. We will know he can do this when he uses his hands to pick up 15 small pieces of food to put in his mouth or uses his hands to pick up his spoon to put at least 10 bites of soft foods in his mouth at each meal for 1 week. Goals should be written so they can be embedded into daily routines and natural settings, emphasize functional skills, and increase participation in activities and settings. Family Outcomes
The IFSP must include at least one family outcome. As with the child outcomes, the family outcomes also are the based on the family’s priorities, resources, and concerns. These outcomes are explicitly focused on setting goals to build the family’s capacity to support their child’s growth and development. The family selects their outcomes based on things they would like to start working on, including the any resources or needs identified through the assessment. Example family outcomes might be (adapted from McWilliam 2010): 1. Janice will keep Maize engaged during at least 30 min of church each week for a month so Janice can start going to church again.
Individualized Family Service Plan
2. All five members of the McDonalds family will spend 1 h each night engaged in a fun activity with the TV off. 3. Anne will rate Alyse’s bedtime routine as successful and easy with 4 months. These family outcomes are based on specific concerns reported by the family, directly related to the families current priorities, and developed in consideration of the family’s available resources and include measurable criterion to ensure the team will know when the outcome has been achieved. Natural Environments
The next section of the IFSP identifies the settings where the family spends most of their time and the activities they enjoy. The natural setting might be the home, child care, relative’s house, or community center. The family’s valued activities and routines are listed to identify needs and opportunities for learning. All child outcomes should be developed with the natural setting, valued activities, and daily routines in mind; the outcomes should support the child’s independent participation in family routines and activities. Also, all service and supports should be provided in the natural settings. In fact, IDEA Part C regulations state: “To the maximum extent appropriate to the needs of the child, early intervention services must be provided in natural environments, including the home and community settings in which children without disabilities participate” 34CFR 303.12(b), and “natural environments means settings that are natural or normal for the child’s age peers who have no disabilities” 34CFR 303.18. Services
The next section of the IFSP delineates the specific services and supports needed to support the child and family in achieving the stated outcomes. The primary service provider will provide or coordinate the provision of material, emotional, or informational supports to build the family’s capacity to promote their child’s development.
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Services and supports are provided through family coaching in the natural setting (often referred to as home visiting) or consultation with the child care center within the child’s daily routines. Material supports might include necessary equipment (e.g., a communication device, supported seating) or basic necessities (e.g., calling an agency to help the family get diapers or housing subsidies). Emotional support might include sensitivity to the family’s culture and needs, positive feedback, and responsivity (e.g., listen to the family, respond to calls, or emails quickly). Informational support might include information about the child’s disability, community resources, and strategies for providing learning opportunities throughout the day. In general, services and supports should be provided directly to the family rather than to the child (Jung 2003). In this manner, the family is supported and given the resources to provide the “intervention” all day long across daily routines and activities with relevant materials. Effective service providers recognize that the family can support their child’s development by providing numerous natural learning opportunities throughout the day between scheduled home visits (McWilliam 2010). Also, supports and services should reflect the family’s culture and experiences. This section also will specify the projected dates of when the services will begin, how often they will occur, and how long they will last. Evaluation
The IFSP also includes a section specifying how the child and family’s progress will be measured and evaluated. This section should include the criteria, procedures, and timelines used to measure progress toward outcomes. Appropriately written outcomes will include a measurable criterion (e.g., 5 times across 5 days, 10 times per day for 1 week). However, this section ensures the child and family’s progress is intentionally and systematically monitored. Progress on each outcome must be measured and documented, and reviewed at least every 6 months. The team will assess progress and determine if the supports are effective or if changes need to be made to the
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IFSP. As mentioned above, the IFSP is meant to delineate an ongoing process of support for the family and child to help the family recognize their own strengths, resources, and capacity to promote their child’s development. Transition Plan
The IFSP includes a section to plan for the transition to special education services (Part B of IDEA which refers to special education services for children 3 years and older) and an IEP, or other appropriate agencies or resources for when the child turns 3. The service coordinator must initiate this conversation and plan with the family at least 6 months prior to the child’s third birthday. Non-part C Services
The final section of the IFSP delineates the activities or services provided by agencies outside of Part C. This is meant to help the service coordinator and family integrate all service and supports. Occasionally, families are charged for these nonPart C services, which should be noted on the IFSP.
Future Directions An Individualized Education Program (IFSP) is the legal documentation of a service plan developed with a team of professionals based on the strengths and needs of one child and their family. IDEA mandates that all children younger than three receiving Part C services have an IFSP. Effective IFSPs ensure that children with autism and their families receive early and intentional intervention in the natural setting to support the family’s capacity to promote their child’s development to prevent or alleviate further delays. Despite the plethora of research on the benefits of early, intentional intervention for children with or at risk for disabilities, early intervention (specifically effective family coaching) is a largely understudied topic. Future research should examine effective family coaching, home visiting, and strategies for developing effective IFSP.
Individualized Family Services Plan (IFSP) Team
See Also ▶ Free Appropriate Public Education ▶ Individual Education Plan ▶ Individualized Plan for Employment (IPE) ▶ Individualized Transition Plan (ITP) ▶ Individuals with Disabilities Education Act (IDEA)
References and Reading Brown, W., Thurman, S. K., & Pearl, L. F. (1993). Family centered early intervention with infants and toddlers: Innovative cross-disciplinary approaches. Baltimore: Paul H. Brookes Publishing. Cripe, J., & Bricker, D. (1993). Family Interest Survey. University of Oregon, unpublished assessment. Jung, L. A. (2003). More is better: Maximizing natural learning opportunities. Young Exceptional Children, 6, 21–27. Jung, L. A. (2010). Can embedding prompts into the IFSP form improve the quality of IFSPs? Journal of Early Intervention, 32, 200–213. Jung, L. A., & McWilliam, R. A. (2005). Reliability and validity of scores on the IFSP Rating Scale. Journal of Early Intervention, 27, 125–136. McWilliam, R. A. (2005). Assessing the resource needs of families in the context of early intervention. In M. J. Guralnick (Ed.), A developmental systems approach to early intervention (pp. 215–234). Baltimore: Paul H. Brookes Publishing. McWilliam, R. A. (2010). Routines-based early intervention. Baltimore: Paul H. Brookes Publishing. McWilliam, R. A., Casey, A. M., & Sims, J. L. (2009). The routines-based interview: A method for assessing needs and developing IFSPs. Infants and Young Children, 22, 224–233. McWilliam, R. A., Ferguson, A., Harbin, G., Porter, D. M., & Vaderviere, P. (1998). The familycenteredness of individualized family services plans. Topics in Early Childhood Special Education, 18, 69–82. Woods, J., & Lindeman, D. P. (2008). Gathering and giving information with families. Infants and Young Children, 21, 272–284.
Individualized Family Services Plan (IFSP) Team ▶ Interdisciplinary Team
Individualized Plan for Employment (IPE)
Individualized Plan for Employment (IPE) Ernst O. VanBergeijk Vocational Independence Program, New York Institute of Technology, Central Islip, NY, USA Threshold Program, Lesley University, Cambridge, MA, USA
Definition An Individualized Plan for Employment (IPE) (also known as an Individualized Employment Plan [IEP]) is a contractual agreement between an individual with a disability and a state office of vocational and rehabilitative services. IPEs help inform the individual regarding what services they will receive and help them make informed choices regarding their vocational training. This information includes the cost, duration, and accessibility of potential services. This document is written collaboratively between the client and vocational rehabilitative counselor. The IPE must contain the following information: (1) the date that services begin and the date by which the vocational objective is to be achieved; (2) the specific services that are to be offered to the client or consumer; (3) objective criteria, evaluation procedures, and schedules for determining progress toward goals; and (4) a description of the Client Assistance Program (CAP) (Vision Aware 2010). For each service described in the IPE, specific information must be listed. Each service must have a date by which the service will be initiated and a date by which the specific vocational goal will be reached. The IPE must describe the services offered to the client in detail. The vocational goals are to be written in a manner that is behaviorally oriented so that the vocational goal is observable and measurable. How the attainment of the goal will be assessed must be written into the IPE. The IPE should be reviewed at least annually (Joint Committee on Administrative Rules n.d.). The list of services available to a client who is eligible for vocational and rehabilitative services from a state office includes the following:
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• Counseling and guidance. • Physical and mental restoration, including corrective surgery and ocular prosthetic devices. • Vocational and other training services. • Services to the family. • Interpretive services for those who are deaf. • Reading services. • Personal adjustment services. • Provision of telecommunications and other technical aids. • Placement in suitable employment. • Post-employment services if the client’s job is in jeopardy. • Assistance in obtaining occupational licenses, tools, equipment, and initial stocks for small businesses. • Job coaching services. • Assistance in obtaining other goods and services that could aid the client in obtaining employment. • In addition, the vocational objective must contain a formal job title that is listed in the Occupational Outlook Handbook, with the code number, if available (Vision Aware 2010).
Current Knowledge The range of unemployment figures varies from 50% to 75% for people with disabilities in general and over 90% for individuals with autism spectrum disorders.
Future Directions A coordination of entitlements must be developed for young adults who are transitioning between secondary school and postsecondary education. There is potential conflict between the transition plans outlined in IDEA, the new provisions of the Higher Education Opportunity Act of 2008, and the Rehabilitation Act Amendments of 1998 in terms of which law or agency will pay for additional training if that training is conducted in an institution of higher education such as through a Comprehensive Transition and Postsecondary Program (CTP) at a college or university.
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See Also ▶ Comprehensive Transition Program ▶ HEA 2008 ▶ Individuals with Disabilities Education Act (IDEA) ▶ Vocational Rehabilitation Act of 1973 ▶ Vocational Training
References and Reading Joint Committee on Administrative Rules. (n.d.). Administrative code. Title 89: Social services. Chapter IV: Department of Human Services. Subchapter b: Vocational rehabilitation. Part 572 Individualized Plan for Employment (IPE). Retrieved 29 June 2011, from http://www.ilga.gov/commission/jcar/admincode/089/ 08900572sections.html U.S. Congress. (1973). The Rehabilitation Act of 1973 (P.L. 93–112). Retrieved 25 July 2011, from http:// www.dotcr.ost.dot.gov/documents/ycr/REHABACT. HTM Vision Aware. (2010). What is an individualized plan for employment (IPE)? Retrieved 29 June 2011, from http://www.visionaware.org/individualized-plan-foremployment Wehman, P. (2001). Life beyond the classroom: Transition strategies for young people with disabilities (3rd ed.). Baltimore: Paul H. Brookes.
Individualized Transition Plan (ITP) Paul Wehman1 and Staci Carr2 1 Department of Physical Medicine and Rehabilitation, Virginia Commonwealth University, Richmond, VA, USA 2 UniqueKids Inc, Moseley, VA, USA
Definition Individualized Transition Plan, or ITP, is a plan based on informal and formal assessments that is used to identify the desired and expected
Individualized Transition Plan (ITP)
outcomes by students and their families once they leave school as well as the supports needed to achieve these outcomes. This plan is developed collaboratively with the student, caregivers, vocational educators, vocational rehabilitation counselors, and current IEP team members. A thorough plan will include the following: postsecondary education opportunities, employment opportunities, living opportunities, financial and income needs, friendship and socialization needs, transportation needs, health and medical needs, and legal/advocacy needs.
Historical Background Transitional planning, for youth with disabilities, was first mandated by federal legislation in 1990 with the passing of PL 101-476, IDEA, 1990. The reauthorization of IDEA in 1997 added new components to the formal process of transitional services. In 1997, the Individuals with Disabilities Education Act was amended to strengthen accountability and academic expectations for children with disabilities across the United States. In particular, transition services were more clearly outlined and defined as a coordinated set of activities for a student, with a disability, that (a) is designed within an outcome oriented process that promotes movement from school to postschool activities, including postsecondary education, vocational training, integrated employment (including supported employment), continuing and adult education, adult services, independent living, or community participation; (b) is based on the student’s needs, taking into account the student’s preferences and interests; (c) includes instruction, related services, community experiences, the development of employment and other post-school objectives, and, when appropriate, acquisition of daily living skills and functional vocational evaluation (Section 602). IDEA ’97 states: (vii)(I) beginning at age 14, and updated annually, a statement of the transition service needs of the
Individualized Transition Plan (ITP) child. . .that focuses on the child’s courses of study (such as participation in advanced-placement courses of a vocational education program)’ (ii) beginning at age 16 (or younger, if determined appropriate by the EP Team), a statement of needed transition services for the child, including when appropriate, a statement of the interagency responsibilities or any needed linkages; (III) beginning at least one year before the child reaches the age of majority under State law, a statement that the child has been informed of his or her rights under this title that will transfer to the child. . .on reaching the age of majority under 615(m) Section 614(d).
This legislature advised that transition services be addressed in the IEP at age 14; therefore, at age 14, the IEP becomes an Individualized Transition Plan or ITP.
Current Knowledge In preparation for the development of this ITP, transitional assessment must already be completed. This was a long range plan for what students will do in their remaining years in school as well as what they will do when they exit school. This plan identifies the supports they will receive in school as well as other nonschool supports. In this statement, interagency supports can be identified as well as activities to address transitional needs within home and community environments. IDEA 2004 changed the age of transitional planning from age 14 to age 16. This is an interesting change considering that in 1990, the U.S. House of Representatives Committee who reported on the authorization of 1990 stated: age 16 may be too late for many students, particularly those at risk for dropping out of school and those with the most severe disabilities. Even for those students who stay in school until age 18, many will need more than two years of transitional services. Students with disabilities are dropping out of school before age 16, feeling that the educational system has little to offer them. Initiating services at a younger age will be critical.
2445 (U.S. House of Representatives Report No. 101-554, 10, 1990) States however, may decide to continue mandating transitional services at age 14.
This model is fairly straightforward, including three major components (Storms et al. 2000). First, every student and his or her family should be advised to (1) think about post-high-school goals and (2) develop a plan including supports and services needed to accomplish these goals. (3) The linkages to post-high-school services, supports, and programs need to be identified and made before the student exits high school. The provisions on transition (IDEA 2004) indicate that transitional services should be based on students’ strengths, needs, interests, and preferences. The addition of “strengths” indicates that transition assessment summary should not just identify areas of need (deficits) but should also provide a description of student’s knowledge and skills relative to functioning in post-school environments as well as the planning that has been accomplished.
See Also ▶ Individualized Plan for Employment (IPE) ▶ Transition Planning
References and Reading Council for Exceptional Children (CEC). CEC provides a summary of the law, their recommendations, and a link to the text of the law. http://www.cec.sped.org/am/ template.cfm?section¼Home Storms, J., O’Leary, E., & Williams, J. (2000). Transition requirements: A guide for states, districts, schools, universities and families. Retrieved 7 Aug 2012 from http://www.cde.state.co.us/cdesped/download/pdf/ TK_TransBasics.pdf Wehman, P. (2006). Life beyond the classroom (4th ed.). Baltimore: Brookes Publishing. Wrightslaw. (2004). IDEA 2004. Wrightslaw provides information on changes in the law, as well as some brief explanatory comments. http://www.wrightslaw. com/law/idea/
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Individuals with Disabilities Education Act (IDEA) Jacqueline Kelleher Education, Sacred Heart University Isabelle Farrington School of Education, Southern Connecticut State University, Fairfield, CT, USA
Definition The Individuals with Disabilities Education Act (IDEA) is an amended version of a landmark federal law passed in 1975 called the Education for All Handicapped Children Act or Public Law 94–142. The IDEA, which has been in place since 1990 with key amendments and revisions occurring as part of reauthorization proceedings in 1997 and 2004, ensures that children and youth with disabilities are afforded basic rights such as a free appropriate public education (FAPE) in the least restrictive environment (LRE), regardless of the nature or severity of the disability. There are four parts to the IDEA that pertain to the following: general provisions under the law (Part A), eligible children ages 3–21 (Part B) and ages 0–2 (Part C), and technical assistance available to state and local education agencies (Part D). In addition to FAPE in the LRE, the IDEA requirements are in place to ensure each state and locality makes provisions regarding identification, due process, individualized education programs (IEPs) or individualized family services plans (IFSPs), nondiscriminatory evaluations, confidentiality, personnel development, and other basic rights established for eligible children and youth with disabilities. The IDEA is known as funding legislation. Part B of the IDEA provides funds to state educational agencies (SEAs) and local educational agencies (LEAs) to help them ensure that children with disabilities, including children aged 3 through 5, have access to a free appropriate public education to meet each child’s unique needs and prepare him or her for further education, employment, and independent living. Part C of the IDEA provides funds to each state-led agency
Individuals with Disabilities Education Act (IDEA)
designated by the governor to implement statewide systems of coordinated, comprehensive, multidisciplinary interagency programs and make early intervention services available to infants and toddlers with disabilities and their families. While all states participate in IDEA mandates and requirements, the law applies only to those agencies receiving federal funds; for example, an independent or private school not accepting federal dollars may be exempt from IDEA requirements and regulations; however, this does not exempt them from civil rights legislation in place for individuals with disabilities such as the Americans with Disabilities Act (ADA) or Section 504 of the Rehabilitation Act.
Historical Background As one effort to address years of widespread discrimination against individuals with disabilities with respect to access to quality education and educational services, the United States Congress passed Public Law 94–142 in 1975, the Education for All Handicapped Children Act, mandating school services for children with disabilities. Over time, the law has been amended and revised into its current iteration, the Individuals with Disabilities Education Act or IDEA, renamed in 1990, to reflect its mandates focused on the educational needs of children and youth with disabilities ages birth through the 22nd birthday. Since the renaming of PL 94–142, the IDEA has added protections and regulations with each reauthorization such as the development and review at least annually of an individualized education program (IEP) for eligible children ages 3–21 or an individualized family services plan (IFSP) for infants and toddlers. Families have been specifically named and included in the IDEA as full participants in the decision-making process with legal protections written into the law to ensure parent and student rights are not violated. As part of its most recent modifications in 2004, the IDEA now includes regulations for special services including transition services and protections addressing disciplinary issues for eligible children under the Act.
Individuals with Disabilities Education Act (IDEA)
Current Knowledge The Individuals with Disabilities Education Act (IDEA) is the primary federal program that authorizes state and local aid for special education and related services for eligible children with disabilities. According to IDEA, the term “children with disabilities” means those children evaluated in accordance with Regs. Secs. 300.530-300.534 as having mental retardation, hearing impairments including deafness, speech or language impairments, visual impairments including blindness, serious emotional disturbance, orthopedic impairments, autism, traumatic brain injury, other health impairments, specific learning disabilities, deaf blindness, or multiple disabilities and who because of those impairments need special education and related services. On December 3, 2004, President Bush signed the Individuals with Disabilities Education Improvement Act (P.L. 108–446), a major reauthorization and revision of IDEA. The IDEA 2004 and its federal statutes and regulations currently serve millions of eligible children and youth with disabilities under its provisions. With a commitment to ensuring the provision of a free appropriate public education for those found eligible for a special education and related services, past and current issues for states and citizens concern funding. The most significant funding stream to state education agencies (SEAs), who in turn disseminate funds to local education agencies (LEAs), is that authorized by Part B, with a majority of funds allocated toward Part B initiatives. Currently, Congress has set a maximum target for the federal contribution to special education spending equal to 40% of the estimated excess cost of educating children with disabilities. Thus, if the program were “fully funded,” the states would receive their maximum grants, calculated at 40% of the national average per pupil expenditure times the number of children with disabilities served in the school year 2004–2005, adjusted for population changes. Under the act, the count of children with disabilities cannot exceed 12% of the state’s total school population. Current knowledge regarding the funding formula and resource allocations to each SEA is that there is a shortfall in IDEA funding, with costs not reimbursed or absorbed by the federal
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government assumed by the states and local school districts. The American Recovery and Reinvestment Act of 2009 (ARRA) appropriated new funding for programs under Parts B and C of the Individuals with Disabilities Education Act (IDEA). The IDEA 2004, which is currently up for reauthorization as of this writing, is monitored and enforced at the federal level by the Office of Special Education Programs (OSEP).
Future Directions The Individuals with Disabilities Education (IDEA) Act of 2004 is under review for reauthorization.
I See Also ▶ Free Appropriate Public Education ▶ Least Restrictive Environment (LRE) ▶ State Educational Agency ▶ Vocational Rehabilitation Act of 1973
References and Reading Connecticut State Department of Education. (2005). Guidelines for identification and education of children and youth with autism spectrum disorders. Retrieved December 30, 2010, from http://www.sde.ct.gov/sde/ lib/sde/PDF/DEPS/Special/Guidelines_Autism.pdf Connecticut State Department of Education. (2006). Steps to protect a child’s right to special education: Procedural safeguards. Retrieved January 15, 2011, from http://www.sde.ct.gov/sde/lib/sde/pdf/DEPS/Special/ Prosaf_fullversion.pdf Connecticut State Department of Education. (2007). A parent’s guide to special education. Retrieved December 15, 2010, from http://www.sde.ct.gov/sde/ lib/sde/PDF/DEPS/Special/Parents_Guide_SE.pdf Federal Education Project. (2010). Individuals with disabilities act funding distribution. Retrieved January 30, 2011, from http://febp.newamerica.net/ background-analysis/individuals-disabilitieseducation-act-funding-distribution http://idea.ed.gov/explore/view/p/%2Croot%2Cdynamic %2CTopicalArea%2C1%2C IDEA Reauthorized: 2004 Amendments to the Individuals with Disabilities Education Act and implementing regulations. (2006). LexisNexis: Matthew Bender & Company.
2448 Smith, D., & McGinnley, K. (2004). Side by side comparison of Senate Bill 1248 (as passed on May 13, 2004) and House Bill 1340 (as passed April 30, 2003) and Parts A and B of IDEA, 1997. Retrieved from http:// www.wrightslaw.com/law/idea/sidebyside.06.04.pdf U.S. Department of Education, Office of Special Education Programs 10.04.06 (2006). Building the Legacy: IDEA 2004. Volkmar, F. R., Paul, R., Klin, A., & Cohen, D. (2005). The handbook of autism and pervasive developmental disorders (3rd ed.). Hoboken: Wiley. Volkmar, F. R., & Wiesner, L. A. (2009). A practical guide to autism: What every parent, family member, and teacher needs to know. Hoboken: Wiley. Wright, P., & Wright, P. (2007). Wrightslaw: Special education law (2nd ed.). Hartfield: Harbor House Law Press.
Other Areas Americans with Disabilities Act: ADA. http://www.ada. gov/ Section 504 of the Rehabilitation Act of 1973. http:// www2.ed.gov/policy/rights/reg/ocr/edlite-34cfr104. html Section 504/IDEA 2004. www.504idea.org
Infant Toddler Checklist ▶ Infant/Toddler Checklist
Infant/Toddler Checklist Tony Charman Centre for Research in Autism and Education, Department of Psychology and Human Development, Institute of Education, University of London, London, UK
Infant Toddler Checklist
social communication development. It has been normed on over 2000 infants and toddlers and can be used with children between 6 and 24 months of age.
Historical Background Wetherby and colleagues (Wetherby et al. 2008; Wetherby et al. 2004) developed an early screening tool that can be used from 6 to 24 months of age – the Infant Toddler Checklist (ITC). The ITC is a broader developmental screen that successfully identifies children with developmental, in particular language and communication, delays as well as children with an autism spectrum disorder (ASD). The ITC is one component of the suite of early social communication assessments, known as the Communication and Symbolic Behavior Scales-Developmental Profile (CSBSDP: Wetherby and Prizant 2002). The ITC can be downloaded for free at: http://firstwords.fsu. edu/toddlerChecklist.html and is currently available in English, Spanish, Slovenian, Chinese, and German. The ITC consists of 24 questions about early social communication behavior (e.g., “Does your child let you know when he/she needs help or wants an object out of reach?”; “When you call your child’s name, does he/she respond by looking or turning toward you?”). Unusually for early ASD screening instruments the ITC has norms developed from a reference sample of over 2000 infants and toddlers (Wetherby and Prizant 2002).
Psychometric Data Synonyms Communication and symbolic behavior scales – developmental profile; CSBS-DP; Infant toddler checklist; ITC
Description The Infant Toddler Checklist (ITC) is a one page developmental screener that asks about early
In an initial study Wetherby et al. (2004) screened 3021 children from a population sample between 6 and 24 months of age. Low scorers ( 100, respectively, suggesting the importance to include the type of family as a variable with an interaction effect (Davis et al. 2013. In contrast, a recent study by Webb et al. (2007) found no association between head circumference and signs of language or social regression in children with autism despite using the same definition from the Autism Diagnostic Interview-Revised. There are less studies between the association of macrocephaly in children with ASD and psychiatric comorbidity. Miles et al. (2005) reported that families of probands with autism and macrocephaly had a significantly lower likelihood of having a family history of ADHD compared with the normocephalic group. In the same lines, Aagaard et al. (2018) through a register-based cohort study of all Danish singletons found that macrocephaly was associated with a decreased risk of ADHD (HR 0.90, 95% CI 0.82, 0.99), but neither microcephaly nor macrocephaly was associated with ASD (HR 1.06; 95% CI 0.94, 1.19, and 1.03; 95% CI 0.90, 1.19). In a clinical setting, Albores-Gallo et al. (2017) demonstrated that 20% of Mexican children with autism had macrocephaly. The frequency of psychiatric comorbidity was very similar in the groups with macrocephaly (78.4%) and without macrocephaly (79%). The most associated comorbidity in the group with autism and macrocephaly was attention deficit hyperactivity disorder (ADHD) with 46%, followed by specific phobia with 16% and separation anxiety with 6.4%.
Future Directions Macrocephaly should be more studied because evidence shows that stratifying by head circumference increases the power to detect differences in genetic studies as suggested by Conciatori et al. (2004). More research is necessary with larger sample size and longitudinal studies. Since cephalic growth patterns associated with autism vary in different countries, it is important to investigate
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some prenatal environmental factors such as diet, use of vitamins and regional teratogens, maternal exposure to stress, viral or bacterial infection, and thalidomide and valproic acid use as triggers for cerebral overgrowth (Grabrucker 2013). Since macrocephaly precedes the emergence of symptoms of autism, it would be interesting to focus on risk factors postnatally like fever, viral or bacterial infections, and nutritional deficiencies, which could trigger cephalic overgrowth. The inclusion of the cephalic type as a specifier in the diagnostic classifications of DSM or ICD would be important for detecting associations in prospective and retrospective studies of autism.
References and Reading Aagaard, K., Bach, C. C., Henriksen, T. B., Larsen, R. T., & Matthiesen, N. B. (2018). Head circumference at birth and childhood developmental disorders in a nationwide cohort in Denmark. Paediatric and Perinatal Epidemiology, 32(5), 458–466. Albores-Gallo, L., Fritsche-García, L., Miranda-Aguirre, A. P., & Avila-Acosta, M. (2017). Brief report: Macrocephaly phenotype and psychiatric comorbidity in a clinical sample of Mexican children and adolescents with autism spectrum disorders. Journal of Autism and Developmental Disorders, 47(9), 2911–2917. Aylward, E. H., Minshew, N. J., Field, K., Sparks, B. F., & Singh, N. (2002). Effects of age on brain volume and head circumference in autism. Neurology, 59(2), 175–183. Barnard-Brak, L., Sulak, T., & Hatz, J. K. I. (2011). Macrocephaly in children with autism spectrum disorders. Pediatric Neurology, 44(2), 97–100. Blanken, L. M. E., Dass, A., Alvares, G., van der Ende, J., Schoemaker, N. K., El Marroun, H., et al. (2018). A prospective study of fetal head growth, autistic traits and autism spectrum disorder: Fetal head growth and autistic traits. Autism Research, 11(4), 602–612. Bray, P. F., Shields, W. D., Wolcott, G. J., & Madsen, J. A. (1969). Occipitofrontal head circumference-an accurate measure of intracranial volume. The Journal of Pediatrics, 75, 303–305. Cederlund, M., Miniscalco, C., & Gillberg, C. (2014). Preschoolchildren with autism spectrum disorders are rarely macrocephalic: A population study. Research in Developmental Disabilities, 35(5), 992–998. Conciatori, M., Stodgell, C. J., Hyman, S. L., O’Bara, M., Militerni, R., Bravaccio, C., et al. (2004). Association between the HOXA1 A218G polymorphism and increased head circumference in patients with autism. Biological Psychiatry, 55(4), 413–419.
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Macrocephaly Phenotype and Psychiatric Comorbidity in Autism Spectrum Disorder
Constantino, J. N., Majmudar, P., Bottini, A., Arvin, M., Virkud, Y., Simons, P., & Spitznagel, E. L. (2010). Infant head growth in male siblings of children with and without autism spectrum disorders. Journal of Neurodevelopmental Disorders, 2(1), 39–46. Courchesne, E., Karns, C. M., Davis, H. R., Ziccardi, R., Carper, R. A., Tigue, Z. D., et al. (2001). Unusual brain growth patterns in early life in patients with autistic disorder an MRI study. Neurology, 57(2), 245–254. Courchesne, E., Carper, R., & Akshoomoff, N. (2003). Evidence of brain overgrowth in the first year of life in autism. JAMA, 290(3), 337–344. http://jama. jamanetwork.com/article.aspx?articleid¼196951& resultclick¼1. Accessed 26 May 2016. Davidovitch, M., Golan, D., Vardi, O., Lev, D., & LermanSagie, T. (2011). Israeli children with autism spectrum disorder are not macrocephalic. Journal of Child Neurology, 26(5), 580–585. Davis, J. M., Keeney, J. G., Sikela, J. M., & Hepburn, S. (2013). Mode of genetic inheritance modifies the association of head circumference and autism-related symptoms: A cross-sectional study. PLoS One, 8(9), e74940. Dawson, G., Munson, J., Webb, S. J., Nalty, T., Abbott, R., & Toth, K. (2007). Rate of head growth decelerates and symptoms worsen in the second year of life in autism. Biological Psychiatry, 61(4), 458–464. Dementieva, Y. A., Vance, D. D., Donnelly, S. L., Elston, L. A., Wolpert, C. M., Ravan, S. A., et al. (2005). Accelerated head growth in early development of individuals with autism. Pediatric Neurology, 32(2), 102–108. Dissanayake, C., Bui, Q. M., Huggins, R., & Loesch, D. Z. (2006). Growth in stature and head circumference in high-functioning autism and Asperger disorder during the first 3 years of life. Development and Psychopathology, 18(2), 381–393. Fidler, D. J., Bailey, J. N., & Smalley, S. L. (2000). Macrocephaly in autism and other pervasive developmental disorders. Developmental Medicine & Child Neurology, 42(11), 737–740. Filipek, P. A., Richelme, C., Kennedy, D. N., Rademaker, J., Pitcher, D. A., Zidel, S., & Caviness, V. S. (1992). Morphometric analysis of the brain in developmental disorders and autism. Annals of Neurology, 32, 475. Fletcher, H. M. (1900). A case of Megalencephaly. London:Pathological Society of London, 51, 230232. Froehlich, W., Cleveland, S., Torres, A., Phillips, J., Cohen, B., Torigoe, T., et al. (2013). Head circumferences in twins with and without autism spectrum disorders. Journal of Autism and Developmental Disorders, 43(9), 2026–2037. Gooskens RH, Willemse J, Bijlsma JB, Hanlo PW (1988) Megalencephaly: definition and classification. Brain Dev, 10(1), 1–7. Grabrucker, A. M. (2013). Environmental factors in autism. Front Psychiatry, 18(3), 118. Gray, K. M., Taffe, J., Sweeney, D. J., Forster, S., & Tonge, B. J. (2012). Could head circumference be used to
screen for autism in young males with developmental delay?: Head circumference: Children with autism. Journal of Paediatrics and Child Health, 48(4), 329–334. Kanner, L. (1943). Autistic disturbances of affective contact. The Nervous Child, 2, 217–250. Korevaar, T. I. M., Muetzel, R., Medici, M., Chaker, L., Jaddoe, V. W. V., de Rijke, Y. B., et al. (2016). Association of maternal thyroid function during early pregnancy with offspring IQ and brain morphology in childhood: A population-based prospective cohort study. The Lancet. Diabetes & Endocrinology, 4(1), 35–43. Lainhart, J. E., Piven, J., Wzorek, M., Landa, R., Santangelo, S. L., Coon, H., & Folstein, S. E. (1997). Macrocephaly in children and adults with autism. Journal of the American Academy of Child & Adolescent Psychiatry, 36(2), 282–290. Lainhart, J. E., Bigler, E. D., Bocian, M., Coon, H., Dinh, E., Dawson, G., et al. (2006). Head circumference and height in autism: A study by the collaborative program of excellence in autism. American Journal of Medical Genetics. Part A, 140(21), 2257–2274. Libero, L. E., Nordahl, C. W., Li, D. D., Ferrer, E., Rogers, S. J., & Amaral, D. G. (2016). Persistence of megalencephaly in a subgroup of young boys with autism spectrum disorder: Megalencephaly in boys with autism. Autism Research, 9(11), 1169–1182. Miles, J. H., Takahashi, T. N., Bagby, S., Sahota, P. K., Vaslow, D. F., Wang, C. H., et al. (2005). Essential versus complex autism: Definition of fundamental prognostic subtypes. American Journal of Medical Genetics, 135A, 171–180. Piven, J., Arndt, S., Bailey, J., Havercamp, S., Andreasen, N. C., & Palmer, P. (1995). An MRI study of brain size in autism. The American Journal of Psychiatry, 152(8), 1145–1149. Sacco, R., Militerni, R., Frolli, A., Bravaccio, C., Gritti, A., Elia, M., et al. (2007). Clinical, morphological, and biochemical correlates of head circumference in autism. Biological Psychiatry, 62(9), 1038–1047. Sacco, R., Curatolo, P., Manzi, B., Militerni, R., Bravaccio, C., Frolli, A., et al. (2010). Principal pathogenetic components and biological endophenotypes in autism spectrum disorders. Autism Research: Official Journal of the International Society for Autism Research, 3(5), 237–252. Sacco, R., Gabriele, S., & Persico, A. M. (2015). Head circumference and brain size in autism spectrum disorder: A systematic review and meta-analysis. Psychiatry Research: Neuroimaging, 234(2), 239–251. Stadelmaier, R., Nasri, H., Deutsch, C. K., Bauman, M., Hunt, A., Stodgell, C. J., et al. (2017). Exposure to sodium valproate during pregnancy: Facial features and signs of autism: Dysmorphic features associated with valproate. Birth Defects Research, 109(14), 1134–1143. Tan, A. P., Mankad, K., Gonçalves, F. G., Talenti, G., & Alexia, E. (2018). Macrocephaly: Solving the
Magnetic Resonance Imaging diagnostic dilemma. Topics in Magnetic Resonance Imaging, 27(4), 197–217. Vinkhuyzen, A. a. E., Eyles, D. W., Burne, T. H. J., Blanken, L. M. E., Kruithof, C. J., Verhulst, F., et al. (2018). Gestational vitamin D deficiency and autismrelated traits: The generation R study. Molecular Psychiatry, 23(2), 240–246. Webb, S. J., Nalty, T., Munson, J., Brock, C., Abbott, R., & Dawson, G. (2007). Rate of head circumference growth as a function of autism diagnosis and history of autistic regression. Journal of Child Neurology, 22(10), 1182–1190. Whitehouse, A. J. O., Holt, B. J., Serralha, M., Holt, P. G., Hart, P. H., & Kusel, M. M. H. (2013). Maternal vitamin D levels and the autism phenotype among offspring. Journal of Autism and Developmental Disorders, 43(7), 1495–1504.
Macrographia Giacomo Vivanti A.J. Drexel Autism Institute, Drexel University, Philadelphia, PA, USA
Definition Macrographia is abnormally large handwriting. It might be seen in patients with cerebellar disease (Haymaker 1956) and basal ganglia dysfunctions such as Huntington disease (Phillips et al. 1994). It has also been observed in a sample of patients with aphasia (Fradis and Leischner 1985). Macrographia has been documented in both children and adults with autism spectrum disorder (Beversdorf et al. 2001; Johnson et al. 2013). In the Johnson et al. study, macrographia was associated with poor manual dexterity. Overall, these findings might reflect abnormalities in the prefrontal/ basal ganglia/cerebellar motor network in the ASD population. However, more empirical research is needed to investigate the nature of handwriting abnormalities in autism.
See Also ▶ Micrographia
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References and Reading Beversdorf, D. Q., Anderson, J. M., Manning, S. E., Anderson, S. L., Nordgren, R. E., Felopulos, G. J., et al. (2001). Brief report: Macrographia in highfunctioning adults with autism spectrum disorder. Journal of Autism and Developmental Disorders, 31(1), 97–101. Fradis, A., & Leischner, A. (1985). Die Zahlenagraphie. European Archives of Psychiatry and Neurological Sciences, 235, 82–91. Haymaker, W. (1956). Bing’s local diagnosis in neurological diseases (pp. 232–234). St. Louis: C.V. Mosby. Johnson, B. P., Phillips, J. G., Papadopoulos, N., Fielding, J., Tonge, B., & Rinehart, N. J. (2013). Understanding macrographia in children with autism spectrum disorders. Research in Developmental Disabilities, 34(9), 2917–2926. Phillips, J. G., Bradshaw, J. L., Chiu, E., & Bradshaw, J. A. (1994). Characteristics of handwriting of patients with Huntington’s disease. Movement Disorders, 9, 521–530.
Magnetic Resonance Imaging Natasa Mateljevic1 and Roger J. Jou2 1 Yale University, New Haven, CT, USA 2 Child Study Center, Yale University School of Medicine, New Haven, CT, USA
Definition Magnetic resonance imaging (MRI) is a noninvasive technique by which detailed images of internal anatomy are constructed by measuring the response of atomic nuclei to strong magnetic fields and innocuous radio waves. The hydrogen proton (1H) is the atomic nuclei of choice due to its abundance in water molecules which makes up most of the human body’s mass. During the procedure, protons emit unique signals which depend on their distinct chemical environment. These signals are then used to construct a series of twodimensional images, which taken together, can provide valuable information of soft tissue (i.e., organ, muscle, etc.) anatomy in three dimensions. MRI uses no ionizing radiation and is well known for being a painless and harmless procedure. This
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is evidenced by its widespread use in the clinical and research arenas in populations spanning the entire age range from infants to the elderly. In clinical practice, MRI is best known as a diagnostic tool used to differentiate diseased from normal tissue. However, due to its tremendous versatility and ability to provide accurate, detailed information, MRI has widespread applications in contemporary clinical and research practice.
Historical Background Nuclear magnetic resonance (NMR) describes the physical phenomenon on which MRI technology is based. The phenomenon of NMR was actually discovered in the 1940s and set the stage for the development of MRI for medical diagnostic use in the 1970s. NMR signals are created by certain atomic nuclei when excited by radio frequency energy in the presence of a strong magnetic field. In 1946, Edward Purcell and Felix Bloch independently developed methods for determining precise nuclear magnetic measurements. This was a monumental achievement, and both were recognized with awards for the Nobel Prize in physics (1952). Spurred by the development of computed tomography in the early 1970s and coupled with advances in the development of new image reconstruction algorithms, chemist Paul Lauterbur and physicist Sir Peter Mansfield created different methods to translate information regarding magnetic spin into cross-sectional images. This seminal work began a rapid progression of continuously improving imaging techniques, and both were jointly awarded the Nobel Prize in physiology or medicine (2003). Many further refinements at various stages have resulted in the acquisition of higher resolution depictions through the improved extraction of signal from tissue over background noise and improvements in the generation of tissue contrast.
Current Knowledge In this section, a brief review is provided on the physics of MRI as there are now numerous texts
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covering this topic in detail (please see References). MRI exploits the magnetic properties of the atomic constituents of biological matter to construct a visual representation of tissue. Specifically in medical MRI, the signal from the nuclei of hydrogen atoms (1H) is used for image generation since hydrogen constitutes two-thirds of all atoms in the human body. The proton of the hydrogen atom is positively charged and possesses spin, an intrinsic property of all elementary particles. In essence, the proton rotates about its axis and is analogous to a spinning top. The proton, which is found in the nucleus of the hydrogen atom, has considerable mass relative to the electron which orbits around the nucleus. As a rotating mass, the proton possesses angular momentum that strives to retain the spatial orientation of its rotational axis. Since the proton is both charged and in constant motion, it additionally has a magnetic moment and can be imagined to behave as a small magnet. What is basically observed in MRI is the motion of the proton’s magnetic axis, which is capable of generating a signal in the MRI scanner’s receiver coil. When an external force acts on the hydrogen proton and tries to alter the orientation of its rotation axis, the proton begins to wobble or precess around its axis of rotation. At the same time, friction caused by the interaction between the external force and the proton withdraws energy from the proton and slows down its rotation. As a result, the proton’s axis of rotation becomes progressively more inclined and if imagined as a spinning top, finally falls over. More specifically, when hydrogen nuclei are exposed to an external magnetic field, the magnetic moments (or spins) align with the direction of the field and simultaneously undergo precession. The precession of the nuclei occurs at a characteristic speed that is proportional to the strength of the applied magnetic field and is called the Larmor frequency. The Larmor frequency is the rate at which spins wobble when placed in a magnetic field. The spin system eventually relaxes and settles into a stable state, but the longitudinal magnetization (Mz) builds up in the z-direction because the magnetic vectors representing the individual magnetic moments accumulate. The spins tend to
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align parallel and antiparallel to the magnetic field, with parallel alignment being slightly more preferred because it is energetically more favorable. Hence, a slightly larger fraction of spins aligns parallel to the main magnetic field. This small difference produces the measurable net magnetization Mz and is represented by the net magnetization vector. This energy difference between the two spin orientations depends on the strength of the external magnetic field with Mz increasing as field strength increases. Furthermore, energy can be introduced into this spin system by applying an electromagnetic wave of the same frequency as the Larmor frequency, a condition known as the resonance state. This wave is generated in a radio transmitter and applied to the object to be imaged using an antenna coil. The result is that the longitudinal magnetization becomes more and more tipped away from the z-axis toward the transverse xy-plane, which is perpendicular to the direction of the main magnetic field. All of the longitudinal magnetization is rotated into the transverse plane by a radio frequency pulse that is both strong enough and applied long enough to tip the magnetization by exactly 90°. This results in Mxy magnetization, which as its name suggests, lies in the xy-plane. Transverse magnetization rotates about the z-axis, which has the effect of an electrical generator and induces an alternating voltage of the same frequency as the Larmor frequency in the MRI scanner’s receiver coil. This is called of the magnetic resonance signal which is collected and processed with sensitive receivers and computers to generate the magnetic resonance image. The magnetic resonance signal rapidly fades due to two independent processes that reduce transverse magnetization and thus return the spin system to the stable state present prior to excitation. These two processes are spin-lattice interaction which causes T1 relaxation and spin-spin interaction which causes T2 relaxation. T1 is defined as the time required for 63% of the original longitudinal magnetization to be recovered. T2 is defined as the time required for transverse magnetization to decrease to 37% of the original value. T1 typically ranges from 200 to 2,000 ms and T2 commonly ranges from 30 to 500 ms. T2
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denotes the process of energy transfer between spins, while T1 refers to the effects of additional field inhomogenities contributing to spins loosing coherence. A key strategy for how differences in T1 and T2 are exploited to generate tissue contrast involves strategic variation of timing and orientation of repetitive radio frequency pulse delivery. MRI’s ability to localize signals in the threedimensional space of the brain is accomplished by using magnetic gradients which are magnetic fields which change gradually along an axis. Encoding of a three-dimensional volume begins by first dividing the tissue mass into slices. Then two distinct magnetic gradients orthogonal to each other are applied, effectively dividing each slice into rows and columns of pixels. By doing this, each pixel possesses unique precessional frequency and direction. A special mathematical operation, called Fourier transform, converts pixel data back into three-dimensional voxels, which are then assembled to form an image volume reconstruction of the original threedimensional tissue mass. Optimal spatial resolution currently approximates 1 mm3 or less. Proton densities and differential T1 and T2 relaxation effects are properties intrinsic to brain tissues, and subsequently, their measurement forms the basis for the provision of differential tissue image contrast in MRI. Different tissue parameters can be differentially weighted, thus, yielding a variety of image types, each providing a unique range of diagnostic information. Different image modalities are usually identified by numeric values of key pulse sequence parameters set during acquisition. Use of shorter repletion time between pulse sequences during image acquisition interrogates tissues at a moment when their intrinsic T1 differences can be exploited to yield different signal intensity production and hence tissue contrast generation. The resulting image, whose contrast is based on tissue differences in T1, is termed T1-weighted. A repetition time less than 500 ms is considered short; a repetition time greater than 1,500 ms is considered long. Use of repetitive 90° pulses to detect T1-based tissue contrast is called a saturation recovery sequence. Alternatively, inversion recovery involves a sequence that begins with a
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180° pulse followed by a 90° pulse. The 180° pulse reverses longitudinal magnetization, and all protons responsible for the net magnetic moment are inverted 180° in the applied longitudinal magnetic field. The signal that is received depends on the time between the 180- and 90° interrogation pulses, which constitutes the repetition time for this particular pulse sequence. On the other hand, a primary strategy for generating T2-weighted images involves use of a pulse sequence termed spin echo. In spin echo, an initial 90° pulse generates transverse magnetization vector component. When 90° pulse is terminated, dephasing follows under the influence of T2. When 180° pulse is administered, the direction of dephasing protons is reversed. More slowly, precessing protons are now ahead of faster ones. Eventually, protons with faster precession frequencies catch up, culminating in proton precession rephrasing and thus detectable signal generation. This process is repeated, and protons again dephase, are again refocused by another 180° pulse, and so on. Therefore, it is possible to obtain more than one signal by repeating the spinecho sequence. In clinical practice, substances with short T1 producing high signals on T1-weighted imaging include fat, methemoglobin in subacute hemorrhage, and paramagnetic contrast agents. Substances with long T1 producing low signals on T1-weighted imaging include cerebral spinal fluid and tissues in which any process that increases local water has occurred (i.e., inflammation). T1 images are best for visualizing normal anatomy. Tissues with longer T2 generate higher signals on T2-weighted images. Because of the greater fluid content of a tissue, the longer the tissue’s T2, the brighter that tissue’s appearance on a T2-weighted image. Thus, T2-weighted images highlight fluidcontaining regions, such as sulci and ventricular system. Also in a healthy brain, T2 constitutes the entirety of high intensity signal. Substances with short T2 produce low signals on T2-weighted images. Examples include iron-containing substances. Also, most brain lesions involve associated change in water content, such as cellular injury (infarction) and extracellular inflammatory lesions (mass lesions) which produce vasogenic
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edema. Because a final common pathway of many brain lesions involves increases in water content of brain, T2-weighted images, which highlight tissue with higher water content, demonstrate brain pathology as higher signal intensities. T2-weighted images are useful for evaluating sulcal widening in cortical atrophic syndromes and for evaluating hydrocephalus.
Future Directions In the decades to come, advances in MRI technology include a widespread transition to higher magnetic field imaging, enhanced MRI coil sophistication incorporating even more channels, ultrashort echo-time imaging, tighter integration of multiple imaging modalities, and advances in molecular MRI agents. Currently, the typically used clinical imaging scanners operate at 1.5 T. While systems operating at even higher field strengths have become more prevalent, they are found mainly at major medical centers. Current clinical interest focuses on transitioning to 3 T scanners; however, it is already evident that much higher field strengths will eventually be used to examine patients (up to 7 T, which so far have only been used in research studies). Available data suggests that magnetic field strengths above 2 T involve no increased risk for patients, but the strongest argument for increasing field strength is the expectation that this will significantly boost signal-to-noise ratio. Improved signal-to-noise ratio leads to increased spatial resolution and/or reduced imaging time. An improved spatial resolution permits better anatomical evaluation. Shorter scan time leads to better tolerability and more cost-effective operation (more patients can be examined per unit time). Finally, imaging at 3 T or higher field strengths has the potential to improve more sophisticated applications of MRI such as functional imaging and carbon-13 or hydrogen-1 spectroscopy. For example, it is known that the chemical shift increases in proportion to magnetic field strength and the larger chemical shift is advantageous in spectroscopy because the spectral lines spread farther apart. Thus, this improves
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spectral resolution and discrimination of the peaks of fat and water, which in turn enables better calibration of the radio frequency pulse for fat suppression, which can significantly diminish the peaks of interest. Also, increasingly higher numbers of radio frequency channels will become standard, having a significant impact in neurological examinations. Commercial products for simultaneous acquisitions of both positron emission tomography and MRI will become commonplace in many centers where their use will be justified by improved throughput, improved patient compliance, and improved image and diagnostic accuracy. Of key importance in combined modalities, however, is the validation of image quality acquired by one modality in the presence of the other. For example, the verification that positron emission tomography image quality is not impaired by placing the detector system into the magnetic field and that MRI quality is not degraded by the presence of the positron emission tomography detectors. In addition to multimodal imaging, the future will also bring a wider application of current methods into other body areas (such as the use of diffusion tensor imaging in non-neurological examinations). Finally, the development of targeted contrast media is a rapidly expanding area in MRI research and is already being frequently used in animal systems. These contrast agents show areas of abnormality in terms of passive accumulation of agent within the tissue, for example, a normal brain will exclude contrast from the parenchyma, but damaged tissue allows passive diffusion of contrast agent through the compromised bloodbrain barrier, leading to image enhancement. The future will also bring very highly sensitivity agents that either are targeted to specific disease processes or provide specific information on regional metabolism.
See Also ▶ Diffusion Tensor Magnetic Resonance Imaging ▶ Functional MRI ▶ Magnetic Resonance Spectroscopy
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References and Reading Blamire, A. D. (2008). The technology of MRI – the next 10 years? British Journal of Radiology, 81, 601–617. Goldstein, M. A., Price, B. H., Dougherty, D. D., Rauch, S. L., & Resenbaum, J. F. (2004). Essentials of neuroimaging for clinical practice. Arlington: American Psychiatric. Gore, J. (2003). Out of the shadows – MRI and the Nobel prize. The New England Journal of Medicine, 349, 2290–2292. Renshaw, P. F., & Parow, A. M. (2002). Psychiatric disease in magnetic resonance imaging of the brain and spine. Philadelphia: Lippincott, Williams & Wilkins. Schlid, H. H. (1999). MRI made easy (5th ed.). Berlin: Schering AG/Berlex Laboratories. Weishaupt, D., Kochli, V. D., & Marincek, B. (2006). How does MRI work? An introduction to the physics and function of magnetic resonance imaging (2nd ed.). Heidelberg: Springer. Westbrook, C., Roth, C. K., & Talbot, J. (2005). MRI in practice (3rd ed.). Oxford, UK: Blackwell.
Magnetic Resonance Spectroscopy Frederick Shic School of Medicine, Yale Child Study Center, Yale University, School of Medicine, New Haven, CT, USA
Definition Magnetic resonance spectroscopy (MRS) is a technique similar to magnetic resonance imaging (MRI) that can extract chemical information about biological tissues. MRS is also known as nuclear magnetic resonance (NMR) spectroscopy, though this term tends to be avoided in in vivo work. MRS can be accomplished in most clinical MRI scanners and is similarly safe. However, rather than providing an image of the tissue as MRI would, MRS provides a “spectrum,” or chemical signature, of the area under examination. From this, metabolic and biochemical disturbances can be identified in a region-specific fashion. MRS has applications across the body; however, one of the most prominent applications of MRS is to examine biochemical disturbances in the brain.
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Historical Background For a brief history of NMR, please see ▶ “Magnetic Resonance Imaging”. Early work in the 1970s showed that MRS could be used to obtain chemical information from living animal tissue, such as blood cells or muscle tissue (de Graaf 2008). However, it was not until the early 1980s that the brain was examined under 1H MRS in living mammals and humans (Ross and Bluml 2001). These studies paved the way for neurospectroscopic applications, ranging from the evaluation of traumatic brain injuries to predictive markers of Alzheimer’s disease risk to studies of neuropsychiatric disorders such as schizophrenia and autism.
Current Knowledge Utility and Applicability of MRS As MRS can typically be conducted with the same equipment as standard MRI, MRS can be considered a very accessible and well-tolerated radiological imaging modality (for comparisons with other modalities, see Siegel and Albers 2006). As with MRI, MRS uses nonionizing radiofrequency pulses for excitation of nuclei and magnetic field gradients for spatial localization. MRS does require, however, specifically built pulse sequences for acquiring data and specialized algorithms and frameworks for analysis. These modifications and additions are widely available on most clinical MR machines. However, it is also important to note that MRS encompasses a wide range of both clinical and research endeavors. Many types of specialized sequences exist (e.g., see section “MRS of GABA in Autism,” below) as do specialized protocols (some of which look at other nuclei besides 1H that are MR-detectable, e.g., 31P, 13C). The bulk of this article will discuss applications that are most relevant to studies of autism, primarily in vivo 1H MRS of the human brain. Differences Between MRI and MRS In standard MRI, pulse sequences are chosen to emphasize specifics regarding the spatial
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distribution of protons, resulting in an image. Thus, the strongest contributors to an MRI profile are water and fats. By contrast, MRS seeks to optimize the provision of spectral information, specifically isolating metabolites and other biochemicals in an organism. Because different nuclei on a molecule may exist in distinct chemical environments, and because each of these chemical environments can result in different levels of “shielding” from the external magnetic field of the MRI machine, this results in certain molecules having “chemical signatures” that are different from other molecules (see section “1H MR Spectra in the Human Brain”). However, the concentrations of these chemicals are often very low, and thus the signal from metabolites, e.g., in the brain, is approximately 10,000 times smaller than that of water. For this reason, additional steps are typically taken both at the time of spectral acquisition and data processing for eliminating dominating signals from water or fats so that other metabolites will be visible (de Graaf 2008; Shic et al. 2010). 1H MR Spectra in the Human Brain The most common form of MRS in use today for studying in vivo brain biochemistry is 1H MRS. This form of MRS uses the same type of head coils as MRI and can typically be conducted as part of a standard clinical imaging examination. A typical MR spectrum is shown below.
MR spectra are typically displayed with frequency (chemical shift) on the x-axis, expressed in parts-per-million (PPM). PPM is the frequency
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difference between a reference chemical (typically tetramethylsilane) and a particular frequency, all divided by the frequency of the MRI machine and multiplied by a million. Since the frequency of the MRI machine scales linearly with its field strength, PPM provides a field-independent axis of chemical shifts for metabolites. The y-axis of MR spectra is the signal intensity, a value that scales with the concentration of a metabolite. A detailed discussion of the clinical utility and biochemical processes underlying MRSdetectable metabolites is beyond the scope of this article (for more details, please see Ross and Bluml (2001) and Lin et al. (2005)), as are the optimizations necessary for obtaining high-quality spectra (for practical advice and theoretical considerations, see Blüml 2011). Briefly, however, primary metabolites seen in in vivo 1H MRS of the brain include: (1) lipids, typically seen in voxels containing subcutaneous fats from the skull or in necrotic tissue; (2) lactate, which appears as a doublet at 1.33 PPM, a by-product of anaerobic glycolysis which is naturally occurring at low concentration levels in the cerebrospinal fluid but which can also be seen in hypoxia and stroke; (3) N-acetyl aspartate (NAA), a marker for healthy neurons, axons, and dendrites, which is decreased in events such as hypoxic or traumatic brain injury; (4) the glutamine and glutamate complex (Glx), which includes both glutamine and glutamate due to overlapping chemical profiles, is involved in inhibitory and excitatory neurotransmission in the human brain and is disturbed in some neuropsychiatric conditions such as schizophrenia; (5) creatine + phosphocreatine (Cr), energy markers of brain metabolism, which can be disturbed in brain trauma and hyperosmolar conditions such as hyponatremia; (6) choline (Cho), a myelin and membrane marker, elevated in certain tumors and in stroke; and (7) myoinositol, a brain osmolyte and a marker for astrocytes found to be elevated in Alzheimer’s disease and diminished in hepatic encephalopathy. Typically, the levels of metabolites are expressed as either a ratio (e.g., the peak height of NAA divided by the peak height of Cr, i.e., NAA/Cr) or as an absolute concentration level
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(typically enclosed in brackets, e.g., [NAA]). Creatine is often used as a reference signal for studies reporting ratios, given its relative homeostatic stability. However, it is important to note that creatine concentrations can be affected by some pathologies (see Ross and Bluml 2001 for details). Several methods exist for quantifying absolute concentration levels of metabolites, but the analysis is more complex and often involves assumptions regarding the spectral shape of metabolites, the creation of model solutions for use as a basis set, and the use of brain water as a reference (Christiansen et al. 1993; Kreis et al. 1993; Poullet et al. 2008). Single-Voxel and Multivoxel Spectroscopy As in MRI, there exist many different protocols for acquiring MRS from the brain. Typical 1H clinical examinations can be broken down into single-voxel techniques and multivoxel techniques. In single-voxel spectroscopy, a small area in the brain is chosen a priori, based on scout images or prior MRI in the same session as the MRS session, and 1H MR spectra acquired from those locations. In multivoxel spectroscopy (also called spectroscopic imaging or chemical shift imaging), spectra are obtained from a wide area under additional phase encoding, thus providing chemical profiles from multiple locations within a larger spatial volume. The advantages of multivoxel techniques include increased ability to detect atypical neurochemical profiles over a larger space and greater time efficiency of detection over the volume compared to comparable serial single-voxel MRS. The disadvantages of multivoxel techniques are increased complexities in data analysis, increased difficulties in controlling for magnetic field inhomogeneities (due to the larger size of the whole volume), and “voxel bleeding” (i.e., contamination of chemical profiles within one voxel with spatially adjacent voxels) (see de Graaf 2008 for details). Short- and Long-Echo Spectroscopy The appearance of 1H MR spectra is highly dependent on parameters of the pulse sequence employed. The width of peaks in MRS is partially determined by T2* time, a property of a molecule that includes both T2, the transverse relaxation
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time of a metabolite, as well as magnetic field inhomogeneity effects. Less mobile molecules, such as lipids, tend to have lower T2 times (and hence lower T2* times), effectively translating into peaks that are broader and shorter. The echo time of an MRS pulse sequence (the TE time) will refocus (i.e., help to eliminate) inhomogeneity effects, but not T2 effects, with the resultant effect that longer TEs result in decreased visibility of short T2 time metabolites. This has advantages as well as disadvantages. The disadvantages include the decreased visibility of important metabolites with shorter TEs which become harder to identify at longer TEs and the generally decreased signalto-noise (SNR) ratio associated with waiting for metabolite signals to decay. The advantages include less complex baselines, i.e., less contamination by “nuisance” molecules which alter the shape underlying the peaks of metabolites of interest. Some contributors to complex baselines include macromolecules, which have very short TEs (Behar et al. 1994). Typical short-echo times are TE ¼ 30–35 ms, whereas long-echo times range from 135 to 144 ms (lactate, which is a j-coupled resonance, inverts in this range of echo times, making it somewhat easier to detect). Other researchers recommend a midrange TE, e.g., 40–50 ms, as a compromise for relatively flat macromolecular contributions and good SNR (Hetherington et al. 2005). 1H MRS Studies of Autism Currently, MRS studies have painted a broad picture regarding neurochemical differences between ASD groups and controls, and some researchers have argued, based on low levels of specific neural markers, that autism may be characterized by disruption of neuronal integrity in a regionspecific or global pattern (Dager et al. 2008a). The earliest examinations with MRS are of children diagnosed with autism at 3–4 years of age (Friedman et al. 2003). Region-specific abnormalities in autism reported by 1H MRS studies include decreased NAA in the hippocampus-amygdala (Gabis et al. 2008; Mori et al. 2001; Otsuka et al. 1999), transverse temporal gyrus (Hisaoka et al. 2001), and medial temporal lobe (Endo et al. 2007). Several
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groups have also noted functional relationships between NAA concentrations and measures of social functioning (Endo et al. 2007; Hardan et al. 2008; Kleinhans et al. 2009; Oner et al. 2009). It is important to note, however, that several groups have found negative results in similar areas or with different subject populations (Kleinhans et al. 2009; Oner et al. 2009; PerichAlsina et al. 2002; Zeegers et al. 2007). Nonetheless, abnormal concentrations of metabolites in the temporal lobe, or relationships between temporal lobe and social functioning, are among the most reported results in 1H MRS as applied to the study of ASD. 1H MRS work by some researchers has also identified abnormalities in levels of brain metabolites other than NAA, such as Cho, mI, Cr, and Glx, in a region-specific fashion (e.g., DeVito et al. 2007; Gabis et al. 2008; Hashimoto et al. 1997; Kahne et al. 2002; Levitt et al. 2003; Mori et al. 2001; Murphy et al. 2002; Sokol et al. 2002; Vasconcelos et al. 2008), though the results are somewhat varied, possibly due to differences in subject populations, acquisition parameters, and experimental protocols. Some groups have also reported what appears to be globally depressed concentrations of all metabolites in autism, primarily as associated with brain gray matter (DeVito et al. 2007; Friedman et al. 2003, 2006; Kleinhans et al. 2007). Friedman et al. (2006), in a particularly relevant study, include as participants the youngest reported group of children with ASD: 45 children 3–4 years of age. Several of these studies, including Friedman et al., also report longer T2 times in autism. T2, also known as spin-spin relaxation time, reflects compartment differences (i.e., where the chemicals are being measured, e.g., intracellularly or extracellularly) and can be interpreted as an indicator of the mobility of the associated metabolite (Dager et al. 2008a). As noted by Dager et al. (2008b), the increased T2 times observed in children with ASD are not compatible with brain overgrowth models of autism which suggest that incomplete neuronal pruning leads to dense neuronal cell packing, as in this case T2 times would be decreased, expressing decreased cellular mobility.
Magnetic Resonance Spectroscopy
MRS of GABA in Autism Though standard 1H MRS protocols can paint a rich picture of neurochemical abnormalities in individuals with ASD, some particularly noteworthy metabolites are difficult to detect using these standard approaches. One such metabolite is GABA, the primary inhibitory neurotransmitter of the central nervous system. GABA exists in low concentrations in the brain, relative to the other metabolites outlined in the section above. Furthermore, the spectrum of GABA is overlapped by other metabolites, obscuring it from view. Recently, Harada et al. (2010) employed a specialized GABA-editing sequence in order to measure GABA levels in the frontal lobe and lenticular nuclei of adults with autism and matched controls. The authors of that study found that adults with autism showed decreased levels of GABA and GABA levels relative to glutamate in the frontal lobe but not the lenticular nuclei, suggesting a frontal lobe–specific disturbance in excitatory-inhibitory neurotransmission. Mitochondrial Dysfunction in Autism One application of MRS has been the study of brain mitochondria dysfunction in ASD. Using optimized proton echo-planar spectroscopic imaging, Corrigan et al. (2011) showed no presence of lactate in a longitudinal sample of children with ASD. Since the presence of elevated lactate levels has been suggested to be associated with mitochondrial dysfunction, the finding of no lactate in the study provided evidence against the belief of widespread mitochondrial dysfunction in ASD and, consequently, the practice of hyperbaric oxygen for the treatment of ASD. This study was followed by a healthy exchange of perspectives by researchers on the Corrigan et al. study and proponents of hyperbaric oxygen treatment (Dager et al. 2011; Rossignol and Frye 2011).
Future Directions Despite some inconsistencies in the literature to date, modern research using 1H MRS is beginning to reveal rich interactions between brain neurochemistry and cognitive, behavioral, and social
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performance in autism. Technological advances, such as more stable spectrometers, increasing field strength, and more reliable and powerful gradients, all contribute to the increased reliability of MRS investigations. Future work will likely replicate and extend prior findings with increasingly specific predictions about the relationships between region-specific neurochemical levels, behavior, and outcome. The continued application of more advanced MRS techniques, such as GABA and glutamate editing, will deepen our understanding of biochemical abnormalities in autism. As in the example of investigations of mitochondrial dysfunction in ASD, these techniques will augment the tool sets available to researchers for providing evidence against or for new and rising hypotheses about the underlying biological mechanisms of autism. However, given the currently high levels of sophistication necessary to enact some of these protocols and process the results, concerted efforts will be required to delineate the limits of these protocols and to make the techniques more easily available and accessible for translational applications. A particularly promising application area for MRS research in autism is the use of MRS in tracking the progression and response of individuals with ASD to pharmacological treatment plans. Such strategies have already proven to be useful in other neuropsychiatric disorders (Mason and Krystal 2006). Another area which has been little explored to date is the use of multinuclear MRS for examining nuclei other than proton (1H), e.g., carbon-13 and phosphorous-31. Multinuclear MRS, especially infusion studies of 13C glucose or acetate (e.g., see Bluml et al. 2002), has the potential to provide accurate descriptions of atypical brain metabolism and energetics in autism, potentially identifying points of biochemical vulnerability in a pathway- and individualspecific manner. When combined with genetic studies which may highlight the corresponding bases for these disorders, and the potential of MRS for monitoring and tracking treatment and disease progression, the role of MRS in the study of autism should only increase in the coming years.
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See Also ▶ Magnetic Resonance Imaging
References and Reading Behar, K. L., Rothman, D. L., Spencer, D. D., & Petroff, O. A. C. (1994). Analysis of macromolecule resonances in 1H NMR spectra of human brain. Magnetic Resonance in Medicine, 32(3), 294–302. https://doi. org/10.1002/mrm.1910320304. Blüml, S. (2011). Magnetic resonance spectroscopy: Physical principles. In S. H. Faro, F. B. Mohamed, M. Law, & J. T. Ulmer (Eds.), Functional neuroradiology (pp. 141–153). Boston: Springer. Retrieved February 1, 2012, from http://www.springerlink.com/content/ l812025616j28319/. Bluml, S., Moreno-Torres, A., Shic, F., Nguy, C. H., & Ross, B. D. (2002). Tricarboxylic acid cycle of glia in the in vivo human brain. NMR in Biomedicine, 15(1), 1–5. Christiansen, P., Henriksen, O., Stubgaard, M., Gideon, P., & Larsson, H. B. W. (1993). In vivo quantification of brain metabolites by 1H-MRS using water as an internal standard. Magnetic Resonance Imaging, 11(1), 107–118. Corrigan, N. M., Shaw, D. W. W., Richards, T. L., Estes, A. M., Friedman, S. D., Petropoulos, H., et al. (2011). Proton magnetic resonance spectroscopy and MRI reveal no evidence for brain mitochondrial dysfunction in children with autism spectrum disorder. Journal of Autism and Developmental Disorders, 42(1), 105–115. https://doi.org/10.1007/s10803-0111216-y. Dager, S. R., Friedman, S. D., Petropoulos, H., & Shaw, D. W. W. (2008a). Imaging evidence for pathological brain development in autism spectrum disorders (p. 361). Autism: Current Theories and Evidence. Dager, S. R., Oskin, N. M., Richards, T. L., & Posse, S. (2008b). Research applications of magnetic resonance spectroscopy (MRS) to investigate psychiatric disorders. Topics in Magnetic Resonance Imaging: TMRI, 19(2), 81. Dager, S. R., Corrigan, N. M., Estes, A., & Shaw, D. W. W. (2011). Further commentary on mitochondrial dysfunction in autism spectrum disorder: Assessment and treatment considerations. Journal of Autism and Developmental Disorders. https://doi.org/10.1007/ s10803-011-1352-4. de Graaf, R. A. (2008). In vivo NMR spectroscopy: Principles and techniques (2nd ed.). Chichester/Hoboken: Wiley-Interscience. DeVito, T. J., Drost, D. J., Neufeld, R. W. J., Rajakumar, N., Pavlosky, W., Williamson, P., et al. (2007). Evidence for cortical dysfunction in autism: a proton magnetic resonance spectroscopic imaging study. Biological Psychiatry, 61(4), 465–473.
Magnetic Resonance Spectroscopy Endo, T., Shioiri, T., Kitamura, H., Kimura, T., Endo, S., Masuzawa, N., et al. (2007). Altered chemical metabolites in the amygdala-hippocampus region contribute to autistic symptoms of autism spectrum disorders. Biological Psychiatry, 62(9), 1030–1037. Friedman, S. D., Shaw, D. W., Artru, A. A., Richards, T. L., Gardner, J., Dawson, G., et al. (2003). Regional brain chemical alterations in young children with autism spectrum disorder. Neurology, 60(1), 100–107. Retrieved October 2, 2009. Friedman, S. D., Shaw, D. W. W., Artru, A. A., Dawson, G., Petropoulos, H., & Dager, S. R. (2006). Gray and white matter brain chemistry in young children with autism. Archives of General Psychiatry, 63(7), 786. Gabis, L., Huang, W., Azizian, A., DeVincent, C., Tudorica, A., Kesner-Baruch, Y., et al. (2008). 1H-magnetic resonance spectroscopy markers of cognitive and language ability in clinical subtypes of autism spectrum disorders. Journal of Child Neurology, 23(7), 766. Harada, M., Taki, M. M., Nose, A., Kubo, H., Mori, K., Nishitani, H., et al. (2010). Non-invasive evaluation of the GABAergic/glutamatergic system in autistic patients observed by MEGA-Editing proton MR spectroscopy using a clinical 3 Tesla instrument. Journal of Autism and Developmental Disorders, 41, 447–454. https://doi.org/10.1007/s10803-010-1065-0. Hardan, A. Y., Minshew, N. J., Melhem, N. M., Srihari, S., Jo, B., Bansal, R., et al. (2008). An MRI and proton spectroscopy study of the thalamus in children with autism. Psychiatry Research: Neuroimaging, 163(2), 97–105. Hashimoto, T., Tayama, M., Miyazaki, M., Yoneda, Y., Yoshimoto, T., Harada, M., et al. (1997). Differences in brain metabolites between patients with autism and mental retardation as detected by in vivo localized proton magnetic resonance spectroscopy. Journal of Child Neurology, 12(2), 91. Hetherington, H., Petroff, O., Jackson, G. D., Kuzniecky, R. I., Briellmann, R. S., & Wellard, R. M. (2005). Magnetic resonance spectroscopy. In R. I. Kuzniecky & G. D. Jackson (Eds.), Magnetic resonance in epilepsy: Neuroimaging techniques (pp. 333–384). Burlington: Elsevier Academic. Hisaoka, S., Harada, M., Nishitani, H., & Mori, K. (2001). Regional magnetic resonance spectroscopy of the brain in autistic individuals. Neuroradiology, 43(6), 496–498. Kahne, D., Tudorica, A., Borella, A., Shapiro, L., Johnstone, F., Huang, W., et al. (2002). Behavioral and magnetic resonance spectroscopic studies in the rat hyperserotonemic model of autism. Physiology & Behavior, 75(3), 403–410. Kleinhans, N. M., Schweinsburg, B. C., Cohen, D. N., Müller, R. A., & Courchesne, E. (2007). N-acetyl aspartate in autism spectrum disorders: Regional effects and relationship to fMRI activation. Brain Research, 1162, 85–97. Kleinhans, N. M., Richards, T., Weaver, K. E., Liang, O., Dawson, G., & Aylward, E. (2009). Brief report:
Magnetoencephalography Biochemical correlates of clinical impairment in high functioning autism and Asperger’s disorder. Journal of Autism and Developmental Disorders, 39(7), 1079–1086. Kreis, R., Ernst, T., & Ross, B. D. (1993). Development of the human brain: in vivo quantification of metabolite and water content with proton magnetic resonance spectroscopy. Magnetic Resonance in Medicine, 30, 424–424. Levitt, J. G., O’Neill, J., Blanton, R. E., Smalley, S., Fadale, D., McCracken, J. T., et al. (2003). Proton magnetic resonance spectroscopic imaging of the brain in childhood autism. Biological Psychiatry, 54(12), 1355–1366. Lin, A., Ross, B. D., Harris, K., & Wong, W. (2005). Efficacy of proton magnetic resonance spectroscopy in neurological diagnosis and neurotherapeutic decision making. NeuroRx, 2(2), 197–214. Mason, G. F., & Krystal, J. H. (2006). MR spectroscopy: Its potential role for drug development for the treatment of psychiatric diseases. NMR in Biomedicine, 19(6), 690–701. Mori, K., Hashimoto, T., Harada, M., Yoneda, Y., Shimakawa, S., Fujii, E., et al. (2001). Proton magnetic resonance spectroscopy of the autistic brain. No to Hattatsu, 33(4), 329–335. Murphy, D. G. M., Critchley, H. D., Schmitz, N., McAlonan, G., Van Amelsvoort, T., Robertson, D., et al. (2002). Asperger syndrome: A proton magnetic resonance spectroscopy study of brain. Archives of General Psychiatry, 59, 885–891. Oner, O., Ozgüven, H. D., Oktem, F., Ya murlu, B., Baskak, B., Olmez, S., et al. (2009). Proton magnetic resonance spectroscopy in Asperger’s syndrome: Correlations with neuropsychological test scores. Turk psikiyatri dergisi (Turkish Journal of Psychiatry), 20(1), 22. Otsuka, H., Harada, M., Mori, K., Hisaoka, S., & Nishitani, H. (1999). Brain metabolites in the hippocampusamygdala region and cerebellum in autism: An 1H-MR spectroscopy study. Neuroradiology, 41(7), 517–519. Perich-Alsina, J., Aduna, P. M., Valls, A., & Muñoz-Yunta, J. A. (2002). Thalamic spectroscopy using magnetic resonance in autism. Revista de Neurologia, 34, S68. Poullet, J.-B., Sima, D. M., & Van Huffel, S. (2008). MRS signal quantitation: A review of time- and frequencydomain methods. Journal of Magnetic Resonance, 195(2), 134–144. https://doi.org/10.1016/j.jmr.2008. 09.005. Ross, B., & Bluml, S. (2001). Magnetic resonance spectroscopy of the human brain. The Anatomical Record, 265(2). Rossignol, D. A., & Frye, R. E. (2011). Substantial problems with measuring brain mitochondrial dysfunction in autism spectrum disorder using magnetic resonance spectroscopy. Journal of Autism and Developmental Disorders. https://doi.org/10.1007/ s10803-011-1276-z.
2795 Shic, F., Lin, A., Brown, J. B., Bluml, S., & Ross, B. D. (2010, May). An open-source platform for routine clinical 1H magnetic resonance spectroscopy processing. Presented at the 2010 Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM), E-Poster, Stockholm, Sweden. Siegel, G. J., & Albers, R. W. (2006). Basic neurochemistry: Molecular, cellular, and medical aspects. Amsterdam/Boston: Academic. Sokol, D. K., Dunn, D. W., Edwards-Brown, M., & Feinberg, J. (2002). Hydrogen proton magnetic resonance spectroscopy in autism: preliminary evidence of elevated choline/creatine ratio. Journal of Child Neurology, 17(4), 245. Vasconcelos, M. M., Brito, A. R., Domingues, R. C., da Cruz, L. C. H., Gasparetto, E. L., Werner, J., et al. (2008). Proton magnetic resonance spectroscopy in school-aged autistic children. Journal of Neuroimaging, 18(3), 288–295. Zeegers, M., Van Der Grond, J., van Daalen, E., Buitelaar, J., & van Engeland, H. (2007). Proton magnetic resonance spectroscopy in developmentally delayed young boys with or without autism. Journal of Neural Transmission, 114(2), 289–295.
Magnetoencephalography Lauren Cornew and Timothy P. L. Roberts Radiology Department, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
Definition Magnetoencephalography (MEG) is a noninvasive neuroimaging technique that detects extracranial magnetic fields produced by electrical activity in the brain, which occurs spontaneously or in response to external stimuli. The extracranial magnetic fields are sampled by an array of sensors, generally arranged in a helmet, within which the head is contained (see Fig. 1). These extracranial magnetic fields, which comprise MEG signals, reflect the synchronous postsynaptic activity of large populations of pyramidal neurons. Electrical activity associated with synaptic transmission produces magnetic fields orthogonal to the direction of the electric currents. The direction of the magnetic fields is governed by the “right-hand rule”: When the right hand is made into a fist with
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Magnetoencephalography, Fig. 1 A child sits in a whole-head biomagnetometer (MEG machine). Typically, somatosensory, visual, or auditory stimuli may be presented, and button-box responses collected synchronously with multichannel (in this case 275) recording of the magnetoencephalogram
the thumb pointing upward, the remaining fingers point in the direction of the flow of magnetic fields. Recording magnetic fields associated with brain activity relies on specialized technology because the magnetic fields in the brain are extremely weak (on the order of 10fT–1pT, approximately 10 million times smaller than the earth’s magnetic field). As such, the coils that detect the brain’s magnetic fields and the superconducting quantum interference devices (SQUIDs) to which the coils are coupled are maintained at extremely low temperatures. This is accomplished by surrounding the sensors with liquid helium and encapsulating them within a dewar. In addition, containing the MEG system within a magnetically shielded room reduces competing environmental magnetism. MEG recording takes place while patients or participants are either seated or lying supine, with the head positioned inside of the helmet that contains the MEG sensors. The size of the helmet is optimal for a typical adult head in the vast majority of MEG systems; however, MEG systems designed for infants and small children are currently being developed. Unlike MRI, MEG recording is silent. Patients or participants may be scanned during rest or during a variety of paradigms, from passive sensory processing to
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active task performance, and the signals recorded can be characterized as spontaneous, evoked, or induced. Spontaneous activity is that which is not related to the presentation of a stimulus and thus constitutes background activity. Evoked activity is strictly time-locked to a stimulus, whereas induced activity arises in response to a stimulus but not in a strict time-locked manner. In order to localize the brain activity recorded at the MEG sensors, the source/s of the activity must be modeled. In so doing, researchers and clinicians are often faced with an inverse problem, that is, the data (in this case, MEG signals recorded outside of the head at the sensors) are used to estimate the model parameters (in this case, the source/s of the MEG signals within the brain). These inverse situations are conceptualized as “problems” because there is no unique solution, and it can be challenging to identify the best possible solution. MEG source modeling is typically accomplished by combining the MEG signals with MRI images. During MEG data collection, several coils are affixed to the individual’s head (usually the nasion and left and right periauricular areas) to establish a three-dimensional coordinate system so that his or her MEG data may be coregistered with his or her structural MRI. The most widely utilized method for MEG source localization is the single equivalent current dipole (ECD) method. This method involves modeling the pattern of MEG activity at relevant sensors with the assumption that the source of the activity is a focal location in the brain (see Fig. 2). An alternative approach to the ECD method is beamforming, which uses rastered spatial filtering to identify the neuromagnetic time course of any given point in the brain. Repeating this process for many points in the brain allows researchers and clinicians to construct images in which the MEG activity of interest is overlaid on the structural MRI. MEG is related to EEG as the two techniques capitalize on the same neurophysiological processes. However, there are several key distinguishing features of MEG. First, whereas EEG signals are susceptible to distortion due to the skull and scalp, MEG signals are impervious
Magnetoencephalography
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Magnetoencephalography, Fig. 2 (a) Spatial and temporal information in MEG represented by the spatial array of sensors each recording the time-varying magnetic field; (b) at any instant, the magnetic field topography can be described; (c) source modeling is used to transition for “sensor space” to “brain, or source, space,” in this case,
two equivalent current dipole sources in bilateral superior temporal gyri accounting for auditory evoked neuromagnetic fields; (d) time courses of neuronal electrical activity form the two brain sources illustrated in (c) during auditory stimulation
to these structures, thus yielding better spatial resolution in MEG than EEG. Second, whereas EEG detects both tangential and radial currents associated with neural activity, MEG detects only tangential currents. This means that MEG is primarily sensitive to activity in sulci. Third, whereas EEG relies on a reference electrode/s, which can be problematic if the reference is active, MEG is reference-free.
epilepsy or brain tumors. In the latter, MEG findings have been demonstrated to be superior to EEG findings and consistent with (and perhaps eventually supplanting) invasive methods such as electrocorticography (ECoG).
Historical Background To date, magnetoencephalography has been utilized predominantly for clinical purposes. The most common applications are identifying abnormal patterns of neural activity, locating seizure focus in epilepsy, and preoperative assessment of eloquent cortex in patients with intractable
Current Knowledge Although MEG is widely considered the “gold standard” for noninvasive characterization of neuropathology in patients with epilepsy and brain tumors and the technique has demonstrated usefulness in the study of disorders such as Parkinson’s disease and schizophrenia, MEG has not been widely applied to the study of autism spectrum disorders (ASD). The earliest MEG studies of ASD were published in the late 1990s, and the relatively small ASD MEG literature can
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be divided into the following broadly defined categories: (a) epileptiform abnormalities, (b) mirror neuron system integrity, (c) face processing, (d) the somatosensory system, and (e) auditory/language processing. Epileptiform Abnormalities One of the earliest MEG studies of ASD (Lewine et al. 1999) investigated the potential presence of abnormal patterns of neuromagnetic activity during sleep in children with regressive ASD, motivated by the substantial clinical overlap between autism and epilepsy. Results indicated a high prevalence of multifocal epileptiform activity in these children, even in the absence of a diagnosable seizure disorder. Similar epileptiform abnormalities have been subsequently reported in children with nonregressive forms of ASD (Muñoz-Yunta et al. 2008). Importantly, this line of research has demonstrated that epileptiform activity in ASD is detectable with MEG even in cases where it goes undetected by simultaneously recorded EEG. Mirror Neuron System Integrity Another small subset of the ASD MEG literature consists of studies examining the integrity of the mirror neuron system. Discovered serendipitously by Rizzolatti and colleagues (Gallese et al. 1996), who observed that neurons in a frontal area in monkeys fired not only when the monkey performed an action itself but also when it observed the experimenter performing a similar action, mirror neurons are purportedly involved in imitation and social cognition. Impairments in the mirror neuron system have been implicated in ASD, mostly with the use of EEG and fMRI. Although an early MEG study investigating electrophysiological signatures of biological motion perception in individuals with ASD (Avikainen et al. 1999) failed to identify abnormalities in ~20 Hz oscillatory activity in primary motor cortex, more recent work has identified spatio- and spectro-temporal abnormalities. Specifically, one finding indicated that although the peak activation latencies between adults with ASD and controls did not differ early in the processing stream (occipital cortex, followed by superior temporal
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sulcus, followed by inferior parietal lobule), the ASD group showed a significantly delayed response downstream in the inferior frontal lobe (Broca’s area), coupled with reductions in the amount of activity in the inferior frontal lobe as well as primary motor cortex (Nishitani et al. 2004). Results from another study revealed that the typical increase in beta (15–25 Hz) activity that occurs after an individual observes an action (the “postmovement beta rebound”) was reduced in adults with ASD in multiple regions in the mirror neuron system, including sensorimotor cortex, premotor cortex, and the superior temporal gyrus, as well as in the medial prefrontal cortex (Honaga et al. 2010). Face Processing Despite an extensive literature reporting faceprocessing abnormalities in ASD using fMRI, only two studies to date have examined face processing in ASD using MEG. One compared the processing of faces to that of mugs and geometrical patterns in adults with ASD (Bailey et al. 2005). Results indicated that the neuromagnetic response to faces at short latencies (30–60 ms) is abnormal in ASD, and equivalent current dipole estimation revealed abnormal face-processing loci in ASD. The second study examined face processing in children with ASD, specifically with respect to processing of faces with direct versus averted gaze (Kylliainen et al. 2006). Findings demonstrated that at early latencies (up to 100 ms), neuromagnetic activity was similar in the ASD and control groups, save a lack of repetition priming in ASD. However, at longer latencies, group differences were more pronounced, with greater responses in ASD to direct versus averted gaze at 240 ms poststimulus (a pattern inconsistent with that of the control group) and weaker responses at 300 ms poststimulus in ASD compared to controls. Somatosensory System Only two studies to date have examined the somatosensory system in ASD. Examining young adults with ASD, one study (Coskun et al. 2009a) investigated the variability of evoked responses to tactile stimulation and failed to find
Magnetoencephalography
abnormalities in the ASD group. The other study examined the organization of somatosensory cortex via somatic maps and found abnormal cortical organization in young adults with ASD. Specifically, the cortical representations of the thumb and lip were further apart in the ASD group compared to the control group (Coskun et al. 2009b). Auditory/Language Processing The majority of the ASD MEG literature to date capitalizes on the ability of MEG to detect lateralized superior temporal gyrus (STG) activity in order to characterize auditory functioning in ASD, with the goal of elucidating biomarkers associated with language impairment in ASD. A number of studies have investigated auditory evoked responses to simple tones, with the hypothesis that impairments in the processing of simple auditory stimuli early in life would likely have cascading detrimental effects on language development. The evoked response most often examined is the M100, a ~100-ms response elicited in passive listening paradigms that is modulated by stimulus parameters such as frequency. Several studies have shown abnormalities in the M100 response in children with ASD. Among these abnormalities are prolonged M100 latencies (Gage et al. 2003; Gandal et al. 2010; Roberts et al. 2010), abnormal hemispheric asymmetry of the M100 (Schmidt et al. 2009), and abnormal modulation of the M100 by tones of different frequencies (Gage et al. 2003). Another feature of auditory processing in ASD that MEG studies have revealed to be abnormal is the mismatch field (MMF). The MMF occurs approximately 200 ms after an auditory stimulus and is a measure of acoustic change detection obtained by subtracting the evoked response to a frequently presented standard stimulus from the evoked response to an infrequently presented deviant stimulus. The presence of an MMF indicates passive, preattentive discrimination of the standard and deviant stimuli. MEG studies have demonstrated latency delays in the MMF in children (Oram Cardy et al. 2005; Roberts et al. 2011) and adults (Kasai et al. 2005) with ASD as well as reduced amplitude, or even absence, of the MMF in children with ASD (Tecchio et al. 2003).
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The M100 and MMF abnormalities may provide insight into the language impairments characteristic of ASD in that atypical early acoustic processing may have cascading downstream effects on the processing of language, which is inherently more complex and involves processing acoustic units presented in rapid temporal succession. Results from an MEG study of auditory processing of pairs of tones support this hypothesis; in children and adolescents with ASD, the majority of those with language impairment failed to show evoked responses to the second of two tones when the two tones were presented in rapid succession (Oram Cardy et al. 2005).
Future Directions A number of novel approaches to the study of neural abnormalities in ASD are emerging in the MEG research community. One such approach is the study of resting-state neuromagnetic activity, which has the potential to shed light on abnormal patterns of neural activity in ASD in the absence of specific sensory processing. In addition, analyses of functional connectivity, or interactions between brain regions, both in the resting state and in sensory and cognitive processing are being implemented with increasing frequency. Functional connectivity analyses have the potential to provide insight into neural abnormalities in ASD at the network level. A clear benefit of MEG in this area of research is the ability to examine network interactions at multiple frequencies and with fine-grained temporal resolution. Other exciting new applications of MEG in ASD research involve multimodal integration of MEG and complementary neuroimaging techniques, including diffusion tensor imaging (DTI) and magnetic resonance spectroscopy (MRS). Multimodal MEG and DTI studies afford the opportunity to investigate associations between electrophysiological measures (e.g., delayed auditory evoked 100-ms responses) and the structural integrity of white matter tracts. Multimodal MEG and MRS studies allow for investigation into associations between electrophysiological measures and neurotransmitter levels. Two particularly
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promising targets in ASD research are GABA and glutamate. With the growing number of applications of MEG to ASD research emerges the potential to develop multidimensional classifiers that will help to further elucidate the neuropathology of ASD.
See Also ▶ Electroencephalogram (EEG) ▶ fMRI
References and Reading Avikainen, S., Kulomaki, T., & Hari, R. (1999). Normal movement reading in Asperger subjects. Neuroreport, 10, 3467–3470. Bailey, A. J., Braeutigam, S., Jousmäki, V., & Swithenby, S. J. (2005). Abnormal activation of face processing systems at early and intermediate latency in individuals with autism spectrum disorder: A magnetoencephalographic study. European Journal of Neuroscience, 21, 2575–2585. Braeutigam, S., Swithenby, S. J., & Bailey, A. J. (2008). Contextual integration the unusual way: A magnetoencephalographic study of responses to semantic violation in individuals with autism spectrum disorders. European Journal of Neuroscience, 27, 1026–1036. Coskun, M. A., Varghese, L., Reddoch, S., Castillo, E., Pearson, D., Loveland, K., et al. (2009a). How somatic cortical maps differ in autistic and typical brains. Neuroreport, 20, 175–179. Coskun, M. A., Varghese, L., Reddoch, S., Castillo, E., Pearson, D., Loveland, K., et al. (2009b). Increased response variability in autistic brains? Neuroreport, 20, 1543–1548. Gage, N. M., Siegel, B., Callen, M., & Roberts, T. P. L. (2003a). Cortical sound processing in children with autism disorder: An MEG investigation. Neuroreport, 14, 2047–2051. Gage, N. M., Siegel, B., & Roberts, T. P. L. (2003b). Cortical auditory system maturational abnormalities in children with autism disorder: An MEG investigation. Developmental Brain Research, 144, 201–209. Gallese, V., Fadiga, L., Fogassi, L., & Rizzolatti, G. (1996). Action recognition in the premotor cortex. Brain, 119, 593–609. Gandal, M. J., Edgar, J. C., Ehrlichman, R. S., Mehta, M., Roberts, T. P. L., & Siegel, S. J. (2010). Validating γ oscillations and delayed auditory responses as translational biomarkers of Autism. Biological Psychiatry, 68, 1100–1106.
Magnetoencephalography Honaga, E., Ishii, R., Kurimoto, R., Canuet, L., Ikezawa, K., Takahashi, H., et al. (2010). Post-movement beta rebound abnormality as indicator of mirror neuron system dysfunction in autistic spectrum disorder: An MEG study. Neuroscience Letters, 478, 141–145. Kasai, K., Hashimoto, O., Kawakubo, Y., Yumoto, M., Kamio, S., Itoh, K., et al. (2005). Delayed automatic detection of change in speech sounds in adults with autism: A magnetoencephalographic study. Clinical Neurophysiology, 116, 1655–1664. Kylliainen, A., Braeutigam, S., Hietanen, J. K., Swithenby, S. J., & Bailey, A. J. (2006). Face- and gaze-sensitive neural responses in children with autism: A magnetoencephalographic study. European Journal of Neuroscience, 24, 2679–2690. Lewine, J. D., Andrews, R., Chez, M., Patil, A., Devinsky, O., Smith, M., et al. (1999). Magnetoencephalographic patterns of epileptiform activity in children with regressive autism spectrum disorders. Pediatrics, 104, 405–418. Muñoz-Yunta, J. A., Ortiz, T., Palau-Baduell, M., MartínMuñoz, L., Salvadó-Salvadó, B., Valls-Santasusana, A., et al. (2008). Magnetoencephalographic pattern of epileptiform activity in children with early-onset autism spectrum disorders. Clinical Neurophysiology, 119, 626–634. Nishitani, N., Avikainen, S., & Hari, R. (2004). Abnormal imitation-related cortical activation sequences in Asperger’s syndrome. Annals of Neurology, 55, 558–562. Oram Cardy, J. E., Flagg, E. J., Roberts, W., Brian, J., & Roberts, T. P. L. (2005a). Magnetoencephalography identifies rapid temporal processing deficit in autism and language impairment. Neuroreport, 16, 329–332. Oram Cardy, J. E., Flagg, E. J., Roberts, W., & Roberts, T. P. L. (2005b). Delayed mismatch field for speech and non-speech sounds in children with autism. Neuroreport, 16, 521–525. Roberts, T. P. L., Khan, S. Y., Rey, M., Monroe, J. F., Cannon, K., Blaskey, L., et al. (2010). MEG detection of delayed auditory evoked responses in autism spectrum disorders: Towards an imaging biomarker for autism. Autism Research, 3, 8–18. Roberts, T. P. L., Cannon, K. M., Tavabi, K., Blaskey, L., Khan, S. Y., Monroe, J. F., et al. (2011). Auditory magnetic mismatch field latency: A biomarker for language impairment in autism. Biological Psychiatry, 70(3), 263–269. Rojas, D. C., Maharajh, K., Teale, P. D., & Rogers, S. J. (2008). Reduced neural synchronization of gammaband MEG oscillations in first-degree relatives of children with autism. BMC Psychiatry, 8, 66. Schmidt, G. L., Rey, M. M., Oram Cardy, J. E., & Roberts, T. P. L. (2009). Absence of M100 source asymmetry in autism associated with language functioning. Neuroreport, 20, 1037–1041. Tecchio, F., Benassi, F., Zappasodi, F., Gialloreti, L. E., Palermo, M., Seri, S., et al. (2003). Auditory sensory processing in autism: A magnetoencephalographic study. Biological Psychiatry, 54, 647–654.
Maladaptive Behavior Wilson, T. W., Rojas, D. C., Reite, M. L., Teale, P. D., & Rogers, S. J. (2007). Children and adolescents with autism exhibit reduced MEG steady-state gamma responses. Biological Psychiatry, 62, 192–197.
Mainstreaming ▶ Integration
Maintenance of Treatment Effects Leona Oakes Department of Clinical and Social Sciences in Psychology, University of Rochester, Rochester, NY, USA
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References and Reading Arnold-Saritepe, A. M., Phillips, K. J., Mudford, O. C., De Rozario, K. A., & Taylor, S. A. (2009). Generalization and maintenance. In J. L. Matson (Ed.), Applied behavioral analysis for children with autism spectrum disorders (pp. 207–224). New York: Springer. McEachin, J. J., Smith, T., & Lovaas, O. I. (1993). Longterm outcome for children with autism who received early intensive behavioral treatment. American Journal of Mental Retardation, 97(4), 359–372. Schreibman, L. (2000). Intensive behavioral/psychoeducational treatments for autism: research needs and future directions. Journal of Autism and Developmental Disorders, 30(5), 373–378. Smith, R. G., Vollmer, T. R., & St. Peter Pipkin, C. (2007). Functional approaches to assessment and treatment of problem behavior in persons with autism and related disabilities. In P. Sturmey & A. Fitzer (Eds.), Autism spectrum disorders: Applied behavioral analysis: Evidence, and practice (pp. 187–234). Austin: Pro-Ed. Stokes, T. F., & Baer, D. M. (1977). An implicit technology of generalization. Journal of Applied Behavioral Analysis, 20(2), 349–367.
Synonyms
Major Depression Durability of treatment effects; Sustained treatment benefits; Treatment gain retention
▶ Mood Disorders
Definition
Major Depressive Disorder The continued improvement in functioning and/or use of skills gained through an intervention over time. Maintenance of behavior change may be strengthened through procedures such as the occasional use of the intervention methods originally used to promote behavior change, provision of reinforcement for displaying the newly learned behavior, and overlearning (teaching until the learner is fluent at displaying the new behavior). Maintenance of treatment effects is often addressed alongside generalization of skills to maximize the retention of treatment gains.
▶ Depressive Disorder
Maladaptive Behavior Sarah A. O. Gray Department of Psychology, University of Massachusetts Boston, Boston, MA, USA Department of Psychology, Tulane University, New Orleans, LA, USA
See Also
Synonyms
▶ Generalization and Maintenance
Problem behaviors
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Definition Maladaptive behavior is defined as behavior that interferes with an individual’s activities of daily living or ability to adjust to and participate in particular settings. Maladaptive behaviors lie along a spectrum from more minor, less impairing behaviors (i.e., nail biting, difficulty separating) to more severely impairing behaviors (i.e., self-injurious or oversexualized behaviors) that seriously interfere with individuals’ ability to maintain relationships with others, learn, and/or engage in adaptive, ageappropriate activities and settings. Because of their impairing nature, maladaptive behaviors are often the target of interventions. Problem behaviors are often a concern for children with developmental disabilities; maladaptive behaviors commonly associated with autism spectrum disorders include self-injurious behaviors (e.g., headbanging), stereotypies, aggression, and temper tantrums. Although maladaptive behavior is related to adaptive behavior, or the personal and social skills necessary for day-to-day functioning, these two domains are distinct both conceptually and functionally. Thus, maladaptive behavior is not simply defined as the absence of adaptive behavior; reciprocally, adaptive behavior is not the absence of maladaptive behavior. Maladaptive behavior is a term that is used most often in the discussion of adaptive behavior. In other contexts, maladaptive behaviors are usually considered under a broader umbrella of social-emotional and behavior problems that are measured with checklists.
See Also ▶ Adaptive Behavior Scales ▶ Maladaptive Behavior ▶ Vineland Adaptive Behavior Scales (VABS)
References and Reading Dominick, K. D., Davis, N. O., Lainhart, J., Tager-Fusberg, H., & Folstein, S. (2007). Atypical behaviors in
Male Turner children with autism and children with a history of language impairment. Research in Developmental Disabilities, 28, 145–167. Horner, R. H., Carr, E. G., Strain, P. S., Todd, A. W., & Reed, H. K. (2002). Problem behavior interventions for young children with autism: A research synthesis. Journal of Autism and Developmental Disorders, 32, 423–446. Matson, J. L., & Nebel-Schwalm, M. (2007). Assessing challenging behaviors in children with autism spectrum disorders: A review. Research in Developmental Disabilities, 28(6), 567–579.
Male Turner ▶ Noonan and Ras/MAPK Pathway Syndromes
Mand Fluency Training Corey Ray-Subramanian Waisman Center, University of Wisconsin-Madison, Madison, WI, USA
Definition Mand fluency training, or mand training, is an intervention approach designed to teach individuals with limited communication skills to express their needs and wants. A mand commonly takes the form of a request and is defined as a verbal act that is reinforced by a specified consequence (Skinner 1957).
Historical Background Mand training is based on B. F. Skinner’s (1957) operant conditioning framework for analyzing the functions of verbal behavior. This behavioral approach to language training has been used extensively with individuals with developmental delays and autism, in particular, as part of early behavioral intervention programs. For example, intervention programs developed by Lovaas (2003) and Sundberg and colleagues (Sundberg
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and Partington 1998) incorporate mand training as a key component for teaching early communication skills.
disabilities who have limited expressive language skills and who do not spontaneously make verbal requests to express their needs and wants.
Rationale or Underlying Theory
Treatment Procedures
Within an operant conditioning framework, mands are controlled by a motivative variable, often referred to as an establishing operation (Sundberg and Michael 2001). For example, hunger and the desire for a cookie may lead an individual to say “I want a cookie.” This behavior is then reinforced by the offering of a small piece of a cookie. A mand training approach can be contrasted with a traditional language perspective that emphasizes teaching the individual the meaning of different words (e.g., learning to say “cookie” when shown a cookie or “ball” when shown a ball; Sundberg and Michael 2001). It has been suggested, however, that some children with autism do not spontaneously request items even if they have demonstrated understanding of the appropriate word for an item and, thus, need to be explicitly taught this skill (Sundberg and Michael 2001). Because of the unique reinforcement provided through the establishing operation, mand training can facilitate children’s willingness to participate in language training, in general (Sundberg and Michael 2001).
Mand training can be used as part of a larger early intervention program focused on developing language, cognitive, self-help, and play skills. For example, the I Want program (Lovaas 2003), which is one component of a large package of intervention techniques for individuals with developmental delays, is designed to teach participants to verbalize their choices when presented with desired and undesired items and to develop spontaneous verbal requesting behavior. The developer of the program recommends starting with teaching the participant to request his or her favorite foods, objects, or activities to reinforce the behavior and possibly reduce problem behavior resulting from frustration with not being able to communicate desires (Lovaas). If necessary, the individual would first need to be taught how to imitate several “I want (item)” statements (e.g., I want ball, I want cookie, I want juice). The participant is then presented with the stimulus “What do you want?” and prompted to say “I want juice,” for example. The participant’s response is then reinforced by having access to a small amount of the item (e.g., a sip of juice, 5 s of playtime with the ball). As the participant reaches specified accuracy benchmarks, the prompts are faded and new items are introduced. Eventually the participant is prompted to request items that are not in sight and then progresses to being reinforced systemically for spontaneous requests that are not prompted. For individuals who are unable to echo sounds or imitate actions, mand training can be done using signs or pictures. Important issues to include when selecting initial words for mand training include:
Goals and Objectives The ultimate goal of mand training is to increase an individual’s spontaneous requesting behavior so that the individual can make his or her needs and wants known to others. Short-term objectives may include imitating mands and requesting basic items, such as food within sight. Longer-term objectives may include requesting items or activities that are not present in the immediate environment.
Treatment Participants Participants in mand training are individuals, typically young children, with developmental
(a) Choosing words that will be reinforcing to the participant and that the instructor can easily control, have access to, and that can be delivered multiple times
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(b) Selecting words the participant can imitate or for which the individual has demonstrated understanding (c) Choosing words that are relatively easy for the participant to say (d) When using sign language, selecting signs that look like the objects they represent and that the individual can already imitate (e) Choosing words for items that are relevant to the participant’s daily life (f) Selecting words for a variety of motivators (e.g., not all foods) (g) Avoiding words that sound alike or rhyme (h) Avoiding words that may represent something negative for the individual (Sundberg and Partington 1998)
Mand Fluency Training
mands in a given period. Clinical intervention programs that include mand training typically use specific accuracy criteria for determining when an individual is ready to progress to the next stage (e.g., from imitating mands to being expected to produce them when prompted).
Qualifications of Treatment Providers Mand training providers are typically professionals or supervised paraprofessionals with formal training in behavioral theory and applied behavioral analysis techniques. In some verbal behavior programs, parents and caregivers are also taught basic mand training techniques to facilitate generalization of the individual’s skills beyond the therapy context.
Efficacy Information Mand training is often implemented as part of a broader behavioral intervention program, and its efficacy has not been examined extensively in isolation. However, smaller studies have provided some evidence for the efficacy of mand training in children with autism (e.g., Drash et al. 1999). There is also some evidence that it may be more efficient than discrete trial instruction for facilitating the acquisition of requesting behavior (Jennett et al. 2008). Adding opportunities for rapid imitation of modeled motor behaviors during mand training has been shown to increase the efficacy of mand training, with evidence of increased spontaneous manding 3 months later (Ross and Greer 2003). In general, however, there is limited information from published studies on the longterm generalization of skills taught during mand training.
Outcome Measurement Efficacy research on mand training has utilized various metrics for progress monitoring and outcomes measurement, such as specified trial accuracy criteria (e.g., 9 out of 10 correct responses), percentage of correct responses, percentage of incorrect responses, or the number of spontaneous
See Also ▶ Applied Behavior Analysis (ABA) ▶ Communication Interventions ▶ Early Intensive Behavioral Intervention (EIBI) ▶ Educational Interventions ▶ Functional Communication Training ▶ Language Interventions ▶ Mands ▶ Operant Conditioning ▶ Skinnerian Categories of Verbal Behavior ▶ Verbal Behavior Interventions
References and Reading Drash, P. W., High, R. L., & Tudor, R. M. (1999). Using mand training to establish an echoic repertoire in young children with autism. The Analysis of Verbal Behavior, 16, 29–44. Jennett, H. K., Harris, S. L., & Delmolino, L. (2008). Discrete trial instruction vs. mand training for teaching children with autism to make requests. The Analysis of Verbal Behavior, 24, 69–85. Lovaas, O. I. (2003). Teaching individuals with developmental delays: Basic intervention techniques. Austin: Pro-Ed. Ross, D. E., & Greer, R. D. (2003). Generalized imitation and the mand: Inducing first instances of speech in young children with autism. Research in Developmental Disabilities, 24, 58–74.
Mania Skinner, B. F. (1957). Verbal behavior. New York: Appleton-Century-Crofts. Sundberg, M. L., & Michael, J. (2001). The benefits of Skinner’s analysis of verbal behavior for children with autism. Behavior Modification, 25, 698–724. Sundberg, M. L., & Partington, J. W. (1998). Teaching language to children with autism or other developmental disabilities. Danville: Behavior Analysts.
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References and Reading Skinner, B. F. (1957). Verbal behavior. New York: Appleton-Century-Crofts. Sundberg, M. L., & Michael, J. (2001). The benefits of Skinner’s analysis of verbal behavior for children with autism. Behavior Modification, 25, 698–724.
Mands
Mania
Corey Ray-Subramanian Waisman Center, University of Wisconsin-Madison, Madison, WI, USA
Dorothy Stubbe Yale University School of Medicine Child Study Center, New Haven, CT, USA
Synonyms
Synonyms
Command; Demand; Request
Elated, euphoric and grandiose; Elevated, expansive, or irritable mood; Overly talkative; Racing thoughts, distractibility, and psychomotor agitation
Definition Mands are verbal acts that are reinforced by a specified consequence (Skinner 1957). This term was introduced by B. F. Skinner and refers to a specific type of verbal action within an operant conditioning framework designed to analyze the functions of verbal behavior. Mands are considered to be the first type of verbal behavior that individuals typically acquire (Sundberg and Michael 2001). As part of a verbal behavior training intervention, mands commonly take the form of requests for items, information, or removal of aversive stimuli (Sundberg and Michael 2001). For example, if an individual who wants a cookie says “Cookie, please” and is then given a cookie, the individual’s statement (i.e., “Cookie, please”) would be considered a mand.
See Also ▶ Applied Behavior Analysis (ABA) ▶ Mand Fluency Training ▶ Operant Conditioning ▶ Skinnerian Categories of Verbal Behavior ▶ Verbal Behavior Interventions
Short Description or Definition The American Psychiatric Association (APA) Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR) defines a manic episode as “a distinct period during which there is an abnormally and persistently elevated, expansive, or irritable mood” (American Psychiatric Association [APA] 2000, p. 357). Symptoms of mania include a high activity level which may be directed toward a specific goal or may consist of psychomotor agitation, inflated self-esteem, a decreased need for sleep, racing thoughts, talkativeness, distractibility, and engaging in pleasure-seeking activities without regard for consequences. Individuals experiencing mania are often noted to have a change in personality, in which they become reckless, boisterous, and engage in high-risk activities. If the individual’s mood is elevated, three of the prior symptoms are required; four if the mood is irritable. To qualify as a manic episode in DSM-IV-TR, the episode must last at least one week, or any duration if symptoms are severe enough to require hospitalization, and must result
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in impairment in the ability to function in daily life. Psychotic features, including delusions (false beliefs) or hallucinations, may be present. The manic episode cannot be caused by the effects of drugs of abuse, medications, exposure to a toxic substance, or a general medical condition (APA 2000).
Categorization A manic episode is one of the essential components of bipolar I disorder, which historically has been referred to as manic-depressive disorder. Although bipolar I disorder usually also includes episodes of depression, it is the occurrence of the manic episode that is the defining feature. A hypomanic episode is a less severe form of mania, which is not severe enough to cause substantial impairment in functioning in daily life, does not necessitate hospitalization, and there are no psychotic symptoms. Bipolar II disorder is characterized as a mood disorder in which hypomania, as opposed to mania, is present (APA 2000).
Epidemiology The lifetime prevalence of bipolar I disorder in the community is estimated at 3.9% (Perlis et al. 2004). There is less diagnostic clarity when the mood symptoms have their initial symptoms presenting in childhood, as many other disorders have overlapping symptoms. Twenty is the mean age of onset for a first manic episode, but retrospective studies suggest that about 60% of adults with bipolar disorder demonstrated some mood symptoms in childhood or adolescence. There has been a trend over time for the age of the first onset of symptoms to occur earlier in life. For adolescents who have suffered from recurrent major depression, about 10–15% will experience a manic episode and thus develop bipolar I disorder (Boris et al. 2007). The lifetime prevalence of bipolar I disorder in the community is estimated at 3.9% (Perlis et al. 2004). There is less diagnostic clarity when the mood symptoms have
Mania
their initial symptoms presenting in childhood, as many other disorders have overlapping symptoms. Twenty is the mean age of onset for a first manic episode, but retrospective studies suggest that about 60% of adults with bipolar disorder demonstrated some mood symptoms in childhood or adolescence. There has been a trend over time for the age of the first onset of symptoms to occur earlier in life. For adolescents who have suffered from recurrent major depression, about 10–15% will experience a manic episode and thus develop bipolar I disorder (Boris et al. 2007). Men and women suffer from bipolar I disorder in about equal numbers. However, women more commonly have a major depressive episode first and suffer from more depressive episodes than manic episodes, whereas men most often experience their first episode as mania and have more equal numbers of manic and depressive episodes. Manic episodes frequently occur after a stressful life event. In about 2/3 of cases, a manic episode immediately follows or precedes an episode of major depression. Most individuals have periods of more normal mood states between episodes of either mania or depression. However, about onequarter of individuals with bipolar I disorder more persistently display a labile (fluctuating) or irritable mood (APA 2000). In individuals over the age of 13 who are diagnosed with autism, approximately 3% suffer from manic episodes (Howlin 2005).
Natural History, Prognostic Factors, and Outcomes Mania, associated with bipolar disorder is a recurrent illness. In general, the earlier the age of onset of the disorder (either depressive or manic symptoms), the more recurrent and chronic the course of illness. Recovery from the first manic episode is very high (over 70%), but a similar number (up to 80%) will experience recurrence of the disorder within 2–5 years. Early-onset bipolar disorder also has a high rate of persistent lower level mood symptoms. Child and adolescent onset of the disorder is often characterized by more rapid fluctuations in
Mania
mood than when the disorder is not evidenced until adulthood. Poor prognostic factors for manic episodes are early age of onset, long duration of symptoms, exposure to trauma and negative life events, low socioeconomic status, mixed or rapid cycling episodes, psychotic symptoms, and co-occurring psychiatric disorders. Suicidal ideation and attempts occur most often when an individual is depressed or using substances. For individuals with bipolar I disorder, the completed suicide rate is estimated at 10–15% (Geller et al. 2004).
Clinical Expression and Pathophysiology Elevated mood is the cardinal symptoms of mania. Individuals who experience mania often feel so elated and omnipotent that they do not recognize that they are ill in any way. It is those around them that notice the change in behavior. The manic individual often demonstrates poor judgment, may change personal appearance to a flamboyant style that is out of character, and may exhibit high-risk behaviors, such as sexual promiscuity, substance use, or spending large sums of money. For youth presenting with mania, increased energy, distractibility, and pressured speech are the most common symptoms, while hypersexuality has been found to be the least frequent (Boris et al. 2007). Very severe mania often includes psychotic symptoms. More than half of manic individuals experience delusions, usually of grandeur (the false belief that one is famous, has unusual powers, or is special in some other way). Religious delusions are also common. Hallucinations, usually auditory, occur in about one-fourth of individuals. Some individuals may experience symptoms of mania and major depression that alternate rapidly, with both experienced almost every day. This is referred to as a “mixed episode.” Agitation, insomnia, rapid changes in appetite, psychotic features, and suicidal thoughts are frequent symptoms for individuals with mixed episode mood disorders (APA 2000). Although there are no specific laboratory tests that are diagnostic of a manic episode, some
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abnormal test results are frequently found in individuals with mania. These include abnormal polysomnograms (electroencephalograms or EEGs that occur during sleep) and increased cortisol secretion. Studies of neurotransmitter metabolites, neuroendocrine function, and pharmacological challenges suggest that there may be abnormalities of many of the central nervous system neurotransmitters: norepinephrine, serotonin, acetylcholine, dopamine, and gamma-aminobutyric acid (GABA). Bipolar disorder has been demonstrated to run in families, according to multiple twin and adoption studies. Having a first-degree relative who has been diagnosed with bipolar disorder confers an increased risk (eight- to tenfold over community samples) of also being diagnosed with the disorder. Symptoms typically occur at an earlier age in children with a close family history of bipolar disorder. The heritability for bipolar disorder has been estimated at over 80% (Boris et al. 2007).
Evaluation and Differential Diagnosis The evaluation for mania includes a patient interview, collateral information from significant others, a medical workup, gathering family history of mood disorder, and the use of assessment scales (see Table 1). Structured and semi-structured interviews (SADS or for children the K-SADS) or a general checklist, such as the Child Behavior Checklist (CBCL), may be helpful diagnostically and for research purposes. For youth, the Young Mania Rating Scale (Young et al. 1978) is the most widely used. Also for youth, parental report is more effective in identifying mania than youth or teacher reports. The Parent Mood Disorder Questionnaire (MDQ) is a widely used rating scale. Mood timelines or diaries may help track mood symptoms, and may also identify events that trigger these symptoms and document response to treatment (Nandagopal and DelBello 2011). Many other disorders have overlapping symptoms with early-onset bipolar disorder. Attentiondeficit/hyperactivity disorder, disruptive behavior disorders and conduct problems, substance abuse disorders, schizophrenia, and pervasive developmental disorder with irritability are most common.
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Mania
Mania, Table 1 Components of a diagnostic evaluation for mania Chief complaint
Context Multiple informants and collateral history
Timeline of mood symptoms Developmental, social, and family history
Patient interview/ mental status examination
Medical evaluation
Rating scales
From patient, family, significant others, referral source In what context does mania occur? History of symptoms from family, significant others, referral source, primary care physician, etc. (with patient consent or from legal guardian if patient is a minor) Frequency, intensity, number, and duration (FIND) of mood episodes Developmental milestones, educational history, level of functioning, social relationships, medical history, and family history of psychiatric disorders Developmentally appropriate interview. Inquire about substance use, high-risk behaviors, suicidality and homicidality, legal problems, trauma history, sleep, and energy level. Assess for psychotic symptoms Routine physical examination; further medical evaluation as indicated: illicit substances (toxicology screen); toxic (lead or toxin levels); autoimmune diseases (screen for lupus, and others); neurological (EEG for epilepsy; brain imaging for multiple sclerosis, etc.); metabolic/infections (thyroid levels, liver function tests, complete blood count, etc.); other diseases (e.g., Cushing’s disease by cortisol level) SADS, K-SADS, Young Mania Rating Scale, Child Behavior Checklist (CBCL), Parent Mood Disorder Questionnaire (MDQ)
Medical disorders that may present with manic symptoms include hyperthyroidism, head trauma, brain tumors, or Cushing’s syndrome. Medications, such as corticosteroids, antidepressants, and
Mania, Table 2 Treatment of mania Treatment: pharmacotherapy Lithium Most evidence base for effectiveness in adults and adolescents. Used as monotherapy or in combination with an antipsychotic or anticonvulsant medication Anticonvulsant The most evidence for medications: valproate, effectiveness in adults and carbamazepine, adolescents are with lamotrigine, valproate, carbamazepine, oxcarbazepine, topiramate and lamotrigine. Used as monotherapy or in combination with lithium or an antipsychotic Atypical antipsychotic May be used as medications: olanzapine, monotherapy but are often risperidone, quetiapine, used to target symptoms of aripiprazole, clozapine, psychosis or for more ziprasidone rapid treatment response in conjunction with lithium or an anticonvulsant medication Treatment: psychosocial Environmental Safe and less stimulating environment, sleep hygiene, and routine. For severe mania, this may include psychiatric hospitalization Psychoeducation and Active listening; support restoration of hope; case management (collaborate with school, job, community resources, family regarding needed services); education about causes, symptoms, treatment, and prognosis Therapeutic interventions Day treatment or partial hospitalization program; individual therapy: cognitive-behavioral therapy, dialectic behavioral therapy, interpersonal therapy, supportive therapy; family therapy; skillbuilding groups
stimulants, may be accompanied by mood fluctuations or precipitate manic symptoms. In individuals diagnosed with autism spectrum disorders, the most common other psychiatric
Manual Sign
diagnoses are attention-deficit/hyperactivity disorder, depression, and anxiety disorders, with mania occurring with no more frequency than the rest of the population (Howlin 2005; Matson and Nebel-Schwalm 2007).
Treatment Current treatments for mania aim to control the agitation, impulsivity, aggression, and psychotic symptoms and to help patients regain their preillness level of functioning. After the first episode of mania, many individuals demonstrate ongoing milder levels of dysfunction, which may or may not resolve fully. Mania, and thus, bipolar I disorder, is a recurrent disorder, and effective treatment must include acute, continuation, and maintenance phases. Effective treatment includes the use of appropriate medications in the mood stabilizing, antiepileptic, and antipsychotic classes. These medications may be used in monotherapy or combined for enhanced effectiveness. Psychosocial interventions, including education about the disease, case management and coordination of services, family therapy, group therapy, and individual supportive and evidence-based treatments are also essential to optimize prognosis. Table 2 highlights the components of effective treatment.
2809 Geller, B., Tillman, R., Craney, J. L., & Bolhofner, K. (2004). Four-year prospective outcome and natural history of mania in children with a prepubertal and early adolescent bipolar disorder phenotype. Archives of General Psychiatry, 61, 459–467. Howlin, P. (2005). Outcomes of autism spectrum disorders. In F. R. Volkmar, R. Paul, A. Klin, & D. Cohen (Eds.), Handbook of autism and pervasive developmental disorders, volume 1: Diagnosis, development, neurobiology and behavior (3rd ed., pp. 201–222). Hoboken: Wiley. Leibenluft, E., Charney, D. D., Towbin, K. E., Bhangoo, R. K., & Pine, D. S. (2003). Defining clinical phenotypes of juvenile mania. American Journal of Psychiatry, 160, 430–437. Matson, J. L., & Nebel-Schwalm, M. S. (2007). Comorbid psychopathology with autism spectrum disorder in children: An overview. Research in Developmental Disabilities, 28(4), 341–352. Nandagopal, J. J., & DelBello, M. P. (2011). Assessment and treatment of childhood and adolescent bipolar disorder. In A. Martin, L. Scahill, & C. Kratochvil (Eds.), Pediatric psychopharmacology: Principles and practice (pp. 466–479). New York: Oxford University Press. Perlis, R. H., Miyahara, S., Marangell, L. B., et al. (2004). Long-term implications of early onset in bipolar disorder: Data from the first 1000 participants in the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD). Biological Psychiatry, 55, 875–881. Young, R. C., Biggs, J. T., Ziegler, V. E., & Meyer, D. A. (1978). A rating scale for mania: Reliability, validity and sensitivity. British Journal of Psychiatry, 133, 429–435.
Manual Sign See Also ▶ Comorbidity
Vannesa T. Mueller Speech-Language Pathology Program, University of Texas at El Paso College of Health Science, El Paso, TX, USA
References and Reading American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders (4th ed., Text Revision). Arlington: Author. American Psychiatric Association. (2002). Practice guideline for the treatment of patients with bipolar disorder (revision). American Journal of Psychiatry, 159, 1–50. Boris, B., Axelson, D., & Pavuluri, M. (2007). Bipolar disorder. In A. Martin & F. R. Volkmar (Eds.), Lewis’s child and adolescent psychiatry: A comprehensive textbook (pp. 513–528). Philadelphia: Lippincott Williams & Wilkins.
Synonyms American Sign Language (ASL); Conceptually accurate signed english (CASE); Linguistics of verbal english (LOVE); Manually coded english (MCE); Seeing essential english (SEE I); Sign language; Signed english; Signing exact english (SEE II)
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Definition Manual sign is a system of communicating visually and spatially through signs created with the hands. The term “manual sign” does not specify a particular sign system such as American Sign Language (ASL) or manual codes of English such as Seeing Essential English (SEE I), Signing Exact English (SEE II), Linguistics of Verbal English (LOVE), and Conceptually Accurate Signed English (CASE). ASL is the natural language of the deaf community in the United States and much of Canada (Neidel et al. 2000). ASL is a distinct language from spoken English (see entry on ▶ “American Sign Language (ASL)”) while manual codes of English are based on spoken English and are an attempt to represent English on the hands. Manual codes of English were created in the 1960s primarily to aid in increasing the literacy skills of deaf and hard-of-hearing children (Schow and Nerbonne 2007). Manual codes of English can be classified as “sign systems” and not “sign languages” because they borrow the semantics and syntax from spoken English. These sign systems follow English word order and use grammatical markers for morphological endings of words. For example, the word “smiling” is signed with two signs SMILE + ing. Additionally, unlike ASL, manual codes of English use signs for function words such as “the” and “an.” Typically, when signing is employed in the language intervention for children with autism, the type of signing systems that are used is manual codes of English rather than ASL. The main reason for this may be the hope that using manual sign will lead to verbal communication, an outcome which has been supported by research (Schlosser and Wendt 2008).
Manually Coded English (MCE) Schlosser, R. W., & Wendt, O. (2008). Effects of augmentative and alternative communication intervention on speech production in children with autism: A systematic review. American Journal of SpeechLanguage Pathology, 17, 212–230. Schow, R. L., & Nerbonne, M. A. (2007). Introduction to audiologic rehabilitation (5th ed.). Boston: Pearson.
Manually Coded English (MCE) ▶ Manual Sign ▶ Sign Language
Marker ▶ Milestone
Marketed as Geodon ▶ Geodon (Ziprasidone)
MAS ▶ Motivation Assessment Scale
Massed Practice Anjileen Singh Counseling, Clinical, and School Psychology, UC Santa Barbara, Santa Barbara, CA, USA
See Also ▶ American Sign Language (ASL) ▶ Sign Language
References and Reading Neidel, C., Kegl, J., MacLaughlin, C., Bahan, B., & Lee, R. G. (2000). The syntax of American sign language. Cambridge, MA: The MIT Press.
Definition The repeated presentation of a task over a short period of time, with no intertrial interval between presentations. The term was first introduced by Hermann Ebbinghaus, and a detailed study was published in the 1885 book Memory: A Contribution to Experimental Psychology. Since then, researchers have found that distributed
Mast Cells and Autism
practice, or spaced practice, in which short periods of learning are interspersed with periods of rest or alternative activities, produces better performance than massed practice in a variety of tasks (Hunter 1929). Studies investigating the influence of task variation on learning suggest that distributed practice produces superior levels of on-task responding and higher levels of child affect in children with autism (Dunlap 1984). Researchers suggest that interspersing alternative activities with varying difficulty levels serves to increase the child with autism’s motivation to respond to the tasks (Koegel and Egel 1979). Massed practice can also refer to a self-directed behavior change technique in which a person performs an undesired behavior repeatedly, which sometimes decreases the future frequency of the behavior (Cooper et al. 2007).
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Massively Parallel Sequencing ▶ Next-Generation Sequencing
Mast Cells and Autism Jonathan Kopel1 and Gregory Brower2 1 Texas Tech University Health Sciences Center (TTUHSC), Lubbock, TX, USA 2 School of Medicine, Texas Tech University Health Sciences Center, Lubbock, TX, USA
Synonyms Mastocyte
See Also
Definition
▶ Distributed Practice ▶ Learning Styles
Despite the increase prevalence of autism spectrum disorders (ASD), the pathogenesis and therapeutic options for ASD remain poorly understood (Theoharides et al. 2012). In recent years, several reports have suggested the importance of brain inflammation in the pathogenesis of several psychiatric conditions (Theoharides et al. 2016). Among ASD patients, many develop worsening symptoms after vaccination, infections, toxins, or other environmental exposures suggesting an underlying immune dysregulation (Theoharides et al. 2016). Specifically, increased inflammatory cytokines, autoantibodies, and allergic responses reported in ASD patients suggest mast cells may have a prominent role in the onset and progression of ASD (Theoharides et al. 2016). Patients with increased mast cells have an increased prevalence of ASD and other cognitive deficits, suggesting an underlying mast cell hyperactivity among ASD patients (Theoharides 2013). Mast cells are bone marrow-derived cells that modulate the innate immune response and contribute several chronic inflammatory and renal diseases (Balakumar et al. 2009; Holdsworth and Summers 2008; Wasse et al. 2012). Within the central nervous system, mast cells are
References and Reading Cooper, J. O., Heron, T. E., & Heward, W. L. (2007). Applied behavior analysis (2nd ed.). Upper Saddle River: Pearson Education. Dunlap, G. (1984). The influence of task variation and maintenance tasks on the learning and affect of Autistic children. Journal of Experimental Child Psychology, 37, 41–64. Ebbinghaus, H. (1885). Memory: A contribution to experimental psychology. Teachers College: Columbia University. Hunter, W. S. (1929). Learning: II. Experimental studies of learning. In C. Murchison (Ed.), The foundations of experimental psychology (pp. 564–627). Worcester: Clark University Press. Koegel, R. L., & Egel, A. (1979). Motivating Autistic children. Journal of Abnormal Psychology, 88(4), 418–426.
Massive Infantile Spasms ▶ Infantile Spasms/West Syndrome
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concentrated in the diencephalon, which modulates several emotional and behavior responses (Theoharides et al. 2016). In recent years, mast cell interactions with microglial have been implicated in the pathogenesis of ASD and other neuroinflammatory and psychiatric disorders (Theoharides et al. 2016). Several studies suggest neuroinflammation interferes with mast cellmicroglial interactions from pruning neural circuits during neurodevelopment producing abnormal microglial growth and activation in ASD patients (Theoharides et al. 2016). Specifically, environmental and psychological stresses during prenatal development may stimulate the release of potent neuropeptides, neurotensin, and corticotropin-releasing factor and stimulate mast cells to increase vascular permeability and disrupt the blood-brain barrier in ASD patients (Theoharides et al. 2016). With the lack of effective treatments for ASD, pharmacological treatments targeting mast cell-microglial interactions may provide a new treatment options for ASD patients and their caregivers (Tsilioni et al. 2015).
Mastocyte Theoharides, T. C., Stewart, J. M., Panagiotidou, S., & Melamed, I. (2016). Mast cells, brain inflammation and autism. European Journal of Pharmacology, 778, 96–102. https://doi.org/10.1016/j.ejphar.2015.03.086. Tsilioni, I., Taliou, A., Francis, K., & Theoharides, T. C. (2015). Children with autism spectrum disorders, who improved with a luteolin-containing dietary formulation, show reduced serum levels of TNF and IL-6. Translational Psychiatry, 5(9), e647–e647. https://doi. org/10.1038/tp.2015.142. Wasse, H., Naqvi, N., & Husain, A. (2012). Impact of mast cell chymase on renal disease progression. Current Hypertension Reviews, 8(1), 15–23. https://doi.org/10. 2174/157340212800505007.
Mastocyte ▶ Mast Cells and Autism
Maternal and Child Health Bureau See Also ▶ Allergies ▶ Leaky Gut Syndrome ▶ Intestinal Permeability Studies
References and Reading Balakumar, P., Reddy, J., & Singh, M. (2009). Do resident renal mast cells play a role in the pathogenesis of diabetic nephropathy? Molecular and Cellular Biochemistry, 330 (1–2), 187–192. https://doi.org/10.1007/s11010-0090132-3. Holdsworth, S. R., & Summers, S. A. (2008). Role of mast cells in progressive renal diseases. Journals of the American Society of Nephrology, 19(12), 2254–2261. https://doi.org/10.1681/asn.2008010015. Theoharides, T. C. (2013). Is a subtype of autism an allergy of the brain? Clinical Therapeutics, 35(5), 584–591. https://doi.org/10.1016/j.clinthera.2013.04.009. Theoharides, T. C., Angelidou, A., Alysandratos, K.-D., Zhang, B., Asadi, S., Francis, K., et al. (2012). Mast cell activation and autism. Biochimica et Biophysica Acta (BBA) – Molecular Basis of Disease, 1822(1), 34–41. https://doi.org/10.1016/j.bbadis.2010.12.017.
Cynthia Zierhut and Sally J. Rogers Department of Psychiatry and Behavioral Sciences, UC Davis M.I.N.D. Institute, Sacramento, CA, USA
Major Areas or Mission Statement The Maternal Child Health Bureau (MCHB), a United States Federal commitment to addressing maternal and child health, was first established in 1912. In 1935, the MCHB was transferred to Title Vof the Social Security Act and then converted to a federal block grant administered by the Health Resources and Services Administration (HRSA) in 1981. The mission of the MCHB is to provide leadership, in partnership with key stakeholders, to improve the physical and mental health, safety, and well-being of the nation’s women, infants, children, adolescents, and their families, including fathers and children with special health-care needs.
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Landmark Contributions
References and Reading
HRSA administers a wide range of programs to pregnant women, mothers, infants, children, and their families. MCHB programs serve more than 34 million women, infants, and children each year in the United States. The MCHB supports programs to reduce infant mortality by providing comprehensive prenatal and postnatal care for women, promotes an advocacy communitybased group for Healthy Start programs, and is a proponent of preventative care (including rehabilitative services and immunizations) for children, research and training, genetic services, and newborn screening and treatments. The HRSA awards grants to improve health care and other services for children and adolescents with autism spectrum disorders and other developmental disabilities. Funds are utilized to enhance awareness and reduce barriers to screening and diagnosis. Funds also support research of evidence-based interventions, evidence-based guidelines for interventions, training professionals to use valid screening tools for diagnosis and intervention, and improving technical assistance to facilities which provide services for children with autism spectrum disorders.
Maternal and Child Health. (2010). Retrieved 17 July 2011, from http://mchb.hrsa.gov/
Major Activities HRSA’s implementation of the Combating Autism Act of 2006 ($48 million) addresses the most urgent issues affecting people with autism and their families. The goal of this act is to enable all infants, children, and adolescents who are at risk for developing or who have autism spectrum disorders and other developmental disabilities to reach their full potential by: • Establishing a system of services, including early screening of children for autism spectrum disorders • Conducting early, multidisciplinary evaluations to diagnose or rule out autism spectrum disorders • Providing evidence-based early interventions when a diagnosis is confirmed
Maternal Hypothyroidism and Autism Stephen Sulkes Division of Developmental and Behavioral Pediatrics, Golisano Children’s Hospital, University of Rochester, Rochester, NY, USA
Synonyms Iodothyronine deiodinase (D2T3); Thyrotropin, Thyroid-stimulating hormone (TSH); Thyroxine (T)
Structure The thyroid gland is part of the endocrine system. It regulates the rate of metabolism and impacts growth by its regulation of other tissues through secretion of hormones triiodothyronine (T3) and thyroxine (T4). The substrates these hormones are synthesized from are iodine and tyrosine. The rate that these hormones are produced is regulated by thyroid-stimulating hormone (TSH) from the anterior pituitary gland. TSH is itself regulated by thyrotropin-releasing hormone (TRH) produced by the hypothalamus. The fetus begins to make T3 at 18–20 weeks’ gestation and T4 by 30 weeks. This may be partially protective from maternal hypothyroidism.
Function Thyroid hormones are critical to normal brain development. The thyroid regulates metabolism and growth and impacts both calcium metabolism and catecholamine effects.
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Typical symptoms of hypothyroidism (low hormone production) postnatally are decreased energy, constipation, weight gain, fatigue, cold intolerance, hair loss, and slowed heart rate. Typical symptoms of hyperthyroidism (increased hormone production) postnatally are mood swings, overactivity, decreased appetite, and increased heart rate.
Pathophysiology Among the prenatal conditions that have been associated with autism spectrum disorders is prenatal exposure to maternal hypothyroidism. Although not clearly demonstrated as a cause of autism, this hypothesis has growing support from neuroembryonic research, and some have suggested links between environmental toxins, maternal hypothyroidism, and the autism “epidemic.” Twenty-five years ago, Gillberg et al. (1992) noted that congenital hypothyroidism was associated with autism spectrum disorders, along with intellectual disability and other physical concerns. In 1999, Haddow et al. confirmed that children exposed in utero to maternal hypothyroidism manifesting as high gestational thyrotropin (thyroid-stimulating hormone, or TSH) levels, arising in response to relative low levels of thyroid hormone, resulted in significant neuropsychologic impacts at school age. Developmental abnormalities were present even in children of women whose hypothyroidism was subclinical or partially treated (Bernal 2007). Thyroid hormone receptors are present in the fetal cortex by 8–9 weeks of gestation and increase tenfold by 18 weeks (Bernal 2007), and brain T3 and iodothyronine deiodinase (D2) activities increase during the second trimester (Kester et al. 2004). D2 is felt to play an important role in brain compensation for variations in ambient thyroid hormone levels. It is well established that thyroid hormone plays a role in brain development during the fetal period, that different parts of the brain are differentially sensitive to thyroid hormone at any one time during development, and that sensitivity to
Maternal Hypothyroidism and Autism
thyroid hormone is controlled, in part, by local control of hormone production. These observations imply that the consequences of thyroid hormone insufficiency during fetal development differ from those of thyroid hormone insufficiency during postnatal development (Zoeller 2005). However, it has also been shown that small fluctuations in maternal thyroid hormone levels can have measurable neurocognitive impacts on the developing fetus. One impact of hypothyroidism shown in animal models is reduced expression of reelin, which regulates neuronal migration (Alvarez-Dolado et al. 1999). Abnormality of reelin expression has been associated with ASDs (Fatemi et al. 2005), and although not universally accepted, abnormalities of reelin genes have been associated with ASDs (Serajee et al. 2006; He et al. 2011). It has been noted that thyroid hormone deficiency before the onset of hearing has negative effects on peripheral and central auditory systems (Knipper et al. 2000). In animal models, maternal hypothyroidism has been shown to result in abnormal cochlear development, in abnormal neurotransmission in the developing auditory brainstem and hippocampus (Friauf et al. 2008) and in cerebrocortical and hippocampal synaptic activity (Taylor et al. 2008). Based on these and other animal studies, it has been proposed that the rat exposed to mild, transient neonatal hypothyroidism may serve as a model for autism (Sadamatsu et al. 2006). High TSH levels have also been noted to be associated with increased risk of breech presentation in term infants, based on samples of Dutch women followed for TSH elevation or evaluated for breech presentation (Kooistra et al. 2010). Inasmuch as breech presentation is one risk factor for ASDs (Bilder et al. 2009), this supports the relationship. The authors note that infants who present in the breech position often have low tone or reasons for less active body movements such as kicking and that suboptimal maternal thyroid function might have a direct motor effect on the fetus. Alternatively, they quote literature (Van der Meulen et al. 2008) that suggests that the sensory responses of fetuses in breech position are different from those in cephalic presentation.
Maternal Hypothyroidism and Autism
Sweeten et al. (2003) replicated the findings of Comi et al. (1999), noting increased frequency of autoimmune disorders, with hypothyroidism being the most common, among parents of children with autism spectrum disorders. They acknowledge that this could be an effect of endocrine abnormality or, alternatively, the result of another immunologic abnormality having a direct effect on brain development. A meta-analysis by Wu et al. (2015) and supported by Brown et al. (2015) reasserted this association based on pooled data, with an increased risk overall and greater risk specifically with maternal hypothyroidism. An extensive review of the hypothyroidism/ ASD association by Román (2007) draws an association between maternal hypothyroidism and neuropathologic features found in individuals with ASDs, including abnormalities in the cortex and hippocampus, “consistent with abnormal neuronal migration and alterations in the number, survival, and orientation of neurons up to the time of olivary cell migration. . .before the end of the third month” of gestation. He notes that, in humans, the fetal thyroid forms about midgestation and that maternal dietary iodine deficiency and associated hypothyroidism prior to the third trimester of pregnancy can result in endemic cretinism. Multiple possible causes for both transient and long-term maternal hypothyroidism are proposed, including dietary iodine deficiency, exposure to food-based antithyroid substances (particularly soy products and other foods with high levels of isoflavonoids), and other environmental antithyroid substances (herbicides, PCBs, mercury, and coal derivatives). In follow-up to earlier studies, Román et al. (2013) evaluated 4039 mother-child pairs to determine if there was a correlation between maternal hypothyroxinemia before 18 weeks of gestation and autism risk. A “probable autistic child” was defined based on a score >98th percentile on the Pervasive Developmental Problems subscale of the Child Behavior Checklist in conjunction with a score in the top 5% on an 18-item short-form version of the Social Responsiveness Scale, administered at about age 6 years. Eighty children were defined as being a probable autistic child; the adjusted odds ratio of being a probable autistic
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child increased 3.9 times when the mother had severe hypothyroxinemia in early gestation, but there was no relation between mild hypothyroxinemia in the mother and autism in the child. Specific ASD diagnosis was not performed, so a significant limitation in this study is the specificity of parent report of problems. Andersen et al. (2014) reported a retrospective population based cohort study of over 30,000 children born to mothers with thyroid disease that suggested an association between ASD (based on medical record review) and hypothyroidism diagnosed in the mother after birth, suggesting that antibody and TSH exposure prenatally could be a factor. Colborn (2004) has also been outspoken in drawing an association between endocrinedisrupting synthetic chemicals and neurodevelopmental disorders such as ADHD and autism spectrum disorders. Some environmental chemicals, e.g., perchlorate, which has sometimes contaminated water supplies, directly interfere with iodine uptake into the thyroid gland. Other chemicals interfere with thyroid hormone signaling; polychlorinated biphenyls (PCBs) have this effect. Bisphenol A (BPA), involved in the manufacture of plastics, also blocks thyroid action on glial cells. Blazewicz et al. (2016) found lower urinary iodine levels in boys with ASDs, compared to controls, although thyroid hormone levels and thyroid volumes were comparable, with a correlation between clinical ASD severity and lower urinary iodine levels. Hamza et al. (2013) reported similar findings in both children with ASD and their mothers, also noting lower reported intake of iodized salt. Whether these differences were cause or effect of ASD was unclear. As Zoeller (2005) notes, “a causal relation between (environmental) contaminants, thyroid hormone signaling, and cognitive development will be difficult to obtain given the fact that even small and transient thyroid hormone insufficiency may be detrimental.” If screening of pregnant women for elevated TSH levels and thyroid supplementation when indicated become routine, epidemiologic studies of autism in the children of these women will be informative. Meanwhile, there is not yet clear evidence to associate prenatal
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exposure to maternal hypothyroidism with autism spectrum disorders, nor are there clear links from environmental toxins to maternal hypothyroidism to autism spectrum disorders, but the hypotheses presented are provocative and speak to the need for well-controlled study.
See Also ▶ Medical Evaluation in Autism
References and Reading Alvarez-Dolado, M., Ruiz, M., Del Rio, J. A., Alcantara, S., Burgaya, F., Sheldon, M., et al. (1999). Thyroid hormone regulates reelin and dab-1 expression during brain development. Journal of Neuroscience, 19, 6979–6993. Andersen SL, Laurbert P, Wu CS, Olsen J. (2014). Attention deficit hyperactivity disorder and autism spectrum disorder in children born to mothers with thyroid dysfunction: a Danish nationwide cohort study. British Journal of Obstetrics and Gynecology (BJOG) 121 (11), 1365–74. Bernal, J. (2007). Thyroid hormone receptors in brain development and function. Nature Clinical Practice. Endocrinology & Metabolism, 3, 249–259. Bilder, D., Pinborough-immerman, J., Miller, J., & McMahon, W. (2009). Prenatal, perinatal, and neonatal factors associated with autism spectrum disorders. Pediatrics, 123, 1293–1300. Blazewicz, A., Makarewicz, A., Korona-Glowniak, I., Dolliver, W., & Kocjan, R. (2016). Iodine in autism spectrum disorders. Journal of Trace Elements in Medicine and Biology, 34, 32–37. Brown, A.S., Surcel, H.M., Hinkka-Yli-Salomaki, S. et al. (2015). Maternal thyroid autoantibody and elevated risk of autism in a national birth cohort. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 57, 86–92. Colborn, T. (2004). Neurodevelopment and endocrine disruption. Environmental Health Perspectives, 112, 944–949. Comi, A. M., Zimmerman, A. W., Frye, V. H., Law, P. A., & Peeden, J. N. (1999). Familial clustering of autoimmune disorders and evaluation of medical risk factors in autism. Journal of Child Neurology, 14, 388–394. Fatemi, S. H., Snow, A. V., Stary, J. M., Araghi-Niknam, M., Reutiman, T. J., Lee, S., et al. (2005). Reelin signaling is impaired in autism. Biological Psychiatry, 57, 777–787. Friauf, E., Wenz, M., Oberhofer, M., Nothwang, H. G., Balakrishnan, V., Knipper, M., et al. (2008). Hypothyroidism impairs chloride homeostasis and onset of
Maternal Hypothyroidism and Autism inhibitory neurotransmission in developing auditory brainstem and hippocampal neurons. European Journal of Neuroscience, 28, 2371–2380. Gillberg, I. C., Gillberg, C., & Kopp, S. (1992). Hypothyroidism and autism spectrum disorders. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 33, 531–542. Haddow, J. E., Palomaki, G. E., Allan, W. C., Williams, J. R., Knight, G. J., Gagnon, J., et al. (1999). Maternal thyroid deficiency during pregnancy and subsequent neuropsychological development of the child. The New England Journal of Medicine, 341, 549–555. Hamza, R. T., Hewedi, D. H., & Sallam, M. T. (2013). Iodine deficiency in Egyptian autistic children and their mothers: Relation to disease severity. Archives of Medical Research, 44, 555–561. He, Y., Xun, G., Xia, K., Hu, Z., Lv, L., Deng, Z., & Zhao, J. (2011). No significant association between RELN polymorphism and autism in case-control and familybased association in Chinese Han population. Psychiatry Research, 187(3), 462–464. Kester, M. H., Martinez de Mena, R., Obregon, M. J., Marinkovic, D., Howatson, A., Visser, T. J., et al. (2004). Iodothyronine levels in the human developing brain: Major regulatory roles of iodothyronine deiodinases in different areas. Journal of Clinical Endocrinology and Metabolism, 89, 3117–3128. Knipper, M., Zinn, C., Maier, H., Praetorius, M., Rohbock, K., Kopschall, I., et al. (2000). Thyroid hormone deficiency before the onset of hearing causes irreversible damage to peripheral and central auditory systems. Journal of Neurophysiology, 83, 3101–3112. Kooistra, L., Kuppens, S. M. I., Hasaart, H. M., Vader, H. L., Wijnen, H. A., Oei, S. G., et al. (2010). High thyrotrophin levels at end term increase the risk of breech presentation. Clinical Endocrinology, 73, 661–665. Lammert, D. B., & Howell, B. W. (2016). RELN mutations in autism spectrum disorder. Frontiers in Clinical Neuroscience, 10, 1–9. Román, G. C. (2007). Autism: Transient in utero hypothyroxinemia related to maternal flavonoid ingestion during pregnancy and to other environmental antithyroid agents. Journal of the Neurological Sciences, 262, 15–26. Román, G. C., Ghassabian, A., Bongers-Schokking, J. J., Jaddoe, V. W. V., Hofman, A., et al. (2013). Association of gestational maternal hypothyroxinemia and increased autism risk. Annals of Neurology, 74, 733–742. Sadamatsu, M., Kanai, H., Xu, X., Liu, Y., & Kato, N. (2006). Review of animal models for autism: Implication of thyroid hormone. Congenital Anomalies, 46, 1–9. Serajee, F. J., Zhong, H., & Mahbubul Huq, A. H. M. (2006). Association of reelin gene polymorphisms with autism. Genomics, 87, 75–83. Sweeten, T. L., Bowyer, S. L., Posey, D. J., Halberstadt, G. M., & McDougle, C. J. (2003). Increased prevalence of familial autoimmunity in probands with pervasive developmental disorders. Pediatrics, 112, e420–e424.
Maternal Obesity and ASD Risk Taylor, M. A., Swant, J., Wagner, J. J., Fisher, J. W., & Ferguson, D. C. (2008). Lower thyroid compensatory reserve of rat pups after maternal hypothyroidism: Correlation of thyroid, hepatic, and cerebrocortical biomarkers with hippocampal neurophysiology. Endocrinology, 149, 3521–3530. Van der Meulen, J. A., Davies, G. A. L., & Kisilevsky, B. S. (2008). Fetal sensory-elicited body movements differ in breech compared to cephalic position. Developmental Psychobiology, 50, 530–534. Wu, S., Ding, Y., Wu, F., Li, R., Xie, G., Hou, J., & Mao, P. (2015). Family history of autoimmune diseases is associated with risk of autism in children: A systematic review and meta-analysis. Neuroscience and Biobehavioral Reviews, 55, 322–332. Zoeller, R. T. (2005). Thyroid hormone and brain development: Environmental influences. Current Opinion in Endocrinology and Diabetes, 12, 31–35.
Maternal Obesity and ASD Risk Yong-Jiang Li1 and Ya-Min Li2 1 Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China 2 Clinical Nursing Teaching and Research Section, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
Definition Obesity is a nutritional and metabolic life-course condition with excessive accumulation of adipose tissue that affects health adversely. Body mass index (BMI) calculated as weight in kilograms divided by height in meters squared was used for the definition of obesity. By this measure, obesity is defined as a BMI 30 kg/m2 for adults. Furthermore, BMI values were classified in the following categories: underweight 5% in the general population, account for the majority of the genetic risk of these disorders (El-Fishawy and State 2010). However, after decades of research, common variation alone was found to account for only a small percentage of the predicted heritability of ASD. By contrast, the rare variant-common disease hypothesis has been gaining in momentum. Evidence for this hypothesis stems in part from findings of an increased risk of de novo variation in ASD in simplex families, as well as the lack of consistent findings from three large, unbiased genome-wide association studies (Anney et al. 2010; Wang et al. 2009; Weiss et al. 2009). In addition, rare variant approaches to candidate gene identification have been clearly pivotal in identifying genes of major effect and highlighting relevant molecular pathways in ASD (Hoffman and State 2010). Regardless of this conceptual shift, it is likely that both common and rare variant alleles contribute to susceptibility in ASD. Because common variants are associated with relatively modest effect sizes, it is likely that common variants may function as modifier genes in ASD. Rare alleles, such as those that are found to be causative in Mendelian syndromes, have large effect sizes. Therefore, with respect to epistasis, one could imagine that a rare allele could be causative, and its expression could be influenced by common alleles at different loci. However, there is emerging evidence that even some rare variants may carry modest effect sizes and, therefore, may also perform a similar role as a modifier to another rare, causative gene in ASD susceptibility (State 2010; State and Levitt 2011). Moreover, pleiotropy has emerged as a central theme in the genetic architecture of ASD such that the same genetic mutations can manifest in vastly different clinical phenotypes in different individuals. One explanation for this may be
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locus heterogeneity, or variation at multiple genetic loci, which together influence the expression of a known risk factor, and have the potential to result in a range of clinical presentations (State and Levitt 2011). Moreover, it is possible that a combination of rare variants with modest effect sizes and/or common variants at different loci serves as modifying genes in ASD. Therefore, modifier genes likely play a key role in autism and contribute to the varying relationship between genetic mutation and clinical manifestation.
Pathophysiology One of the challenges of identifying modifier genes and gene interactions that contribute to ASD susceptibility is the need for appreciable power in genome-wide studies intended to identify alleles associated with small effect sizes. An alternate approach taken in some studies is to identify endophenotypes, or heritable phenotypes with a presumed genetic etiology. These studies utilize quantitative trait loci (QTLs), which are definable traits that are associated with a particular disorder. The rationale for using QTLs in genetic studies is that it can be used to identify the genetic risk for a particular trait, as opposed to relying on all of the diagnostic criteria for a disorder. Clinical diagnostic categories may be associated with multiple alleles, which complicates the assessment of heritable genetic risk (Abrahams and Geschwind 2008). Further, the presence of subclinical symptoms in family members of an affected individual argues for the relevance of QTL-based approaches (Abrahams and Geschwind 2008) and suggests that there may be common variants with modest effect sizes that influence phenotype in family members as well. For example, studies have shown that siblings and parents of boys with ASD have quantifiable differences in social responses compared to the general population, suggesting that they share some but not all of the risk alleles that contribute to ASD (Constantino et al. 2006; Virkud et al. 2009).
Modifier Genes and Autism Susceptibility
Multiple QTLs may be defined for ASD, given the range of behaviors associated with this group of disorders. One QTL which was utilized in a study to identify common variants associated with ASD was “age at first word,” in order to identify impairments in language development (Alarcon et al. 2008). The rationale for the use of this QTL in part is that many unaffected siblings of children diagnosed with ASD experience speech and language delay. This study identified a QTL on 7q34-7q36, which falls in contactin-associated protein-like 2 (Alarcon et al. 2008), a gene that has been strongly implicated in ASD by multiple rare variant studies as well. Therefore, QTLs represent a potentially informative approach to identify susceptibility genes with smaller effect sizes in highly heterogeneous disorders with complex genetic architecture. Moreover, our current conceptualization of the genetic architecture of ASD is that it involves the complex interplay of rare variation, both at the level of the sequence of DNA and the structure of chromosomes, which may function in a causative or modifying manner, depending on the effect size of a particular variant, and common alleles, which have a modifying role, together with environmental factors. At this time, larger genome-wide association studies are needed to obtain the power to identify modifier genes with relatively modest effect sizes.
See Also ▶ Common Disease-Rare Variant Hypothesis ▶ Pleiotropy
References and Reading Abrahams, B. S., & Geschwind, D. H. (2008). Advances in autism genetics: On the threshold of a new neurobiology. Nature Reviews Genetics, 9(5), 341–355. Alarcon, M., Abrahams, B. S., Stone, J. L., Duvall, J. A., Perederiy, J. V., Bomar, J. M., et al. (2008). Linkage, association, and gene-expression analyses identify
Moldova and Autism CNTNAP2 as an autism-susceptibility gene. American Journal of Human Genetics, 82(1), 150–159. Anney, R., Klei, L., Pinto, D., Regan, R., Conroy, J., Magalhaes, T. R., et al. (2010). A genome-wide scan for common alleles affecting risk for autism. Human Molecular Genetics, 19(20), 4072–4082. Constantino, J. N., Lajonchere, C., Lutz, M., Gray, T., Abbacchi, A., McKenna, K., et al. (2006). Autistic social impairment in the siblings of children with pervasive developmental disorders. The American Journal of Psychiatry, 163(2), 294–296. Cordell, H. J. (2002). Epistasis: What it means, what it doesn’t mean, and statistical methods to detect it in humans. Human Molecular Genetics, 11(20), 2463–2468. El-Fishawy, P., & State, M. W. (2010). The genetics of autism: Key issues, recent findings, and clinical implications. The Psychiatric Clinics of North America, 33(1), 83–105. Gupta, A. R., & State, M. W. (2007). Recent advances in the genetics of autism. Biological Psychiatry, 61(4), 429–437. Hoffman, E. J., & State, M. W. (2010). Progress in cytogenetics: Implications for child psychopathology. Journal of the American Academy of Child and Adolescent Psychiatry, 49(8), 736–751, quiz 856–737. Jorde, L. B., Carey, J. C., & Bamshad, M. J. (2010). Jorde: Medical genetics (4th ed.). Philadelphia: Mosby. State, M. W. (2010). The genetics of child psychiatric disorders: Focus on autism and Tourette syndrome. Neuron, 68(2), 254–269. State, M. W., & Levitt, P. (2011). The conundrums of understanding genetic risks for autism spectrum disorders. Nature Neuroscience, 14(12), 1499–1506. Virkud, Y. V., Todd, R. D., Abbacchi, A. M., Zhang, Y., & Constantino, J. N. (2009). Familial aggregation of quantitative autistic traits in multiplex versus simplex autism. American Journal of Medical Genetics. Part B, Neuropsychiatric Genetics, 150B(3), 328–334. Wang, K., Zhang, H., Ma, D., Bucan, M., Glessner, J. T., Abrahams, B. S., et al. (2009). Common genetic variants on 5p14.1 associate with autism spectrum disorders. Nature, 459(7246), 528–533. Weiss, L. A., Arking, D. E., Daly, M. J., & Chakravarti, A. (2009). A genome-wide linkage and association scan reveals novel loci for autism. Nature, 461(7265), 802–808.
Modifier Loci ▶ Modifier Genes and Autism Susceptibility
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Moebius Syndrome ▶ Möbius Syndrome
Moldova and Autism Roald A. Øien1,2 and Marina Calac3 1 Department of Psychology/Department of Education, UIT – The Arctic University of Norway, University of Tromsø, Tromsø, Norway 2 Yale Child Study Center, Yale Autism Program, Yale University School of Medicine, New Haven, CT, USA 3 Center for Early Intervention Volnickel, Chisinau, Republic of Moldova
Historical Background Autism in Moldova has a very short history. The story of autism spectrum disorders in Moldova started in 2004, when the first child was diagnosed as childhood autism. However, this is synonymous to autism spectrum disorders (ASD) not existing in Moldova. For many years, especially during the soviet period, children with autism were diagnosed as mentally retarded or some other diagnosis. Most of them had missed the optimal treatment window (early intervention) because of inadequate institutions, insufficient finances, and parental ignorance. Many of the children with autism have been placed into institutions. Because of the poor economic situations and inadequateness of services of social assistance, families were no longer able to care for the medical, physical, and psychological needs of their children and found institutions to be the only viable alternative. This attitudes and approach had lasted for decades in Moldova. The lack of knowledge on ASD in professionals, who are involved in work with children with autism spectrum disorders (ASD), lack of standardized protocols for diagnosis and
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assessment tools, and precarious use of international classifications and diagnostic tools are resulting in low and late detection of autism. Officially in Moldova ICD-10 (1992) diagnostic manual is used, but these days, children with autism spectrum disorders in formal statistics data are getting the diagnosis “mental and behavioral disorders of nonpsychotic character.” So there are no reliable data about the statistics on how many children with ASD are diagnosed in Moldova. The last years, due the immense influence of nongovernmental organization, supported by Western countries, and the activism of parents associations of children with autism in Moldova, have increased awareness regarding autism in professionals and parents. The organization, “SOS Autism”– one of the most powerful parents associations in Moldova founded in 2008 – is a leader in advocacy of children with ASDs. This organization is lobbying for the development of professional intervention services, inclusive education for children with autism, counseling, informational support, and social assistance for parents raising this children. Today in Moldova, all the burdens related with ASD fall on the shoulders of the parents. In capital city of Moldova, there are several private organizations that offer very expensive ABA – therapy for children with autism. Activities of these companies are not controlled and regulated by the state. The government published its first autism strategy in 2009. In different regions in Moldova, there have been opened community centers in mental health. But the efficiency of these centers in order to help people with Autism is very low because of lack of professionals working in these centers.
Moldova and Autism
But in reality inclusion of a child with autism in the kindergarten or school depends on parents’ education, insistency, and patience. Often parents with higher education are promoters of right approaches toward the children with autism and have a powerful influence in implementations on laws.
Overview of Current Treatments and Centers The clinic for early intervention in Chisinau is regarded as one of the best clinics working with intervention on kids with ASDs. SOS Autism – is another clinic in Moldova Hippo – behavioral intervention center – private center offering ABA sessions There are somewhere around ten clinics in total in Moldova.
Overview of Research Directions There is currently no research going on in Moldova. Overview of Training Training is in Moldova left to the parents after they have received help in the clinics. Very few of these kids have personal assistants in school and are left to themselves often.
Social Policy and Current Controversies Legal Issues, Mandates for Service Law no. 60 of 30.03.2012 on the social inclusion of people with disabilities, Chapter IV seels tp årpvode Education, raining, and development of persons with disabilities to participate in all areas of life without discrimination, at the same level with other members of society.
Socially, the society is looking down on those with an autism spectrum disorder. They are looked down on, bullied, and regarded as less valuable than children with typical development. Parents often complain that friends push back when they find out that families have a son or a daughter with ASDs. So it is huge social stigma connected to having an ASD diagnosis.
Monotone
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Molindone
Monotone
Lawrence David Scahill Nursing and Child Psychiatry, Yale Child Study Center, Yale University School of Nursing, New Haven, CT, USA Marcus Autism Center, Children’s Healthcare of Atlanta, Atlanta, GA, USA Department of Pediatrics, Emory University, Atlanta, GA, USA
Lisa Edelson-Fries Department of Psychology, Boston University, Boston, MA, USA Neurocognition, Department of Brain Health, Nestlé Institute for Health Sciences, Lausanne, Switzerland
Synonyms Flat prosody; Monotonic voice
Synonyms Moban
Definition Molindone is a traditional antipsychotic medication developed for the treatment of schizophrenia. It is more potent than chlorpromazine, but less potent than antipsychotic medications such as haloperidol (▶ Antipsychotics: Drugs, ▶ Haloperidol). Molindone has somewhat unique feature in that it is not associated with weight gain, and, indeed, patients treated with molindone may even lose weight. Molindone has been studied in children but has not been studied in children or adults with autism.
Definition Monotone speech refers to a flat prosodic pattern in which the voice varies little in pitch or rhythm. This style of speaking can come across as dry and lacking in affective qualities. Many individuals with autism spectrum disorder use this style of speech, which can make them stand out as “different” in conversational situations and can be an obstacle to forming social relationships.
See Also ▶ Intonation ▶ Prosody ▶ Singsong
See Also
References and Reading
▶ Haloperidol
Diehl, J. J., & Berkovits, L. (2010). Is prosody a diagnostic and cognitive bellwether of autism spectrum disorders? In A. Harrison (Ed.), Speech disorders: Causes, treatments, and social effects (pp. 159–176). New York: Nova Science. McCann, J., & Peppé, S. (2003). Prosody in Autism spectrum disorders: A critical review. International Journal of Language & Communication Disorders, 38(4), 325–350. Paul, R., Augustyn, A., Klin, A., & Volkmar, F. R. (2005). Perception and production of prosody by speakers with
References and Reading Martin, A., Scahil, L., & Christopher, K. (2010). Pediatric psychopharmacology: Principles and practice (2nd ed.). New York: Oxford University Press.
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2954 Autism spectrum disorders. Journal of Autism and Developmental Disorders, 35(2), 205–220. Shriberg, L. D., Paul, R., McSweeny, J. L., Klin, A., Cohen, D. J., & Volkmar, F. R. (2001). Speech and prosody characteristics of adolescents and adults with high-functioning Autism and Asperger syndrome. Journal of Speech, Language, and Hearing Research, 44(5), 1097–1115.
Monotonic Voice ▶ Monotone
Monotropism: An InterestBased Account of Autism Dinah Murray National Autistic Taskforce, London, UK
Synonyms Attention tunnelling; Ecological model; Resource distribution
Definition The idea of monotropism makes most sense in terms of a basic model which sees minds as made up of active interests shaped by their histories of paying attention in a world (and in an imagined world). This interest model of mind is ecological, embodied, and exploratory. Instead of applying emotionally charged values to categorize humans, it offers a more objective way of thinking about autistic and other human variations: it does not pathologize them. This is not just semantics, current diagnostic practice stamps “Rejected!” on the core nature of a large part of the human race, with profound repercussions, as history relates if we attend to it. The model was developed initially as an approach to relevance or salience, so it is about
Monotonic Voice
what matters to a person; it is about what you pay attention to and why. Interests in action retain – are formally changed by – information acquired as they proceed. Homeostasis and reliability of action are pervasive values which are in tension with each other as bodies move through a world viewed through the lens of prior encounters. Thus interests are adaptively informed by their engagement with attractions, emotions, and possibilities. “Interests” range from fleeting moments to lasting passions; from narrow fixations and selfseeking plots to duties and universal concerns; and communities of interest can extend from family and friends, teams, gangs, brands, and nations with long histories to casual folk at bus stops; and from mutual aid to the self-seeking vested interests of corporations and their actors, and the bureaucratic interests of the state. Interests can be aroused, expressed in the world or the imagination, and informed. Their basic resource is emotional – an energetic force; its quantity as well as its direction and “flavor” tend to incessant change in any individual or set of individuals, against relatively stable patterns of bound energy within a vast network of n-dimensions and gradients, itself a value system. The will to proceed and take some degree of risk depends on assuming reliable grounds for your actions: all that stuff you do not need to worry about, because it is already settled – sometimes referred to as your “priors” – informs all active interests. Active interests constantly probe at a person’s (or any living being’s) worldengaged edge. They involve having expectations: rough and ready and often disappointed. They can be aroused, informed, expressed/enacted, connected or disconnected, expanded or shrunk, and boosted or undermined. Emotional valences and gradients within an n-dimensional matrix change: possibilities get assessed; certainty is sought; boundaries are created; other goods are sought. Flows and waves and turbulence occur. The monotropism idea about autism proposes that a leading interest will tend to get a larger share of the scarce resource, which is diverted at boundaries creating channelled flows. The corollary of this larger share can be rich, interest based, interconnections in some areas, with real gaps in other
Monotropism: An Interest-Based Account of Autism
areas which may take others by surprise. Flows may also be held back by a significantly higher proportion of resource being assigned to “guard” the boundary relative to a non-monotropic pattern. When a boundary is at its least permeable, patterns which are hard to dislodge will emerge. Cases both of extreme avoidance and extreme attraction are likely and can be problematic as well as rewarding, even for the individual experiencing them. These patterns of resource distribution can mean virtually undistracted flow versus virtually uncrossable boundaries. The flow states can be immensely satisfying and sometimes highly productive; the barriers can be like cliffs, “frightful, sheer” (Hopkins 1880). It is particularly important to avoid catastrophic events in this “landscape” as far as possible, as they are atypically hard to recover from. Catastrophes involving sudden loss of capacity (see Thom 1976) may be precipitated by input of any sort: sensory, social, and imaginary; resources may be so scattered and unavailable, and the emotional temper so unsettled that much recovery time is necessary. A high intensity outburst is hard to get over and an inability to send out soothing vibes may get worse all round, if there is no let-up in the pressure. Monotropic people are less likely to have the resources available to repair the ruptured community of interest, therefore it is only fair to ensure they have a chance to get over it. Neurotypicals have to lead on this as they do have the resources, and must avoid projecting assumptions especially negative ones, or may risk causing lasting harm. This can be the downside of so-called “Theory of Mind”: high levels of very unreliable and often pejorative projections of meaning by “typical” people onto others (borderline “gaslighting”) are especially problematic if the others have no means to talk back. In every way possible, the more verbally capable people should gently, with minimum pressure, try to find out more from the inarticulate person and not assume they know best. The embedding of the all or nothing monotropic disposition, into a very busy environment steeped in uncertainty and egotistical calculation, causes the problems that attract a diagnosis. Like
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everybody else, autistic people prefer to operate in a reliable and generally predictable world (see, e.g., Friston 2016). Autistic people’s ability to recover from the impact of unanticipated events which seem harmless to others can be so strongly diminished that a return to capable processing is long delayed. Some of this can be worsened by social pressures from neurotypical people for autistic people to conform. Pushing someone is always the worst, least mutual way to relate to them; doing that while emitting the hostile emotional vibes of the self-righteous can cause the worst possible experience for the socially wary.
See Also ▶ Double Empathy ▶ Ecological Model of Autism
References and Reading Asma, S. T., & Gabriel, R. (2019). The emotional mind: The affective roots of culture and cognition. Cambridge: Harvard University Press. Friston, K. (2016). Commentary the Bayesian savant. Biological Psychiatry, 80, 87–89. www.sobp.org/journal Hopkins, G. M. (1880). Poem: No worst, there is none, pitched past pitch of grief. In G. M. Hopkins (Ed.), Poems and Prose. London: Penguin Classics, 1985. Lawson, W. (2001). Understanding and working with the spectrum of Autism: An insider’s view. London: JKP. Lawson, W. (2010). The passionate mind. London: JKP. Mike, L., & Dinah, M. (1998/2020). Mind as a dynamical system from Durham conference papers. Reprinted in D. Milton (Ed.), The Neurodiversity Reader. London: Pavilion, in press. Deborah, L., & Richards, W. (2009). Managing meltdowns: Using the S.C.A.R.E.D. calming technique with children and adults with Autism. Durham/Brighton: JKP & Kindle. Milton, D. et al. (2020). The double empathy problem. Encyclopedia. Miserandino, C. (2003). Cited in Memmott, A (2018), Autism and spoon theory. http://anns autism.blogspot.co.uk/2018/02/autism-and-spoon-the ory.html. Accessed 28 Feb 2018. Murray, D. (2020). Dimensions of difference. In D. Milton (Ed.), The neurodiversity reader. Brighton: Pavilion. Murray, D. 1st edition of this encyclopedia. Murray, D., Lesser, M., & Lawson, W. (2005). Attention, monotropism and the diagnostic criteria for autism. Autism, 9, 139–156.
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2956 Murray, F. (2019). Me and monotropism: A unified theory of autism. The Psychologist, 32, 44–49. Thom, R. (1976). Structural stability and morphogenesis an outline of a general theory of models. London: Benjamin. Wood, R. (2019). Inclusive education for autistic children helping children and young people to learn and flourish in the classroom. Foreword by Dr. Wenn B. Lawson, illustrated by Sonny Hallett. London: JKP. Woods, R. (2020). Commentary: Demand avoidance phenomena, a manifold issue? Intolerance of uncertainty and anxiety as explanatory frameworks for extreme demand avoidance in children and adolescents – a commentary on Stuart et al. (2019) Child and Adolescent Mental Health Volume, 2020. https://doi.org/10.1 111/camh.12368.
Monozygotic (MZ) Twins Paul El-Fishawy State Laboratory, Child Study Center, Yale University, New Haven, CT, USA
Synonyms Identical twins
Definition Twins are two individuals who are the result of the same pregnancy. Monozygotic twins each carry the identical genetic information or deoxyribonucleic acids (DNA). Monozygotic twins form as follows. When a sperm from the father fuses with an egg from the mother, they form a single cell called a zygote, the earliest stage of an embryo. Further cell divisions occur during embryonic development. At an early point in this development, the embryo can be split into two separate embryos, the cells of each having originated from the initial zygote. Each of these embryos goes on to develop into a separate and complete individual. Since each originated from the same initial cell, each individual is identical in his or her genetic composition. Barring very rare genetic occurrences, since monozygotic
Monozygotic (MZ) Twins
twins share the same genetic material, their physical appearance will be identical, and they will be of the same sex. In contrast, in the case of dizygotic twins, two separate eggs are released at one time. Each of these is then fertilized by a separate sperm and forms a distinct zygote and embryo. Since each sperm and egg carry distinct genetic material, each of the two embryos will, thus, carry distinct genetic material. On average, dizygotic twins’ genetic material is only about 50% identical, as compared to 100% for monozygotic twins. Since each dizygotic twin carries distinct genetic material, his or her physical appearance will be distinct. Dizygotic twins may be of the same sex. However, they might also be of different sexes. The occurrence of monozygotic twins is rare and more rare, in general, than the occurrence of dizygotic twins. Monozygotic twinning occurs at a rate of approximately 4 in every 1,000 live births worldwide. Monozygotic twins comprise approximately one third of all twins. Two thirds are dizygotic twins. Studies indicate that the rate of monozygotic twinning in the absence of fertility treatments is fairly uniform across different populations around the world. This is in contrast to the uneven ethnic worldwide distribution of dizygotic twinning in the absence of fertility treatments. The rate of both monozygotic and dizygotic twins has increased worldwide since the 1970s. The majority of the increase has resulted from the increase in dizygotic twins born as a result of fertility treatments. Twin pregnancies of either type increase pregnancy risk and especially the risk of preterm delivery and low birth weight. Approximately 51% of twins are born preterm compared to 9.4% in singletons.
See Also ▶ DNA ▶ Genetics ▶ Twin Studies in Autism
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References and Reading
Short Description or Definition
Alexander, G. R., Kogan, M., Martin, J., & Papiernik, E. (1998). What are the fetal growth patterns of singletons, twins, and triplets in the United States? Clinical Obstetrics and Gynecology, 41(1), 115. Bortolus, R., Parazzini, F., Chatenoud, L., Benzi, G., Bianchi, M. M., & Marini, A. (1999). The epidemiology of multiple births. Human Reproduction Update, 5(2), 179. Gilbert, S. F. (1994). Developmental biology. Sunderland: Sinauer Associates. Hall, J. G. (2003). Twinning. The Lancet, 362(9385), 735–743. Machin, G. A. (1996). Some causes of genotypic and phenotypic discordance in monozygotic twin pairs. American Journal of Medical Genetics, 61(3), 216–228. Reeve, E. C. R., & Black, I. (2001). Encyclopedia of genetics. London: Routledge. Smits, J., & Monden, C. (2011). Twinning across the developing world. PLoS One, 6(9), e25239. Strachan, T., & Read, A. P. (2004). Human molecular genetics. New York: Garland Press. Vitthala, S., Gelbaya, T. A., Brison, D. R., Fitzgerald, C. T., & Nardo, L. G. (2009). The risk of monozygotic twins after assisted reproductive technology: A systematic review and meta-analysis. Human Reproduction Update, 15(1), 45.
Autism spectrum disorders (ASDs) are a heterogeneous group of neurodevelopmental disorders characterized by impairments in social interaction and communication skills, as well as stereotypic behaviors and restricted activities and interests. Also included within ASDs are conditions which were previously classified as Asperger syndrome (AS) and Pervasive Developmental Disorder-Not Otherwise Specified (PDD-NOS), but now all contained within the diagnosis ASD (DSM-5 2013). Recent studies in Europe and the United States indicate a prevalence of ASD ranging from 1.4 to 212 per 10,000 individuals (CDC 2012; Elsabbagh et al. 2012). Males, compared to females, are circa four times more frequently affected (Rinehart et al. 2002; Williams et al. 2013). The severity specifiers (level 1: requiring support; level 2: requiring substantial support; level 3: requiring very substantial support) may be used to describe succinctly the current symptomatology, with the recognition that severity may vary by context and fluctuate over time (DSM-5 2013). Recent studies show that more than 70% of all children diagnosed with ASD have at least one and 41% have at least two comorbid psychiatric disorders (Simonoff et al. 2008). In adults with ASD, comorbid psychiatric disorders are common as well. The most common coexisting psychiatric conditions in children and in adults with ASD are intellectual disability, attentiondeficit/hyperactivity disorder (ADHD), anxiety disorders, and mood disorders (Geurts et al. 2010). This section will focus on the epidemiology, natural history, prognostic factors, outcomes, clinical expression, pathophysiology, evaluation, differential diagnosis, and treatment of mood disorders in patients with ASD.
Mood Disorder ▶ Mood Disorders
Mood Disorders Manon H. J. Hillegers, Angelo T. R. Sivathasan and Karen S. van der Aalst Department of Child and Adolescent Psychiatry/ Psychology, Erasmus Medical Center-Sophia Children’s Hospital, Rotterdam, The Netherlands
Categorization Synonyms Clinical depression; Major depression; Unipolar depression, Mood disorder
The term “mood disorder” describes a group of various psychiatric disorders characterized by mood disturbance. According to the DSM-5, this group can be divided into “depressive
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disorders” and “bipolar and related disorders.” Depressive disorders include the major depressive disorder, disruptive mood dysregulation disorder, persistent depressive disorder (dysthymia), premenstrual dysphoric disorder, substance/medication-induced depressive disorder, depressive disorder due to another medical condition, other specified depressive disorder, and unspecified depressive disorder. Major depressive disorder (also called major depression, unipolar depression, or clinical depression) is the most common depressive disorder. The disruptive mood dysregulation disorder (DMDD) resulted from the DSM-5 Work Group raised concerns that many youths with severe, nonepisodic irritable mood are inappropriately diagnosed with bipolar disorder. DMDD refers to the presentation of children with persistent irritability and frequent episodes of extreme behavioral dyscontrol which must be present for at least a year. Dysthymia is a condition characterized by a chronic low mood. The unspecified depressive disorder contains any depressive disorder that does not meet criteria for a specific mood disorder. Bipolar disorder is a complex disorder in which the core feature is pathological disturbance in mood ranging from extreme elation or mania to severe depression usually accompanied by disturbances in thinking and behavior, which may include psychotic symptoms. It is an episodic, chronic illness, usually with recovery between episodes. Bipolar disorders include the bipolar I disorder, bipolar II disorder, cyclothymic disorder, substance/ medication-induced bipolar and related disorder, bipolar and related disorder due to another medical condition, other specified bipolar and related disorder, and unspecified bipolar and related disorder. The diagnosis bipolar I disorder requires that a person has suffered one or more episodes of mania in their life with or without episodes of depression at other times. Patients with bipolar II disorder experience a milder form of a manic episode, known as a hypomanic episode as well as major depressive episodes. The diagnosis of cyclothymic disorder is given to adults who experience at least 2 years (children: 1 year) of both hypomanic and depressive periods without
Mood Disorders
ever fulfilling the criteria for an episode of mania, hypomania, or major depression (DSM-5 2013).
Epidemiology Depressive symptoms are the most common psychiatric concern in patients with ASD and are more likely to occur in adolescence and adulthood. Patients with ASD show a higher risk for developing depressive symptoms compared to the general population (Sterling et al. 2007). Prevalence estimates for any type of co-occurring depressive disorder range from 0.9% to 29% in children and adolescents, which is higher than the prevalence of depression in the general population (Leyfer et al. 2006; Simonoff et al. 2008). Likewise, 77% of outpatient adults with ASD have ever had a diagnosis of depression, compared to 46% of outpatient, non-ASD adults (Joshi et al. 2013). Reported prevalence rates of mood disorders in ASD vary due to differences in study populations, duration of follow-up, and variation in methods (Stewart et al. 2006; Geurts et al. 2010). In addition, autistic symptomatology tends to mask cardinal features of depression and presentation of depressive symptoms might be atypical in patients with ASD, causing a bias in the prevalence rates (Simonoff et al. 2008; Lainhart and Folstein 1994). In study of adults (age 16–60 years) with ASD, the lifetime prevalence rate of depression was 53% and in a younger study population this prevalence ranged from 4% to 38% (Hovander et al. 2009). Leyfer et al. (2006) showed that around 10–15% of children and adolescents (mean age 9) with ASD, ever had depressive symptoms, 10% met criteria for a major depressive disorder, and about 2% met criteria for a depression not otherwise specified. In patients with the former Asperger Syndrome (AS), the prevalence of comorbid depression is reported most frequently with a prevalence of around 30% (Matson and Nebel-Schwalm 2007). Little is known about the prevalence of dysthymia in persons with ASD. Dysthymia is less common compared to depressive disorders (Simonoff et al. 2008). Skokauskas and Frodil (2015) found the
Mood Disorders
average prevalence of bipolar disorder in persons with ASD to be 7%, with variations between 2% and 31%. It often emerges during adolescence (Vannucchi et al. 2014). Clinical studies on the comorbidity of ASD and bipolar disorder in children are scarce and in particular in the form of case reports. In the following sections, we will focus on the depressive disorder and its symptoms.
Natural History, Prognostic Factors, and Outcomes Mood disorders are episodic disorders with either a marked change in the context of type of symptoms and variability in intensity of symptoms over time (Leyfer et al. 2006; Matson and NebelSchwalm 2007). Due to difficulties in diagnosing a coexisting depression in these patients (see also the sections “Clinical Expression and Pathophysiology” and “Evaluation and Differential Diagnosis”), symptoms are often severe and/or prolonged at time of diagnosis (Skokauskas and Gallagher 2010). Clinical evidence suggests that the outcome of autism might be affected by the presence of coexisting psychiatric disorders in ASD (Ghaziuddin et al. 2002). Especially, depression can have a negative impact on long-term outcome. Effects on a patient and a patient’s family can be wide ranging. Patients with ASD can show a regression of behavioral skills, social withdrawal, and aggressive and oppositional behavior. These changes can interfere with a person’s community participation and his or her social relationships. Furthermore, depression in ASD is associated with poor quality-of-life, inpatient hospitalization, and medical illness (Greenlee et al. 2016; Righi et al. 2017). Depression may also put an autistic patient at risk for catatonia and suicide, especially when symptoms are severe. There are few reports on suicidal behavior among patients with ASD. Causes of underreporting may be the low rate of suicidal behavior among children and preadolescents and the underdiagnosing of ASD in the adult psychiatric setting. Comorbidity is often reported with mood disorders and psychotic disorders, so it is difficult to determine if
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suicidality is specifically associated with ASD (Richa et al. 2014).
Clinical Expression and Pathophysiology Symptoms of ASD and depression show a considerable overlap, which makes it harder to diagnose depressive disorders. Signs of ASD like abnormal speech patterns and social withdrawal might be confused with symptoms of depression such as psychomotor retardation or fatigue. Depressive patients with ASD show a difference in symptom presentation compared to patients without ASD, since expression of depressive symptoms is colored by ASD behavior. Many of these patients are not able to express their feelings, because of difficulties in expressing and communicating emotion due to poor integration between different modalities of nonverbal expressions and insufficient language skills to verbalize changes in feelings and mood (Perry et al. 2001; Stewart et al. 2006; Skokauskas and Gallagher 2010). As in depressed persons without ASD, in depressed patients with ASD one of the main features is a depressed mood. However, in patients with ASD, its presence is more often reported by a patient’s close relative than by self-report (Stewart et al. 2006). This is also the case with behavioral changes, appetite, and sleep disturbance. The majority of patients with ASD and comorbid depression show a decrease in self-care and increased social withdrawal. In youth with ASD, preoccupation with themes of death has also been noted during depressive periods (Ghaziuddin et al. 1995; Perry et al. 2001). When severity of depressive symptoms increases, self-esteem becomes more negative in individuals with ASD. Due to the limited communication skills experienced by many individuals with intellectual disability, symptoms of depression are likely to manifest behaviorally as a regression in adaptive skills and an increase in aggressive and selfinjurious behaviors (Magnuson and Constantino 2011; Stewart et al. 2006; Turygin et al. 2013). With the onset of depression in patients with ASD, both decrease and intensification of autistic symptoms have been reported. Specific
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depressive symptoms for individuals with ASD, might be the loss of interest in subject of previous preoccupation and a decline in rigid behavior (Ghaziuddin et al. 2002; Stewart et al. 2006). However, these changes in behavior might be often interpreted as an improvement by their close relatives; in fact, these changes could be a sign of the presence of a depression. Presented symptoms are not always mood-related. The new onset of a depression, for example, is usually associated with a new onset or exacerbation of maladaptive behavior consisting of an increase in aggressive behavior, agitation, irritability, automutilation, compulsive behavior, hypoactivity, or a decline in daily skills (Geurts et al. 2010; Ghaziuddin et al. 2002; Stewart et al. 2006). Especially, self-injury and aggression are often reported. Thus, in all individuals diagnosed with ASD, it is important to investigate whether or not behavior is related to the natural history of ASD, or has changed due to a comorbid disorder. Every ASD patient, especially those with an accompanying intellectual impairment, should be screened for depression if there is a recent history of aggressive outbursts, in the setting of irritability, appetite, and sleep disturbance. In those patients, the most frequent signs of depression are a change in level of functioning; a regression in skills such as incontinence, severe appetite, sleep, and weight disturbance; and the presence of aggression. More unusual features are catatonia and psychotic symptoms (Ghaziuddin et al. 2002). The exact pathophysiology of depression in patients with ASD remains unclear. Depression is known to be caused by genetic and environmental factors. Children diagnosed with ASD suffering from a depression are more likely to have a family history of mood disorders (Gillberg and Billstedt 2000). Autism and depression seem to cluster in some families, suggesting that in a subgroup of patients, common genetic factors seem to be responsible. However, there is also evidence for an independent genetic origin of these factors (Hallett et al. 2009). The excess of depression in these families does not seem to be related to the stress caused by raising a child with ASD. Life events play an important role in the onset of depressive symptoms. Psychological processes
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such as emotion dysregulation and intolerance of uncertainty have an impact on mood in autistic patients (Hill et al. 2004; Cai et al. 2018). Stressful situations can be overwhelming for patients with ASD, and even to mild life events, they can respond intense. Children with ASD who develop a major depression experienced more negative life events such as family illness, parental divorce, and bereavement (Ghaziuddin et al. 1995). Data on this correlation in adults have not been described yet. Autistic patients might respond with depressive symptoms to negative events because of a genetic predisposition to depression (Ghaziuddin et al. 2002). Social relationships in patients with ASD seem to be related to the development of depression as well. Interpersonal conflicts with family members have been strongly associated with depressive symptoms in children with autism spectrum disorders. In addition, autistic children with more friendships of poorer quality were found to have the highest levels of depression (Lopata et al. 2010). Factors affecting the occurrence of depression might be age, level of intelligence, social functioning, and the presence of other psychiatric or other medical disorders. Most cases of depression in patients with ASD have been described in adolescents and adults (Ghaziuddin et al. 2002; Matson and Nebel-Schwalm 2007; Sterling et al. 2007). This could be due to problems of assessment of psychiatric conditions in children with ASD. However, clinical experience suggests that the rate of depression in patients with ASD rises with age (Ghaziuddin et al. 1998; Greenlee et al. 2016). An explanation for this increase with age could be the more pronounced developmental gap between these patients and their same-aged peers and the lack in adaptive skills of executive planning abilities to take necessary steps toward independent living (Sterling et al. 2007). Some researchers have hypothesized that individuals who are higher functioning (e.g., higher verbal capacities, theory of mind, introspective capacities, social strength) may be more likely to experience depression, due to greater awareness of differences and challenges with social interactions (Lerner et al. 2017; Mazurek 2014). Also, higher
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functioning autistic adults may be more likely to endorse cognitive symptoms of depression, such as guilty feelings, sense of failure, and pessimism, compared to normal developing individuals (Gotham et al. 2015). It is not clear, however, whether a lack of insight in one’s own level of impairment in lower functioning children confers a protection from depressive states. Though, in severe autistic patients with a lower IQ, fewer depressive symptoms were reported (Magnuson and Constantino 2011). Better adaptive skills are associated with increased rates of depression as well.
Evaluation and Differential Diagnosis As previously described, a coexisting mood disorder in children and adults with ASD can be difficult to diagnose due to impairments in cognitive functions and an overlap in symptoms between different psychiatric conditions. For patients, these impairments make it difficult to describe experiences and mental state. Symptoms are often reported by a patient’s close relative instead of by the patient himself/herself. Therefore, it is important to involve the patient’s parents and teachers in the diagnostic process. Kanne et al. (2009) found that when using the Achenbach System of Empirically Based Assessment, a higher percentage of parents reported their children as having depressive symptoms than their teachers did. Using the Youth Self-Report form, Hurtig et al. (2009) found strong agreement among adolescent patients with ASD, their parents, and their teachers, indicating awareness of such difficulties. A mood diagnosis is based on clinical expression of depressive symptoms reported by a patient, its close relatives, and clinician’s observation and interpretation. Due to a lack of a “gold standard” diagnostic tool, many different diagnostic instruments have been used for assessment of a depressive disorder in patients with ASD. Traditional measures of diagnosis have, in general, not been tested for validity and reliability in ASD populations. Diagnostic criteria used to identify psychiatric disorders in developmentally disabled
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patients may identify only the “tip of the iceberg” (Leyfer et al. 2006). In children and adolescents, the Diagnostic Interview for Children and Adolescents, DICA IV, the Child Behavior Checklist CBCL, the Leiter-R-Questionnaires, the Kiddie Schedule for Affective Disorders and Schizophrenia K-SADS-PL, and the Children Depression Inventory CDI (based on the Hamilton Rating Scale for Depression) have been often used. One study used a modification of the Kiddie Schedule for Affective Disorders and Schizophrenia in children and adolescents with autism for the assessment of comorbid psychiatric disorders. They developed additional screening questions and new coding options and found and reported a sensitivity of 100% and a specificity of 93.7% for major depression in their sample (Leyfer et al. 2006). Reiss and Valenti-Hein (1990) developed the Reiss scales for the screening of comorbid psychiatric disorders in children and adolescents with intellectual disability (ID). Results from their study population suggest that these instruments are particularly well suited for screening and for helping in the analysis of the relationships between certain behavior problems and psychopathology in patients with ID (Reiss and Valenti-Hein 1990). In general, observations of behavior and activities are important in the assessment of depression because of their limited verbal abilities and their tendency to express thoughts and feelings through play. In adults, the Beck Depression Inventory (BDI) and the Hamilton Rating Scale for Depression (HRSD) are two of the most widely used scales to assess the severity of depressive symptoms (Stewart et al. 2006). The Beck Depression Inventory, a multiple choice self-report questionnaire containing 21 items relating to symptoms of depression, was found to be an adequate screening instrument for depression in adolescent and adult males with AS (Cederlund et al. 2010). However, even if questions of the BDI and the HRSD have been adapted, some questions may remain difficult for patients with ASD and their relatives. For example, these diagnostic instruments ask patients to rate their mood and if they have feelings of guilt or worthlessness. Therefore, some patients with ASD may not express these feelings
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during a depressive episode because these feelings are not in their repertoire (Leyfer et al. 2006; Stewart et al. 2006). During the lifespan, different problems related to ASD can appear resulting in differences in a person’s behavior. To determine whether a person’s behavior is due to an underlying autism spectrum disorder or due to a comorbid psychiatric condition, a clinician can assess a patient’s developmental history, focusing on the consistency of symptoms over time and pervasivity in different situations. Thus, it will be easier to determine whether a comorbid disorder was already present and if symptoms of comorbidity or ASD are changing (Geurts et al. 2010). As mentioned before, if a change in symptoms of an autistic patient occurs, it is important to screen him or her for comorbid disorders. The differential diagnosis of mood disorders in patients with ASD is comprehensive because of an atypical presentation of symptoms and includes depressive disorders, bipolar disorders, anxiety disorders, and disruptive disorders. Determined by the presence of episodic manic or hypomanic symptoms, a patient can be diagnosed with a comorbid bipolar I disorder and bipolar II disorder, respectively. When a change of or increase in symptoms is reported in an autistic patient, one should be aware that these symptoms can still exist due to an autism spectrum disorder. An increase in obsessive compulsive behavior can point towards the onset of a depression but be a sign of an obsessive-compulsive disorder as well. Anxiety is often present in patients with ASD, but coexisting anxiety and depressive disorders should be excluded. In school-aged children, a depressive disorder is characterized by agitation and externalizing behavior problems. Therefore, differential diagnostic disruptive disorders have to be considered, such as ADHD and ODD. Psychotic disorders are a part of the differential diagnosis of depression in patients with ASD as well.
Treatment There is a lack of studies that directly assess the effects of residential programs, treatment
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programs, and education programs on depressive symptoms in patients with ASD. However, there are interventions that can reduce mood and related behavioral problems in patients with ASD. Some high-functioning depressive patients with good verbal skills may benefit from psychodynamic psychotherapy. In these cases, a highly structured and directive approach is required. Structured psychotherapy combined with appropriate behavioral and educational interventions are recommended by some studies. In patients with good cognitive capacities and older patients, cognitive behavioral therapy might help them to cope with anger and other depressive symptoms. This therapy is seldom successful without integration and repeating of learned skills in ongoing treatment. Studies in adults with ASD have shown mindfulness-based therapy and social and vocational skills training to be effective for symptoms of depression, but neither intervention has been studied as a treatment for depressive symptoms in children or adolescents with ASD (Spek et al. 2013; Hillier et al. 2011). The main treatment for depression in patients diagnosed with ASD is pharmacological including selective serotonin reuptake inhibitors (SSRIs), mood stabilizers, antipsychotics, and hypnotics. SSRIs seem to be the most effective drugs in depressive adult patients with ASD (Stewart et al. 2006). These agents are being increasingly used in ASD to control depression and aggression (Ghaziuddin et al. 2002). Serotonin is linked to the mediation of psychological processes such as mood, social interaction, sleep, obsessive-compulsive behaviors, and aggression. Increased rates of platelet serotonin transport and levels of whole blood and platelet serotonin (5-hydroxytryptamine, 5-HT) have been reported in people diagnosed with ASD, and therefore, SSRIs are associated with an improvement of ASD symptoms (Williams et al. 2013). Moreover, treatment in autistic patients with depressive symptoms is in some study populations including children, adolescents, and adults associated with a decrease in low mood, sleep disturbances, and aggressive and self-injurious behavior and an increased capacity for self-care as well (Hollander et al. 2003; Perry et al. 2001; Stewart
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et al. 2006). In nondepressed ASD children, low doses of SSRIs (venlafaxine) improved repetitive behaviors, hyperactivity, and communication and language functions (Hollander et al. 2000). However, a Cochrane review found no evidence for the effectiveness of SSRIs as a treatment for children with ASD (Williams et al. 2013). This review focused on the treatment of ASD using SSRIs and did not specifically focus on a coexisting depression. The Hamilton Rating Scale for Depression (HRSD) is developed to evaluate or report depressive symptoms in nonautistic patients. Since depressive symptoms in patients with ASD show a considerable overlap with these ASD symptoms, but other important signs like increased aggressive behavior are missed, this HRSD is not very sensitive and easy to use in an autistic population. A study by Buchsbaum et al. (2001) reported no significant improvement of depressive symptoms seen in autistic adults treated with SSRIs in depression. However, using the HRSD, some small positive effects of fluoxetine and fluvoxamine on aggressive behavior and anxiety, respectively, in this study population of adults with ASD have been described. At this time, SSRIs cannot be recommended as a treatment for children with ASD in general. SSRIs are poorly tolerated, and also lack evidence in reducing restricted repetitive behaviors (Goel et al. 2018). Decisions about the use of these agents in depressed patients with ASD should be made on a case by case basis (Williams et al. 2013). Reported side effects of different SSRIs are headaches, sedation, aggressiveness, agitation, and lip dyskinesia by using citalopram. Fluoxetine can cause weight gain and increase agitation, and venlafaxine can cause aggression, nausea, and behavioral activation. Mood stabilizers such as valproate and lithium might improve symptoms related to affective instability and aggression in ASD patients; however, more research is needed on its efficacy (Canitano 2015). The atypical antipsychotic agent risperidone and aripiprazole can improve disruptive behavior in patients with ASD. Tremor, weight gain, and increased appetite are reported adverse effects (Fung et al. 2016;
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Hollander et al. 2003). A Cochrane review found that there is limited and inconsistent evidence of the effectiveness of tricyclic antidepressants (TCAs), making clear recommendations impossible at this time (Hurwitz et al. 2012). Recently, it is hypothesized that the impairment of gut microbiota plays a role in the development of ASD and mood disorders, but the mechanism through which it does is not fully clear. The application of therapeutic modulators of gut microbiota to autism and mood disorders has been experienced only in experimental settings to date, with few but promising results. Well-designed research studies with more participants are needed to provide more evidence that supports the effectiveness of these treatments (Mangiola et al. 2016; Li et al. 2017).
See Also ▶ Affective Disorders (Includes Mood and Anxiety Disorders) ▶ Antidepressant Medications ▶ Attention Deficit/Hyperactivity Disorder ▶ Broader Autism Phenotype ▶ Comorbidity ▶ Depressive Disorder
References and Reading American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: American Psychiatric Association. Buchsbaum, M., Hollander, E., Haznedar, M., Tong, C., Spiegal Cohen, J., & Wei, T. (2001). Effects of fluoxetine on regional cerebral metabolism in autistic spectrum disorders: A pilot study. International Journal of Neuropsychopharmacology, 4, 119–125. Cai, R. Y., Richdale, A. L., Dissanayake, C., & Uljarević, M. (2018). Brief report: Inter-relationship between emotion regulation, intolerance of uncertainty, anxiety, and depression in youth with autism spectrum disorder. Journal of Autism and Developmental Disorders, 48, 316–325. Canitano, R. (2015). Mood stabilizers in children and adolescents with autism Spectrum disorders. Clinical Neuropharmacology, 38(5), 177–182. Cederlund, M., Hagber, B., & Gillberg, C. (2010). Asperger syndrome in adolescent and young adult males. Interview, self- and parent assessment of social,
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2964 emotional and cognitive problems. Research in Developmental Disabilities, 31, 287–298. Centers for Disease Control and Prevention. (2012). Prevalence of autism spectrum disorders – Autism and developmental disabilities monitoring network, 14 sites, United States. Morbidity and Mortality Weekly Report, 61(3), 1–19. Elsabbagh, M., Divan, G., Koh, Y. J., Kim, Y. S., Kauchali, S., Marcín, C., et al. (2012). Global prevalence of autism and other pervasive developmental disorders. Autism Research, 5(3), 160–179. Fung, L. K., Mahajan, R., Nozzolillo, A., et al. (2016). Pharmacologic treatment of severe irritability and problem behaviors in autism: A systematic review and meta-analysis. Pediatrics, 137, S124–S135. Geurts, H. M., Deprey, L., & Ozonoff, S. (2010). De diagnostiek van comorbiditeit bij patiënten met een autismespectrumstoornis. Tijdschrift voor Psychiatrie, 52(11), 753–761. Ghaziuddin, M., Alessi, N., & Greden, J. (1995). Life events and depression in children with pervasive developmental disorders. Journal of Autism and Developmental Disorders, 25, 495–502. Ghaziuddin, M., Weidmer-Mikhail, E., & Ghaziuddin, N. (1998). Comorbidity of Asperger syndrome: A preliminary report. Journal of Intellectual Disability Research, 4, 279–283. Ghaziuddin, M., Ghaziuddin, N., & Greden, J. (2002). Depression in persons with autism: Implications for research and clinical care. Journal of Autism and Developmental Disorders, 32, 299–306. Gillberg, C., & Billstedt, E. (2000). Autism and Asperger syndrome: Coexistence with other clinical disorders. Acta Psychiatrica Scandinavica, 102, 321–330. Goel, R., Hong, J. S., Findling, R. L., & Ji, N. Y. (2018). An update on pharmacotherapy of autism spectrum disorder in children and adolescents. International Review of Psychiatry, 30(1), 78–95. Gotham, K., Unruh, K., & Lord, C. (2015). Depression and its measurement in verbal adolescents and adults with autism spectrum disorder. Autism, 19, 491–504. Greenlee, J. L., Mosley, A. S., Shui, A. M., VeenstraVander Weel, J., & Gotham, K. O. (2016). Medical and behavioral correlates of depression history in children and adolescents with autism spectrum disorder. Pediatrics, 137(Supplement), S105–S114. Hallett, V., Ronald, A., & Happe, F. (2009). Investigating the association between autistic-like and internalizing traits in a community based twin sample. Journal of the American Academy of Child and Adolescent Psychiatry, 2009(48), 618–627. Hill, E., Berthoz, S., & Frith, U. (2004). Brief report: Cognitive processing of own emotions in individuals with autistic spectrum disorder and in their relatives. Journal of Autism and Developmental Disorders, 34, 229–235. Hillier, A. J., Fish, T., Siegel, J. H., & Beversdorf, D. Q. (2011). Social and vocational skills training reduces self-reported anxiety and depression among young
Mood Disorders adults on the autism spectrum. Journal of Developmental and Physical Disabilities, 23, 267–276. Hollander, E., Kaplan, A., Cartwright, C., & Reichman, D. (2000). Venlafaxine in children, adolescents, and young adults with autism spectrum disorders: An open retrospective clinical report. Journal of Child Neurology, 15, 132–135. Hollander, E., Phillips, A. T., & Yeh, C. (2003). Targeted treatments for symptom domains in child and adolescent autism. Lancet, 362, 32–34. Hovander, B., Delorme, R., Chaste, P., Nyden, A., Wentz, E., Stahlberg, S., et al. (2009). Psychiatric and psychosocial problems in adults with normalintelligence autism spectrum disorders. BioMed Central Psychiatry, 9, 35. Hurtig, T., Kuusikko, S., Mattila, M. L., Haapsamo, H., Ebeling, H., Jussilia, K., et al. (2009). Multi-informant reports of psychiatric symptoms among highfunctioning adolescents with Asperger syndrome or autism. Autism, 13, 583–598. Hurwitz, R., Blackmore, R., Hazell, P., Williams, K., & Woolfenden, S. (2012). Tricyclic antidepressants for autism spectrum disorders (ASD) in children and adolescents (Review). The Cochrane Collaboration. Cochrane Database Syst Rev, 2012(3), Art. No.: CD008372. Joshi, G., Faraone, S. V., Wozniak, J., Petty, C., Fried, R., Galdo, M., et al. (2013). Psychiatric comorbidity and functioning in a clinically reffered population of adults with autism spectrum disorders: A comparative study. Journal of Autism and Developmental Disorders, 44, 2117–2126. Kanne, S. M., Abbacchi, A. M., & Constantino, J. N. (2009). Multi-informant ratings of psychiatric symptom severity in children with autism spectrum disorders: The importance of environmental context. Journal of Autism and Developmental Disorders, 39, 856–864. Lainhart, J. E., & Folstein, S. E. (1994). Affective disorders in people with autism: A review of published cases. Journal of Autism and Developmental Disorders, 24, 587–601. Lerner, M. D., Mazefsky, C. A., Weber, R. J., Transue, E., Siegel, M., & Gadow, K. D. (2017). Verbal ability and psychiatric symptoms in clinically referred inpatient and outpatient youth with ASD. Journal of Autism and Developmental Disorders, 48(11), 3689–3701. Leyfer, O. T., Folstein, S. E., Bacalman, S., Davis, N. O., Dinh, E., Morgan, J., et al. (2006). Comorbid psychiatric disorders in children with autism: Interview development and rates of disorders. Journal of Autism Developmental Disorders, 36, 849–861. Li, Q., Han, Y., Dy, A. B. C., & Hagerman, R. J. (2017). The gut microbiota and autism Spectrum disorders. Frontiers in Cellular Neuroscience, 11, 120. Lopata, C., Toomey, J. A., Fox, J. D., Volker, M. A., Chow, S. Y., Thomeer, M. L., Lee, G. K., Rodgers, J. D., McDonald, C. A., & Smerbeck, A. M. (2010). Anxiety and depression in children with
Mood Stabilizers HFASDs: Symptom levels and source differences. Journal of Abnormal Child Psychology, 38(6), 765–776. Magnuson, K. M., & Constantino, J. N. (2011). Characterization of depression in children with autism spectrum disorders. Journal of Developmental & Behavioral Pediatrics, 32, 332–340. Mangiola, F., Ianiro, G., Franceschi, F., Fagiuoli, S., Gasbarrini, G., & Gasbarrini, A. (2016). Gut microbiota in autism and mood disorders. World Journal of Gastroentology, 22(1), 361–368. Matson, J. L., & Nebel-Schwalm, M. S. (2007). Comorbid psychopathology with autism spectrum disorder in children: An overview. Research in Developmental Disabilities, 28, 341–352. Mazurek, M. O. (2014). Loneliness, friendship, and well-being in adults with autism spectrum disorders. Autism, 18, 223–232. Perry, D. W., Marston, G. M., Hinder, S. A. J., Munden, A. C., & Roy, A. (2001). The phenomenology of depressive illness in people with a learning disability and autism. Autism, 5, 265–275. Reiss, S., & Valenti-Hein, D. (1990). Development of a psychopathology rating scale for children with mental retardation. Journal of Consulting and Clinical Psychology, 62(1), 28–33. Richa, S., Fahed, M., Khoury, E., & Mishara, B. (2014). Suicide in autism Spectrum disorders. Archives of Suicide Research, 18(4), 327–339. Righi, G., Benevides, J., Mazefsky, C., Siegel, M., Sheinkopf, S. J., Morrow, E. M., et al. (2017). Predictors of inpatient psychiatric hospitalization for children and adolescents with autism spectrum disorder. Journal of Autism and Developmental Disorders, 48, 1–11. Rinehart, N. J., Bradshaw, J. L., Brereton, A. V., & Tonge, B. J. (2002). A clinical and neurobehavioural review of high-functioning autism and Asperger’s disorder. Australian and New Zealand Journal of Psychiatry, 36, 762–770. Simonoff, E., Pickles, A., Charman, T., Chandler, S., Loucas, T., & Baird, G. (2008). Psychiatric disorders in children with autism spectrum disorders: Prevalence, comorbidity, and associated factors in a populationderived sample. Journal of the American Academy of Child and Adolescent Psychiatry, 47, 921–929. Skokauskas, N., & Frodil, T. (2015). Overlap between autism spectrum disorder and bipolar affective disorder. Psychopathology, 48, 209–216. Skokauskas, N., & Gallagher, L. (2010). Psychosis, affective disorders and anxiety in autistic spectrum disorders: Prevalence and nosological considerations. Psychopathology, 43(1), 8–16. Epub 2009 Nov 6. Review. Spek, A. A., van Ham, N. C., & Nyklicek, I. (2013). Mindfulness-based therapy in adults with an autism spectrum disorder: A randomized controlled trial. Research in Developmental Disabilities, 34, 246–253. Sterling, L., Dawson, G., Estes, A., & Greenson, J. (2007). Characteristics associated with presence of depressive
2965 symptoms in adults with autism spectrum disorder. Journal of Autism and Developmental Disorders, 38, 1011–1018. Stewart, M. E., Barnard, L., Pearson, J., Hasan, R., & O’Brien, G. (2006). Presentation of depression in autism and Asperger syndrome: A review. Autism, 10, 103–116. Turygin, N. C., Matson, J. L., MacMillan, K., & Konst, M. (2013). The relationship between challenging behavior and symptoms of depression in intellectually disabled adults with and without autism spectrum disorders. Journal of Developmental and Physical Disabilities, 25, 475–484. Vannucchi, G., Masi, G., Toni, C., Dell’Osso, L., et al. (2014). Bipolar disorder in adults with Asperger’s syndrome: A systematic review. Journal of Affective Disorders, 168, 151–160. Williams, K., Brignell, A., Randall, M., Silove, N., & Hazell, P. (2013). Selective serotonin reuptake inhibitors (SSRIs) for autism spectrum disorders (ASD) (Review). The Cochrane Collaboration. Cochrane Database Syst Rev, 2013(8), Art. No.: CD004677.
Mood Stabilizers Lawrence David Scahill Nursing and Child Psychiatry, Yale Child Study Center, Yale University School of Nursing, New Haven, CT, USA Marcus Autism Center, Children’s Healthcare of Atlanta, Atlanta, GA, USA Department of Pediatrics, Emory University, Atlanta, GA, USA
Definition Mood stabilizer is a term used to describe a group of medications that are used in the treatment of bipolar disorder. Currently, mood stabilizers including lithium, valproic acid, and its derivatives, carbamazepine and several antipsychotic medications, are now approved for the treatment of bipolar disorder.
See Also ▶ Lithium
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References and Reading Kowatch, R. A., Strawn, J. R., & Danielyan, A. (2011). Mood stabilizers: Lithium, anticonvulsants and others. In A. Martin, L. Scahill, & C. Kratochvil (Eds.), Pediatric psychopharmacology: Principles and practice (pp. 297–311). New York: Oxford University Press.
Moral Cognition Ulrich Max Schaller1 and Reinhold Rauh2 1 Department of Psychiatry and Psychotherapy Medical Center, University of Freiburg, Faculty of Medicine University of Freiburg, Germany, Freiburg, Germany 2 Department of Child and Adolescent Psychiatry, Psychotherapy, and Psychosomatics; Medical Center, University of Freiburg, Faculty of Medicine University of Freiburg, Germany, Freiburg, Germany
This chapter is intended to show what is to be understood by the term of moral cognition, as it has evolved throughout history, philosophy, and psychology, how it is connected to autism spectrum disorders, and how novel approaches in moral research can improve knowledge and understanding on relationships between moral cognition and pathogenetic models of ASD. A glance at the current research literature underlines two topical issues: First, moral research in the field of ASD is still a niche product, and second, while many of the studies suggest a deficit in moral reasoning, only few try to analyze the causes behind these deficits in a way that also considers the content of the presented moral tasks.
Definition A universally valid and uniform definition of what is meant by the term morality is virtually impossible because of so many different approaches and theories arising from disciplines like philosophy, psychology, and sociology. However, it is undisputed that in most definitions,
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morality is described as a regulatory, normreferenced, and value-oriented system which accrues from cultural and religious experience in the context of a social community and serves as a code of conduct within this very community (Gert 2005; Gert and Gert 2017). From the perspective of an individual, moral is a special type of communication that enables one to express respect or disrespect for a person or an action. It is therefore a binary code of good and evil, respectively, good and bad. The repertoire of moral values arises from conditioning and acquisition of conditions of respect and disrespect (Luhmann 2008). Concerning the concept of moral cognition, there is also no unequivocal definition. But, most researchers in the field would agree with Greene (2015, p. 40) who states: “. . . the field of moral cognition does not study a distinctive set of cognitive processes. [. . .] Instead, it studies a set of psychological phenomena bound together by a common function.” And many proponents of this functional characterization would argue that the core function of morality is to promote and sustain cooperation (see, e.g., Greene 2015). This view also entails that there is no specialized “moral organ” (Hauser 2006), but to the contrary “that the ‘moral brain’ is, more or less, the whole brain, applying its computational powers to problems that we, on non-neuroscientific grounds, identify as ‘moral’.” (Greene 2015, p. 1013). Based on this functional characterization of the concept of moral cognition and emphasizing the role of cooperation, it is clear that moral cognition and social cognition and their developmental courses are heavily entwined: The beginning of human life is in stark contrast to the fully expanded potential a human being can develop over lifetime. We are physiologically premature, deficient, and obligatory interdependent (Gehlen 2014; Portmann 1941). Without social bonding, intense care, and dissemination of information, we would be unable to bring into being this potential. Thus, human beings are innately intended to fit into a social and cultural context. Therefore cooperation and pro-social behavior has been an evolutionary advantage and can be seen as behavioral expression of social cognition. Social cognition can be regarded as a dynamic constructive and conceiving process of
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perception, categorization, recall, and evaluation of social stimuli, and it is a prerequisite for moral cognition (Schaafsma et al. 2015). The combination of implicit, automatic, and perceptual categories with explicit categories of attention and experience of agency and concentration has influence on the way we perceive social situations with moral content. From a psychological point of view, moral or moral behavior can be seen as a function of mind and environmental structures. The objective is that (moral) decisions have to be made in an uncertain world under the constraints of limited knowledge, resources, and time. What is at issue is the match or mismatch of social cognition with the structure of a social environment (Gigerenzer 2010). The difficulties in communication and social reciprocal interactions, typically associated with a diagnosis of ASD, already indicate that especially the preconditions for normative moral assessments, namely, adequate perception and categorization of social stimuli, do not appear to be available. The result could be altered moral judgments and moral behavior in individuals with ASD.
Historical Background Since human beings have developed writing, they have registered codes of conduct. Listing, how to behave in a particular situation under certain circumstances, was not only a way to develop a complex progression of rules in terms of social coexistence but also an effective instrument to influence the perceptions, emotions, and intuitions (Haidt and Kesebir 2010) of the society for which those codices were written. These virtue-based and descriptive considerations of moral were always anchored in a cultural and religious context and reached from classical antiquity to modern times. ln the late eighteenth century, moral philosophy tried to separate the code of conduct from cultural and religious influences and the philosophical approaches consider moral either from a deontological or a consequential perspective. The deontological perspective – with its most important representative
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Kant (1788/ 2003) – focuses on duties and prohibits actions that don’t comply with certain rights and obligations, while the consequential approach focuses on the best possible outcome and judges actions only by their consequences (Bentham 1823/ 2008). Although there were first accounts by Freud (1923/ 2001) and Durkheim (1925/ 1973) for a psychological and sociological perspective on moral, the first empirical attempts were conducted by Piaget (1932/ 1965) and Kohlberg and Kramer (1969) from a developmental perspective: In Piaget’s studies on “genetic epistemology,” up to 6 year old typically developing children judged the culpability of a person by rather use of the outcome than use of the motive. Kohlberg’s method was to interview children, adolescents, and adults about hypothetical moral dilemmas. They investigated moral development by analyzing the answers of the participants explaining their decisions. Based on the resultant narrations, Kohlberg formulated a theory of moral development in the sense of universally ordered stages, including pre-conventional, conventional, and postconventional moral. However, these rationalistic psychological theories of moral development were heavily criticized and do not have many proponents anymore, whereas some of their methods, especially the presentation of moral dilemmas, were maintained and are still applied in many studies. The complexity of moral cognition can be illustrated by means of being confronted with moral dilemmas: Participants confronted with a moral dilemma have to deal with many different aspects of social cognition. The common band of all dilemmas is the conflict, respectively, the unsolvability of the given problem associated with antagonistic moral rules, making it impossible not to fail in one of the two options of action.
Current Knowledge In a commentary on the annual research review of Happé and Frith (2014), Charles A. Nelson III (2014) describes moral as a highly complex behavior. He suggests that high-level social abilities are dependent on more elementary core
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social constructs. Furthermore, he argues that many aspects of social development are experience-expectant or experience-dependent. In his book A natural history of human morality, Michael Tomasello (2016) describes the development of human values and norms from an evolutionary perspective. He suggests that development of human morality took place in a two-stage process, whose beginning is marked by the ability to be compassionate and empathetic: The joint intentionality of human beings is based on the principle to project one’s self into the other’s perspective. Both partners have to know the normative standards of each role. The ability to cooperate and to take joint action allows for a first step of what is called a second-personal morality. The second step in the history of moral development took place 150,000 years ago, following from the enlargement of the social unions and tribes. Within these cultural associations, a collective intentionality developed from the second-personal morality that yielded cultural conventions, norms, and institutions (Searle 1995). This group-related objective morality describes what is seen as good or morally adequate within these cultural associations. However, a closer consideration of moral reasoning reveals a large number of underlying highly complex processes: The moral content of a social situation has to be perceived and categorized. Actual information content needs to be adjusted on the basis of internalized social norms, own experience, and memory. Various possible courses of action must be balanced against the perceived situation and compared with imagined and anticipated consequences of a possible decision (Beißert and Hasselhorn 2016; Derryberry et al. 2005). Since Beißert and Hasselhorn (2016) have provided evidence that moral development measured with everyday life moral transgression scenarios is not correlated with general intelligence, it must be assumed that processes of moral reasoning follow a different principle. The spectrum of social context expands with advancing age. Besides other members of the family, this can be kindergarten, school, narrow circle of friends, peer group, and media. The acquisition
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of these values and moral rules from the extended social environment develops through imitation and identification (Oerter 1974). Kohlberg and Kramer (1969) also assume that even adolescents and adults process and internalize those values and rules predominantly without undertaking an explicit analysis of meaning and source of these behavior rules. Thus, if the majority of moral impartation takes place by means of expressed emotions, the recipient of moral values must be able to “read” human behaviors (Schaafsma et al. 2015), especially those expressing emotions like respect or contempt. Being a crucial component of human emotional experience and social interaction, empathy is defined as the ability to share affective states and allows us to predict and understand the feelings, motivations, and actions of others (Bernhardt and Singer 2012). In the range of moral research, there is sufficient evidence that empathy rather than the ability to mindreading is a necessary aspect for adequate moral reasoning (Batson et al. 1983; Haidt and Kesebir 2010; Myyry et al. 2010). While Kohlberg represented a rationalistic approach, defined as conviction that explicit and deliberate reasoning is the most important and most reliable way to gain moral knowledge, in the 1980s and especially in the 1990s, many researches began to doubt on rationalism, so that implicit cognitive processes became more and more the focus of scholarly interest in reasoning, judgment, and decision-making in general and in moral cognition in particular. The following current psychological theories of moral cognition have all in common that they emphasize the role of implicit, automatic processes. They differ however with respect (i) to which implicit processes are involved (affective vs. non-affective processes) and (ii) to which degree implicit processes impact moral cognition (exclusive vs. complementary). At present, the following three psychological theories of moral cognition are discussed: (1) the theory of moral grammar (Hauser 2006; Mikhail 2007); (2) the “social-intuitionist” theory by Haidt (2001); and (3) dual-process accounts, especially the variant put forward by Greene et al. (2001).
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The theory of moral grammars assumes an innate and universal set of abstract principles on the one hand and culturally switchable parameters on the other for building specific instances of moral systems (Hauser 2006; Mikhail 2007). Hauser writes (2006, p. 298): “Every newborn child could build a finite but large number of moral systems. When a child builds a particular moral system, it is because the local culture has set the parameters in a particular way. If you are born in Pakistan, your parameters are set in such a way that killing women who cheat on their husbands is not only permissible but obligatory, and the responsibility of family members.” Once the parameters have been set, the grammar automatically and unconsciously generates judgments of right and wrong for an infinite variety of acts and inactions. Therefore, moral judgments don’t depend neither on conscious reasoning, nor on emotions. Instead, moral judgments trigger emotions, which are “downstream, pieces of psychology triggered by an unconscious moral judgment” (Hauser, pp. 30–31). Consequently, emotions are a subsequent process stage resulting from a preceding unconscious and automatic moral grammar module. The social intuitionist model (SIM) by Jonathan Haidt (2001) emphasizes the role of emotion with recourse to David Hume (1739/2011), who claimed that “reason is the slave of passion.” Consequently, moral cognition is based on an implicit, automatic process (e.g., passion, emotion, or intuition). Furthermore, the degree of emotionality depends on the type of moral dilemmas presented to the participants (Greene et al. 2001; Shenhav and Greene 2014). Conscious reasoning about moral concerns is a subsequent process preceded by (moral) intuitions, being “the sudden appearance in consciousness of a moral judgment, including an affective valence (good-bad, like-dislike), without any conscious awareness of having gone through steps of searching, weighing evidence, or inferring a conclusion” (Haidt 2001, p. 818). Consequently “moral intuitions (including moral emotions) come first and directly cause moral judgments” (Haidt 2001, p. 814). The dual-process theory for moral decisions of Greene et al. (2001) also operates with the
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distinction between cognitive and affective subcategories. Dual-process theories differentiate between fast automatic inferences describable as heuristics and slower, conscious decisions demanding complex computations caused by normative principles such as “thou shall not kill” (Kahneman 2011). Greene et al. (2001) states that the influence of affect on moral judgments depends on given contextual factors that frame the moral dilemma. Depending on the personal emotional involvement, the judgment on the one hand can follow utilitarian approaches if the contextual factors suggest low emotional involvement, and on the other hand the judgment is based on categorical principles if the context indicates high emotional involvement. According to this, moral actions in which the individual is involved very closely and personally might be driven by an emotional and automatic component and as opposed to this actions that are remote and impersonal elicit a rational decision. Greene assumes that conscious and rational decisions are utilitarian and on the contrary a high level of emotionality and participation evokes a more categorical behavior, thus turning Kant upside down and putting him on his feet; in other words, rationality is the basis of utilitarian decisions, and emotional involvement leads to categorical moral decisions and actions. FMRI studies have shown that moral reasoning correlates with the representation of emotions and intuitions (Reniers et al. 2012) and that the degree of emotionality depends on the type of moral dilemma presented to the participants (Greene et al. 2001; Shenhav and Greene 2014). Nevertheless a meta-analysis of imaging studies, concerning different aspects of moral thought (Bzdok et al. 2012), comes to the conclusion that due to neurotopographical aspects, the neural network of moral decisions is domain-global and can be divided in cognitive as well as affective subsystems. From a psychological perspective, the task to make a moral judgment can only then be successful if the one who judges is able to empathize with the person on whom the moral judgment has to be imposed (Krahn and Fenton 2009). From an evolutionary point of view, the
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ability to moral behavior and moral reasoning can be derived from the interdependence hypothesis (Tomasello 2016): All social species within a social group are mutually dependent regarding their survival. The need of collaborative effort urged early humans to cooperate and thus to consider and anticipate the needs of their counterpart. In order to develop a theory of mind that is able to draw conclusions about emotions, thoughts, and intention of the cooperating partner, especially in complex and dangerous situations, it was necessary to decode and categorize facial expression, gesture, and body posture and to derive an inner state of the counterpart. Consequently, if ToM is necessary for moral reasoning and moral judgment, then individuals with ASD should have difficulties to pass moral judgments in the same way as typically developed individuals. Kohlberg’s approach assumes well-developed language and expressive abilities. Since individuals with ASD have deficits in communication skills (WHO 1993), they can make less use of words describing mental states. Since Takeda et al. (2007) could show significant correlations between moral reasoning and verbal ability in ASD, it is obvious that the linguistic quality of their replies leads to lower scores compared to typically developed peers (Shulman et al. 2012). Turiel (1983) emphasized the ability to distinguish between moral norms on the one hand and mere social conventional norms on the other. Therefore, he developed tasks, where the display takes the form of pictorial representations, and in addition to the description of those pictorial-presented transgressions, child participants were asked for an evaluation of the seriousness of the transgression. The first use of this task including individuals with ASD showed that children with ASD are able to differentiate between moral and conventional transgressions. Furthermore, there was no correlation between theory of mind abilities (level of ability to solve false belief tasks) and the tendency to differentiate between moral and conventional transgressions (Blair 1996). Typically developed individuals base their judgments of the moral acceptability of behavior
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on their emotional response to that behavior. Brewer et al. (2015) suggest that individuals with ASD do not seem to use emotional information and may rely more on rules to judge moral acceptability. Others tend to explain differences between neurotypically developed individuals and individuals with ASD by analyzing the justifications for a moral decision: While participants with typical development explained their decisions with significantly more abstract rules, participants with ASD tended to nonspecific condemnations of the described behavior to justify their judgment (Shulman et al. 2012). In their theoretical analysis Autism, empathy and questions of moral agency, Krahn and Fenton (2009) propose a distinction between cognitive and affective empathy. Dziobek et al. (2008) showed that individuals with high-functioning autism have access to affective empathy but reveal deficits in cognitive empathy. Based on this evidence, it is to be assumed that there is a capability in ASD to show empathy which is a prerequisite for moral cognition and moral behavior. Suggesting that HFA are capable of moral agency, they encourage to find methods to unfold the term of morality. Based on this evidence, it is to be assumed that there is a capability in ASD to show empathy which is a prerequisite for moral cognition and moral behavior. A further study focused on the difference between quantitative results and reported personal experience. The results in terms of quantitative analysis suggest that adolescents with ASD demonstrate similar empathetic concern like typically developed peers but have significantly higher personal distress and deviant moral reasoning. In the qualitative survey, adolescents with ASD see themselves as individuals with empathetic concern but without the ability to use these feelings in sociomoral situations (Senland and Higgins-D’Alessandro 2013). The question of the role of implicit processes in moral reasoning remains open. It is still unclear to what extent empathy and the subsequent moral judgment depend on the social relationships of the protagonists depicted in the moral tasks or dilemmas, respectively what access do
Moral Cognition
participants with ASD have to such social schemas of relationship. In a study by Patil et al. (2016), hypothetical moral choices were investigated in adults with high-functioning autism, and here again, the role of empathy was the focus of the investigation. The results suggest that adults with ASD are able to differentiate between personal and impersonal dilemmas, they make similar behavioral choices compared to controls, and they exhibit equivalent moral judgments despite deficits in social cognition and emotional processing. Thus again, the claim of Krahn and Fenton (2009) for an unfolding of moral cognition and social cognition in terms of research in ASDs should be addressed. Although compared to other areas of social cognition the number of studies that survey moral reasoning are few and far between, nevertheless there is evidence that the impairment in understanding others’ mind hinders the development of an intent-based moral judgment in children with ASD (Margoni and Surian 2016). People with ASD do not seem to use emotional information and may rely more on rules to judge moral acceptability (Brewer et al. 2015). In the study of Fadda et al. (2016), children with ASD who showed difficulties in perspective-taking, nevertheless were able to take account for the consequences of actions but not for psychological information, in particular the subjective state of an agent in a task, where they should judge the consequences of moral and non-moral actions. Baez et al. (2012) describe a pattern of social cognition deficits characterized by the decreased ability to implicitly encode and integrate contextual information in order to gain access to the social meaning. Comparing moral judgments and the evaluation of those by adults with ASD and neurotypical controls, regarding intentional action of protagonists in moral situations, only subtle differences had been found (Buon et al. 2013). In a qualitative analysis of self-rating the own empathetic concern in sociomoral situations, individuals with ASD envision themselves as empathetic but unable to apply those feelings in moral reasoning conditions (Senland and Higgins-D’Alessandro 2013).
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Future Directions The approaches of Haidt (2001) and Greene et al. (2001) seem to be well suited for appropriate experimental designs to investigate moral cognition of individuals with ASD, especially since the emphasis of their accounts is on emotions. In contrast the application of the moral grammar model by Hauser (2006) appears to be a less researched issue regarding the performance of ASD. A new approach to unfold morality and emotion is the psychological constructionist account of Cameron et al. (2015). It proceeds from a general correspondence of morality and emotion, emerging from basic cross-domain psychological processes. Perception, memory, emotions, and moral judgments are not separate and isolated entities but emerge from a combination of basic psychological elements that can be combined flexibly and yield different mental states (Barrett and Satpute 2013). The relevant ingredients of this large-scale, domain-general network can always be newly combined, creating a coherent system of functional properties matching with every perceived situation. The interaction of a core affect and conceptualized knowledge in a specific situation leads to discrete emotions and an ensuing judgment of what was perceived. In this context core affect means a physiological state and a non-reflective feeling (Russell 2003), perceptible as valence (positive versus negative) and arousal (high versus low). Important for the process of situative conceptualizing is the transformation of core affect by means of conceptual knowledge comprising general semantic knowledge, autobiographical memories, and situationspecific knowledge (Lindquist 2013). Thus, moral reasoning evoked by moral dilemmata induces a neurophysiological state of valence and arousal, and reconciled with conceptual knowledge, it leads to a moral judgment. A closer look upon the constructionist model of core affect and conceptual knowledge shows that there is an evident congruousness with the concept of schemas. The combination of affective states with conceptual knowledge can be understood as a constructionist pattern containing causal representations that
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epitomize integrative situational models. Following the definition of Dienstbier et al. (1980), a schema is a “complex of values, attitudes, cognitions, and affective responses that are elicited by and interact with new relevant information. That interaction determines the resultant attributions, decisions, and behaviors” (Dienstbier et al. 1980, p. 194). Schemas represent knowledge of all kinds of content, from ideologies to culture and from behavior in social situations to the meaning of a certain word. Schemas do not only include structural elements but also have a processual component and for this unfold several executive functions (Spada and Mandl 1988, p. 126). Development of social cognition as a prerequisite of moral abilities finds its social world of experience in numberless commonly experienced situations that comprise moral values, attitudes, and the affective disposition of the perceiver, having impact on the development of moral schemas. The development of mindreading is a seminal process including qualitative changes in representational ability (Christensen and Michael 2016), and the construction of integrated situation models requires access to a theory of mind (ToM). Since a schema serves as an implicit (intuitive) automatism to categorize social perceptions, Kintsch and van Dijk (1978) consider schemas as a construction of a framework that integrates information and highlights key relationships in order to provide a model for certain social situations (e.g., moral dilemmas). If so, a person who lacks adequate schemas fails to pick out, structure, and organize the relevant causal factors of a situation and will be unable to understand the context and to predict how the situation will develop. In order to make such multilayered details of a moral problem present, an investigation of holistic inferential processes, having recourse on social schemas, can show what kind of social schemas are deficient in ASD. Using a schema-theoretical perspective in the field of moral cognition, patients with ASD seem to show a different decision and response behavior, particularly when the dilemmas are based on social schemas involving close social relationships (Schaller et al. 2019).
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In order to determine the influence of implicit and explicit processes, moral grammar, and social schemas on moral decision-making in its overall complexity, future studies should develop refined methodological procedures to unfold the factors influencing the process of moral cognition.
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2974 autistic spectrum disorder. Frontiers in Psychology, 7 (SEP), 1–5. https://doi.org/10.3389/fpsyg.2016.01478. Mikhail, J. (2007). Universal moral grammar: Theory, evidence and the future. Trends in Cognitive Sciences, 11(4), 143–152. https://doi.org/10.1016/j. tics.2006.12.007. Myyry, L., Juujärvi, S., & Pesso, K. (2010). Empathy, perspective taking and personal values as predictors of moral schemas. Journal of Moral Education, 39(2), 213–233. https://doi.org/10.1080/ 03057241003754955. Nelson, C. A. (2014). Commentary: Becoming social–a commentary on Happé & Frith (2014). Journal of Child Psychology and Psychiatry, and Allied Disciplines, 55(6), 578–581. https://doi.org/10.1111/ jcpp.12254. Oerter, R. (1974). Moderne Entwicklungspsychologie (9. Auflage ed.). Donauwörth: Auer. Patil, I., Melsbach, J., Hennig-Fast, K., & Silani, G. (2016). Divergent roles of autistic and alexithymic traits in utilitarian moral judgments in adults with autism. Scientific Reports, 6, 23637. https://doi.org/10.1038/ srep23637. Piaget, J. (1965). The moral judgement of the child (trans: Gabain, M.). New York: Free Press (original work published 1932). Portmann, A. (1941). Die biologische Bedeutung des ersten Lebensjahres beim Menschen. Schweizerische Medizinische Wochenschrift, 71(1941), 921–924. Reniers, R. L. E. P., Corcoran, R., Völlm, B. A., Mashru, A., Howard, R., & Liddle, P. F. (2012). Moral decision-making, ToM, empathy and the default mode network. Biological Psychology, 90(3), 202–210. https://doi.org/10.1016/j.biopsycho.2012.03.009. Russell, J. A. (2003). Core affect and the psychological construction of emotion. Psychological Review, 110(1), 145–172. https://doi.org/10.1037/ 0033-295X.110.1.145. Schaafsma, S. M., Pfaff, D. W., Spunt, R. P., & Adolphs, R. (2015). Deconstructing and reconstructing theory of mind. Trends in Cognitive Sciences, 19(2), 65–72. https://doi.org/10.1016/j.tics.2014.11.007. Schaller, U. M., Biscaldi, M., Fangmeier, T., Tebartz van Elst, L., & Rauh, R. (2019). Intuitive moral reasoning in high-functioning autism spectrum disorder: A matter of social schemas? Journal of Autism and Developmental Disorders, 49(5), 1807–1824. https://doi.org/10.1007/s10803-01803869-y. Searle, J. R. (1995). The construction of social reality. New York: Free Press. Senland, A. K., & Higgins-D’Alessandro, A. (2013). Moral reasoning and empathy in adolescents with autism spectrum disorder: Implications for moral education. Journal of Moral Education, 42(2), 209–223. https://doi.org/10.1080/03057240. 2012.752721. Shenhav, A., & Greene, J. D. (2014). Integrative moral judgment: Dissociating the roles of the amygdala
More Than Words and ventromedial prefrontal cortex. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 34(13), 4741–4749. https://doi.org/10. 1523/JNEUROSCI.3390-13.2014. Shulman, C., Guberman, A., Shiling, N., & Bauminger, N. (2012). Moral and social reasoning in autism spectrum disorders. Journal of Autism and Developmental Disorders, 42(7), 1364–1376. https://doi.org/10.1007/s10803-011-1369-8. Spada, H., & Mandl, H. (1988). Wissenspsychologie. In H. Mandl, & H. Spada (Eds.), München: Beltz Psychologie Verlags Union. Takeda, T., Kasai, K., & Kato, N. (2007). Moral judgment in high-functioning pervasive developmental disorders. Psychiatry and Clinical Neurosciences, 61(4), 407–414. https://doi.org/10.1111/j.1440-1819. 2007.01678.x. Tomasello, M. (2016). A natural history of human morality. Cambridge, MA: Harvard University Press. Turiel, E. (1983). The development of social knowledge: Morality and convention. Cambridge: Cambridge University Press. World Health Organization (WHO). (1993). The ICD-10 classification of mental and behavioral disorders. Diagnostic criteria for research. Geneva: World Health Organization.
More Than Words Kelly Macy Department of Communication Sciences, The University of Vermont, Burlington, VT, USA
Definition More Than Words is a parent-based socialpragmatic intervention program for children with autism spectrum disorders (ASD). It includes education and social support for parents and early language intervention for children with ASD under the age of 5. The program is led by a Hanen-certified speech-language pathologist (SLP) and aims to help parents learn more about communication and, in turn, support their children in increasing their language and communication skills.
Historical Background The Hanen Centre is a Canadian charitable organization that was founded in 1975 by Ayala Hanen
More Than Words
Manolson to increase knowledge and training of adults to help children become more effective communicators. More Than Words was adapted from another Hanen program, It Takes Two to Talk, to help meet the needs of parents and their children on the autism spectrum. The parent guidebook was written by Fern Sussman, a speech-language pathologist, and was published in 1999 by the Hanen Centre in Toronto, Ontario (The Hanen Centre 2007).
Rationale or Underlying Theory The goal of this program is to “empower parents to become the primary facilitator of their child’s communication and language development thereby maximizing the child’s opportunities to develop communication skills in everyday situations” (The Hanen Centre 2007). More Than Words reflects a family-centered model of intervention. This program believes that since the parent or caregiver is the most important and constant element in the child’s life, they have an invaluable opportunity to provide consistent interventions in a natural setting to assist their children in experiencing meaningful interactions. A social-pragmatic theory of language acquisition is the theoretical foundation for More Than Words, asserting that the development of communication occurs in the context of the interaction between the child and the important adults in his or her life. Strategies taught in the program are intended to help parents become more responsive to their children’s communication attempts in order to facilitate their language skills. Parents also learn ways to manipulate the environment to increase joint attention and the motivation to communicate.
Goals and Objectives The More Than Words program outlines three main objectives for parents in helping to support their children’s communication skills: parent education, early language intervention, and social support for parents (The Hanen Centre 2007). As part of the parent education component, parents develop a
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better understanding of their child’s strengths and challenges by learning about their child’s learning style and sensory preferences. They also learn about basic concepts relating to language and communication and what stage of communication their child is in. This helps them to set goals and recognize communication attempts. The second objective of More Than Words is for parents to carry out early communication and language intervention. The Hanen-certified speech-language pathologist (SLP) works collaboratively with parents to develop goals and implement responsive strategies that can be applied to interactions with their child across a variety of contexts. This is a flexible process and is intended to become a consistent, yet natural part of the dayto-day exchanges between the parent and child. As part of this intervention, parents video-record interactions and strategy implementation with their child. The SLP then reviews the video with the parents and provides feedback to help increase their awareness of their interactions along with the communicative behaviors demonstrated by their child. The SLP facilitates the parents’ metacognitive thinking in the video review to encourage their consistent use of the new strategies. The third objective is to provide social support for parents, who are at greater risk for fatigue, frustration, depression, and anxiety than parents without a child with developmental disabilities. Support within this context is valuable because it comes from a professional who understands the unique strengths and challenges of their child, as well as from other parents in the group who can empathize and share experiences.
Treatment Participants More Than Words was designed for parents and their children under the age of 5 with ASD. It can be used with children who are verbal or nonverbal.
Treatment Procedures The program is led by a Hanen-certified SLP and is offered to groups of up to eight families at one
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time. There are three major components of this program. First, there is a preprogram assessment and baseline video recording of an interaction between the parent and child. Next, parents participate in seven sessions of group training, totaling a minimum of 17.5 h. These sessions are led by the SLP and include discussion, PowerPoint presentations, and video clips that aim to help parents learn about early communication and language, how to manipulate the environment to help their child communicate, identify their child’s stage of communication, and how to set realistic communication goals. The parents’ interactions with their children are video recorded, and three of these recordings are reviewed with the SLP. As part of the video feedback sessions, the parent observes and then describes the strategies they implemented with their child. The SLP probes parent understanding of what was observed and how what a parent did either facilitated or failed to facilitate communication change. The SLP provides coaching to the parents, based on the strategies taught during the parent education sessions. The program is supported by a guidebook, DVD, and PowerPoint slides with video clips.
Efficacy Information This program seeks to target the communication challenges that are present in children with autism spectrum disorders. The guidebook provides illustrations and step-by-step examples, giving parents a readable and easy to understand resource with opportunities to practice what they learn in the context of everyday activities. Intervention strategies used in this approach, including modeling, reinforcement, and imitation, are commonly used best practices in early education and speechlanguage therapy and are directly related to the individual child’s communication goals. The video-recorded interactions between the child and parent are periodically reviewed by the certified SLP, ensuring that the strategies are implemented in an effective and consistent manner. Two studies have examined the effectiveness of More Than Words for families and children with ASD. The first examined the facilitation of parental understanding of ASD and the social
More Than Words
communication of their children following implementation of the More Than Words program (McConachie et al. 2005). Results indicated that parents increased their use of facilitative strategies and children with ASD increased their vocabulary size. A second study found similar results for three families of children 2.8–3.2 years of age with parents increasing their use of responsive strategies and children increasing their vocabulary (Girolametto et al. 2007).
Outcome Measurement A number of studies provide empirical evidence demonstrating positive outcomes of a socialinteractionist intervention for children with ASD (Chapman et al. 1986; Duchan 1989; Mirenda and Dollennan 1986). There is also a recent body of literature that supports this type of intervention being successfully carried out by parents (Aldred et al. 2004; Mahoney and Perales 2003). Two published studies specifically examine the efficacy of More Than Words. The first was a controlled trial measuring the effectiveness of parent training on the use of facilitative interaction strategies to enhance the communication skills and behaviors of their children (McConachie et al. 2005). This quasi-experimental study included 26 children and parents in an experimental group and 25 children and parents in a control group. Parents in the experimental group participated in the More Than Words training program. The results showed a measureable effect on both the parents’ and their children’s communication skills. The parents of children diagnosed with ASD showed an increase in responsiveness. The children whose parents attended More Than Words had significantly greater vocabulary size than those in the control group, as measured by parental report on the MacArthur-Bates Communicative Development Inventory (CDI) (McConachie et al. 2005). The CDI is a frequently used measure of word and gesture comprehension and word and sentence production that is used to assess outcomes following the More Than Words program. Another study examined the social interaction of three preschool children with ASD following
Moro Reflex
their mothers’ participation in More Than Words (Girolametto et al. 2007). This was a multiple case study design that aimed to extend the previous study by using microanalytic coding procedures on the recordings of parent–child interactions. The results found that mothers increased their use of spontaneous interaction strategies and their children demonstrated increased vocabulary development and social interaction (Girolametto et al. 2007).
Qualifications of Treatment Providers The More Than Words program is led by a certified speech-language pathologist who has completed a specialized training by the Hanen Centre. First, the SLP needs to be trained in the It Takes Two to Talk program. Then, they can complete the 3-day, intensive, small-group training on More Than Words, led by a Hanen instructor.
See Also ▶ Hanen Approach ▶ Language Acquisition
References and Reading Aldred, C., Green, J., & Adams, C. (2004). A new social communication intervention for children with autism: Pilot randomized controlled treatment study suggesting effectiveness. Journal of Child Psychology and Psychiatry, 40, 1–11. Chapman, K., Leonard, L., & Mervis, C. (1986). The effects of feedback on young children’s inappropriate word use. Journal of Child Language, 13, 101–117. Duchan, J. (1989). Evaluating adults’ talk to children: Assessing adult attunement. Seminars in Speech and Language, 10, 7–27. Girolametto, L., Weitzman, E., & Greenburg, J. (2003). Training day care staff to facilitate children’s language. American Journal of Speech-Language Pathology, 12, 299–311. Girolametto, L., Sussman, F., & Weitzman, E. (2007). Using case study methods to investigate the effects of interactive intervention for children with autism spectrum disorders. Journal of Communication Disorders, 40(6), 470–492.
2977 Mahoney, G., & Perales, F. (2003). Using relationshipfocused intervention to enhance the social-emotional functioning of young children with autism spectrum disorders. Topics in Early Childhood Special Education, 23(2), 77–89. McConachie, H., Val Randle, V., Hammal, D., & Le Couteur, A. (2005). A controlled trial of a training course for parent of children with suspected autism spectrum disorders. Journal of Pediatrics, 147, 335–340. Mirenda, P., & Dollennan, A. (1986). Effects of adult interaction style on conversational behavior in students with severe communication problems. Language, Speech, and Hearing Services in Schools, 17, 126–141. Pearce, P., Girolametto, L., & Weitzman, E. (1996). The effects of focused stimulation intervention on mothers of late-talking toddlers. Infant-Toddler Intervention, 6, 213–227. Pepper, J., & Weitzman, E. (2004). It takes two to talk: A practical guide for parents of children with language delays (2nd ed.). Toronto: The Hanen Centre. Siller, M., & Sigman, M. (2002). The behaviors of parents of children with autism predict the subsequent development of their children’s communication. Journal of Autism and Developmental Disorders, 32(2), 77–89. Sussman, F. (1999). More than words: Helping parents promote communication and social skills in children with autism spectrum disorder. Toronto: The Hanen Centre. The Hanen Centre. (2007). More than words research summary. Retrieved from http://www.hanen/web/Por tals/0/HostedFiles/MTWResearchSummary2007.pdf Weitzman, E., & Greenburg, J. (2002). Learning language and loving it: A guide to promoting children’s social, language, and literacy development in early childhood settings (2nd ed.). Toronto: The Hanen Centre. Woods, J., Kashinath, S., & Goldstein, H. (2004). Effects of embedding caregiver implemented teaching strategies in daily routines on children’s communication outcomes. Journal of Early Intervention, 26, 175–193.
Moro Reflex Gianluca Esposito Nanyang Technological University, Wako, Saitama, Singapore University of Trento, Rovereto, TN, Italy Kuroda Research Unit, RIKEN Brain Science Institute, Wako-shi Saitama, Japan
Synonyms Startle reflex
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Definition The Moro reflex, also known as the startle reflex, was initially described by the Austrian pediatrician Ernst Moro (1874–1951). It represents one of the infantile reflexes, and it may be observed since birth in all newborns up to 5 months of age. The reflex is triggered when (1) a baby’s head falls backward or quickly changes position and/or (2) the baby is startled by an unexpected loud noise. The reflex causes the baby initially to extend the neck and spread out widely the arms and legs (abduction) and then pull the arms back in a clasping motion (adduction). These movements are sometimes associated with cry. The Moro reflex, as well as other infantile reflexes (e.g., the grasping reflex), is considered a residual behavior from when nonhuman primates clung to their mothers swinging through the trees (Thies and Travers 2001). The absence, persistence, and/or exacerbation of the Moro reflex in childhood and adulthood is generally associated with profound disorders of the motor system (e.g., cerebral palsy). The assessment of the Moro reflex is carried out by pediatricians to evaluate the integration of the central nervous system in early infancy. As early primitive reflexes, such as the Moro reflex, are gradually extinguished at around 5 months of age, research in this area in the ASD population is very limited. A retrospective study by Teitelbaum et al. (2004) suggested that infants later diagnosed with ASD show abnormal movement patterns that can be interpreted as primitive reflexes “gone astray”; that is, some reflexes are not extinguished at the appropriate age in development, whereas others fail to appear when they should. More empirical research is needed to specifically investigate the development of the Moro reflex in the ASD population.
References and Reading Teitelbaum, O., Benton, T., Shah, P. K., Prince, A., Kelly, J. L., & Teitelbaum, P. (2004). Eshkol-Wachman movement notation in diagnosis: The early detection of Asperger’s syndrome. Proceedings of the National
Morocco and Autism Academy of Sciences of the United States of America, 101(11), 909–11914. Thies, M., & Travers, J. F. (2001). Human growth and development through the lifespan. Thorofare: Slack.
Morocco and Autism Ismail El Hailouch School of Public Health, Child Study Center, Yale University School of Medicine, New Haven, CT, USA
Historical Background As the only country in North Africa that was able to conserve its political stability, Morocco has garnered a stellar reputation amongst foreign investors. The growing interest in the Moroccan kingdom by business leaders has undoubtedly contributed to its economic growth over the past decade, which further embellished the country’s reputation. In fact, the country ranked 68th out of 190 nations on the Doing Business 2017 survey. However, behind the country’s growing success lie many striking weaknesses in some of its most vital sectors. Moroccan education, for instance, has been controversial for a long time and remains a focus of political debate. After Morocco’s independence in 1956, the country’s educational system has been suffering from numerous challenges. Healthcare is a sector that is unarguably underdeveloped. The quality of care provided and the serious lack of staff and resources are a challenge. There are even more challenges for psychiatric care. While Morocco is ahead of its neighboring countries when it comes to mental health policy, the rules are rarely enforced. The lack of trained providers is another challenge. The diagnosis and management autism has not been a topic of interest until recently. Autism is considered a “disability” in Morocco and polices and legislation focus more globally on disability than on autism per se. The main laws that have been issued in Morocco to protect disabled people (including
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individuals with autism spectrum disorder) have been the following: 1. Law 92-07 concerning social protection of disabled people – adopted in 1993. 2. Law 03-10 relative to accessibility in addition to a certain number of decrees about the application of laws issued in 2003. 3. Several decrees following the previous laws that urge the applications of laws. However, despite the activeness of the government in issuing these laws to protect the rights of disabled people, there has not been much improvement made in the field. This reflects the lack of resources to implement the issued laws, the limited emphasis on inclusion of people with disabilities, and issues of lack of acceptance – all factors that contribute to a certain extent to perpetuating disability. In 2004, a National Disability Survey revealed some quite shocking numbers about the case of disabilities in Morocco. • One million and a half Moroccan (5.12%) live with a disability, and that on average, a household of four people counts at least one disabled person. • One out of five disabled people in Morocco never go to a specialized institution. • Only 1% of disabled people in Morocco benefit from health insurance. • More than 70% of disabled people don’t have any level of instruction. • The rate of schooling amongst disabled people in Morocco is of only 32.4% as opposed to a 92.6% among the nondisabled. • 88.6% of disabled people of age above 15 have no professional activity. • The poverty rate among the disabled is multiple times higher than that amongst the nondisabled. Since the release of this report in 2004, the government has been active in terms of social protection legislations. Attempts have been made to provide obligatory medical insurance; medical assistance policy and labor legislation has been
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enacted. The Moroccan government has acted to protect the rights of people with disabilities and these efforts occur in five main fields: legal capacity, education, institutionalization, women, and disability. In 2008, the government elaborated the project of law 62-09 which is meant to consolidate the rights of disabled people which have been achieved through a national conference in 2008, followed by four regional meetings. Unfortunately implement of this has been difficult. The adoption of 2011 constitution and through the high orders of King Mohamed 6 has encouraged adoption of this law.
Legal Issues and Mandates for Service In a recent legislative effort by the Moroccan parliament intended for the protection and advancement of persons with disabilities, an initiative known as the Draft Framework Law or the Draft Law 97.13 was adopted, marking the first legislation addressing the rights of the disabled to be considered by the Moroccan parliament since it ratified the International Treaty on Disability Rights in 2009. Although hopeful for the future of disabled Moroccan citizens, the draft was met with much criticism, particularly from the Human Rights Watch. Disabled citizens of Morocco have often been treated as objects of charity as opposed to equal citizens, which has inevitably led to an environment of discrimination and stigmatization. Many see this draft law as an opportunity to initiate a large-scale change in the perception of disabilities in Morocco, but as it stands, the Draft Framework Law maintains outmoded perceptions of disabilities and in actuality, places even more restraints on the rights of disabled persons. Morocco’s recent signing of the International Human Rights Treaty, which established the right of disabled persons outlined in the Draft Framework Law as below the international standard, preceded the consideration of the Draft Framework Law. In a letter to the Moroccan parliament, the Human Rights Watch noted the many flaws present in the current Draft Framework law,
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among which includes the absence of a clear establishment of the right to education for the disabled due to the segregation of students with special needs with students in regular classrooms, which further amplifies the societal disassociation of disabled persons with the general population. This leads many parents to withdraw special needs students from school. In their letter, the Human Rights Watch demanded proper accommodations in educational settings for special needs students in Morocco. In addition, the general Moroccan population does not adhere to the Draft Framework Law due to a lack of awareness, and certain initiatives should be put in place in order for the law to be as effective as possible.
Current Treatments and Treatment Centers According to Soumia Amrani, the vice president of Moroccan Collection for Promoting Handicap Rights, resources and treatment centers for disabled persons are readily available in Morocco, but are grossly overpriced and unaffordable for most Moroccan families, especially given the increasing unemployment rate. However, the professional disciplines necessary for the treatment and diagnosis autism are relatively new in Morocco. Starting in the 1980s, and especially after 2000, Moroccan parent-activists have been reaching out to foreign experts in order to change the status quo. They have taken their lead from the Anglo-American Neurocognitive behavioral model of the treatment and diagnosis of autism, rather than the French psychoanalytic one. Moroccan activists have been focusing on raising autism awareness, lobbying government agencies, and establishing the proper infrastructure required for the identification and education of autistic children. Recently, as part of the National Initiative for Human Development (INDH), King Mohammad VI inaugurated the “Ashourouk” Care Center for Children with Autism, which can enroll up to 100 children, in order to ensure that children with autism and special needs are provided with adequate educational and social integration. The
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king noted that among the themes outlined by this initiative was assisting those with special needs in preserving their dignity and preventing them from falling into extreme poverty or isolation. Although much progress has been made in recent years, Morocco lags behind European countries with respect to providing resources a care for autistic children.
Overview of Research Directions Unfortunately, the majority of ASD research has been conducted in the United States, and this commitment to provide accommodations and understand people with this disorder has not been equally reflected in other countries, such as Morocco. Because of this, parents and families of individuals with autism have been pushed to establish start-up organizations with the sole purpose of providing resources and care for individuals affected with autism. These start-up organizations are working with larger, international organizations in an attempt to advance the understanding of those affected with autism in Morocco. One start-up, in particular theAssociation Amal pour Enfants aux Benzoins Specifiques Mentaux (A.A.E.B.S.M), was founded by Moroccan activists for disability rights – many of whom had a relative affected by the disorder – and is currently centered in Casablanca. The primary goal of this organization is to provide support for individuals and families affected by ASD by improving public perception and educating the general public about ASD. Along with local organizations, larger international organizations, such as Autism Speaks, are dedicated to providing care and support for those affected by ASD and have similar objectives to locally run organizations. Autism Speaks has recently began an initiative in Morocco dedicated to providing effective resources to Moroccan individuals and families affected by ASD. However, the incomplete analysis of the resources available in the country has been an obstacle and as a result, Autism Speaks has partnered with students from Worcester Polytechnic Institute in a project designed to collect and analyze data regarding
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treatment and education options currently available to autistic children in Rabat, Morocco. The analysis and conclusions of this study will be made available to parents of autistic children in Morocco and will hopefully assist in future projects aimed to advance autism treatment in Morocco.
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See Also ▶ Autism Speaks ▶ Egypt and Autism ▶ Sub-Saharan Africa and Autism
References and Reading Overview of Training Historically, Moroccan psychiatry has been heavily influenced by French psychoanalysis. In the 1980s and 1990s, the very first child psychiatrists in Morocco received specialized training in France, since at the time, there were no training programs for child psychiatry in Morocco. This psychoanalytical approach to psychiatry proved to be ineffective, as many of the children who were treated by these psychiatrists were later admitted into adult psychiatric hospitals. The year 2008 marked a significant advancement in the field of child psychiatry in Morocco, with the opening of two new child psychiatry departments in Casablanca and Salé, pioneering the first child psychiatry training programs in Morocco, which distanced itself from the usual psychoanalytic approach, focusing instead on a biological approach. In fact, over the past couple of decades, Moroccan psychiatry has generally been moving away from psychoanalysis and gearing towards a more biologically based psychiatry. Major psychiatric hospitals in Morocco have been operating in a style which uses international scientific consensus of the DSM or ICD diagnoses while promoting psychotherapy as well as behavioral and cognitive approaches. In general, the younger generation of psychiatrists in Morocco takes an “eclectic” approach to psychiatry, as many were trained in psychoanalytic theory and as a result see some benefit in thinking psychodynamically; however, they were exposed to neurobiology and pharmacology and believe that cognitive and behavioral therapy play a role in the treatment of mental illness and developmental disorders. They also tend to view the treatment methods of the previous generation of French and Moroccan psychiatrists as damaging and counterproductive.
Admin. One man’s death points to shortcomings in Morocco’s mental health infrastructure. REPORTING MOROCCO, 14 Feb 2017. morocco.roundearthmedia. org/one-mans-death-points-shortcomings-moroccosmental-health-infrastructure Alami, A. Fewer than 30 percent of Moroccans have health Insurance. The New York Times, 27 Mar 2013. www. nytimes.com/2013/03/28/world/middleeast/fewer-than30-percent-of-moroccans-have-health-insurance.html Bibeau, G. (1997). Cultural psychiatry in a creolizing world. Transcultural Psychiatry, 34(1), 9–41. Chamak, B. (2008). Autism and social movements: French parents’ associations and international autistic individuals’ organizations. Sociology of Health & Illness, 30(1), 76–96. Crapanzano, V. (1973). The Hamadsha: A study in Moroccan ethnopsychiatry. Berkeley: University of California Press. Educational facilities and classes launched for children with autism. Morocco World New, 5 May 2017. www. moroccoworldnews.com/2017/05/215832/educationalfacilities-classes-launched-children-autism/ Elmallem, S. Morocco: A few key facts about your next business destination. La Chambre De Commerce Du Montréal Métropolitain, CCMM, 28 Sept 2017. www. ccmm.ca/en/news-morocco-a-few-key-facts-about-yournext-business-destination/ Hart, B. G. (2016). Making an autism world in Morocco: Parent activism, therapeutic practice, and the proliferation of a diagnosis, Columbia University Academic Commons. https://doi.org/10.7916/D8W66KJN. How is autism treated? Autism Speaks, 25 July 2012. www. autismspeaks.org/what-autism/treatment Letter to Moroccan Parliament on draft disability law. Human Rights Watch, 26 Oct 2015. www.hrw.org/news/2015/10/ 26/letter-moroccan-parliament-draft-disability-law Moroccan Educational System: Problems and solutions. Morocco World News, 9 Mar 2016. www. moroccoworldnews.com/2016/03/181000/moroccaneducational-system-problems-and-solutions/ Jawad Garmah. (2016). Moroccan Educational System: Problems and solutions. Morocco World News. www. moroccoworldnews.com/2016/03/181000/moroccaneducational-system-problems-and-solutions/ Morocco: Inauguration of Center for Children With Autism in Bouskoura – Consolidated Roy approach for comprehensive human development. All Africa, Maghreb Arabe Presse, 31 Jan 2013. allafrica.com/ stories/201302011199.html
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Mortality
Current Knowledge
Mortality Svend Erik Mouridsen Child and Adolescent Psychiatry Centre, Bispebjerg University Hospital, Copenhagen, Denmark
Definition The term “autism” is used in this entry to describe all autism spectrum disorders, which include the various types of pervasive developmental disorders according to DSM-IV (American Psychiatric Association [APA] 2000) and ICD-10 (World Health Organization [WHO] 1992). The life expectancy of people with autism is of interest to parents, health professionals, and service providers concerned with these peoples’ lifetime needs. Mortality information that includes the causes of death is important as it can help parents and professionals focus on reducing associated risks and ultimately the rate of mortality among people with autism.
Historical Background Occasional deaths have been reported in all general follow-up studies of individuals with autism (Ballaban-Gil et al. 1996; Billstedt et al. 2005; Fombonne et al. 1989; Howlin et al. 2004; Kobayashi et al. 1992; Larsen and Mouridsen 1997). Causes of death include traffic accidents (Fombonne et al. 1989; Larsen and Mouridsen 1997); unrecognized volvulus in a woman in a long-term psychiatric institution (Larsen & Mouridsen); status epilepticus (Howlin et al. 2004); and cases of drowning, pneumonia, and complications arising from long-term psychotropic medication (Ballaban-Gil et al. 1996). However, systematic studies dealing with mortality and causes of death in autism are rare. There are difficulties in obtaining an adequate sample size to calculate specific risks, in studying samples over a suitable time span, and in establishing the cause(s) of death.
Standardized mortality ratio (SMR) is frequently used to measure excessive mortality. The SMR is the quotient of the observed to the expected numbers of deaths, and an SMR of 1 indicates that the observed number of deaths is no different from what would be predicted for the general population: An SMR value greater than 1 indicates that the observed mortality exceeds expectations in the group under study. Only three systematic follow-up studies specifically dealing with mortality and causes of death in individuals with autism have been published so far. Shavelle et al. (2001) and Pickett et al. (2006) reported mortality and causes of death in 13,111 people with autism who were receiving services from the California Department of Developmental Services between 1983 and 2002. The overall SMR was 2.4, indicating a mortality rate more than twice as high as for the general population. The increase in mortality was larger in females than in males. Mortality was associated with mental retardation with an SMR of 3.1 for participants with moderate, severe, or profound retardation, whereas the SMR was 1.4 for individuals with no or only mild mental retardation. Epilepsy was strongly associated with death risk. SMR was 36.9 in participants with epilepsy and moderate, severe, or profound mental retardation against 22.6 in those with no or mild mental retardation. Suffocation (SMR ¼ 51.4 and 5.7, respectively) and drowning (SMR ¼ 13.7 and 3.9, respectively) were also noted to be frequent specific causes of death and, like epilepsy, associated with the level of cognitive impairment. Similar results were obtained in a Danish study (Isager et al. 1999; Mouridsen et al. 2008) in which an overall SMR of 1.9 was reported in a nationwide cohort of 341 individuals with autism (average age 43 years) observed between 1960 and 2007. Again, the SMR was particularly high in females. Epilepsy (SMR ¼ 35.0) and infectious diseases were among the most common causes of death. Epilepsy was notably marked in deceased females; five of the eight deceased females had epilepsy. Different kinds of infectious diseases (meningitis,
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pneumonia, appendicitis) were associated with death in seven individuals. In a Swedish community-based study of 120 individuals with autism, mean age 33.2 years, Gillberg et al. (2010) found an overall SMR of 5.6. They also noted that the mortality rate was particularly high in females. Associated medical diseases (including epilepsy with cognitive impairment) and accidents accounted for most of the observed causes of deaths. Prevention efforts to decrease mortality in people with autism need to address the conditions that are the immediate causes of death (i.e., infectious diseases, epilepsy, and accidents). However, the behavioral characteristics that define autism often make care difficult and can lead to exacerbations in the state of somatic illness or diagnostic delay, eventually leading to deathcausing illness (Larsen and Mouridsen 1997; Mouridsen et al. 2008). Assessing pain and discomfort in a cognitively impaired, nonverbal patient is difficult, and of paramount importance is the involvement of the parent(s) or other care providers, if available. The best pain assessment by proxy is that provided by caregivers or family members who know the patient well. Only they can identify changes from a patient’s base line behavior that may signify pain and discomfort. Patients may be uncooperative or combative if they do not understand the need for help. It is important to realize that many people with autism react badly to any change in their environment, and therefore, a visit to a general practice or a hospital can be very alarming for them, in particular, if an unplanned acute hospital admission is necessary (Scarpinato et al. 2010; Souders et al. 2002). In some patients, conventional peri- and postoperative management is impossible and alternative strategies are needed (Dell et al. 2008; Van Der Walt and Moran 2001). Likewise, venipunctures, intravenous insertions, and initiation or change of medical treatment can be difficult to carry through. It is also noteworthy that Williams et al. (2000) found that 62% of parents reported difficulty giving medication to their children with autism. An association between autism and epilepsy has been consistently reported, and the literature
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presents a wide range of co-occurrence estimates from 5% to 46% (Spence and Schneider 2009). These authors also report that more severe intellectual disability is associated with greater rates of epilepsy. Considering the high prevalence of epilepsy in autism, it is therefore important to suspect the comorbidity of epilepsy in every individual presenting with autism. It follows that understanding the needs of this group is particularly important within any major epilepsy service. Since epilepsy is strongly associated with death risk, careful monitoring of anticonvulsant treatment is essential in those individuals with a concomitant occurrence of autism and epilepsy. However, the clinical identification of seizures may be difficult in some cases. The diagnosis of partial complex seizures, in particular, can be complicated by the presence of atypical body movements and behavioral patterns often seen in association with autism (Bauman 2010). Obtaining a high-quality EEG can also be difficult, and effective communication usually has to be through a caregiver. Since accidents are often avoidable, special attention should be paid to the safety of community surroundings. In the Mouridsen et al. (2008) study, two participants, who both lived in specialized institutions for autistic people, managed to swallow dangerous objects and choke on them during an unsupervised period. According to the standards of the caregivers, the two patients should never be left unsupervised. As the rates of autism diagnoses increase (Rutter 2005), it becomes more and more imperative for health care providers in any setting – family, institutions, and primary or acute care – to understand the unique challenges of people with autism. The findings published so far underscore the importance of maintaining sufficient competence levels in the fields of internal medicine and neurology within the autism care system to recognize and properly manage somatic as well as neurological diseases. As there is little information about the best practice for providing care to people with autism in institutional or hospital settings, it is important to develop nursing standards that address care for people with autism.
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Future Directions To conclude, mortality is increased in people with autism, and individuals with diminished cognitive functioning or epilepsy are at particularly high risk of a reduction in life expectancy. However, increased understanding of the most common causes of death can help parents and other caregivers focus on reducing risk and, ultimately, the rate of mortality among people with autism.
References and Reading American Psychiatric Association. (2000). Diagnostic and statistical manual (4th ed., Text Rev.) Washington, DC: APA Press. Ballaban-Gil, K., Rapin, I., Tuchamn, R., & Shinnar, S. (1996). Longitudinal examination of the behavioural, language, and social changes in a population of adolescents and young adults with autistic disorder. Pediatric Neurology, 15, 217–223. Bauman, M. (2010). Medical comorbidities in autism: Challenges to diagnosis and treatment. Neurotherapeutics, 7, 320–327. Billstedt, E., Gillberg, I. C., & Gillberg, C. (2005). Autism after adolescence: Population-based 13- to 22-year follow-up study of 120 individuals with autism diagnosed in childhood. Journal of Autism and Developmental Disorders, 35, 351–360. Dell, D. D., Feleccia, M., Hicks, L., Longstreth-Papsun, E., Politsky, S., & Trommer, C. (2008). Care of patients with autism spectrum disorder undergoing surgery for cancer. Oncology Nursing Forum, 35, 177–182. Fombonne, E., Talan, I., Bouchard, F., & Lucas, G. (1989). A follow-up study of childhood psychosis. Acta Paedopsychiatrica, 52, 12–35. Gillberg, C., Billstedt, E., Sundh, V., & Gillberg, I. C. (2010). Mortality in autism: A prospective longitudinal community-based study. Journal of Autism and Developmental Disorders, 40, 352–357. Howlin, P., Goode, S., Hutton, J., & Rutter, M. (2004). Adult outcome for children with autism. Journal of Child Psychology and Psychiatry, 45, 212–219. Isager, T., Mouridsen, S. E., & Rich, B. (1999). Mortality and causes of death in pervasive developmental disorders. Autism, 3, 7–16. Kobayashi, R., Mutara, T., & Yoshinaga, K. (1992). A follow-up study of 201 children with autism in Kyushu and Yamaguchi areas, Japan. Journal of Autism and Developmental Disorders, 22, 395–411. Larsen, F. W., & Mouridsen, S. E. (1997). The outcome in children with childhood autism and Asperger syndrome originally diagnosed as psychotic: A 30-year follow-up study of subjects hospitalized as children. European Child and Adolescent Psychiatry, 6, 181–190.
Motivating Operation Mouridsen, S. E., Brønnum-Hansen, H., Rich, B., & Isager, T. (2008). Mortality and causes of death in autism spectrum disorders: An update. Autism, 12, 403–414. Pickett, J. A., Paculdo, D. R., Shavelle, R. M., & Strauss, D. J. (2006). 1998–2002 update on “Causes of death in Autism”. Journal of Autism and Developmental Disorders, 36, 287–288. Rutter, M. (2005). Incidence of autism spectrum disorders: Changes over time and their meaning. Acta Paediatrica, 94, 2–15. Scarpinato, N., Bradley, J., Kurbjun, K., Bateman, X., Holtzer, B., & Ely, B. (2010). Caring for the child with an autism spectrum disorder in the acute care setting. Journal for Specialists in Pediatric Nursing, 15, 244–255. Shavelle, R. M., Strauss, D. J., & Pickett, J. (2001). Causes of death in autism. Journal of Autism and Developmental Disorders, 31, 569–576. Souders, M. C., DePaul, D., Freeman, K. G., & Levy, S. E. (2002). Caring for children and adolescents with autism who require challenging procedures. Pediatric Nursing, 28, 455–562. Spence, S. J., & Schneider, M. T. (2009). The role of epilepsy and epileptiform eggs in autism spectrum disorders. Pediatric Research, 65, 599–606. Van Der Walt, J. H., & Moran, C. (2001). An audit of perioperative management of autistic children. Pediatric Anaesthesia, 11, 401–408. Williams, G. P., Dalrymple, N., & Neal, J. (2000). Eating habits of children with autism. Pediatric Nursing, 26, 259–264.
Motivating Operation Amanda P. Laprime The Center for Children with Special Needs, Glastonbury, CT, USA University of Rochester Medical Center, Rochester, NY, USA
Definition Motivating operations (MO) is a general term to describe antecedent events which momentarily alter the effects of a reinforcing or punishing consequence, and therefore alter the future frequency of behavior related to that consequence. Motivating operations alter the effectiveness of reinforcers and punishers in two ways, which designates different classifications of MOs.
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Establishing operations (EO) increase the effectiveness of a consequence (see ▶ “Establishing Operations”), and abolishing operations (AO) decrease the reinforcing or punishing effectiveness (see ▶ “Abolishing Operations”) (Laraway et al. 2003). The term MO is important because it refers to “not only how much someone wants something, but how hard they will work to get it” (Langthorne and McGill 2009, p. 22).
Historical Background Establishing operation was a term originally coined by Keller and Schoenfeld (1950) to describe a series of motivational events and their effect on behavior. Since, the designation, conceptualization, and distinctions of MOs have evolved considerably. Moving from the term Establishing Operation to Motivating Operation: Researchers in the field of behavior found that the term EO was limited as it only included events resulting in the relative increase in the effectiveness of a consequence; discarding those variables which decreased the value of a consequence. While the inherent limitations to this term were recognized (Michael 1982, 1993, 2000), it was deemed cumbersome to have different terms to describe each operation. More recently, there has been a push to utilize the general term, MO, and distinguish between EOs and AOs based on their relative effects on consequences and behavior (Laraway et al. 2003). For the remainder of this entry, EO will describe only variables that increase the reinforcer/punisher effectiveness, AO will denote only events resulting in the decreased reinforcer/punisher effectiveness, and MO will denote the general class of variables that alter the effectiveness of such consequences. The designations are important due to the essential role that all MOs, including both EOs and AOs, have on the understanding of human behavior. The current conceptualization of MOs has been found to be important in the role of reinforcing or punishing consequences for behavior. Importantly, interventions to address challenging behavior and skill deficits in individuals
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with autism spectrum disorders (ASD) have benefited from an analysis of motivating operations as an independent variable. Role in operant conditioning: Historically, operant behavior has been conceptualized as a three-term contingency (Antecedent-BehaviorConsequence), in which a stimulus readily evokes a behavior because of a past history of reinforcement in the presence of that stimulus. The conceptualization of MOs added a variable that provides an explanation for momentary differences of such operants (Sundberg 1993). The importance of the MO cannot be understated. Motivation is paramount to understanding behavior. Although all operant behavior occurs due to its history with reinforcing or punishing consequences, the probability of such behavior being emitted is dependent on the relative effectiveness of such consequences. Specifically, if a reinforcer is more effective due to an EO, behavior will be more likely to be emitted at that time. If, in contrast, there is an AO in effect, the same behavior would be less likely to occur. In its simplest form, motivation can be described by its biological basis in states of deprivation or satiation. More complexly, motivation can come under the control of arbitrary relationships due to conditioning paradigms. Regardless of etiology, the analysis of the MO provides the conceptual foundation for why a reinforcer is effective for a person, at any moment in time (McGill 1999). Conditioned motivating operations: A series of papers by Michael (e.g., 1982, 1993, 2000) distinguished between MOs that are unconditioned and those that are conditioned. Unconditioned MOs, or UMOs, are those events that change the effectiveness of a reinforcer or punisher with no learning history (e.g., food deprivation, sleep deprivation), while conditioned MOs, or CMOs, are those that are learned. Three distinct CMOs have been classified based on the process by which this learning occurs: the reflexive conditioned motivating operation (CMO-R), the transitive conditioned motivating operation (CMO-T), and the surrogate conditioned motivating operation (CMO-S). Each of these alter the value of some other stimulus, such that its presentation is momentarily establishing or abolishing.
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The CMO-S refers to a previously neutral stimulus that has been correlated with another MO, and therefore acquires the same effects on behaviors and consequences as the original MO. The CMO-T refers to motivational events that establish another stimulus as a reinforcer. The CMO-R refers to motivational events which signal an improving or worsening set of conditions, therefore altering its own removal as a type of reinforcement or punishment (Langthorne and McGill 2009). Although complex, a conceptual understanding of these CMOs has enhanced intervention around challenging behavior and teaching language skills to individuals with ASD. Distinguishing between different antecedent events: Motivating operations have often been confused with other antecedent variables, such as discriminative stimuli (SDs, see ▶ “Stimulus Control”). The important distinction is that while SDs signal the availability of reinforcement or punishment for a response, MOs increase or decrease the relative value of a reinforcer or punisher as a consequence for that response. This value altering effect (Laraway et al. 2003) results in a relative increase or decrease in the probability of a behavior occurring at that moment, rather than the consistency of responding under similar conditions in the future. In an applied example, a water fountain signals the availability of reinforcement for stopping and drinking (i.e., SD) but whether you stop and drink depends on whether you are thirsty in the moment. Thirst, a noted state of deprivation, is an unconditioned EO for water as a reinforcer.
Current Knowledge The concept of the MO is influential for any intervention that relies on reinforcement or punishment for its effect. Without an understanding of the MO, one cannot truly understand the impact of an intervention, as well as the variables in play if one is not working. For example, if a reinforcement program is put into place to increase academic behavior, it is paramount that the child is motivated for the reinforcer (i.e., there is an EO in place). Motivation for a reinforcer in this situation
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may be one component of an effective intervention. Conversely, if in the previous example the child is not motivated for the reinforcer, academic work behavior may not increase, and the intervention may be deemed ineffective. This would be an error in the analysis, as in this situation, reinforcer saliency may be impeding progress. This same analysis applies to any situation in which the development of adaptive behaviors, or the omission of challenging behavior is contingent on delivery of a reinforcer. Most prominently, the MO has been highlighted in the areas of verbal behavior, decreasing challenging behavior, and skill acquisition instruction. Verbal behavior: Motivating operations have been instrumental to providing a full account of language development and the acquisition of skills related to language (see ▶ “Analysis of Verbal Behavior (AVB)”). A thorough analysis of language development is pertinent for learners with ASD who may have deficits in basic communication skills. To illustrate, the concept of the MO is paramount for teaching mands, otherwise known as requests. Skinner (1957) described the “mand” as reliant on a salient EO as an antecedent condition. Meaning, requesting only occurs in the presence of motivation for something and is specifically reinforced by access to that which was requested. For example, requesting for food items will only occur when someone is hungry (a noted EO, state of deprivation). The specific reinforcer for this request would be food. “Mand training” is a particularly popular and important skill acquisition program for early learners with ASD. Requesting for items is dependent on motivation for that item or activity. Meaning, if a child is not motivated for the cookie, they are not going to independently ask for the cookie, regardless of whether they have the skill in their repertoire or not. This analysis is important in understanding how to teach these types of language skills. Sweeney-Kerwin et al. (2007) implemented a procedure to teach requesting to children with ASD. Importantly, their participants were dependent on seeing the things they were requesting which is inconsistent with the way in which requesting typically evolves. The importance of motivation was a critical component of the intervention as it
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was necessary that the individuals were able to request for things, only when there was motivation in place. By manipulating aspects of the EO, the intervention was successful in teaching individuals with ASD to request for the things they wanted, without needing to see them in the environment. This type of requesting is paramount to the development of a requesting repertoire that is independent and generalizes across a variety of variables. Similarly, the CMO-T has been used to support teaching the skill of asking for information (Hall and Sundberg 1987; Sundberg et al. 2002). In these interventions, individuals were taught to seek out information. Importantly, information had to become momentarily valuable for the questions to occur. An analysis of the CMO-T was pertinent in establishing motivation for information. This was done by repeatedly presenting individuals with preferred items in containers. Then, the instructor removed the items from the containers. Since the individuals had previously experienced the container and the item together, the presentation of the container without the item resulted in motivation for information about the location of the item, evoking the questions of “Where is the item?” This study effectively increased “question asking” as a skill for individuals with ASD who previously did not inquire for information. Without this careful analysis and application of CMOs, it is unlikely that the skill of asking for questions would have developed and persisted for learners with deficits in this complex area of language development. Challenging behavior: The concept of the MO has implications in the area of challenging behavior as well. Many individuals with ASD engage in challenging behavior that impacts their access to a variety of environments and experiences (See ▶ “Challenging Behavior”). Literature on functional assessment (See ▶ “Functional Behavior Assessment; ▶ Functional Analysis”) has suggested that MOs play a primary role in the understanding of challenging behavior and its functional relationship to environmental events. A series of studies on functional analyses have shown that the presence or absence of MOs have directly influenced the relative success with
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understanding the environmental variables related to challenging behavior during behavioral assessments (Call et al. 2004; Iwata et al. 1994). Similarly, MOs have been integral to interventions intended to decrease challenging behaviors in individuals with ASD (Smith and Iwata 1997). Motivation as an independent variable has been shown to impact the efficacy of behavior reduction procedures for stereotypy (Lang et al. 2009), destructive behavior (McComas et al. 2003), aggression (McGuinnis et al. 2010), self-injury (McComas et al. 2003), and noncompliance (Call et al. 2004). One example of this has been conceptualized in the literature on the CMO-R. Carbone and colleagues (2010) discussed the role of the CMO-R as it relates to problem behavior during discrete trial instruction (DTI; see ▶ “Lovaas Approach”). Specifically, this line of work has provided a conceptual analysis for the conditions during DTI which may establish instruction as aversive, and some practical modifications for reducing said aversiveness, resulting in a decrease in challenging behavior. Another important example relates to a common consequence for challenging behavior time-out. Removal from an environment, contingent on a challenging behavior, is intended to punish or decrease the future frequency of the challenging behavior. Unless there is motivation (i.e., an EO) for the time-in environment, it is unlikely that the time-out procedure, will suppress or decrease challenging behavior. This analysis of motivation is pertinent in understanding the mechanisms responsible for successful interventions. Skill acquisition: The concept of the MO has also been employed to enhance skill acquisition instruction for individuals with ASD. Several instructional strategies have been identified as AOs to decrease escape-maintained behaviors during work and increase appropriate participation (Carbone 2013). For example, fast-paced instruction (Roxburgh and Carbone 2012), modified task difficulty (McComas, Thompson, implementing choice options (McComas et al. 2000), and the high probability (high-p) request sequence (Mace et al. 1988) have all been demonstrated to increase compliance during instruction by abolishing the aversive value of demands.
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In addition, a series of studies have shown that contriving MOs during instruction can enhance repertoires such as engagement (Neely et al. 2013), play skills (Lang et al. 2010), discrimination (Lotfizadeh et al. 2012), eye contact (Carbone et al. 2013), social initiations (Taylor et al. 2005), and joint attention (Dube et al. 2004; Isaksen and Holth 2009). For example, Lang and colleagues (2010) found that when an AO for stereotypy was created prior to play sessions, children with ASD were more likely to engage in functional play with their peers during subsequent play sessions. Taylor et al. (2005) manipulated an establishing operation to increase social interactions for children with ASD. Here, the authors contrived motivations for a reinforcer that a peer was in control of and taught the participants to request for the items from their peers. The results of the study demonstrated that only when there was motivation for the item would the children request from their peers. Additionally, the authors reported that outside of the study, the children who participated were observed to request across other peers. One child requested in an untrained situation and in a novel environment as well. These data suggest the importance of motivation when planning for instruction with learners with ASD.
Future Directions An understanding of MOs has been demonstrated to increase the efficacy of interventions, understand more fully behavioral presentation, and enhance the overall acquisition of necessary skills for individuals with ASD. Continued research and application of interventions based in the conceptualization of MOs are necessary to expand the analysis of behavior as it relates to these areas.
See Also ▶ Abolishing Operations ▶ Analysis of Verbal Behavior (AVB) ▶ Establishing Operations ▶ Functional Behavior Assessment ▶ Reinforcement
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References and Reading Call, N. A., Wacker, D. P., Ringdahl, J. E., Cooper-Brown, L. J., & Boelter, E. W. (2004). An assessment of antecedent events influencing noncompliance in an outpatient clinic. Journal of Applied Behavior Analysis, 37, 145–157. Carbone, V. J. (2010). The role of the conditioned motivating operation (CMO-R) during discrete trial instruction of children with autism. Journal of Early Intensive Behavioral Intervention, 4, 658–680. Carbone, V. (2013). The establishing operation and teaching verbal behavior. The Analysis of Verbal Behavior, 29, 45–49. Carbone, V., O’Brien, L., Sweeney-Kerwin, E. J., & Albert, K. M. (2013). Teaching eye contact to children with autism: A conceptual analysis and single case study. Education and Treatment of Children, 36, 139–159. Dube, W. V., MacDonald, R. P. F., Mansfield, R. C., Holocomb, W. L., & Ahearn, W. H. (2004). Toward a behavioral analysis of joint attentions. The Behavior Analyst, 27, 197–207. Hall, G., & Sundberg, M. L. (1987). Teaching mands by manipulating conditioned establishing operations. The Analysis of Verbal Behavior, 5, 41–53. Isaksen, J., & Holth, P. (2009). An operant approach to teaching joint attention skills to children with autism. Behavior Interventions, 24, 215–236. Iwata, B. A., Dorsey, M. F., Slifer, K. J., Bauman, K. E., & Richman, G. S. (1994). Toward a functional analysis of self-injury. Journal of Applied Behavior Analysis, 27, 197–209. Keller, F. S., & Schoenfeld, W. N. (1950). Principles of psychology: A systematic text in the science of behavior. E. Norwalk: Appleton-Century Crofts. Lang, R., O’Reilly, M., Sigafoos, J., Lancioni, G. E., Machalicek, W., Rispoli, M., & White, P. (2009). Enhancing the effectiveness of a play intervention by abolishing the reinforcing value of stereotypy: A pilot study. Journal of Applied Behavior Analysis, 42, 889–894. Lang, R., O’Reilly, M., Sigafoos, J., Machalicek, W., Rispoli, M., Lancioni, G. E., & Fragale, C. (2010). The effects of an abolishing operation intervention component on play skills, challenging behavior, and stereotypy. Behavior Modification, 34(4), 267–289. https://doi.org/10.1177/0145445510370713 Langthorne, P., & McGill, P. (2009). A tutorial on the concept of the motivating operation and its importance to application. Behavior Analysis in Practice, 2(2), 22–31. Laraway, S., Snycerski, S., Michael, J., & Poling, A. (2003). Motivating operations and some terms to describe them: Some further refinements. Journal of Applied Behavior Analysis, 36, 407–414. Lotfizadeh, A. D., Edwards, T. L., & Redner, R. (2012). Motivating operations affect stimulus control: A largely overlooked phenomenon in discrimination learning. The Behavior Analyst, 35(1), 89–100. Mace, F. C., Hock, M. L., Lalli, J. S., West, B. J., Belfiore, P., Pinter, E., et al. (1988). Behavioral momentum in the
Motivation treatment of noncompliance. Journal of Applied Behavior Analysis, 21, 123–141. McComas, J., Hoch, H., Paone, D., & El-Roy, D. (2000). Escape behavior during academic tasks: A preliminary analysis of idiosyncratic establishing operations. Journal of Applied Behavior Analysis, 33, 479–493. McComas, J., Thompson, A., & Johnson, L. (2003). The effects of presession attention on problem behavior maintained by different reinforcer. Journal of Applied Behavior Analysis, 36, 297–307. McGill, P. (1999). Establishing operations: Implications for the assessment, treatment, and prevention of problem behavior. Journal of Applied Behavior Analysis, 32, 393–418. McGuinnis, M. A., Houchins-Juarez, N., McDaniel, J. L., & Kennedy, C. G. (2010). Abolishing and establishing operation analysis of social attention as positive reinforcement for problem behavior. Journal of Applied Behavior Analysis, 43, 119–123. Michael, J. (1982). Distinguishing between discriminative and motivational functions of stimuli. Journal of the Experimental Analysis of Behavior, 37, 149–155. Michael, J. (1993). Establishing operations. The Behavior Analyst, 16, 191–206. Michael, J. (2000). Implications and refinements of the establishing operation concept. Journal of Applied Behavior Analysis, 33, 401–410. Michael, J. (2007). Motivating operations. In J. O. Cooper, T. E. Heron, & W. L. Heward (Eds.), Applied behavior analysis (2nd ed., pp. 374–391). Upper Saddle River: Merrill Prentice Hall. Neely, L., Rispoli, M., Camargo, S., Davis, H., & Boles, M. (2013). The effect of instructional use of an iPad [R] on challenging behavior and academic engagement for two students with autism. Research in Autism Spectrum Disorders, 7, 509–516. Roxburgh, C., & Carbone, V. J. (2012). The effects of varying teacher presentation rates on responding during discrete trial training for two children with autism. Behavior Modification, 37, 1–26. Skinner, B. F. (1957). Verbal behavior. Englewood Cliffs: Prentice Hall. Smith, R. G., & Iwata, B. A. (1997). Antecedent influences on behavior disorders. Journal of Applied Behavior Analysis, 30, 343–375. Sundberg, M. L. (1993). The application of establishing operations. The Behavior Analyst, 16, 211–214. Sundberg, M. L., Loeb, M., Hale, L., & Eigenheer, P. (2002). Contriving establishing operations to teach mands for information. The Analysis of Verbal Behavior, 18, 14–28. Sweeney-Kerwin, E. J., Carbone, V. J., O’Brien, L., Zecchin, G., & Janecky, M. N. (2007). Transferring control of the mand to the motivating operation in children with autism. The Analysis of Verbal Behavior, 23, 89–102. Taylor, B. A., Hoch, H., Potter, B., Rodriguez, A., Spinnato, D., & Kalaigian, M. (2005). Manipulating establishing operations to promote initiations toward peers in children with autism. Research in Developmental Disabilities, 26, 385–392.
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Motivation Lynn Kern Koegel1,2, Robert L. Koegel1,3 and Mi Na Park4 1 Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA 2 Koegel Autism Center, Eli and Edythe L. Broad Center for Asperger Research, University of California, Santa Barbara, CA, USA 3 Koegel Autism Center/Clinical Psychology, Gevirtz Graduate School of Education, University of California, Santa Barbara, CA, USA 4 Department of Counseling, Clinical, and School Psychology, University of California The Gevirtz School, Santa Barbara, CA, USA
Definition An important body of research relates to interventions that improve motivation in children with autism. This literature has been particularly relevant, as individuals with autism often appear unmotivated to engage in social and learning interactions. It has been hypothesized that a lack of motivation to engage in social and learning interactions may be caused by external variables (see below). Therefore, environmental manipulations should be effective in changing the response-reinforcer relationships that led to the development of these motivational deficits. Along those lines, a number of studies have focused on identifying specific variables that effectively improve motivation in children with autism. To start, child-driven approaches, such as providing the child with the choice of stimulus materials, activities, and conversational topics, have been shown to improve responding and attention (Koegel et al. 1987). This has also been referred to in the literature as “following the child’s lead,” and its effectiveness has been well documented in regard to improving a range of behaviors such as communication, academics (Koegel et al. 2010), and socialization (Koegel et al. 1987). Such approaches have been compared and contrasted with previous empirically
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based approaches that focus on an adult-driven curriculum without any input or consideration of the child with autism’s changing reinforcer hierarchy and have been shown to be more effective. Another variable that improves motivation to respond is the interspersal of new (acquisition) tasks that the child has not yet mastered with previously mastered (maintenance) tasks (Dunlap 1984; Koegel and Koegel 1986). Rather than repeatedly presenting new target tasks that are likely to be difficult, interspersing maintenance tasks with acquisition tasks results in increased correct responding in addition to improved overall responsiveness. Similarly, when tasks are varied frequently, rather than being taught in a drill-like context, motivation improves (Dunlap and Koegel 1980). Another body of research relates to the manipulation of consequences for child responses. As opposed to using a strict shaping paradigm that only rewards responses that are as good or better than the previous response, child motivation can be improved by rewarding child’s attempts to respond correctly that are free of disruptive or repetitive behaviors (Koegel and Egel 1979; Koegel et al. 1988). Related to this, providing direct and natural rewards, which are inherently linked to the child’s response, results in improved motivation in terms of more rapid acquisition of the target behaviors (Koegel and Williams 1980; Williams et al. 1981). That is, rather than providing an arbitrary unrelated reward (e.g., food item or token) contingent upon a correct response, the child is provided with rewards that are directly related to their behaviors. For example, a food may be given to a child learning first words contingent upon labeling the food item so that it is directly related to the child’s response. The aforementioned procedures can be used in combination with one another and have been documented to improve motivation as measured by decreased levels of disruptive behavior; improved performance in academic, language, and social domains; and improved child affect (Koegel et al. 1987; Koegel and Koegel 2006). Another variable that has been shown to affect child motivation for some (but not all) children is clinician affect. For example, research suggests that some children respond more
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favorably when the clinician shows specific types of affect while providing reinforcers, such as either strong positive affect or low affect depending on the child. As a whole, these studies suggest that motivation appears to be measurable and treatable. At this point, the most effective programs use a combination of motivational procedures, applied simultaneously, to produce large changes in the behavior of children with autism. This body of literature has led researchers to conceptualize the construct of motivation as “pivotal” such that positive changes in a number of untreated symptoms/behaviors occur when a child with autism is motivated to respond (Koegel and Koegel 2006). Thus, the literature on pivotal response treatment (PRT) uses motivational procedures as a core underlying principle of the intervention, and motivational components have effectively been incorporated into interventions for improving articulation (Koegel et al. 1998), first words (Koegel et al. 1987), academic engagement (Koegel et al. 2010), and social conversation (Koegel et al. 1987) in children with autism. Additionally, studies have shown that collateral improvements in disruptive behavior occur when such motivational variables are employed without needing to target the disruptive behavior directly. While the general concept of motivation can be conceptualized by feelings of an individual, such as the desire or incentive to engage in activities, motivation also can be defined by observable and measureable behaviors. Specifically, motivation has been measured by task engagement, responsiveness, decreases in response latency, initiations, and affect, as well as an absence of avoidance behavior (Koegel et al. 2001). During the intervention sessions when motivational procedures are incorporated into the teaching, the children show an increase in the number of responses and decrease in response latency, initiate new related behaviors, and demonstrate positive affect. Task engagement and responsiveness can be monitored by the number or rate of responses the child emits. Latency can be measured by assessing the amount of time that passes before the child begins to engage in a learning task or activity. Initiations are important in that if the children are motivated to engage in
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the tasks, they are likely to spontaneously engage in the task, sustain participation, and initiate future activities. This also relates to generalization, as motivated children may spontaneously demonstrate the targeted responses across settings, behaviors, and people. Changes in affect can also be seen in motivated children. Affect is often rated by Likert scales in general categories of positive, neutral, and negative affect and has been defined as interest (e.g., child is alert and involved in the activity), happiness (e.g., the child smiles, laughs, and seems to be enjoying self), and enthusiasm (Baker et al. 1998; Koegel and Egel 1979; Koegel et al. 2009). In addition to child affect, motivation can be measured by lowered levels of disruptive behaviors (Dunlap and Koegel 1980; Koegel et al. 1992). Multiple measures showing improved latency, affect, happiness, enthusiasm, and levels of disruptive behavior have been used to measure motivation with high degrees of reliability (Dunlap and Koegel 1980).
Historical Background The interest in the construct of motivation, as it relates to autism spectrum disorder (ASD), came about as a result of the apparent lethargy and disinterest individuals with autism may demonstrate in regard to social conversation, learning, and other activities (Lovaas 1977). This led researchers to hypothesize theories for this lack of motivation. A possible theoretical explanation is related to the concept of “learned helplessness.” Specifically, it has been hypothesized that during exposure to an uncontrollable outcome, a lack of contingency between the behavior and the outcome is learned (Seligman and Altenor 1980). Simply put, if an individual receives noncontingent rewards or punishers, the individual will not initiate behaviors and will appear lethargic and inactive. Early animal studies that postulated the learned helplessness theory were carefully controlled and showed the effect of uncontrollable events causing a disruption in behavior. Specifically, the once responsive animals became unresponsive and lethargic. Subsequently, studies replicated this phenomenon using human
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subjects in controlled settings. They showed that if individuals’ responses were not reinforced, they too simply stopped responding (Gatchel et al. 1975). Learned helplessness has also been hypothesized to occur in other human conditions, such as stress and depression (Miller and Seligman 1975; Price et al. 1978). Some studies have also shown that physiological changes, such as norepinephrine depletion, weight loss, and ulcers occur at a higher rate when consequences are noncontingent and outcomes are uncontrollable (Abramson et al. 1978; Seligman and Weiss 1980). Data also suggest that this theory may be highly relevant for autism as well (Koegel and Egel 1979; Koegel and Koegel 2006). The theory of motivation is an important concept in regard to intervention for autism, as it supports the notion that some, or all, of the disability may relate to environmental variables. That is, the state of an apparent lack of motivation is hypothesized to occur when the individual’s responses are uncontrollable, or “learned helplessness.” In regard to children with autism, reduced social responding that begins very early in life due to central nervous system dysfunction may result in the children experiencing high degrees of noncontingent consequences. This may create a situation wherein the child learns that responding and reinforcement are independent. This weakening of the response-reinforcer relationship may cause a decrease in social responding and lower levels of motivation in children with autism.
Current Knowledge The lack of responsiveness and long response latencies demonstrated by children with autism as well as an apparent lack of motivation and curiosity have been discussed since the 1960s when principles of applied behavior analysis were beginning to show promise with children with autism. Within the broad framework of environmentally manipulated behaviors, researchers discussed the role of reinforcers in this apparent lack of motivation. They proposed that the potency or availability of reinforcers may not be
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adequate when a child with autism engages in difficult tasks (Koegel and Egel 1979), thereby creating a lack of motivation. Koegel and Egel (1979) were the first authors to present the possibility of learned helplessness and the importance of motivation to address the problem. Following that publication, a number of studies addressed individual areas of intervention that appeared to directly improve motivation. In 1987, Koegel, O’Dell, and Koegel published work using an intervention package, containing a large number of motivational variables (discussed above), was especially effective.
Future Directions Although the literature describes specific procedures for improving motivation, there continues to be a need for future research relating to the measurement of motivation, the refinement of existing procedures that improve motivation, and additional intervention procedures for improving motivation.
References and Reading Abramson, L. Y., Seligman, M. E., & Teasdale, J. D. (1978). Learned helplessness in humans: Critique and reformulation. Journal of Abnormal Psychology, 87(1), 49–74. Baker, M., Koegel, R. L., & Koegel, L. K. (1998). Increasing the social behavior of young children with autism using their obsessive behaviors. The Journal of the Association for Persons With Severe Handicaps, 23, 300–308. Dunlap, G. (1984). The influence of task variation and maintenance tasks on the learning and affect of autistic children. Journal of Experimental Child Psychology, 37(1), 41–64. Dunlap, G., & Koegel, R. L. (1980). Motivating autistic children through stimulus variation. Journal of Applied Behavior Analysis, 13(4), 619–627. Gatchel, R. J., Paulus, P. B., & Maples, C. W. (1975). Learned helplessness and self-reported affect. Journal of Abnormal Psychology, 84(6), 732–734. Koegel, R. L., & Egel, A. L. (1979). Motivating autistic children. Journal of Abnormal Psychology, 88(4), 418–426. Koegel, L. K., & Koegel, R. L. (1986). The effects of interspersed maintenance tasks on academic
Motivation performance in a severe childhood stroke victim. Journal of Applied Behavior Analysis, 19(4), 425–430. Koegel, R. L., & Koegel, L. K. (2006). Pivotal response treatments for autism: Communication, social, & academic development. Baltimore: Paul H Brookes Publishing. Koegel, R. L., & Williams, J. A. (1980). Direct versus indirect response-reinforcer relationships in teaching autistic children. Journal of Abnormal Child Psychology, 8(4), 537–547. Koegel, R. L., Dyer, K., & Koegel, L. K. (1987). The influence of child-preferred activities on autistic children’s social behavior. Journal of Applied Behavior Analysis, 20(3), 243–252. Koegel, R. L., O’Dell, M., & Dunlap, G. (1988). Producing speech use in nonverbal autistic children by reinforcing attempts. Journal of Autism and Developmental Disorders, 18(4), 525–538. Koegel, R. L., Koegel, L. K., & Surratt, A. (1992). Language intervention and disruptive behavior in preschool children with autism. Journal of Autism and Developmental Disorders, 22(2), 141–153. Koegel, R. L., Camarata, S., Koegel, L. K., Ben-Tall, A., & Smith, A. E. (1998). Increasing speech intelligibility in children with autism. Journal of Autism and Developmental Disorders, 28(3), 241–251. Koegel, R. L., Koegel, L. K., & McNerney, E. K. (2001). Pivotal areas in intervention for autism. Journal of Clinical Child Psychology, 30(1), 19–32. Koegel, R. L., Vernon, T. W., & Koegel, L. K. (2009). Improving social initiations in young children with autism using reinforcers with embedded social interactions. Journal of Autism and Developmental Disorders, 39(9), 1240–1251. Koegel, L. K., Singh, A. K., & Koegel, R. L. (2010). Improving motivation for academics in children with autism. Journal of Autism and Developmental Disorders, 40(9), 1057–1066. Lovaas, O. I. (1977). The autistic child: Language development through behavior modification. Oxford: Irvington. Miller, W. R., & Seligman, M. E. (1975). Depression and learned helplessness in man. Journal of Abnormal Psychology, 84(3), 228–238. Price, K. P., Tryon, W. W., & Raps, C. S. (1978). Learned helplessness and depression in a clinical population: A test of two behavioral hypotheses. Journal of Abnormal Psychology, 87(1), 113–121. Seligman, M. E. P., & Altenor, A. (1980). Part II: Learned helplessness. Behaviour Research and Therapy, 18(5), 462–473. Seligman, M. E., & Weiss, J. M. (1980). Coping behavior: Learned helplessness, physiological change and learned inactivity. Behaviour Research and Therapy, 18(5), 459–512. Williams, J. A., Koegel, R. L., & Egel, A. L. (1981). Response-reinforcer relationships and improved learning in autistic children. Journal of Applied Behavior Analysis, 14, 53–60.
Motivation Assessment Scale
Motivation Assessment Scale Corey Ray-Subramanian Waisman Center, University of WisconsinMadison, Madison, WI, USA
Synonyms MAS
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designed to be a more efficient alternative to direct functional behavior analysis methods (Durand and Crimmins 1988). Based on previous literature identifying common functions of self-injurious behavior (i.e., social attention, tangible consequences, escape from unpleasant situations, and sensory consequences), Durand and Crimmins (1988) created four subscales, each representing a specific function, that comprise the MAS. The items were generated following interviews with teachers, clinicians, and parents of children with developmental disabilities.
Description Psychometric Data The Motivation Assessment Scale (MAS) is a rating scale that assesses functions of problem behavior in individuals with developmental disabilities through informant responses. It includes 16 questions and is comprised of four subscales that each represents a possible function of the behavior: attention, escape, sensory, and tangible. Each question has six response options (0 ¼ never, 1 ¼ almost never, 2 ¼ seldom, 3 ¼ half the time, 4 ¼ usually, 5 ¼ almost always, and 6 ¼ always). Scores are calculated by summing the item ratings within a particular subscale/function and calculating the mean rating for that subscale. High scores for one or more of the subscales suggest that those functions may be maintaining the individual’s problem behavior (Durand and Crimmins 1988), although the authors of the instrument do not specify what constitutes a high score. An example item on the MAS is “Does this behavior occur when you are talking to other persons in the room?” (Durand & Crimmins, p. 102). The MAS is considered an indirect assessment method because it is removed in time and place from the behaviors being measured (Floyd et al. 2005).
Historical Background The MAS was originally developed to assess the functions of self-injurious behavior, in particular (Durand and Crimmins 1988), within a framework of applied behavior analysis. It was
In their original study based on 50 children with developmental disabilities who exhibited frequent self-injurious behavior, Durand and Crimmins (1988) reported interrater reliability coefficients ranging from .66 to .92 for the individual items and .80 to .95 for the raw mean subscale scores. Test-retest reliability, over a 30-day period, was calculated and the coefficients ranged from .89 to .98 for the individual items and .92 to .98 for the mean subscale raw scores. The authors also found that teachers’ ratings on the MAS were highly predictive of student’s behavior in an analogue experimental condition (Durand & Crimmins). However, the psychometric properties of the MAS have since been questioned by other researchers (e.g., Paclawskyj et al. 2001), as subsequent studies examining the validity and reliability of the measure have produced inconsistent results. It has also been argued that research on the MAS and other indirect functional assessment measures has not carefully examined validity evidence based on scale content and informant response processes, evidence of user satisfaction with the measure, or the treatment utility of the scale (Floyd et al. 2005). Some factor analytic research has provided support for the intended four-factor structure of the MAS (i.e., sensory, escape, attention, and tangible), although the factor structure was somewhat different for a sample with lower frequency problem behaviors (Singh et al. 1993). Other researchers have not found support for the four-
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factor model, specifically the sensory function subscale (Kearney et al. 2006). Joosten and Bundy (2008) also failed to find support for the construct validity of the MAS in the form of a four-factor structure or as a unidimensional measure of motivating factors for stereotyped behavior. In addition, Duker and Sigafoos (1998) results did not fully support the factor structure of the MAS, and their results suggest that the type of problem behavior may be differentially related to reliability and factor structure. Research examining the internal consistency of the MAS has also produced inconsistent results. Cronbach’s alpha coefficients for the subscales ranged from .68 to .86 in a study by Spreat and Connelly (1996) and .68 to .87 in a study by Duker and Sigafoos (1998). However, Shogren and Rojahn (2003) reported values ranging from .80 to .96. Subsequent research on the interrater reliability of the rating scale has produced lower values than those reported in the original Durand and Crimmins (1988) study. Shogren and Rojahn (2003) found interrater reliability coefficients for the four subscales ranging from .35 to .73, and Streat and Connelly (1996) reported values ranging from .31 to .57. Another study found that interrater agreement depended upon the type of problem behavior (Duker and Sigafoos 1998). For example, raters were less likely to agree on the functions of destructive behaviors (e.g., aggressive behavior) as compared to maladaptive (e.g., self-injurious behavior) and disruptive (e.g., screaming) behaviors (Duker & Sigafoos). Research examining other types of reliability evidence for the MAS has also failed to provide clear evidence that the instrument is psychometrically sound. For example, Shogren and Rojahn (2003) reported test-retest reliability coefficients ranging from .71 to .89 for the four subscales, which are lower values than those reported originally by Durand and Crimmins (1988). Streat and Connelly (1996) also found evidence of poor reliability in the difference scores between subscales, which are used to make clinical decisions regarding the function of the problematic target behavior.
Motivation Assessment Scale
Clinical Uses The MAS has been used clinically to identify possible factors that are maintaining an individual’s problem behavior. The indentified function can then be used to develop an intervention program to reduce the problem behavior and to select reinforcers for appropriate behavior. Some have argued that the MAS should be used in conjunction with direct observation methods because of its psychometric limitations (e.g., Duker and Sigafoos 1998; Shogren and Rojahn 2003; Spreat and Connelly 1996).
See Also ▶ Applied Behavior Analysis (ABA) ▶ Behavioral Assessment ▶ Functional Analysis ▶ Functional Behavior Assessment
References and Reading Duker, P. C., & Sigafoos, J. (1998). The motivation assessment scale: Reliability and construct validity across three topographies of behavior. Research in Developmental Disabilities, 19, 131–141. Durand, V. M., & Crimmins, D. B. (1988). Identifying the variables maintaining self-injurious behavior. Journal of Autism and Developmental Disorders, 18, 99–117. Floyd, R. G., Phaneuf, R. L., & Wilczynski, S. M. (2005). Measurement properties of indirect assessment methods for functional behavioral assessment: A review of research. School Psychology Review, 34, 58–73. Joosten, A. V., & Bundy, A. C. (2008). The motivation of stereotypic and repetitive behavior: Examination of construct validity of the motivation assessment scale. Journal of Autism and Developmental Disorders, 38, 1341–1348. Kearney, C. A., Cook, L. C., Chapman, G., & Bensaheb, A. (2006). Exploratory and confirmatory factor analyses of the motivation assessment scale and resident choice assessment scale. Journal of Developmental and Physical Disabilities, 18, 1–11. Paclawskyj, T. R., Matson, J. L., Rush, K. S., Smalls, Y., & Vollmer, T. R. (2001). Assessment of the convergent validity of the questions about behavioral function with analogue functional analysis and the motivation assessment scale. Journal of Intellectual Disability Research, 45, 484–494.
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Shogren, K. A., & Rojahn, J. (2003). Convergent reliability and validity of the questions about behavioral function and the motivation assessment scale: A replication study. Journal of Developmental and Physical Disabilities, 15, 367–375. Singh, N. N., Donatelli, S. S., Best, A., Williams, D. E., Barrera, F. J., Lenz, M. W., et al. (1993). Factor structure of the motivation assessment scale. Journal of Intellectual Disability Research, 37, 65–74. Spreat, S., & Connelly, L. (1996). Reliability analysis of the motivation assessment scale. American Journal on Mental Retardation, 100, 528–532.
References and Reading
Motor Control
Motor Development Assessment
Kolb, B., & Whishaw, I. Q. (2009). An introduction to brain and behavior (3rd ed.). New York: Worth.
Motor Cortex ▶ Motor Control ▶ Precentral Gyrus
Su Mei Lee Child Neuroscience Lab, Yale Child Study Center, New Haven, CT, USA
▶ Psychomotor Development Index
Synonyms
Motor Planning
Cerebellum; Motor cortex
Casey Zampella and Loisa Bennetto Department of Clinical and Social Sciences in Psychology, University of Rochester, Rochester, NY, USA
Definition Motor control refers to the coordination of information exchanged between the neocortex in the brain and the muscular and somatosensory systems to bring about controlled, or voluntary, movements. Controlled movements are planned, organized, and initiated in the prefrontal, premotor, and primary motor cortices. Instructions for the movement are then sent through the spinal cord to the appropriate muscles to execute the movement. The somatosensory system provides feedback about the movement, and the basal ganglia, brain stem, and cerebellum fine-tune the movement by adjusting force and correcting for movement errors.
See Also ▶ Cerebellum ▶ Motor Cortex
Definition Motor planning can be broadly defined as the capacity to plan the necessary steps to achieve purposeful movements. It is often considered under the larger concept of praxis, which comprises the conceptualization, organization, and execution of an action sequence. Although the terms “motor planning” and “praxis” are sometimes used interchangeably, motor planning may be more accurately thought of as one element of praxis, distinct from both the idea behind the movement and its actual implementation. In addition to being dependent in part on neural motor systems, motor planning may be affected by the multimodal integration of sensory processes involved in proprioception, vestibular functioning, and vision, and by capabilities for preemptively formulating a mental sequence of actions.
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The clinical assessment of motor planning typically occurs as part of a broader evaluation of motor functioning. Few clinical tests specifically measure motor planning; however, a number of standardized instruments can provide relevant information. These include batteries that assess a range of motor abilities, such as fine and gross motor skills, limb and verbal praxis, and sensorimotor functioning. In addition, the assessment of adaptive skills and motor performance during everyday activities can provide some indication of motor planning ability. A thorough developmental history is also usually conducted for children with motor planning or praxis difficulties, including the documentation of major motor milestones and early motor behaviors. Disorders related to motor planning and praxis include apraxia and dyspraxia. Apraxia is typically thought of as an acquired neurological condition in which skilled movement is impaired in the presence of intact basic sensorimotor and attentional functioning. The term “dyspraxia” is often used to describe similar symptoms that are present early in development and have a less concrete etiology. Deficits in praxis in autism spectrum disorder (ASD) generally fall under the category of dyspraxia or developmental dyspraxia. Disorders of praxis can affect multiple modalities, including fine motor functioning, gross motor functioning, oculomotor control, orofacial movement, and speech production.
Historical Background General impairments in motor functioning have been documented in individuals with ASD since both Kanner’s and Asperger’s initial descriptions of the conditions. Since then, basic motor deficits have been consistently found in ASD, with many individuals displaying abnormalities in gait, postural control, coordination, balance, tone, and/or strength. Furthermore, there is evidence that aspects of motor dysfunction are often identifiable early in development, present throughout the lifespan, and pervasive across levels of cognitive functioning. The study of praxis has been an area of increasing interest within the larger domain of motor
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functioning in ASD. One focus of earlier work related to praxis was the assessment of clumsiness, or motor incoordination, as a possible symptom to differentiate subtypes within the autism spectrum. Historically, clumsiness was often considered a particular characteristic of Asperger syndrome, whereas individuals with classic autism were thought to have preserved motor function. Most evidence now suggests that motor clumsiness does not discriminate among subtypes; individuals with both Asperger syndrome and autism show similar degrees of impairment. Although clumsiness and dyspraxia are sometimes used synonymously, it is important to recognize that clumsiness is a clinical description that does not address the specific nature of an underlying motor impairment. Current research has taken a more systematic and componential approach to studying dyspraxia, with the goal of identifying the precise roots of the movement disturbances seen in individuals with ASD.
Current Knowledge Dyspraxia in ASD The growing body of literature focused on dyspraxia in ASD has, on the whole, confirmed that both children and adults with ASD display deficits on overall measures of praxis. In fact, difficulties with skilled limb movements have become one of the more reliable motor findings in the field. Impairments have been found in the ability to produce actions in response to command, imitate actions, and carry out actions involving tools. Moreover, these impairments are seen in transitive movements, or those involving objects, as well as intransitive movements, which do not include objects and are often more symbolic. Although basic motor skills are associated with praxis ability, individuals with ASD consistently appear to display deficits in praxis beyond what can be accounted for by general motor dysfunction. This suggests that praxis is a specific area of weakness for individuals with ASD within the larger motor domain. In addition, although correlations between praxis and both age and cognitive ability have been reported, the
Motor Planning
available data largely indicate that symptoms of dyspraxia in ASD remain even after controlling for these factors. Of particular interest, praxis impairment has been shown to predict the severity of autistic symptoms, whereas correlations between basic motor skill and ASD symptomatology have not been found. Motor Planning in ASD Research has also sought to examine the motor planning component of praxis more directly. One approach has utilized paradigms in which potential impairments at the planning or preparation stage of a goal-directed motor act can be assessed separately from impairments at the execution stage. An example is a task in which participants are required to generate hand movements toward alternating targets. The planning stage is defined as the time between the appearance of the target and the point at which the hand begins to move; the execution stage is defined as the time in which the hand is in motion. Research employing these types of tasks has found that the planning stage of movement is generally atypical in individuals with ASD, while the execution stage is similar to typically developing controls. Motor planning abilities in individuals with ASD have also been investigated using a reach, grasp, and place paradigm, in which participants are instructed to reach for an object, pick it up, and place it in a specified location or orientation. In one study in which children were required to reach, grasp, and place a rod in a certain position, typically developing children consistently chose initial movements that allowed for a comfortable end state. In contrast, children with ASD were more likely to favor an easier initial grip, even when this forced an uncomfortable final hand posture. In a similar study, children were asked to pick up an object and place it in either a small or large container. Typically developing children took longer during both the reach/grasp and place stages on the trials with the smaller compared to the larger target container. This suggests global planning of actions, such that when the final step of a motor act requires more precision, the execution of earlier steps is also affected. In contrast, for children with ASD, only the place
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stage was longer, signifying that although they were affected by the increased precision required at the end, they were less likely to plan for this in their initial actions. Overall, the performance of individuals with ASD on reach, grasp, and place tasks indicates that they are less likely than typically developing individuals to plan goal-directed actions holistically. Instead, they seem to execute the necessary steps for a motor act independently, such that knowledge of the end goal does not influence the initial movements in an action sequence. However, there is some evidence to suggest that the motor planning behavior of children with ASD may not differ from their peers when tasks have fewer higher order cognitive demands. For example, on a more constrained grip selection paradigm in which children reached for and grasped a handle and turned it in a specified direction, differences in performance were not found between age-matched groups. Related research has found that some individuals with ASD may have difficulty with coordinating the stages of a reach-to-grasp movement, failing to temporally couple the velocity of the reach stage with the timing of their hand opening during the grasp stage. One study found that lower functioning children with ASD did not seem to synchronize the timing of their reach and grasp based on target location and size in the same way as controls. Instead, they performed the movement in two distinct stages, completing the reaching movement first, and then opening their hand to grip the object. However, high-functioning children with ASD performed similarly to typically developing children, with the timing of the reach and handgrip movements well synchronized. A final area of research on motor planning in ASD has explored how the advanced presentation of information about a target affects goal-oriented movements. One hypothesis is that goal-directed actions are planned in a predictable sequence, where the appropriate hand is chosen first, followed by the direction of the movement, then by the amount of movement necessary to achieve the target. This model has been tested through the use of a precue technique, in which prior information about hand, direction, and/or movement
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extent is given to the participant. Results indicate that young adults with and without ASD display a similar, predictable pattern on this task, suggesting that they can effectively use advanced information to plan movements. Individuals in both groups tend to derive the most benefit from information pertaining to which hand to use, followed by information about direction, and finally by information about the amount of movement. Nevertheless, the reaction times of individuals with ASD were slower and more variable than those of their peers. Clinical Significance Motor deficits are not a diagnostic characteristic of ASD, yet the importance of studying how various aspects of motor dysfunction may contribute to the condition has been increasingly recognized. Without a doubt, impairments in planning purposeful movement can have deleterious effects on many aspects of daily functioning, including basic tasks like feeding and dressing. In addition, the impact of difficulties with motor planning on social and communicative functioning is a topic of growing interest. Some theories posit that the motor system plays a foundational role in certain interpersonal skills. The ability to effectively plan and produce movements within an appropriate time frame may be crucial for reciprocal social interaction. In addition, praxis ability has been empirically associated with imitation. Difficulties with imitation have been widely documented in ASD across ages and functioning levels: individuals with ASD have been shown to imitate less than their peers, display poorer approximations of imitated movements, and consistently make specific types of errors during imitation tasks. Furthermore, abnormalities have been found for the imitation of nonmeaningful movements, as well as movements with more social or symbolic significance, suggesting that a fundamental problem with skilled motor functioning may be involved in imitation impairments in ASD.
Future Directions The extant research has established motor planning as an area worthy of further investigation in ASD.
Motor Planning
An important direction for future research will be to build on existing work exploring components of motor acts to identify the specific aspects of goaldirected movement that are affected in ASD, both globally and at different points in development. There is also a need for further research on how functioning in other domains may impact the components of movement. Although basic motor functioning, general intellectual ability, attentional skills, motor learning, and sensory abilities have all been implicated in the organization of movement, the relationship between these various skill domains and motor planning in individuals with ASD is still not well understood. Recent work has specifically highlighted the need to distinguish between motor planning and higher order planning, the latter of which is thought to rely on executive functioning skills. Motor planning is distinctly dependent on the ability to implement learned movements, whereas executive planning requires abstraction and mental sequencing of goal-oriented decisions. Both motor and executive planning are often necessary to effectively prepare and execute actions, making it difficult to pin down the precise nature of movement planning difficulties in ASD. One viewpoint has been that individuals with ASD perform poorly on motor planning tasks because of executive functioning impairments, and that they would demonstrate intact performance on tasks that do not require executive skills. However, at least one study has shown that individuals with ASD perform poorly across a range of motor planning tasks regardless of the level of executive demand, suggesting that motor planning may be impaired even in situations that do not require higher order abilities. Another direction for future research is continued exploration of the neurobiological underpinnings of motor planning and related functions in ASD. Although research in ASD has implicated abnormalities in a number of regions important in motor functioning, less work has specifically addressed functional atypicalities during motor tasks. The neurobiology of the motor system in typically developing individuals is well understood. Thus, studying the neural bases of motor dysfunction in ASD may offer a more straightforward avenue for identifying structural and
Motor Planning
connectivity abnormalities in this disorder, potentially leading to a better understanding of the neural bases of other affected domains. In addition, functional neuroimaging studies can provide further information on how individuals with ASD approach motor planning tasks, helping to answer questions about the degree to which executive and other skills play a role in motor planning in this condition. Finally, further research is needed to better understand if and how motor planning difficulties might influence the pathogenesis of core symptoms in ASD, including social and communication impairments. The answers to these questions will benefit from more research on the developmental timeframe of motor planning in ASD, and how this interfaces with other aspects of development.
See Also ▶ Apraxia ▶ Developmental Apraxia ▶ Developmental Dyspraxia ▶ Dyspraxia ▶ Executive Function (EF) ▶ Imitation ▶ Praxis
References and Reading Baranek, G. T., Parham, L. D., & Bodfish, J. W. (2005). Sensory and motor features in autism: Assessment and intervention. In F. R. Volkmar, R. Paul, A. Klin, & D. Cohen (Eds.), Handbook of autism and pervasive developmental disorders (pp. 831–857). Hoboken: Wiley. Dewey, D., & Bottos, S. (2006). The effect of motor disorders on imitation in children. In S. J. Rogers & J. H. G. Williams (Eds.), Imitation and the social mind: Autism and typical development (pp. 399–430). New York: Guilford Press. Dewey, D., Cantell, M., & Crawford, S. G. (2007). Motor and gestural performance in children with autism spectrum disorders, developmental coordination disorder and/or attention deficit hyperactivity disorder. Journal of the International Neuropsychological Society, 13, 246–256. Dowell, L. R., Mahone, E. M., & Mostofsky, S. H. (2009). Associations of postural knowledge and basic motor skill with dyspraxia in autism: Implication for
2999 abnormalities in distributed connectivity and motor learning. Neuropsychology, 23(5), 563–570. Dziuk, M. A., Gidley Larson, J. C., Apostu, A., Mahone, E. M., Denckla, M. B., & Mostofsky, S. H. (2007). Dyspraxia in autism: Association with motor, social, and communicative deficits. Developmental Medicine & Child Neurology, 49, 734–739. Fabbri-Destro, M., Cattaneo, L., Boria, S., & Rizzolatti, G. (2009). Planning actions in autism. Experimental Brain Research, 192, 521–525. Gidley Larson, J. C., & Mostofsky, S. H. (2006). Motor deficits in autism. In R. Tuchman & I. Rapin (Eds.), Autism: A neurological disorder of early brain development (pp. 231–247). London: Mac Keith Press. Glazebrook, C. M., Elliott, D., & Szatmari, P. (2008). How do individuals with autism plan their movements? Journal of Autism and Developmental Disorders, 38, 114–126. Hughes, C. (1996). Brief report: Planning problems in autism at the level of motor control. Journal of Autism and Developmental Disorders, 26(1), 99–107. Leary, M. R., & Hill, D. A. (1996). Moving on: Autism and movement disturbance. Mental Retardation, 34, 39–53. Mari, M., Marks, D., Marraffa, C., Prior, M., & Castiello, U. (2003). Autism and movement disturbance. In U. Frith & E. Hill (Eds.), Autism: Mind and brain (pp. 225–246). New York: Oxford University Press. Mostofsky, S. H., Dubey, P., Jerath, V. K., Jansiewicz, E. M., Goldberg, M. C., & Denckla, M. B. (2006). Developmental dyspraxia is not limited to imitation in children with autism spectrum disorders. Journal of the International Neuropsychological Society, 12, 314–326. Nazarali, N., Glazebrook, C. M., & Elliott, D. (2009). Movement planning and reprogramming in individuals with autism. Journal of Autism and Developmental Disorders, 39, 1401–1411. Rinehart, N. J., Bradshaw, J. L., Brereton, A. V., & Tonge, B. J. (2001). Movement preparation in highfunctioning autism and Asperger disorder: A serial choice reaction time task involving motor reprogramming. Journal of Autism and Developmental Disorders, 31(1), 79–88. Rinehart, N. J., Bellgrove, M. A., Tonge, B. J., Brereton, A. V., Howells-Rankin, D., & Bradshaw, J. L. (2006). An examination of movement kinematics in young people with high-functioning autism and Asperger’s disorder: Further evidence for a motor planning deficit. Journal of Autism and Developmental Disorders, 36(6), 757–767. Rogers, S. J., & Williams, J. H. G. (2006). Imitation in autism: Findings and controversies. In S. J. Rogers & J. H. G. Williams (Eds.), Imitation and the social mind: Autism and typical development (pp. 277–309). New York: Guilford Press. Smith, I. M. (2000). Motor functioning in Asperger syndrome. In A. Klin, F. R. Volkmar, & S. S. Sparrow (Eds.), Asperger syndrome (pp. 97–124). New York: Guilford Press. Smith, I. M. (2004). Motor problems in children with autistic spectrum disorders. In D. Dewey & D. E.
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Tupper (Eds.), Developmental motor disorders: A neuropsychological perspective (pp. 152–168). New York: Guilford Press. Smith, I. M., & Bryson, S. E. (1994). Imitation and action in autism: A critical review. Psychological Bulletin, 116(2), 259–273. van Swieten, L. M., Williams, J. H. G., Plumb, M. S., van Bergen, E., Wilson, A. D., Kent, S. W., et al. (2010). A test of motor (not executive) planning in developmental coordination disorder and autism. Journal of Experimental Psychology: Human Perception and Performance, 36(2), 493–499.
ICC MABC MABC-2 PDD PDMS-2 SD SDDMF TOMI TOMI-H
Movement Assessment Battery for Children, Second Edition ▶ Movement Assessment Battery for Children: Second Edition (MABC-2)
UK
Intra-class Correlation Coefficient Movement Assessment Battery for Children Movement Assessment Battery for Children, Second Edition Pervasive Developmental Disorders Peabody Developmental Motor Scales – Second Edition Standard Deviation Specific Developmental Disorder of Motor Function Test of Motor Impairment Test of Motor ImpairmentHenderson Revision United Kingdom
Synonyms MABC; MABC-2; Movement assessment battery for children, second edition
Movement Assessment Battery for Children: Second Edition (MABC-2) Ted Brown Department of Occupational Therapy, School of Primary and Allied Health Care, Faculty of Medicine, Nursing and Health Sciences, Monash University – Peninsula Campus, Frankston, VIC, Australia
Abbreviations AB ADHD AS ASD BOTMP BOT-2 CFA DCD
Age bands Attention deficit/hyperactivity disorder Asperger’s Syndrome (AS) Autism Spectrum Disorder Bruininks-Oseretsky Test of Motor Proficiency Bruininks-Oseretsky Test of Motor Proficiency, Second Edition Confirmatory Factor Analysis Developmental Coordination Disorder
Description The Movement Assessment Battery for Children, Second Edition (MABC-2) (Henderson et al. 2007) is a revision of the Movement Assessment Battery for Children (MABC) (Henderson and Sugden 1992) and is one of the most widely used assessment tools by occupational therapists, physiotherapists, psychologists, and educational professionals (Barnett and Henderson 1998; Brown and Lalor 2009; Wiart and Darrah 2001). The purpose of the MABC-2 is the identification and description of impairments in children’s motor function. It is composed of two parts: the Performance Test and the Checklist (see Table 1). The Psychometrics Centre at the Cambridge Judge Business School (n.d.) states that the MABC-2 Performance Test or the Checklist “can be used to identify a child with motor difficulties by comparing the child’s score to the normative data. However, neither instrument should be used on its own as a predictive screening instrument” (para 1). The MABC-2 Performance Test involves children completing a series of fine and gross motor
Movement Assessment Battery for Children: Second Edition (MABC-2) Movement Assessment Battery for Children: Second Edition (MABC-2), Table 1 Movement Assessment Battery for Children, Second Edition (MABC-2; Henderson
et al. 2007) summary of Performance Test and Checklist data
Test section Purpose
MABC-2 performance test The identification and screening of children with delay or impairment of their motor development, provision of appropriate intervention planning, clinical exploration, program evaluation, and as a research tool with children who are believed to be at risk of motor difficulties
Test age range
3:0–16:11 years across three age bands (AB) AB1 3:0–6:11 years AB2 7:0–10:11 years AB3 11:0–16:11 years Individuals who are certified by a professional organization recognized by Pearson assessment or who have a graduate and/or postgraduate qualification relevant to their profession. This qualification code would include psychologists, speech therapists, physiotherapists, occupational therapists, mental health professionals, health practitioners, and education professionals. No additional specialized training is required; however, familiarity with test items is required. It is envisaged in the future that specialized training modules will be set up to allow a wider range of professionals’ access to the MABC-2 Individual administration usually takes approximately 20–40 min depending on the age of the children; however, 50 min is suggested for setup, testing, and completion of the record form 10–15 min Stopwatch, clipboard, pencil, correct age band record form, test manual, and test kit containing full set of testing materials and manipulatives Physical demonstration of all items is required to ensure each child assessed fully understands the verbal instructions for each task. Verbal instructions are not scripted, allowing for the assessor to alter their language according to the age of the child and the child’s level of comprehension. Important features of the physical demonstration are emphasized verbally to the child. The child is provided a practice phase (that is not scored/rated) and a formal trial (which is scored/rated)
Who can administer?
Time to administer
Time to score Materials/ equipment required Method of administration
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MABC-2 checklist The efficient and economical assessment of the movement competence of children and to identify children who are likely to have difficulty with their movement. The MABC-2 checklist takes assessment into the everyday situations in which the child has to function, including the extent to which a child’s attitudes and feelings about motor tasks are situation specific or more generalized. Therapists can also obtain parents’ or teachers’ views on a child’s movement in everyday settings 5:0–12:11 years
Available to psychologists, classroom teachers, special education teachers, physical education specialists, pediatricians, occupational therapists, speech therapists, and physiotherapists. A child’s parent or primary caregiver may also contribute to the completion of MABC-2 checklist if requested by a professional
The MABC-2 checklist can be completed with a group or individual and takes the respondent approximately 10 min to complete 10 min Checklist and pencil
A basic list of specific motor behaviors is outlined for the adult rater to observe and rate the child’s performance and competency. A total score is obtained by summing the ratings, and this is then mapped onto a “traffic light” scoring system (outlined below under the “scores provided” heading)
(continued)
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Movement Assessment Battery for Children: Second Edition (MABC-2)
Movement Assessment Battery for Children: Second Edition (MABC-2), Table 1 (continued) Test section Response format
MABC-2 performance test The child’s performance on a task can be recorded in 1 of 4 ways 1 2
The child’s best result for an individual task * An “F” is recorded for a failed attempt
An “R” is recorded for refusal to attempt or complete a task 4 An “I” is recorded if it is inappropriate for a child to attempt a task * if a child completes a task successfully according to the item parameters, the item is scored according to criteria and then converted to a scaled score reported as 1–5, where 5 indicates a poor performance. Some items are scored based on number of correct repetitions, length of time, or accuracy of responses
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Scale construction/ test structure
Eight tasks are administered across three specific components: (a) manual dexterity, (b) aiming and catching, and (c) balance. Adding all eight scores yields a total motor impairment score (maximum 40). Overall scores that are below the 15th percentile are deemed at risk; those below the 5th percentile indicate a definite movement impairment
Standardization sample
The norms of the MABC-2 are derived from a stratified sample of 1172 children (48.3% male, 51.7% female) in the United Kingdom (UK), who were assessed between November 2005 and July 2006. This sample closely approximated the UK 2001 census data. The test sample included 12 UK geographic regions, 16 age levels (e.g., ages 3–16 years), four ethnic groups (e.g., white, 90.2%; black, 2.6%; Asian, 3.9%; and other, 3.2%), and five levels of parental education
Scores provided
Standard scores and percentile ranks are provided. “Total standard scores are calculated and converted into percentiles to determine how a child’s motor coordination compares to typically developing children of the same age” (Camden et al. 2013, p. 1). Additional score interpretation according to a “traffic light” system is provided. This system is the same for both the MABC-2 performance test and checklist, allowing for direct comparison if required. Any child whose score falls at or below the 5th percentile is regarded as having a significant movement difficulty (red zone), between the 6th and 15th percentile at risk (amber zone), and above the 16th percentile as unlikely to have a movement difficulty (green zone)
MABC-2 checklist In sections A and B, the rater has four alternative responses for the child’s task competence 0 – very well 2 – almost 1 – just OK 3 – not close The summed score of sections A and B represents the total motor score and interpreted with the “traffic light” system outlined below. Section C allows for either a yes or no response to factors that may affect a child’s movement and is not meant to be summed. Rather, these factors should be reviewed by the assessor to determine how much the child is prevented from demonstrating their true capability due to the influence of the observed factors The checklist comprises three sections Section A: 15 items regarding movement in a static or predictable environment Section B: 15 items regarding movement in a dynamic or unpredictable environment Section C: 13 items regarding non-motor factors that may affect movement 656 children of the 1172 sample fell into the age range of the MABC-2 checklist. Of the 656, teachers for 395 children (50.9% male, 49.1% female) completed the MABC-2 checklist. The test sample included 12 UK geographic regions, 8 age levels (e.g., ages 5–12 years), four ethnic groups (e.g., white, 90.4%; black, 2.6%; Asian, 3.5%; and other, 3.5%), and five levels of parental education. This sample closely approximated the UK 2001 census data A percentile cute score is provided. The “traffic light” system shows whether a child is in the “green zone” which is within the age-expected normal range, in the “amber zone” indicating a need for monitoring due to minor delay or movement problem, or in the red zone where it is highly likely that child has a serious movement problem
Movement Assessment Battery for Children: Second Edition (MABC-2)
tasks on which they are scored and rated. The Performance Test is designed for use with children aged 3–17 years in one of three age bands (AB) (AB1, 3:0–6:11 years; AB2, 7:0–10: 11 years; and AB3, 11:0–16:11 years) and evaluates motor skills under three categories: (1) Manual Dexterity, (2) Aiming and Catching, and (3) Balance. The MABC-2 Checklist requires an adult who knows the children being assessed well to rate their motor competence on a 30-item scale. Since the MABC-2 is a revision of an existing instrument, it is important for professionals who use the motor skill battery to be familiar with its age range, scoring format, standardization sample, reliability, validity, and clinical utility. Details of the purpose, age range, administration, response format, scale construction, standardization, and scores provided of the MABC-2’s Performance Test and Checklist are reported in Table 1. The MABC (Henderson and Sugden 1992) was normed with children from Canada, the United States, and the United Kingdom and has subsequently been translated into several European languages (including Swedish, Danish, Dutch, Italian, and Finnish) (Livesey et al. 2007) and Chinese (Chow and Henderson 2003). Predominantly used throughout the United Kingdom, Canada, Australia, several European countries (e.g., the Netherlands, Belgium, Italy, Finland, Denmark, Sweden, and Greece), and Asia to assess pediatric motor impairments, it has been shown to correlate positively with other pediatric motor assessments used worldwide (SmitsEngelsman et al. 1998; Missiuna et al. 2006) and is well recognized as one of the most extensively utilized motor impairment assessments in the world (Chow and Henderson 2003; Chow et al. 2006a, 2006b; Croce et al. 2001; Tan et al. 2001; Wiart and Darrah 2001). The MABC norms have been evaluated in studies completed in other cultural contexts including Sweden (Rosblad and Gard 1998), Japan (Miyahara et al. 1998), the Netherlands (Smits-Engelsman et al. 1998), Hong Kong (Chow et al. 2001; Chow et al. 2006), Taiwan (Chow et al.), Israel (Engel-Yeger et al. 2010),
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United States (Van Waelvelde et al. 2008), Greece (Ellinoudis et al. 2008), and Singapore (Wright et al. 1994). Generalizability across European cultures has shown to be satisfactory; however, those studies conducted in Hong Kong, Taiwan, Singapore, and Japan suggested some cultural differences existed and that the MABC norms for those countries may need some adjustment (Livesey et al. 2007). In addition to the worldwide recognition that the MABC has, the instrument has been utilized in many international studies covering a broad range of diagnostic categories such as developmental coordination disorder (DCD), learning disabilities, and autism spectrum disorder (ASD). Additionally, the MABC has been noted to identify more children with motor impairments more successfully (e.g., greater sensitivity and specificity) than that of the “gold standard” BruininksOseretsky Test of Motor Proficiency (BOTMP) (Dewey and Wilson 2001), while Crawford et al. (2001) have found that the MABC appears to be more sensitive and is able to better identify children with additional problems associated with learning or attention. The authors have revised the MABC to generate the MABC-2, a “reliable, easily administered and valid measure of competence in three broad and carefully selected areas of motor performance” (Henderson et al. 2007, p. 117). The three broad motor skill categories that are assessed are (1) Manual Dexterity, (2) Aiming and Catching, and (3) Balance. Changes undertaken to produce the second edition involved revising existing items and introducing some new items. These changes are outlined in two sections below: test content changes and test structure changes (see Tables 2, 3, and 4 for details). The MABC-2 test content changes are described under three areas: (1) materials, (2) tasks, and (3) instructions: 1. Materials: Brightly colored plastic pieces have been introduced to replace pieces originally made of wood. This change was undertaken to standardize the pieces and eliminate any room for variation between kits as well as
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Movement Assessment Battery for Children: Second Edition (MABC-2), Table 2 Changes made to age band 1 (3–6 years) from initial version to second edition Motor skill task Manual dexterity 1 Manual dexterity 2 Manual dexterity 3 Aiming and catching 1 Aiming and catching 2 Balance 1 Balance 2 Balance 3
MABCa task age band 1 Post coins Threading beads Bicycle trail Catching beanbag Rolling ball between goal posts One-leg balance Walking heels raised Jumping over cord
MABC-2b task age band 1 Post coins Threading beads Drawing trail 1 (shape of visual trail has changed) Catching beanbag Throwing beanbag onto mat (new item) One-leg balance Walking heels raised Jumping on mats (new item)
Henderson et al. 2007, p. 118 MABC Movement Assessment Battery for Children b MABC-2 Movement Assessment Battery for Children, Second Edition a
Movement Assessment Battery for Children: Second Edition (MABC-2), Table 3 Changes made to age band 2 (7–10 years) from initial version to second edition Motor skill task Manual dexterity 1 Manual dexterity 2 Manual dexterity 3 Aiming and catching 1 Aiming and catching 2 Balance 1 Balance 2 Balance 3
MABCa task age bands 2/3 Placing pegs/shifting pegs by rows
MABC-2b task age band 2 Placing pegs (new starting position and layout)
Threading lace/threading nuts on bolt
Threading lace (lacing board is longer)
Flower visual trail/flower visual trail
Drawing trail 2 (shape of visual trail has changed)
Two-hand catch/one-hand bounce and catch Throwing beanbag/throwing beanbag into box Stork balance/one-board balance Heel-to-toe walking/ball balance Jumping in squares/hopping in squares
Catching with two hands Throwing beanbag onto mat (mat with target now used instead of box) One-board balance Walking heel-to-toe forward Hopping on mats (mats used for this task)
Henderson et al. 2007, p. 118 MABC Movement Assessment Battery for Children b MABC-2 Movement Assessment Battery for Children, Second Edition a
taking into account health and safety regulations regarding item pieces when used with children in various settings. 2. Tasks: The test has maintained the original structure of the test by retaining eight test items across each age band organized into the three motor skill categories of Manual Dexterity, Aiming and Catching, and Balance. However, individual items have been altered as well as new individual items being introduced. The task changes across the age bands are outlined in Tables 2, 3, and 4.
3. Instructions: In previous research articles, the test instructions of the MABC have been called into question. Although no standardized verbal instructions are included in the second edition, clarification of the administration, scoring of the test, and aspects of tasks to emphasize during demonstration have been provided to minimize the potential for ambiguity. The MABC-2 authors state that this provides flexibility in the mode of presentation and allows the assessor to ensure the examinee understands individual tasks.
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Movement Assessment Battery for Children: Second Edition (MABC-2), Table 4 Changes made to age band 3 (11–16 years) from initial version to second edition Motor skill task Manual dexterity 1 Manual dexterity 2 Manual dexterity 3 Aiming and catching 1 Aiming and catching 2 Balance 1 Balance 2 Balance 3
MABCa task age band 3 Turning pegs Cutting out elephant visual trail with scissors Flower visual trail
MABC-2b task age band 3 Turning pegs Triangle with nuts and bolts (new item)
One-hand catch
Drawing task 3 (shape of visual trail has changed) Catching with one hand
Throwing ball at wall-mounted target
Throwing ball at wall-mounted target
Two-board balance Walking backward Jumping and clapping
Two-board balance Walking toe-to-heel backward Zigzag hopping (new item)
Henderson et al. 2007, p. 118 MABC Movement Assessment Battery for Children b MABC-2 Movement Assessment Battery for Children, Second Edition a
The MABC-2 test structure changes are described under two areas: (1) age extension and (2) reduction of age bands: 1. Age Extension: The MABC-2 has been extended to encompass the assessment of children aged 3 years 0 months–16 years 11 months. The MABC-2 authors believed that a gap existed for the appropriate motor performance assessment for children aged 3 years. In order to assess children of this age, items were slightly adjusted to ensure the attention of a typical 3-year-old could be maintained as well as being fun, easily understood, and requiring minimal verbal communication. Similarly, as the MABC has been utilized and widely recognized for its use with assessment of children with DCD, two sources of requests for suitable motor performance assessments for children aged between 11 and 16 years have been identified. The first is due to the increase in the recognition of DCD as a clinical diagnosis and the importance of early assessment, identification, and intervention service provision to assist with coordination difficulties encountered so as to maximize children’s developmental and motor skills. Current intervention for DCD occurs predominantly in during the primary school years due to the
effectiveness of early intervention. However, the MABC-2 authors have noted that an increasing proportion of adolescents have not been identified earlier with coordination difficulties or have failed to benefit from intervention. The second source identified was due to ongoing worldwide research into children who have been born “at risk” of damage to their nervous system (e.g., born prematurely). This group of children exhibit cognitive skills within normal limits and do not meet the diagnostic criteria for cerebral palsy, but do experience severe difficulties with their motor performance which in turn affects their academic progression. Therefore, with the development and revision of the MABC, the MABC-2 can provide improved motor performance measurement instrument for younger and older children. This in turn will enable therapists and researchers to develop new intervention techniques, allow for more accurate and sound intervention, and provide a greater understanding of motor performance problems and their consequences over time. 2. Reduction of Age Bands: Previously the MABC had 4 ABs; however, pilot work by the test authors provided evidence that existing items could be revised and/or adapted across
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the extended age bands. Thus, AB1 and the previous AB4 had an age range of 4 years, and so AB2 and AB3 were collapsed in to one age band. Additionally, and perhaps more importantly, the test authors were aware of problems associated with children changing between age bands in the MABC during the course of an intervention program. Although correlations between similar items across the age bands were high, the authors recognized that they were not perfect. Hence, the final age bands for the MABC-2 are AB1: 3–7 years, AB2: 7–11 years, and AB3: 11–17 years. The Qualitative Observations section remains a part of the MABC-2 test and allows the testing clinician to supplement the formal test results gathered from administering the Performance Test to a child. A chapter of the manual provides some assistance regarding the process of observation and how to utilize the Qualitative Observations section of the test in conjunction with the formal scores as well as taking into account both motor and non-motor factors that can affect movement and a child’s motor performance.
Historical Background The MABC-2 is a composite of two complementary assessments: the Performance Test and the Checklist. The performance-based portion of the MABC-2 was developed from the Test of Motor Impairment (TOMI) (Stott et al. 1972). Development of the TOMI began in 1966 with a primary focus of identifying impaired or nonstandard motor skill performance. Since the TOMI provided little overall motor ability information about the children who were assessed with it, the TOMI was revised in 1984 (see Table 5). This revision, known as the Test of Motor Impairment-Henderson Revision (TOMI-H) (Stott et al. 1984), decreased the number of items that a child was required to complete, added a behavioral checklist, and included room for recording qualitative observations related to children’s motor skill performance.
Movement Assessment Battery for Children: Second Edition (MABC-2), Table 5 Evolution of the versions of the Movement Assessment Battery for Children – Second Edition (MABC-2) 1. Test of Motor Impairment (TOMI) (Stott et al. 1972) 2. Test of Motor Impairment – Henderson Revision (TOMI-H) (Stott et al. 1984) 3. Movement Assessment Battery for Children (MABC) (Henderson and Sugden 1992) 4. Movement Assessment Battery for Children-2 (MABC-2) (Henderson et al. 2007)
In 1992, the Henderson and Sugden Movement Assessment Battery for Children (MABC) was published and retained the same items as the TOMI-H (see Table 5). However, the MABC was developed as a means of identifying children aged between 4 and 12 years considered being at risk of a motor impairment, and thus, normative data was added, and the scoring criteria and item descriptions were revised. The MABC consisted of 32 tasks that increased in difficulty across four age bands (AB): 4–6 years, 7–8 years, 9–10 years, and 11–12 years. Simultaneously, Keogh (1968), and subsequently Sugden (1972), developed a teacher checklist which served as preliminary to further evaluative assessments of a child’s motor performance and to alert “teachers to the existence of children with movement difficulties” (Henderson et al. 2007, p. 113). Further development has led to it becoming the MABC Checklist (Henderson and Sugden 1992; Reynard 1975; Sugden 1972).
Psychometric Data Types of reliability data often reported for scales, tests, and measures include internal consistency, test-retest/time sampling reliability/temporal stability, inter-rater/inter-scorer reliability, intra-rater/intra-scorer reliability, alternate form reliability, and split-half reliability (American Educational Research Association [AERA], American Psychological Association [APA], & National Council on Measurement in Education [NCME] 1999; Anastasi and Urbina 1997). Types
Movement Assessment Battery for Children: Second Edition (MABC-2)
of validity often reported for tests include face validity, content validity, criterion-related validity, and construct validity (AERA et al. 1999). Two subtypes of criterion-related validity frequently included are concurrent validity and predictive validity, while subtypes of construct validity often reported include factor analysis validity, discriminant validity, convergent validity, divergent validity, diagnostic validity, and rating scale validity (Fawcett 2007). Details of the reliability and validity of the MABC-2 are reported in its manual. The MABC-2 is a major revision of the wellknown and frequently used MABC (Barnett and Henderson 1998; Henderson et al. 2007). The test authors assume that the reliability data and validity information reported for the MABC are generalizable to the MABC-2. “Confidence in the MABC-2 score interpretation can be derived not only from the UK standardisation study but also from the extensive validation data reported in this and earlier manuals” (Henderson et al. 2007, p. 132). The MABC and MABC-2 may assess the same motor skill constructs in a similar format, but since the MABC-2 has added four new items, revised some of the retained items, reduced number of age ABs (the MABC had 4 ABs, while the MABC-2 has 3), and increased age range that it covers (the MABC-2 now covers ages 3–17 years), it is essentially a new, discrete test that needs to have its own specific measurement properties evaluated singly. This appears to be an inaccurate assumption made by the MABC-2 authors. The MABC-2 test manual reports some preliminary reliability data for its Performance Test based on the results of several studies completed by other investigators that involved experimental versions of the MABC-2 AB1 and AB3 tasks (see Table 6). Visser and Jongmans (2004) investigated the test-retest results for the MABC-2 AB1 with a group of 55 3-year-old children from the Netherlands. Chow et al. (2002), in another study involving a sample of 31 adolescents, evaluated the inter-rater and test-retest reliability of a translated version of the MABC-2 AB3 tasks into Chinese. Smits-Engelsman et al. (2008) reported about the inter-rater reliability of the MABC-2. In
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another inquiry involving 64 young adults, Faber and Nijhuis van der Sanden (2004) examined the intra-rater and inter-rater reliability of a total score calculated for the MABC-2 AB3 tasks originally used by Chow et al. (2002). Details of the translation process for the Chinese version of the MABC-2 AB3 tasks used in the Chow et al. (2002) study were not reported in the test manual. Similarly, it was not reported if the version of the MABC-2 AB1 tasks used in the Visser and Jongmans (2004) investigation was translated into another language since it was used in the Netherlands. The studies completed by Visser and Jongmans (2004) and Faber and Nijhuis van der Sanden (2004) are both unpublished manuscripts, thus have not been peer reviewed nor are they readily accessible for review. The Faber and Nijhuis van der Sanden (2004) study involved 64 young adults aged between 18 and 28 which were outside the age limits of the MABC-2 and the geographical location where the study was completed was not reported. With all of these studies, there are issues of cultural context, translation of the MABC-2 Performance Test items, and only evaluating one AB at a time. Henderson et al. (2007) reported a test-retest study involving 20 3-year-old children. Pearson product moment correlations ranged from 0.86 to 0.91 for the three Manual Dexterity tasks, while the Aiming and Catching and Balance tasks were less reliable with coefficients of 0.48 and 0.68. The test authors suggested that the test-retest reliability problems lie with younger children, aged between 3 and 4 years. In another study completed by the test authors, the test-retest reliability of the whole test involving all three ABs was completed. Sixty children, 20 from each AB, were included. Using the standard scores for the three test sections (Manual Dexterity, Aiming and Catching, and Balance) as well as the total test score, the Pearson product moment correlations were 0.77, 0.84, 0.73, and 0.80, respectively (Henderson et al. 2007). This indicates reasonable test-retest reliability results for the MABC-2. It would have been informative if the test authors had completed similar studies evaluating the intra-rater reliability, inter-rater reliability, and internal consistency of the Manual Dexterity, Aiming and Catching,
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Movement Assessment Battery for Children: Second Edition (MABC-2)
Movement Assessment Battery for Children: Second Edition (MABC-2), Table 6 Movement Assessment Battery for Children – Second Edition (MABC-2; Henderson Test section Reliability data reported
Validity data reported
et al. 2007) summary of MABC-2 Performance Test and Checklist validity and reliability data
MABC-2 performance test Limited reliability data reported is reported in the manual that is specific to the MABC-2. A study completed by Visser and Jongmans (2004) that reports test-retest results for AB1 for a group of 55 3-year-old children from the Netherlands is included in the test manual. Pearson product moment correlations ranged from 0.49 to 0.70 The test authors reported a second test-retest study involving 20 3-year-old children. Pearson product moment correlations ranged from 0.86 to 0.91 for the three manual dexterity tasks, while the aiming and catching tasks were less reliable with coefficients of 0.48 and 0.68. The test authors suggest that the test-retest reliability problems lie with younger children, aged between 3 and 4 years In another study that involved a translated version of AB3 tasks into Chinese, inter-rater reliability and test-retest reliability were evaluated on a sample of 31 teenagers by Chow et al. (2002). Intraclass correlation (ICC) coefficients for interrater reliability ranged from 0.92 to 1.00, while test-retest coefficients ranged from 0.62 to 0.92 In another study involving 64 young adults, Faber and Nijhuis van der Sanden (2004) examined the intra-rater and inter-rater reliability of a total score calculated for the AB3 tasks originally used by Chow et al. (2002). The ICC scores were 0.79 (intra-rater) and 0.79 (inter-rater) In a final study completed on the test authors, the test-retest reliability of the whole test involving all three ABs was completed. Sixty children, 20 from each AB, were included. Using the standard scores for the three test sections (manual dexterity, aiming and catching, and balance) as well as the total test score, the Pearson product moment correlations were 0.77, 0.84, 0.73, and 0.80, respectively Extensive validity data about the MABC is reported in the manual, and limited preliminary validity evidence (content, face, criterion-related) about the MABC-2 is also reported. Content validity of the MABC-2 was established by input of an expert panel. According the test manual, the expert panel was unanimous that the MABC-2 contents/items were representative of the motor domain it was intended to evaluate The test manual reports that face validity has been established by feedback obtained from a wide range of professionals who have used the MABC including psychologists, therapists, educational
MABC-2 checklist The test manual states that no reliability data has been collected on the MABC-2 checklist; thus, none is reported. The test authors do report reliability data about the MABC checklist, but this is redundant since the MABC-2 checklist has been revised
Two validity studies are reported in the test manual. The first study involves a group of 20 children with DCD ages 6–11 years (Barnett et al. 2007). The MABC checklist was completed by the children’s parents. Scatterplots of section A and section B of the checklist suggested a weak relationship between the two section scores. The MABC-2 checklist traffic light system was able to indicate that the children continued to have motor skill difficulties in 19 of the 20 children. The test authors claim that this is evidence of criterionrelated validity, but this is not clearly demonstrated (continued)
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Movement Assessment Battery for Children: Second Edition (MABC-2), Table 6 (continued) Test section
Clinical utility
MABC-2 performance test
MABC-2 checklist
professionals, and physicians. The face validity appears to be based on the MABC and not the MABC-2 The test manual reports subtest and total test standard score correlations as evidence of the MABC-2 subtests evaluating related, but different motor abilities. The manual dexterity subtest was correlated with the aiming and catching (0.26), balance (0.36), and total test score (0.76). The aiming and catching subtest was correlated with the balance (0.25) and the total test score (0.65). The balance subtest was correlated with the total test score (0.73) Evidence of criterion-related validity of the MABC-2 was reported in three studies in the test manual. First, using a sample of 31 Cypriot children, Kavazi (2006) examined the relationship MABC-2 manual dexterity items and the children’s performance on the Goodenough and Harris draw-a-man test. Correlations with the posting coins and drawing trail items and the draw-a-man test were 0.66 The preliminary results of a second study completed by the test authors involving a small sample of 20 children who were initially assessed with the MABC and were reassessed using the MABC-2. The MABC-2 was able to indicate that the children continued to have motor skill difficulties. The type of validity being evaluated here is not clearly reported. The third study reports the results of 25 boys diagnosed with Asperger’s syndrome who were assessed by the MABC-2 (Siaperas et al. 2007). The MABC-2 total test scores indicated that the majority of the boys (21/25) have impaired motor skills. The test authors are purporting that this is evidence of the MABC’s discriminative validity The MABC-2 appears to have reasonable to good clinical utility. The international recognition of the MABC and its use with identifying children with DCD as well as other motor impairments has seen it become one of the most commonly utilized motor assessments. In addition, the brevity of the test across a wide age range of children furthermore enhances the utility of the MABC-2 in various clinical, educational, and research contexts. In summary, the MABC-2 is an improvement over the MABC and is best used as a screening instrument. Blank et al. (2012) noted that attentional problems in children may impact on their performance on the MBC-2 and that “there seems to be a training effect of the M-ABC if repeated within 4 weeks, although this effect seems to be less in children with severe DCD” (p. 71)
The second study involves 24 boys with Asperger’s syndrome with a mean age of 13.3 years (Siaperas et al. 2007). The MABC checklist was completed by the children’s parents. The MABC-2 checklist traffic light system was able to indicate that in 23 of the 24 boys with Asperger’s syndrome experienced motor skill difficulties. Again, the test authors make the claim that this is evidence of criterion-related validity, but how this is actually demonstrated is not clear
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Movement Assessment Battery for Children: Second Edition (MABC-2)
and Balance test sections as well as the total test score involving all three MABC-2 ABs. At this time, this fundamental information is lacking. The test manual states that no reliability data has been collected on the MABC-2 Checklist; thus, none is reported. The test authors do report reliability data about the MABC Checklist, but this is redundant since the MABC-2 Checklist has been revised. Test-retest reliability and internal consistency reliability data needs to be reported about the MABC-2 Checklist. When it comes to the validity of the MABC, the test authors include a large body of evidence in the MABC-2 test manual (Barnett and Henderson 1998; Henderson et al. 2007). However, generalizations from the MABC’s validity findings to the MABC-2’s validity cannot be readily made for a number of reasons. First, the age range is different between the two test versions with the MABC-2 now including children as young as 3 years and adolescents as old as 16 years. The ABs have fundamentally changed between the two test versions. The MABC ABs 2 and 3 have been collapsed into the MABC-2 AB 2. Finally, a number of test items have been revised, and four new test items have been added to the MABC-2. Hence, the validity properties of the first test cannot be generalized to the newer revised version in the opinion of this author. Limited preliminary content, face, criterionrelated validity, and evidence about the MABC-2 Performance Test are reported in its test manual. Content validity of the MABC-2 Performance Test was established by input of an expert panel. According to the test manual, the expert panel was unanimous that the Performance Test contents/ items were representative of the motor domain it was intended to evaluate. The content validity of the MABC-2 Performance Test appears reasonable (see Table 6). The test manual reports that face validity has been established by feedback obtained from a wide range of professionals who have used the MABC including psychologists, therapists, educational professionals, and physicians. However, the face validity appears to be based on the MABC and not the MABC-2 Performance Test. Similarly, face validity of an instrument does not have inherent psychometric,
objective, or numeric data attached to it to ensure that an instrument is evaluating the skills, attributes, or constructs it claims to assess. Face validity is mainly based on an overall subjective impression of a test (AERA et al. 1999). The test manual reported section and total test standard score correlations as evidence of the three MABC-2 Performance Test sections evaluating related but distinct motor skill abilities (see Table 6). The Manual Dexterity section was correlated with the Aiming and Catching section (0.26), Balance section (0.36), and MABC-2 total test score (0.76). The Aiming and Catching section was correlated with the Balance section (0.25) and the total test score (0.65). The Balance section was correlated with the total test score (0.73). This provides evidence that the three MABC-2 Performance Test sections are measuring related but discrete skills and “the fact that each component correlates well with the Total Test Score is re-assuring” (Henderson et al. 2007, p. 142). Evidence of criterion-related validity of the MABC-2 Performance Test was reported in the form of three studies in the test manual (see Table 6). First, enlisting a sample of 31 Cypriot children, Kavazi (2006) examined the relationship MABC-2 Manual Dexterity section items and the children’s performance on the Goodenough and Harris Draw-a-Man Test (Goodenough and Harris 1963). Correlations with the Posting Coins and Drawing Trail items and the Draw-aMan Test were 0.66. Limitations of this study were a small sample size (31 children) and correlating the children’s fine motor skill performance with their performance on an outdated person drawing test. Given the fact that the Kavazi study only involved the Manual Dexterity section items from one MABC-2 AB1, the concurrent validity results are not generalizable to the whole measure. The scope of the Kavazi (2006) study was very narrow. The preliminary results of a second study completed by the test authors involved a small sample of 20 children who were initially assessed with the MABC and were reassessed using the MABC-2 Performance Test (Henderson et al. 2007). The MABC-2 was able to indicate that the children
Movement Assessment Battery for Children: Second Edition (MABC-2)
continued to have ongoing motor skill difficulties. The type of validity being evaluated here was not evident to the reader. It would have been more prudent to compare the whole MABC-2 to a well-established motor skill tests such as the Peabody Developmental Motor Scales – 2nd edition (PDMS-2) or the Bruininks-Oseretsky Test of Motor Proficiency, 2nd edition (BOT-2). Spironello et al. (2010) examined the construct validity and concurrent validity between the short form of the BOTMP and the MABC, while Van Waelvelde et al. (2007a) investigated the convergent validity between the MABC and the PDMS-2. The third study reported the results of 25 boys diagnosed with Asperger’s syndrome who were assessed by the MABC-2 Performance Test Siaperas et al. (2007). The MABC-2 Performance Test total scores indicated that the majority of the boys (21/25) had impaired motor skills. The test authors purported that this is evidence of the MABC-2 Performance Test’s discriminative validity. The study by Siaperas et al. (2007) did not include a matched control group for comparison purposes, therefore, claims that the MABC-2 exhibits discriminant validity cannot really be supported. One major weakness with the validity evidence reported in the MABC-2 test manual is the lack of construct validity evidence. There is no evidence that clearly indicated the MABC-2 Performance Test items are actually evaluating the motor skill constructs they claim they are. Evidence of construct validity can include divergent/convergent validity, factor analysis validity, diagnostic validity, rating scale validity, and/or discriminative validity. The completion of a confirmatory factor analysis, a component of classical test theory, would provide valuable evidence if the items from the Manual Dexterity, Aiming and Catching, and Balance sections load on three discrete factors and whether the eight items that make the whole MABC-2 load on one overall motor impairment factor. Hypotheses about the MABC-2 Performance Test factor structure need to be explored further (Anastasi and Urbina 1997). Blank et al. (2012) noted that there “is a lack of research on the discriminant validity of the M-ABC” (p. 71).
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In regard to the MABC-2 Checklist, two validity studies are reported in the test manual (see Table 6). The first study involves a group of 20 children with DCD ages 6–11 years (Barnett et al. 2007). The MABC Checklist was completed by the children’s parents. Scatterplots of Section A and Section B of the Checklist suggested a weak relationship between the two section scores. The MABC-2 Checklist Traffic Light system was able to indicate that 19 of the 20 children continued to have motor skill difficulties. The test authors claim that this is evidence of criterion-related validity, but how this is demonstrated is not clear. The second MABC-2 Checklist study involved 24 boys with Asperger’s syndrome with a mean age of 13.3 years (Siaperas et al. 2007). The MABC-2 Checklist was completed by the children’s parents. The MABC-2 Checklist Traffic Light system was able to indicate that 23 of the 24 boys with Asperger’s syndrome experienced motor skill difficulties. Again, the test authors make the claim that this is evidence of criterionrelated validity, but how this is actually is demonstrated is not clear. Similar to the MABC-2 Performance Test, the Checklist has limited content, concurrent, and construct validity evidence. Further validity evidence is needed since one is left asking the question: do the MABC-2 Checklist items load on the constructs they claim to measure? A number of studies have been completed by other researchers that have evaluated different aspects of the MABC-2’s reliability. These studies have been completed in a variety of cross-cultural settings as well including Greece, Norway, Japan, mainland China, Taiwan, Israel, and Australia. This provides valuable insights about how suitable the MABC-2 is for use in cross-cultural settings. For example, Engel-Yeger et al. (2010) examined the construct validity of the MABC-2 and its appropriateness to use in Israel. Participants in the study were 249 typically developing children between four and 12 years of age. The findings indicated that children’s age and gender, as well as mother’s education level and socioeconomic status had an influence on the children’s motor performance as assessed by the MABC-2.
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Movement Assessment Battery for Children: Second Edition (MABC-2)
Engel-Yeger et al. (2010) concluded that the “MABC may serve as a suitable tool for examining the motor performance of children in Israel” (p. 87). In a Greek setting, Ellinoudis et al. (2011) completed an investigation of the reliability and validity of the age band 1 of the MABC-2 involving 183 children ranging in from 36 to 64 months old (mean ¼ 50 months, SD ¼ 9 months). “Cronbach’s alpha coefficient values were 0.51, 0.70 and 0.66 for manual dexterity, aiming and catching, and balance task groups, respectively” (Ellinoudis et al. 2011, p. 1049). Thirty children completed the age band 1 of the MABC-2 items on two occasions 1 week apart. The test-retest reliability correlations for the eight items ranged from 0.66 to 0.96 and for the manual dexterity, aiming and catching, balance task and total scores were 0.82, 0.61, 0.90, and 0.85. “A confirmatory factor analysis was performed to test the goodness-of-fit of the items. The analysis revealed values for the three-domain model (manual dexterity, aiming and catching and balance) that are similar to the structure proposed by Henderson et al. (2007)” (Ellinoudis et al. 2011, p. 1049). Wuang et al. (2012a) examined the internal consistency, test-retest reliability, and responsiveness to change of the MABC-2 in a group of 144 children from Taiwan diagnosed with DCD. A Cronbach alpha of 0.90 was reported for the internal consistency coefficient for the MABC-2 while the coefficients for the Manual Dexterity, Aiming and Catching, and Balance subscales were 0.81, 0.84, and 0.88, respectively. An intraclass correlation coefficient of 0.97 was reported as the test-retest reliability for the MBC-2 total score for a 20-day interval between test administrations and the ICCs for the items ranging between 0.88 and 0.88. “The minimal detectable change was 0.28 points whereas the minimal important difference (MID) values were from 2.36 to 2.50” (Wuang et al. 2012, p. 160). The authors suggest that the minimal detectable change and minimal important difference scores for the MABC-2 provide the point of reference for practitioner decision-making in clinical management of children presenting with DCD.
Similar to the study by Ellinoudis et al. (2011), Hua et al. (2013) evaluated the internal consistency, test-retest reliability, criterion-related validity, and construct validity of the age band 1 of MABC-2. Children from mainland China were recruited from 10 public nursery schools with resultant sample size of 1823 participants (915 boys and 908 girls) aged 36–72 months old (mean ¼ 61.284 months, SD ¼ 10.212 months). The Cronbach’s alpha coefficient for the eight age band 1 MABC-2 items ranged from 0.43 to 0.52 (Hua et al. 2013). The test-retest reliability (n ¼ 184) ranged between 0.83 and 0.99 for the eight age band 1 MABC-2 items with a 14-day interval between test administrations (Hua et al. 2013). The inter-rater reliability (n ¼ 184) for the eight age band 1 MABC-2 items ranged between 0.89 and 0.99 (Hua et al. 2013). A subsample of 184 children completed both the MABC-2 and the PDMS-2 and their scores were correlated. The correlations between the PDMS-2 total scale scores and the MABC-2 Manual Dexterity, Aiming and Catching, and Balance subscales and Total scores were 0.38, 0.063, 0.17, and 0.63, respectively (Hua et al. 2013). “The results showed that the total standard scores on the two tests were correlated moderately with each other” (Hua et al. 2013, p. 805). The authors completed a confirmatory factor analysis to examine the three-factor structure of the MABC-2 with the eight items as proposed by Henderson et al. (2007). The CFA results indicated that a six-item version of the MABC-2 met the goodness-of-fit indices of the adjusted model (each above 0.9), indicating a reasonable fit to the model. “The factor loadings of two items (‘Drawing trail’ and ‘Walking heels raised’) for their domains in the model were not significant statistically” (Hua et al. 2013, p. 807). Kita et al. (2016) investigated the applicability of the MABC-2 age band 2 to Japanese children. A group of 132 typically developing Japanese children completed the age band 2 assessment items. The internal consistency, factorial validity, gender differences, and a comparison to the MABC-2 normative sample was examined. Cronbach’s alpha coefficient for the eight test items was 0.602 and ranged from 0.53 to 0.58.
Movement Assessment Battery for Children: Second Edition (MABC-2)
All indices from the confirmatory factor analysis of the eight MABC-2 items indicated “a good fit between the postulated model and the data” and “these results demonstrated high factorial validity of the AB2 in the Japanese samples” (Kita et al. 2016, p. 710). When the item scores were in the Japanese sample were compared with the MABC-2 normative sample, the results suggested that the sample of Japanese children overall tended to have higher scores on the age band 2 compared with the group of MABC-2 normative sample who were British children (Kita et al. 2016). When the performance of Japanese boys and girls were compared on the three MABC-2 domain scores, girls obtained higher scores on Manual Dexterity and Balance while there were no significant gender differences in Aiming and Catching. Thirty-eight children and parents was recruited from Victoria, Australia were recruited by Kennedy et al. (2013) to investigate whether the Physical Self-Description Questionnaire completed by children or the MABC-2 Checklist completed by the parents of the children were predictive of the children’s BOT-2 performance results. The findings indicated that two significant predictive relationships were identified based on parents’ report of their children’s motor skills: (i) the total score of the MABC-2 Checklist was found to be a significant predictor of the BOT-2 Manual Coordination motor composite score, accounting for 8.35% of its variance, and (ii) the MABC-2 Checklist total score was a significant predictor of the BOT-2 Strength and Agility motor composite score, accounting for 11.6% of its variance (Kennedy et al. 2013). No predictive relationships were identified between the PSDQ and the BOT-2 performance scores. Holm et al. (2013) investigated the intra- and inter-rater reliability of the MABC-2 amongst a group of 45 healthy children from Norway (mean age 8.7 years). Thirty children were included in the inter-rater reliability component of the study, 29 children in the intra-rater part, 14 of them took part in both studies. Qualified physiotherapists completed the MABC-2 assessments. The intrarater reliability test administrations occurred 1–2 weeks apart. The ICC values for the intra-
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rater reliability of eight MABC-2 items ranged from 0.24 to 0.77 (Holm et al. 2013). For the three age band 2 domains, the intra-rater reliability ICCs were 0.62 for manual dexterity, 0.49 for aiming and catching, and 0.49 for balance (Holm et al. 2013). For the MABC-2 total test score, the intra-rater reliability ICC was 0.68. In relation to inter-rater reliability, for the MABC-2 age band 2 items, the ICCs ranged from 0.39 to 0.68. For the three age band 2 domains, the inter-rater reliability ICCs were 0.63 for manual dexterity, 0.77 for aiming and catching, and 0.29 for balance (Holm et al. 2013). The inter-rater reliability for the MABC-2 age band 2 total test score was 0.62. The authors concluded that while the MABC-2 exhibited reasonable intra- and inter-rater reliability, the “findings may indicate that the MABC-2 might be more suitable for diagnostic or clinical decision making purposes, than for evaluation of change over time” (Holm et al. 2013, p. 799). Lane and Brown (2015) investigated the convergent validity between BOT-2 and the MABC-2 age bands 2 and 3. A sample of 50 children from Australia with no history of motor or intellectual problems was recruited. The results of the study indicated that MABC-2 age band 3 was significantly associated with the BOT-2; however, there were no significant correlations between the MABC-2 age band 2 and the BOT-2.
Clinical Uses The MABC, and now the MABC-2, is one of the most widely used pediatric motor impairment assessments (Getchell et al. 2007; Livesey et al. 2007; Slater et al. 2010; Van Waelvelde et al. 2007b). It has been used to assess a variety of diagnostic groups including children with (1) attention deficit/hyperactivity disorder (ADHD) (Miyahara et al. 2006; Pitcher et al. 2003), (2) autistic spectrum disorders (ASD) (Green et al. 2002; Liu and Breslin 2013; Smith 2004), (3) special language impairments (Hill et al. 1998), (4) developmental coordination disorder (DCD) (Cairney et al. 2009; Chen et al. 2009; Chow et al. 2006; Cox et al. 2015; Niemeijer et al. 2006; Rosblad and Gard 1998;
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Movement Assessment Battery for Children: Second Edition (MABC-2)
Ruckser-Scherb et al. 2013; Spittle et al. 2013), (5) cognitive impairments or learning difficulties (Henderson and Sugden 1992; Jongmans et al. 2003), and (6) hearing impairment (De Kegel et al. 2012). Other diagnostic studies where the MABC has been used include congenital hypothyroidism (Kooistra et al. 1998), childhood encephalitis (Rantala et al. 1991), epilepsy (Beckung et al. 1994), neurofibromatosis 1 (North et al. 1994), generalized joint hypermobility (de Boer et al. 2015), birth-related brachial plexus injury (Bellows et al. 2015), hemiplegia (Mercuri et al. 1999), preterm infants at older ages who are at risk for poor motor outcomes (Brown et al. 2015; Keunen et al. 2016; Setänen et al. 2016), and intellectual disability (Wuang et al. 2012). Individuals diagnosed with ASD, Asperger’s syndrome (AS), and other related pervasive developmental disorders (PDD) often present with motor skill difficulties (Dziuk et al. 2007; Fournier et al. 2010; Fuentes et al. 2009; Gidley Larson and Mostofsky 2006; Green et al. 2009; Jasmin et al. 2009; Ozonoff et al. 2008; Pan et al. 2009; Provost et al. 2007; Staples and Reid 2010). Hence, the MABC-2 is a useful and relevant measure to use when assessing children with ASD, AS, PDD, and other related diagnoses. The MABC-2 Performance Test and Checklist items provide invaluable information for practitioners related to the identification of and intervention with children presenting with motor difficulties. It can be used in a range of settings including clinical, community-based, child care, early intervention, early childhood education, primary and secondary school settings, private practice, hospital, and rehabilitation. It can be used as either a quick screening instrument or as one component of a comprehensive diagnostic evaluation. The MABC-2 Performance Test and Checklist items can be used by a variety of health and education professionals (including occupational therapists, psychologists, early childhood education teachers, social workers, counselors, physiotherapists, speech-language pathologists, nurses, pediatricians, physicians, and early intervention specialists among others) to evaluate the motor skills of children and adolescents aged 3–17 years.
It is recommended that other sources of information (e.g., medical records, clinical observations, interviews with parents and teachers) be accessed as well. The clinical purposes the MABC-2 could be used for are establishing the baseline of a client’s gross and fine motor skills (often done in the context of an initial assessment), assisting with the formulation of a client’s intervention goals, monitoring a client’s progress after receiving intervention/remedial services, or providing evidence to justify the need for a client to receive continued services. The MABC-2 Performance Test and Checklist can also be used for research purposes such as evaluating the effectiveness of educational, psychological, therapeutic, or medical services provided to pediatric clients. Two limitations were recently noted about the MABC-2 by Blank et al. (2012): (i) the potential for floor effects in task age band 1 for children 3–6 years of age and (ii) the “‘discontinuation’ of the scales moving from one age band to another may be a problem in longitudinal comparisons, when children, for example, move from kindergarten to school age and for the comparison of children in first grade (6- to 7-year-olds)” (p. 71) since these age bands are often significant for the diagnosis and treatment monitoring of DCD. In a recent review of assessment tools that are recommended for use with children presenting with suspected DCD at the 2011 European Academy for Childhood Disability, Blank et al. (2012) stated that the studies published about the MABC-2 “show good to excellent interrater reliability, good to excellent test–retest reliability and fair to good validity (construct validity and concurrent validity with BOTMP)” (p. 71). Blank et al. (2012) also remarked that considering the overall “strengths and limitations of the M-ABC, the level of evidence on quality and suitability of the M-ABC (-2) for the diagnosis of DCD (SDDMF) is rated as moderate to good” (p. 71). In summary, the MABC-2 is highly recommended for use by practitioners. It can play an important role in the assessment of and intervention planning for children and adolescents presenting with ASD, AS, and PDD.
Movement Assessment Battery for Children: Second Edition (MABC-2)
See Also ▶ Bruininks-Oseretsky Test of Motor Proficiency ▶ Fine Motor Development ▶ Gross Motor Skills ▶ Occupational Therapy (OT) ▶ Peabody Developmental Motor Scales (PDMS) ▶ Procedural Reliability ▶ Sensorimotor Development ▶ Standardized Tests ▶ Validity
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the short form of the Bruininks–Oseretsky Test of Motor Proficiency and the Movement ABC. Child: Care, Health and Development, 35, 402–408. Camden, C., Rivard, L., Pollock, N., & Missiuna, C. (2013). Facilitating a DCD diagnosis: Movement Assessment Battery for Children (MABC-2). Retrieved on 4 Feb 2017, from http://elearning.canchild.ca/dcd_ pt_workshop/assets/identification/mabc-2.pdf Chen, Y. W., Tseng, M. H., Hu, F. C., & Cermak, S. A. (2009). Psychosocial adjustment and attention in children with developmental coordination disorder using different motor tests. Research in Developmental Disabilities, 30, 1367–1377. Chow, S. M. K., & Henderson, S. E. (2003). Brief report – Interrater and test-retest reliability of the Movement Assessment Battery for Chinese preschool children. American Journal of Occupational Therapy, 57(5), 574–577. Chow, S. M. K., Henderson, S. E., & Barnett, A. L. (2001). The Movement Assessment Battery for Children: A comparison of 4-year-old to 6-year-old children from Hong Kong and the United States. American Journal of Occupational Therapy, 55, 55–61. Chow, S. M. K., Chan, L. L., Chan, C. P. S., & Lau, C. H. Y. (2002). Reliability of the experimental version of the Movement ABC. British Journal of Therapy and Rehabilitation, 9, 404–407. Chow, S. M. K., Hsu, Y., Henderson, S. E., Barnett, A. L., & Lo, S. K. (2006a). The Movement ABC: A crosscultural comparison of preschool children from Hong Kong, Taiwan, and the USA. Adapted Physical Activity Quarterly, 23(1), 31–48. Chow, S. M. K., Hsu, Y.-W., Henderson, S. E., & Yu, T.-Y. (2006b). The Movement Assessment Battery for Children: A within-culture and cross-cultural comparison of pre-school children from Hong Kong, Taiwan and the USA. Adapted Physical Activity Quarterly, 23, 31–49. Cox, L. E., Harris, E. C., Auld, M. L., & Johnston, L. M. (2015). Impact of tactile function on upper limb motor function in children with Developmental Coordination Disorder. Research in Developmental Disabilities, 45, 373–383. Crawford, S. G., Wilson, B. N., & Dewey, D. (2001). Identifying developmental coordination disorder: Consistency between tests. Physical & Occupational Therapy in Pediatrics, 20, 29–50. Croce, R. V., Horvat, M., & McCarthy, E. (2001). Reliability and concurrent validity of the Movement Assessment Battery for Children. Perceptual and Motor Skills, 93, 275–280. de Boer, R. M., van Vlimmeren, L. A., Scheper, M. C., Nijhuis-van der Sanden, M. W. G., & Engelbert, R. H. H. (2015). Is motor performance in 5.5-year-old children associated with the presence of generalized joint hypermobility? Journal of Pediatrics, 167(3), 694–701. De Kegel, A., Maes, L., Baetens, T., Dhooge, I., & Van Waelvelde, H. (2012). The influence of a vestibular dysfunction on the motor development of
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disorder of motor function. Journal of Child Psychology and Psychiatry, 43(4), 655–688. Green, D., Charman, T., Pickles, A., Chandler, S., Loucas, T., Simonoff, E., & Baird, G. (2009). Impairment in movement skills of children with autistic spectrum disorders. Developmental Medicine and Child Neurology, 51(4), 311–316. Henderson, S. E., & Sugden, D. A. (1992). Movement Assessment Battery for Children. Kent: The Psychological Corporation. Henderson, S. E., Sugden, D. A., & Barnett, A. L. (2007). Movement Assessment Battery for Children-2 second edition (Movement ABC-2). London: The Psychological Corporation. Hill, E. L., Bishop, B. V. M., & Nimmo-Smith, I. (1998). Representational gestures in developmental coordination disorder and specific language impairment: Error-types and the reliability of ratings. Human Movement Science, 17, 655–678. Holm, I., Tveter, A. T., Aulie, V. S., & Stuge, B. (2013). High intra- and inter-rater chance variation of the Movement Assessment Battery for Children 2, ageband 2. Research in Developmental Disabilities, 34(2), 795–800. Hua, J., Gu, G., Meng, W., & Wu, Z. (2013). Age band 1 of the Movement Assessment Battery for ChildrenSecond Edition: Exploring its usefulness in mainland China. Research in Developmental Disabilities, 34(2), 801–808. Jasmin, E., Couture, M., McKinley, P., Reid, G., Fombonne, E., & Gisel, E. (2009). Sensori-motor and daily living skills of preschool children with autism spectrum disorders. Journal of Autism and Developmental Disorders, 39(2), 231–241. Jongmans, M. J., Smits-Engelsman, B. C. M., & Schoemaker, M. M. (2003). Consequences of comorbidity of developmental coordination disorders and learning disabilities for severity and pattern of perceptual-motor dysfunction. Journal of Learning Disabilities, 36(6), 528–537. Kavazi, E. (2006). Motor competence in young Cypriot children. An examination of cross-cultural differences and the value of human figure drawings in motor assessment. Unpublished Master’s thesis, Oxford Brookes University, Oxford. Kennedy, J., Brown, T., & Stagnitti, K. (2013). Top-down and bottom-up approaches to motor skill assessment of children: Are child-report and parent-report perceptions predictive of children’s performance-based assessment results? Scandinavian Journal of Occupational Therapy, 20, 45–53. Keogh, J. F. (1968). Incidence and severity of awkwardness among regular school boys and educationally subnormal boys. Research Quarterly, 39, 806–808. Keunen, K., Išgum, I., van Kooij, B. J. M., Anbeek, P., van Haastert, I. C., Koopman-Esseboom, C., Fieretvan Stam, P. C., Nievelstein, R. A. J., Viergever, M., de Vries, L. S., Groenendaal, F., & Benders, M. J. N. L. (2016). Brain volumes at term-equivalent age in
Movement Assessment Battery for Children: Second Edition (MABC-2) preterm infants: Imaging biomarkers for neurodevelopmental outcome through early school age. Journal of Pediatrics, 172, 88–95. Kita, Y., Suzuki, K., Hirata, S., Sakihara, K., Inagaki, M., & Nakai, A. (2016). Applicability of the Movement Assessment Battery for Children-Second Edition to Japanese children: A study of the Age Band 2. Brain & Development, 38(8), 706–713. Kooistra, L., Schellekens, J. M. H., Schoemaker, M. M., Vulsma, T., & van der Meere, J. J. (1998). Motor problems in early treated congenital hypothyroidism: A matter of failing cerebellar control? Human Movement Science, 17, 609–629. Lane, H., & Brown, T. (2015). Convergent validity of two motor skill tests used to assess school-age children. Scandinavian Journal of Occupational Therapy, 22(3), 161–172. Liu, T., & Breslin, C. M. (2013). The effect of a picture activity schedule on performance of the MABC-2 for children with autism spectrum disorder. Research Quarterly for Exercise and Sport, 84(2), 206–212. Livesey, D., Coleman, R., & Piek, J. (2007). Performance on the Movement Assessment Battery for Children by Australian 3- to 5-year-old children. Child: Care, Health and Development, 33(6), 713–719. Mercuri, E., Jongmans, M., Bouza, H., Haataja, L., Rutherford, M., Henderson, S., et al. (1999). Congenital hemiplegia in children at school age: Assessment of hand function in the non-hemiplegic hand and correlation with MRI. Neuropediatrics, 30, 8–13. Missiuna, C., Rivard, L., & Bartlett, D. (2006). Exploring assessment tools and the target intervention for children with developmental coordination disorder. Physical & Occupational Therapy in Pediatrics, 26(1/2), 71–89. Miyahara, M., Tsuji, M., Hanai, T., Jongmans, M., Barnett, A. L., Henderson, S. E., et al. (1998). The Movement Assessment Battery for Children: A preliminary investigation of its usefulness in Japan. Human Movement Science, 17, 679–697. Miyahara, M., Piek, J., & Barrett, N. (2006). Accuracy of drawing in a dual-task and resistance-to-distraction study: Motor or attention deficit. Human Movement Science, 25, 100–109. Niemeijer, A. S., Schoemaker, M. M., & Smits-Engelsman, B. C. M. (2006). Are teaching principles associated with improved motor performance in children with developmental coordination disorder? A pilot study. Physical Therapy, 86, 1221–1230. North, K., Joy, P., Yuille, D., Cocks, N., Mobbs, P., McHugh, K., et al. (1994). Specific learning difficulties in children with neurofibromatosis type 1: Significance of MRI abnormalities. Neurology, 44, 878–883. Ozonoff, S., Young, G. S., Goldring, S., Greiss-Hess, L., Herrera, A. M., Steele, J., Macari, S., Hepburn, S., & Rogers, S. J. (2008). Gross motor development, movement abnormalities, and early identification of autism. Journal of Autism and Developmental Disorders, 38(4), 644–656.
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Pan, C., Tsai, C., & Chu, C. (2009). Fundamental movement skills in children diagnosed with autism spectrum disorders and attention deficit hyperactivity disorder. Journal of Autism and Developmental Disorders, 39(12), 1694–1705. Pitcher, T. M., Piek, J. M., & Hay, D. A. (2003). Fine and gross motor ability in males with ADHD. Developmental Medicine and Child Neurology, 45(8), 525–535. Provost, B., Heimerl, S., & Lopez, B. R. (2007). Levels of gross and fine motor development in young children with autism spectrum disorder. Physical & Occupational Therapy in Pediatrics, 27(3), 21–36. Psychometrics Centre, Cambridge Judge Business School, University of Cambridge. (n.d.). Movement Assessment Battery for Children (MABC). Retrieved 4 Feb 2017 from http://www.psychometrics.cam.ac. uk/services/psychometric-tests/mabc-ii Rantala, H., Uhari, M., Saukkonen, A., & Sorri, M. (1991). Outcome after childhood encephalitis. Developmental Medicine and Child Neurology, 33, 858–867. Reynard, C. L. (1975). The nature of motor expectancies and difficulties in kindergarten children. Unpublished M.Sc thesis, University of California at Los Angeles, Los Angeles, CA. Rosblad, B., & Gard, L. (1998). The assessment of children with developmental coordination disorders in Sweden: A preliminary investigation of the suitability of the Movement ABC. Human Movement Science, 17, 711–719. Ruckser-Scherb, R., Roth, R., Lothaller, H., & Endler, C. (2013). Motor abilities and coping in children with and without developmental coordination disorder. British Journal of Occupational Therapy, 76(12), 548–555. Setänen, S., Lehtonen, L., Parkkola, R., Matomäki, J., & Haataja, L. (2016). The motor profile of preterm infants at 11 y of age. Pediatric Research, 80(3), 389–394. Siaperas, P., Holland, T., & Ring, H. (2007). Discriminative validity of the Movement ABC Test and Checklist for use with children with Asperger Syndrome. In S. E. Henderson, D. A. Sugden, & A. L. Barnett (Eds.), Movement Assessment Battery for Children-2 second edition (Movement ABC-2). London, UK: The Psychological Corporation. Slater, L. M., Hillier, S. L., & Civetta, L. R. (2010). The clinimetric proper-ties of performance-based gross motor tests used for children with developmental coordination disorder: A systematic review. Pediatric Physical Therapy, 22, 170–179. Smith, I. M. (2004). Motor problems in children with autistic spectrum disorders. In D. Dewey & D. E. Tupper (Eds.), Developmental motor disorders: A neuropsychological perspective (pp. 152–169). New York: The Guildford Press. Smits-Engelsman, B. C. M., Henderson, S. E., & Michels, C. G. J. (1998). The assessment of children with developmental coordination disorders in the Netherlands: The relationship between the Movement Assessment Battery for Children and the
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Movie and TV Depictions of Autism Spectrum Disorder Wiart, L., & Darrah, J. (2001). Review of four tests of gross motor development. Developmental Medicine and Child Neurology, 43, 279–285. Wright, H. C., Sugden, D. A., Ng, R., & Tan, J. (1994). Identification of children with movement problems in Singapore: Usefulness of the Movement ABC Checklist. Adapted Physical Activity Quarterly, 11, 150–157. Wuang, Y.-P., Su, J.-H., & Su, C.-Y. (2012a). Reliability and responsiveness of the Movement Assessment Battery for Children-Second Edition Test in children with developmental coordination disorder. Developmental Medicine and Child Neurology, 54(2), 160–165. Wuang, Y.-P., Su, C.-Y., & Huang, M.-H. (2012b). Psychometric comparisons of three measures for assessing motor functions in preschoolers with intellectual disabilities. Journal of Intellectual Disability Research, 56(6), 567–578.
Movie and TV Depictions of Autism Spectrum Disorder Anders Nordahl-Hansen1,2 and Roald A. Øien3,4 1 Department of Special Needs Education, UiO, University of Oslo, Oslo, Norway 2 Faculty of Education, Østfold University College, Halden, Norway 3 Department of Psychology/Department of Education, UIT – The Arctic University of Norway, University of Tromsø, Tromsø, Norway 4 Yale Child Study Center, Yale Autism Program, Yale University School of Medicine, New Haven, CT, USA
Definition This chapter focuses on the topic of character portrayals with autism spectrum disorders (ASD) in film and TV-series. We address this topic by presenting research from the general psychiatric field before we discuss the research on portrayals of characters with ASD. We will also provide the reader with some examples of films and TV-series that have been given particular attention in the popular media and that will be known to many, also outside of the autism community. We address both advantages and disadvantages that are consequences of popular-media portrayals of ASD.
Movie and TV Depictions of Autism Spectrum Disorder
Historical Background In the last few years, there has been a surge of new characters in fictional film and in TV-shows that either are labelled as having an autism spectrum disorder by the makers or by the public due to resembling traits and behaviors of the characters with that on the autism spectrum. In general, portrayals of people with psychiatric disorders in film and TV-series are not an uncommon topic of research particularly in disciplines like psychiatry and media-science (Butler and Hyler 2005; Stuart 2006) but may also be found in fields like sociology in, for instance, analyzing trends in society. Conditions like memory loss or disorders like schizophrenia, depression, anxiety, and split personality have been thoroughly addressed (REF). Schneider notes that the link between psychiatry and movies go as far back as to the start of the twentieth century with films like “The Escapees from Charenton” in 1901 (released in Britain under the name “Off to Bedlam,” and in USA under the name “Off to Bloomingdale Asylumn”) (Schneider 1987). Whether it is portrayals of psychiatrists or persons with psychiatric disorders, there are a lot of films, from drama to thrillers, from horror to comedy, available. Some of the films may be regarded as classics and films like “The Three Faces of Eve” (1957), “Psycho” (1960), “One Flew Over the Cuckoo’s Nest” (1975), “Fight Club” (1999), or “Shutter Island” (2010) and have had an impact on public perceptions of psychiatric disorders. Such portrayal on the screen influences the individual patient and their family, healthcare professionals, and policymakers as well as contributing to affect global, societal attitudes (Wahl 1995). However, a problem is that representations of psychiatric disorders in the mass media are often inaccurate and often overrepresent links between, for instance, violence and mental illness (Edney 2004; Stout et al. 2004; Thornton and Wahl 1996: Wahl 2003). Also, fictional portrayals of persons with psychiatric disorders or neurological conditions on film have also contributed to mystification and misconceptions. For instance, portrayals of amnesia on film and TV usually bear little resemblance to reality and in turn distort
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public conception of the condition (Baxendale 2004; Hacking 2009). Further, Butler and Hyler (2005) argue that it is a common feat in Hollywood movie portrayals of children and adolescents with mental health issues or psychiatric disorder to be presented in a mythical fashion, which may be harmful in terms of misguiding public understanding of different conditions and disorders. During the past couple of decades, portraying characters with autism spectrum disorders (ASD) have become very popular in films and in TV-shows. So much that autism is now, by many, regarded as part of the general pop-culture (Belcher and Maich 2014; Hacking 2009). Since TV and film are highly influential sources in shaping public perception of ASD, surpassing sources like clinicians, researchers, or actual encounters with people on the spectrum, it is important to address and investigate in what ways characters in TV and film are portrayed.
Current Knowledge ASD, once considered a rare disorder, is today regarded as a common neurodevelopmental disorder or condition with estimates of prevalence somewhere around 1% in the population (Baird et al. 2006; Kogan et al. 2009). Following the breakthrough film Rain Man (1988), the increase in characters with ASD in films and TV-series has been substantial (Conn and Bhugra 2012; Hacking 2010). Of course, there are examples of characters that have been portrayed as having autistic traits and behaviors before the 1980s, for example, “Philip” in “Run Wild, Run Free” from 1969, and “Chance, the gardener” in “Being There” from 1979, but the knowledge of autism at that point in time was limited and the disorder was not even a formal diagnosis in DSM before 1980. Either way it is safe to say that the release of the movie Rain Man marks a starting point for when autism, both as a term and as a disorder, reached a larger part of the society’s perception. As discussed by Damjanovic et al. (2009) and Draaisma (2009), characters with ASD on film
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and TV may contributed in influencing public knowledge and shaping attitudes towards the condition. But how are characters with ASD portrayed on the screen? As we noted, earlier films with characters with psychiatric disorders are known to presenting disorders in an unfortunate way in terms of, for instance, violent behaviors. However, autistic characters are typically not portrayed in this way. Instead, some argue that characters with ASD have a tendency to be portrayed more like heroes (Belcher and Maich 2014) and with special talents (Hacking 2009) and although this perhaps looks fine at first glance it may not give an accurate picture of ASD. Publications in scholarly journals on the topic of ASD on TV can be found in various disciplines such as film history, media, philosophy, sociology, psychology, and psychiatry to name a few. Many of the publications take the form of in-depth analyses on advantages and disadvantages of portrayals of ASD on screen. Examples of key publications here are Douwe Draaisma’s article on Stereotypes of autism (2009) and Stuart Murray’s article where the public consciousness of autism is analyzed in light of the increased fascination of the disorder (2006). Other publications are case studies where a character in a film or TV-show is being dissected in light of his or her ASD-diagnosis or his or her autistic like traits or behaviors (see for instance Cockain 2016; Tobia and Thoma 2015). There are few studies that report on quantitative data from larger samples of films portraying characters with ASD. Conn and Bhugra (2012) described a sample of 23 films noting that most films with characters with ASD are in the drama genre. They also argue that although there are concerns as to whether portrayals are realistic or not, there are possibilities for using fictional films for educational purposes within health sciences such as medicine and psychiatry. Another quantitative approach on autism and film was undertaken in a PhD thesis published in 2014 (Garner 2014) as well as a publication the following year (Garner et al. 2015). Garner and colleagues used the Childhood Autism Rating Scale – Second edition (CARS2: Schopler et al. 2010) to score 15 characters with ASD from
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various films. The authors concluded that character portrayals, compared to an average autismaffected population, were more severe in their display of autism symptomology (Garner et al. 2015). A recent study used the DSM-5 (APA 2013) to investigate whether diagnostic criteria lined up with behaviors and traits displayed by fictional portrayals of characters with ASD (NordahlHansen et al. 2017). In the sample of 26, there were 22 films and 4 TV-series that included characters in highly popular TV-series that has been linked to the autism spectrum (Community, 2009–2015, The Big Bang Theory, 2007–, The Bridge, 2011–, Alphas, 2011–2012). The results from this study was much in line with results found in Garner et al. (2015) as a majority in the sample was in very close alignment with DSM-5 diagnostic criteria for an autism spectrum diagnosis. In general, the researchers conclude that character portrayals are somehow overemphasized when it comes to displaying autistic traits, which in turn may reinforce stereotypes of the disorder. This is interesting in considering a common criticism from the autism community that most portrayals, at least this past decade, are so-called “high-functioning” verbal characters with low levels of support needed in, for instance, everyday living. In this respect, it is interesting to note that a character like Abed Nadir in the TV-show “Community” has the lowest score on autistic traits in the Nordahl-Hansen et al. (2017) sample but is one of the most embraced characters in various “Asperger-communities.” A topic of very high interest to the people making films and TV-shows about fictional characters with ASD that display exceptional skills or savant skills. It is easy to understand that savant skills are fascinating and just as easy to understand that screenwriters “give” these features to their characters in the hopes of making them more interesting. However, this has led to the impression that savant skills are something that most people with ASD have. But, for instance, the DSM-5 criteria for ASD (APA 2013) do not make particular notice of savant skills or savant syndrome although fixated or special interests
Movie and TV Depictions of Autism Spectrum Disorder
may or may not be regarded as savant like skills. It is not clear how many individuals within the ASD population that actually have savant skills much due to varying definitions of what constitutes savant skills. However, research indicates that the prevalence might be somewhere between 10% (Treffert 2014) to approximately 30% (Howlin et al. 2009) within the ASD population. Although these prevalence estimates are higher than in other populations, it is not a common feat in persons with ASD. Even so, there is a tendency to portray characters with ASD on screen as having savant skills (Conn and Bhugra 2012; Garner et al. 2015; Nordahl-Hansen et al. 2017) or as being intellectually stimulating geniuses (Belcher and Maich 2014).
Future Directions Although there are similarities between many films and TV-shows in the ways characters with ASD are portrayed, there are also large differences both in terms of the portrayals but also of thematic difficulties addressed. Some films show scenes that will relate to families and people on the spectrum. Problems as a consequence of hypersensitivity to environmental stimuli, problems in school and struggles with academic success to severe bullying are some recurring themes. Further, difficulties regarding dating, sex, and relationships are frequent topic but also more overarching existential issues and general difficulties with social communication that leads to feelings of social ostracism. Nevertheless, critique has been raised about the overrepresentation of middle-class white males either in their teens or young adults that are so-called “high functioning.” This is particularly evident in the typical “Hollywood” mainstream portrayals of ASD and the debate has increased in strength following the release of TV-series like “The Big Bang Theory” and more recently “The Good Doctor” and “Atypical,” the latter two both aired for the first time in autumn 2017. For the purposes of giving the public a nuanced view of the heterogeneity that resides within the autistic spectrum, a variety of portrayals of
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persons with ASD on screen would be a step in the right direction. That being said, one character portrayal in a film or TV-show cannot capture the complexity that resides with the entire autism spectrum, nor do justice to the variety of autistic lived experience (Nordahl-Hansen 2017). There are, however, fictional films and TV-series available that portray characters with ASD in a wide variety of ways but many of these depictions do not reach as wide an audience as the so-called mainstream “Hollywood” productions. In addition to more films and TV-series portraying characters with ASD that have higher needs of everyday support, it would be welcoming to see more films with characters that are minimally or nonverbal or even display self-injurious behaviors. Further, although there are exceptions, like Snow Cake (2006), The Bridge (2011–), Fly Away (2011), Molly (1999), and Mozart and the Whale (2005) to name a few, more female characters with ASD on film and TV would also be welcome. Additionally, portrayals of autism and aging is almost entirely missing; so although there has been a marked increase in characters with ASD in fictional films and TV-shows, there are still many gaps to fill. Many of the films and TV-series that are available can be viewed by children but in most cases, parental guidance are encouraged but British and American TV-channels have started including characters with ASD in TV-programs that target the child as the main consumer. It is of high importance that character portrayals of persons with ASD on TV for kids do not lead to stereotype thinking of the disorder. If portrayed with care, respect, and adjusted to the target age groups, characters with ASD on the screen may teach and inform children in ways to act and behave in an inclusive manner. Of course, herein there is a great potential in reducing bullying. Furthermore, feedback from the adult autism community on portrayals of characters with ASD is that although many do not familiarize with characters on screen, many others do. This may be of particular importance for children who are experiencing being different from other peers of similar age and thus having difficulty fitting in. Characters with autistic traits in TV-shows for children could be a
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potential help for children with ASD to better understand their condition. Finally, portrayals of characters with ASD in film and TV have been proposed for use in higher education, for example, in training of healthcare personnel (Conn and Bhugra 2012; Gabbard and Horowitz 2010; Safran 1998; Sartorius et al. 2010; Butler and Hyler 2005). However, to what extent portrayals of individuals with ASD are good or bad, or right or wrong, is not easy to determine since individual portrayals cannot capture the heterogeneity that resides within the autism spectrum. Therefore, although on-screen portrayals of autism can be a good point of departure for further discussion in educational settings (Conn and Bhugra 2012; Gramaglia et al. 2013; Nordahl-Hansen et al. 2017), competent educators with in-depth knowledge and experience with persons with ASD will be key as character portrayals can never capture real life. To make use of insights from actually autistic people would further strengthen the potential of usage of film and TV for educational purposes.
References and Reading American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (DSM-5 ®). American Psychiatric Pub. Baird, G., Simonoff, E., Pickles, A., Chandler, S., Loucas, T., Meldrum, D., & Charman, T. (2006). Prevalence of disorders of the autism spectrum in a population cohort of children in South Thames: The Special Needs and Autism Project (SNAP). Lancet, 21, 210–215. Baxendale, S. (2004). Memories aren’t made of this: Amnesia at the movies. British Medical Journal, 329, 1480–1483. Belcher, C., & Maich, K. (2014). Autism spectrum disorder in popular media: Storied reflections of societal views. Brock Education: A Journal of Educational Research and Practice, 23, 97–115. Butler, J. R., & Hyler, S. E. (2005). Hollywood portrayals of child and adolescent mental health treatment: Implications for clinical practice. Child and Adolescent Psychiatric Clinics of North America, 31, 509–522. Cockain, A. (2016). Making autism through Ocean Heaven (Haiyang tiantang) and the possibilities of realizing disability differently. Disability & Society, 31(4), 535–552. Conn, R., & Bhugra, D. (2012). The portrayal of autism in Hollywood films. International Journal of Culture and Mental Health, 1, 54–62.
Movie and TV Depictions of Autism Spectrum Disorder Damjanović, A., Vuković, O., Jovanović, A. A., & Jašović-Gašić, M. (2009). Psychiatry and movies. Psychiatria Danubina, 21, 230–235. Draaisma, D. (2009). Stereotypes of autism. Philosophical Transactions of the Royal Society B: Biological Sciences, 27, 1475–1480. Edney, D. R. (2004). Mass media and mental illness: A literature review. Ontario: Canadian Mental Health Association. Gabbard, G., & Horowitz, M. (2010). Using media to teach how not to do psychotherapy. Academic Psychiatry, 1, 27. Garner, A.R. (2014). What’s showing: Film industry portrayals of autism spectrum conditions and their influences on preservice teachers in Australia, University of Wollongong. http://ro.uow.edu.au/theses/4282 Garner, A., Jones, S., & Harwood, V. (2015). Authentic representations or stereotyped ‘outliers’: Using the CARS2 to assess film portrayals of autism Spectrum disorders. International Journal of Culture and Mental Health, 8, 414–425. Gramaglia, C., Jona, A., Imperatori, F., Torre, E., & Zeppegno, P. (2013). Cinema in the training of psychiatry residents: Focus on helping relationships. BMC Medical Education, 13(1), 90. Hacking, I. (2009). Autistic autobiography. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 364(1522), 1467–1473. Hacking, I. (2010). Autism fiction: A mirror of an internet decade? University of Toronto Quarterly, 79(2), 632–655. Howlin, P., Goode, S., Hutton, J., & Rutter, M. (2009). Savant skills in autism: Psychometric approaches and parental reports. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 27, 1359–1367. Kogan, M. D., Blumberg, S. J., Schieve, L. A., Boyle, C. A., Perrin, J. M., Ghandourm, R. M., et al. (2009). Prevalence of parent-reported diagnosis of autism spectrum disorder among children in the US, 2007. Pediatrics, 1, 1395–1403. Nordahl-Hansen, A. (2017). Atypical: A typical portrayal of autism? The Lancet Psychiatry, 4(11), 837–838. Nordahl-Hansen, A., Tøndevold, M., & FletcherWatson, S. (2017). Mental health on screen: A DSM-5 dissection of portrayals of autism spectrum disorders in film and TV. Psychiatry Research. https:// doi.org/10.1016/j.psychres.2017.08.050. Safran, S. P. (1998). Disability portrayal in film: Reflecting the past, directing the future. Exceptional Children, 64(2), 227–238. Sartorius, N., Gaebel, W., Cleveland, H. R., Stuart, H., Akiyama, T., Arboleda-Flórez, J., et al. (2010). WPA guidance on how to combat stigmatization of psychiatry and psychiatrists. World Psychiatry, 9(3), 131–144. Schneider, I. (1987). The theory and practice of movie psychiatry. American Journal of Psychiatry, 144(8), 996–1002. Schopler, E., Van Bourgondien, M. E., Wellman, G. J., & Love, S. R. (2010). The childhood autism rating scale, (CARS2). Los Angeles: WPS.
Mu Rhythm
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Behavioral analysis may be an effective way to analyze the function of scripting for some children. By analyzing the antecedents and consequences of scripting for children and adults with ASD, it may be possible to identify functional replacements for this behavior.
Stout, P. A., Villegas, J., & Jennings, N. A. (2004). Images of mental illness in the media: Identifying gaps in the research. Schizophrenia Bulletin, 30, 543–561. Stuart, H. (2006). Media portrayal of mental illness and its treatments. CNS Drugs, 1, 99–106. Thornton, J. A., & Wahl, O. F. (1996). Impact of a newspaper article on attitudes toward mental illness. Journal of Community Psychology, 24(1), 17–25. Tobia, A., & Toma, A. (2015). Rethinking Asperger’s: Understanding the DSM-5 diagnosis by introducing Sheldon Cooper. Journal of Communication Disorders, Deaf Studies & Hearing Aids, 3, 146. Treffert, D. A. (2014). Savant syndrome: Realities, myths and misconceptions. Journal of Autism and Developmental Disorders, 44, 564–571. Wahl, O. (1995). Media madness. New Brunswick: Rutgers University Press. Wahl, O. (2003). Depictions of mental illnesses in children’s media. Journal of Mental Health, 12(3), 249–258.
See Also
Movie Talk
MSEL:AGS
Moira Lewis Speech-Language Pathologist, Marcus Autism Center Children’s Healthcare of Atlanta, Atlanta, GA, USA
▶ Mullen Scales of Early Learning
Synonyms
▶ Echolalia
References and Reading Rydell, P., & Prizant, B. (1995). Assessment and intervention strategies for children who use echolalia. In K. Quill (Ed.), Teaching children with autism (p. 110). New York: Delmar.
M MTL ▶ Medial Temporal Lobe
Delayed echolalia; Echolalia; Parroting; Scripting
Mu Rhythm Definition Repeating or reciting lines heard in TV shows, movies, songs, books, etc., is a common language pattern among children with ASD. “Movie talk” refers to repetitive speech taken from such a source, as opposed to a child’s using novel, spontaneous language. Also known as scripting, the behavior involves reciting phrases or “chunks” of previously heard language and is synonymous with delayed echolalia. Some researchers believe “movie talk” is a form of self-stimulation used to tune others out or to work through a stressful or anxiety-producing moment. Others believe it is a means to initiate communication with others, or as a way to talk about an area of special interest.
Beau Reilly Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA
Synonyms m; m rhythm; Arciform rhythm
Definition Mu (m) rhythm is a type of emitted brain wave that can be measured via electroencephalography (EEG). The mu rhythm frequency band is defined by activity falling between 8 and 13 Hz and
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recorded by scalp electrodes over the sensorimotor cortex during waking neural activity. The mu rhythm band is posited to reflect the conductance of synchronized activity in large groupings of pyramidal neurons in the brain’s motor cortex (Pfurtscheller et al. 1997) but has also been proposed to reflect activity of the mirror neuron system (Pineda 2005). The gradual loss of intensity and desynchronicity of neural activity, referred to as attenuation, is suggestive of an increased load on those specific cells and indicative of significant activation (Pfurtscheller et al. 1997). Attenuation of the mu rhythm has been noted during both the execution and observation of actions falling within one’s behavioral repertoire. Physical movement in the body itself does not account for mu wave attenuation during an individual’s observation of other’s actions. The first neurological evidence of mu rhythm attenuation came from the work of Gastaut and Bert (1954) during observations of activity and response characteristics of subjects during motor tasks. It was noted that desynchronization of mu rhythm activity from centrally located scalp electrodes occurred not only during the active movements of the subjects, but also during subjects’ passive observation of the same actions performed by others in the absence of any other overt motor activity. Because of the observed attenuation of the EEG mu rhythm during the execution and observation of actions, it has been proposed to reflect mirror neuron system activity (Pineda 2005). Given the noted impairments in imitation, empathy, and social understanding in ASD and given that the mirror neuron system has been proposed to subserve many of these social cognitive abilities, the mu rhythm has been examined among ASD populations as an assessment of the mirror neuron system. Oberman and colleagues first found that children with ASD showed reduced attenuation of the EEG mu rhythm in response to the observation of simple actions (Oberman et al. 2005), providing support for the theory that the mirror neuron system is disrupted in ASD. Bernier, Dawson, Webb, and Murias (2007) replicated this finding in adults and observed that attenuation of the EEG mu rhythm is correlated with imitative abilities. This finding provided further support that the EEG mu rhythm may reflect activity of an
Mullen Scales of Early Learning
execution/observation matching system, such as the mirror neuron system and that this system may be related to imitation abilities. Although the existence of a human mirror neuron system is controversial and the literature contains conflicting findings regarding the presence of a disruption of the EEG mu rhythm in ASD, the EEG mu rhythm continues to be utilized in the investigation of action execution and observation among individuals with ASD.
See Also ▶ Electroencephalogram (EEG) ▶ Mirror Neuron System
References and Reading Bernier, R., Dawson, G., Webb, S., & Murias, M. (2007). EEG mu rhythm and imitation impairments in individuals with autism spectrum disorder. Brain and Cognition, 64(3), 228–237. Gastaut, H. J., & Bert, J. (1954). EEG changes during cinematographic presentation. Electroencephalography and Clinical Neurophysiology, 6, 433–444. Oberman, L. M., Hubbard, E. M., McCleery, J. P., Altschuler, E. L., Ramachandran, V. S., & Pineda, J. E. (2005). EEG evidence for mirror neuron dysfunction in autism spectrum disorders. Cognitive Brain Research, 24(2), 190–198. Pfurtscheller, G., Neuper, C., Andrew, C., & Edlinger, A. (1997). Foot and hand area mu rhythms. International Journal of Psychophysiology, 26, 121–135. Pineda, J. E. (2005). The functional significant of mu rythms: Translating “seeing” and “hearing” into “doing”. Brain Research Reviews, 50, 57–68.
Mullen Scales of Early Learning Evon Batey Lee Pediatrics, Kennedy Center/Vanderbilt University, Nashville, TN, USA
Synonyms MSEL:AGS; Mullen scales of early learning, american guidance service edition
Mullen Scales of Early Learning
Abbreviations ELC
Early learning composite
Description The Mullen Scales of Early Learning: American Guidance Service Edition is an individually administered, multidomain measure of early development designed for children from birth to 68 months of age. It was developed by Eileen M. Mullen, Ed.D., and published in 1995. The MSEL:AGS consists of five individual scales: four cognitive scales that cover the age range from birth through 68 months (visual reception, fine motor, receptive language, and expressive language) and one gross motor scale that is administered from birth to 33 months. The four cognitive scales can be combined to produce an Early Learning Composite (ELC) that is said to represent “g” or “general intelligence.” The MSEL:AGS was based on the author’s information processing and neurodevelopmental theories that conceptualize intelligence “as a network of interrelated but functionally distinct cognitive skills” (Manual, p. 1). By assessing these individual areas of development independently, the examiner can identify specific areas of strength and weakness. From the author’s perspective, this developmental profile can be used to aid in making diagnoses and also in developing strategies for individualized intervention plans (Manual, p. 33). Brief descriptions of each scale are included below. The visual reception scale consists of 33 items. It assesses the child’s processing of visually presented information and includes tasks that require visual discrimination, categorization, and visual memory skills. While the scale is not completely “nonverbal,” it was designed to minimize the amount of language needed to understand instructions and give a response. Items range from visually fixating and tracking moving objects in early infancy to matching shapes, pictures, letters, and words and remembering visual forms for older children. The fine motor scale consists of 30 items and (similar to visual reception) also requires minimal receptive and expressive language skills. Fine motor items measure visual-motor planning and
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control, motor imitation, unilateral and bilateral manipulation of objects, and writing readiness skills. Individual items range from reflexively holding objects and bringing fist to mouth to stacking blocks, cutting with scissors, and copying shapes and letters with a pencil. The receptive language scale consists of 33 items. This area focuses on the child’s ability to process linguistic input and assesses auditory comprehension and auditory memory skills. At the earliest age levels, the items assess the infant’s orienting to sounds and responding to voices. As the item difficulty increases, the young child is asked to follow verbal directions and respond to questions about spatial, color, size, length, and number concepts. Most items can be answered by pointing to a picture or handing over an object, but some of the more advanced items require brief spoken responses, such as answering general knowledge questions. The expressive language scale consists of 28 items and involves the production of sounds/ words and the use of auditory memory. Items range from vocalizing early vowel and cooing sounds in infancy to defining vocabulary words, repeating sentences, and solving verbal analogies. The gross motor scale consists of 35 items. The manual states that this scale measures “central motor control and mobility” (p. 2) in a variety of positions that range from being held in the caregiver’s arms to the child being fully upright and independently walking, hopping, and running. Time required for administration of the full MSEL:AGS varies by the child’s chronological age and ability level. The manual estimates administration takes approximately 15 min for 1-year-olds, 30 min for 4-year-olds, and 60 min for 5-year-olds. Directions include information on positioning infants up to 8–10 months of age and supplemental drawings. Some items can be scored based on parent report and therefore do not have to be directly observed. This is particularly helpful in expressive language items where it is often difficult to elicit vocalizations or spoken responses from young children in an unfamiliar setting. It is also permissible to vary the order of administration of both the scales and individual items within each scale and to readminister some items after a child has “warmed up.” For children
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born prematurely (at or before 36 weeks gestation), scores may be calculated based on the child’s corrected age up to 2 years. The components of a complete MSEL:AGS include the Mullen test kit, stimulus booklet, record forms, and two manuals. The test kit contains most of the objects needed for item administration. However, some of these items lack the durability needed for young children (e.g., picture cards), and others present a potential choking hazard (e.g., small beads). The examiner must also supply a number of items not contained in the test kit, such as paper, cereal, large ball, colored tape or white chalk line, 6-in.-high bench, and stairs. Consequently, some preparation time is needed before each administration to insure that all the necessary materials are available. The stimulus booklet contains black and white line drawings, primarily for use on visual matching, prewriting, receptive language, and expressive vocabulary tasks. On the record form, items are arranged in increasing order of difficulty for each domain. Arrows denote starting points for different ages, but examiners may use their judgment about which starting point to choose (e.g., the examiner may begin at an earlier start point for children with developmental delays). Brief descriptions are included for each item, along with cues for positioning infants and a scoring column. The MSEL:AGS Item Administration Book provides instructions for giving items in all five scales. For each item, this manual includes a list of needed materials, the recommended position for the younger infants (e.g., supported sitting, standing), administration directions, and scoring criteria. The instructions are generally understandable, particularly for professionals with experience in infancy and early childhood. The MSEL:AGS Manual provides information about the test’s theoretical background, scale descriptions, general administration, scoring procedures, standardization and psychometric data, and raw score conversion tables. There are also three case studies provided to illustrate the use and interpretation of the MSEL:AGS. Scoring for each scale varies from 1 point for a correct response to 0 points for an incorrect response on some items – to items where multiple
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points can be earned for correctly completing component parts. Basal and ceiling rules are the same for each scale. A basal has been established for a scale when a child earns at least 1 point for each of three consecutive items beginning at a starting point. Testing continues until the child obtains scores of 0 points on three consecutive items. The sums of raw scores for each scale are used to compute derived scores that include T scores (mean ¼ 50; SD ¼ 10), percentile ranks, and age equivalents for each motor and cognitive scale and an Early Learning Composite (mean ¼ 100; SD ¼ 15) based on the four cognitive scales. Descriptive categories are also available (e.g., very high to very low).
Historical Background The Mullen Scales of Early Learning: AGS Edition (1995) represents a revision of the original Mullen Scales (1992) and combines the Infant MSEL (1989) and the Preschool MSEL (1992) into a single test with norms that span the age range from birth through 68 months. The author, Eileen M. Mullen, Ed.D., developed the MSEL:AGS based on 30 years of clinical experience testing young children with developmental disabilities. She designed it to include multiple developmental domains based on the assumption that specific abilities mature at different rates in infants and young children. Viewed from this perspective, a test yielding only a global score would not provide information about patterns of cognitive strengths and weaknesses that can be used to plan interventions (Manual, p. 9). To arrive at the item sets for the current edition, item analyses were conducted using the Rasch one-parameter IRT model in order to obtain item difficulty estimates and indices of goodness of fit. A few items were dropped from the earlier versions, and the remaining items were sequenced by order of difficulty for the MSEL:AGS. The structure of the individual scales was built on the author’s theory of information processing where gross motor learning is considered to be the foundation for conceptual development in both visual and auditory modalities (Manual, p. 7).
Mullen Scales of Early Learning
Tasks from each of the four cognitive scales were analyzed in terms of the component processes required to perform them. Individual items in each scale are purported to target intrasensory (auditory or visual) processing or intersensory (auditory-visual) processing. More specifically, the visual reception and fine motor scales are designed to assess visual intrasensory processing, while the receptive language and expressive language scales are designed to assess auditory intrasensory processing. In addition, approximately half of the items on the receptive language scale are reported to assess auditory-visual intersensory processing. However, when one carefully analyzes the individual items, it is difficult to categorize them into purely one type of sensory processing since problem solutions often require multiple skills across multiple modalities. For example, solving the form-board task on the visual reception scale involves understanding the spoken directions (auditory), matching the shapes to their recesses (visual), using fine motor control to place the shapes into their respective holes (visual-motor), and so on. The MSEL:AGS is the second most widely used measure of early development after the Bayley Scales of Infant and Toddler Development, Third Edition (2006) (Chawarska and Bearss 2008, p. 55). Before the Bayley III (2006) was redesigned to include cognitive, language (receptive and expressive communication), and motor (fine and gross motor) scales to replace the previous more global mental and motor scales, the Mullen Scales was frequently chosen for research studies because investigators wanted to be able to assess individual developmental domains (Klin et al. 2005). In addition, the relatively long age span (birth to 68 months) was very helpful for longitudinal projects (e.g., studies of the development of baby siblings of children with autism spectrum disorders).
Psychometric Data The Mullen Scales was standardized on a sample of 1,849 children ranging in age from 2 days to 69 months and grouped into 16 age groups
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ranging from 2-month age intervals from birth to 14 months to 5-month age intervals from 15 to 68 months. However, there were uneven numbers of children across the 16 age groups, varying from a low of 84 children at 11–12 months to a high of 156 children at 27–32 months. Standardization utilized 71 clinicians from a variety of disciplines and took place at more than 100 sites across the United States. It extended over an 8-year period and was conducted in two phases that covered different time periods and included different geographic regions. Phase 1 spanned June 1981 to February 1986 and included children from the Northeast. Phase 2 covered the period between December 1987 and April 1989 and included samples from the South, West, and North, and South Central areas of the United States. The standardization sample is described as “geographically diverse,” but it is not representative of the entire United States. For example, no children in the 1–14-month age range were recruited from the North/South Central region (Bradley-Johnson 1997). For the total sample, there were 48.7% females and 51.3% males which were very close to the US population estimates in 1990. However, there was some variability among the 16 age groups, e.g., males were overrepresented at 5–6 months (59.8%) and underrepresented at 27–32 months (43.6%). Parental consent and information on demographic variables (age, sex, race/ethnicity, father’s occupation, and urban/ rural residence) were obtained for all children in the sample. Settings for testing included kindergarten, day care, nursery, and homes. All children in the sample were from homes where English was the primary language. It should also be noted that children with known physical or mental disabilities were excluded from the standardization sample. Chapter 6 of the manual provides information about the reliability and validity of the MSEL: AGS. In terms of reliability, information is provided about internal consistency, test-retest reliability, and interscorer reliability. As a measure of internal consistency, the manual presents splithalf reliability coefficients for the 16 age groups of the standardization sample (Manual, p. 56). Modified split-half internal consistency
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coefficients were calculated for the five scales and the Early Learning Composite. The median values ranged from 0.75 to 0.83 for the scales and 0.91 for the ELC. However, four coefficients that were significantly lower than this range were omitted from the estimation of the median split-half coefficients. The author explained these omissions as being due to a ceiling effect on visual reception for the two oldest age groups and floor and ceiling effects on receptive language for the youngest and oldest age groups. With these same four age groups omitted, the standard error of measurement had medians ranging from 4.1 to 5.0 T score points for the scales and 4.5 standard score points for the ELC. Test-retest reliability was reported in the manual for two samples based on administration of the original Mullen Scales of Early Learning. The first sample included 50 children from 1 to 24 months of age and the second sample included 47 children ranging from 25 to 56 months. The retest interval ranged from 1 to 2 weeks, with a mean test-retest interval of 11 days. For the younger sample, testretest reliability was high for gross motor (0.96) and ranged from 0.82 to 0.85 for the cognitive scales. For the older sample (which did not include children from 57 to 68 months), the median stability coefficients ranged from 0.71 to 0.79. Interscorer reliability was reported to be high (0.91 to 0.99), but the number of scorers was not reported, and the sample only went up to 44 months. The manual provides information about construct, criterion-related, and concurrent validity. Support for construct validity is provided by showing an increase in mean raw scores with increasing chronological age across all five scales. Intercorrelations and squared correlations were calculated for the five Mullen Scales for 5 age groups and the total standardization sample. Scales correlated with each other at low to moderate levels. This was interpreted as indicating both unique and shared variances. Exploratory factor analyses were also conducted for the five scales by 5 age groups. Only one factor was reportedly extracted at each age level and this was interpreted as supporting the Mullen ELC as a measure of “g” or general cognitive ability.
Mullen Scales of Early Learning
However, establishing construct validity for this measure is problematic because there is a lack of supportive data for the author’s theory about intrasensory and intersensory processing leading to the identification of the child’s learning style and its subsequent implications for remediation (Bradley-Johnson 1997). Evidence for concurrent validity is based on three types of studies: (1) studies analyzed by the publisher correlating the MSEL:AGS with other measures such as the Bayley Scales of Infant Development (1969), Preschool Language Assessment (1979), and Peabody Developmental Motor Scales (1983), (2) studies reported in the manuals for the original Infant and Preschool Mullen Scales, and (3) studies reported in the research literature which primarily focused on differences between low-birth-weight and normal-birth-weight children. The MSEL:AGS correlated moderately well with the measures mentioned above – with higher correlations obtained when the specific Mullen scale more closely matched what the other instrument was purported to measure. For example, the receptive language scale on the Mullen correlated more highly with Auditory Comprehension on the Preschool Language Assessment (1979) than with the Bayley Scales (1969) Mental Development Index (that includes both verbal and nonverbal items).
Clinical Uses The MSEL:AGS was designed for use by early childhood professionals working in a variety of settings, such as early intervention programs, preschool and kindergarten special education, universities, research laboratories, hospitals, rehabilitation centers, and private practice. The author described three major uses: (1) eligibility determination for early intervention and special education, (2) diagnostic assessment of children with special needs, and (3) individualized program planning (Manual, p. 5–6). Under the Individuals with Disabilities Education Act (IDEA), infants and young children must meet specific eligibility criteria in order to qualify for early intervention or special education
Multidisciplinary Evaluation
services. The MSEL:AGS includes three of the five areas of functioning that must be assessed (i.e., physical (including gross and fine motor skills), cognition, and communication), but additional assessment with another measure is needed to cover personal-social and adaptive/self-help skills. Because the MSEL:AGS includes five individual scales, it is useful in diagnostic assessments where clinicians are trying to determine whether children are progressing within the typical range of development or whether they are demonstrating significant delays in one or more areas of development. Determining a profile of strengths and weaknesses is essential in diagnostic assessments of children suspected to have a wide variety of disabilities, such as specific speech-language impairments, motor impairments (e.g., cerebral palsy), autism spectrum disorders, and global developmental delays. Clinicians also use patterns of performance to formulate recommendations for skills to address in intervention. However, as noted in the validity discussion above, the modality-specific approach to individualized program planning proposed in the MSEL:AGS has not been substantiated. In summary, the MSEL:AGS has been widely used in developmental assessments of infants and young children. Both clinicians and researchers have appreciated the multiple domain structure and relatively broad age range. Although it is regularly used for diagnostic evaluations and eligibility determination for services, children with physical and mental disabilities were excluded from the standardization sample. In addition, much of the data used for standardization was gathered in the 1980s, so the norms are outdated, and there are no current plans to renorm this measure.
References and Reading Akshoomoff, N. (2006). Use of the Mullen scales of early learning for the assessment of young children with autism spectrum disorders. Child Neuropsychology, 12, 269–277. Bayley, N. (1969). Bayley scales of infant development. San Antonio: Psychological Corporation.
3029 Bayley, N. (2006). Bayley scales of infant and toddler development (3rd ed.). San Antonio: Harcourt Assessment. Bradley-Johnson, S. (1997). Mullen scales of early learning. Psychology in the Schools, 34, 379–382. Chawarska, K., & Bearss, K. (2008). Assessment of cognitive and adaptive skills. In K. Chawarska, A. Klin, & F. R. Volkmar (Eds.), Autism spectrum disorders in infants and toddlers: Diagnosis, assessment and treatment (pp. 50–75). New York: The Guilford Press. Klin, A., Saulnier, C., Tsatsani, K., & Volkmar, F. R. (2005). Clinical evaluation in autism spectrum disorders: Psychological assessment with a transdisciplinary framework. In F. R. Volkmar, R. Paul, A. Klin, & D. Cohen (Eds.), Handbook of autism and pervasive developmental disorders (Vol. 2, pp. 772–798). Hoboken: Wiley. Mullen, E. M. (1989). Infant Mullen scales of early learning. Cranston: T.O.T.A.L. Child. Mullen, E. M. (1992). Mullen scales of early learning. Cranston: T.O.T.A.L. Child. Mullen, E. M. (1995). Mullen scales of early learning: AGS edition. Circle Pines: American Guidance Service.
Mullen Scales of Early Learning, American Guidance Service Edition ▶ Mullen Scales of Early Learning
Multidisciplinary Evaluation Sarah Melchior School Psychologist, Services for Students with Autism Spectrum Disorders Montgomery County Public Schools, Silver Spring, MD, USA
Definition Multidisciplinary evaluation refers to the process of gathering both formal and informal data from a variety of sources to determine whether a student is eligible for special education services and to provide information about his or her current levels of functioning. “Multidisciplinary” implies that evaluations will be conducted using a team approach in which a number of professionals (e.g.,
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speech/language pathologists, occupational therapists, physical therapists, psychologists) gather assessment data about the student.
Historical Background Prior to the enactment of Public Law 94-142, many students with disabilities were either not being served in public schools or were not receiving appropriate education services. Lack of adequate or appropriate assessments contributed to inaccurate labeling as well as education of students with disabilities. With the support of family associations, the federal government began to develop practices for children with disabilities in the 1950s and 1960s. The Elementary and Secondary Education Act (PL 89-10) and the State Schools Act (PL 89-313), both enacted in 1965, provided states with funding to improve the quality of education for students with disabilities. In 1975, the Education for All Handicapped Children Act, now referred to as IDEA (Individuals with Disabilities Education Act), guaranteed all students with disabilities the right to a free, appropriate public education. Another provision of IDEA included the need for a multidisciplinary assessment of students suspected of having a disability. Using a multidisciplinary approach to assessment, involving a variety of professionals with expertise in each area of need, contributed to more accurate diagnosis of children and the provision of more appropriate special education services for students with disabilities.
Multidisciplinary Evaluation
assessment and evaluation of the student’s communication abilities, assessment data from other sources such as occupational therapy or physical therapy are also valuable. Outside of the school setting, pediatric, neurologic, and genetic assessment data are also important (Klin et al. 2005). Autism spectrum disorders are characterized by atypical development in multiple areas, including cognitive development, communication abilities, social interactions, and play skills. A comprehensive approach to assessment that addresses all areas of concern is therefore necessary. To assess these multiple areas of functioning, a multidisciplinary team of professionals with expertise in these various areas must be involved in the assessment process (Noland and Gabriels 2004).
Future Directions While a multidisciplinary approach to assessment is essential for children suspected of having an autism spectrum disorder, data from various professionals allows for multiple and, potentially, conflicting views of the child. In order to avoid this possibility, it is recommended that professionals operate in a transdisciplinary manner by which the team collaborates to present a single, coherent picture of the child’s strengths and needs. By working together to present this single picture of the child, professionals are able to offer a more accurate view of the child and more fully address the impact of deficits in one area on other areas of functioning (Klin et al. 2005).
Current Knowledge References and Reading Developmentally based assessments including information about a child’s cognitive, communication, social emotional, and adaptive skills are integral to the diagnosis and decision-making process for those suspected of having an autism spectrum disorder (ASD). Current research supports the need for evaluation of students suspected of having an ASD to take place within a framework that incorporates the knowledge and expertise of various disciplines. In addition to psychological
Brock, S. E., Jimerson, S. R., & Hansen, R. L. (2006). Identifying, assessing, and treating autism at school. New York: Springer. Burnette, J. (2000). Assessment of culturally and linguistically diverse students for special education eligibility. Retrieved from http://www.cec.sped.org/Content/ NavigationMenu/NewsIssues/TeachingLearningCente r/Assessment_of_Culturally_and_Linguistically_Dive rse_Students_for_Special_Education_Eligibility.htm Klin, A., Saulnier, C., Tsatsanis, K., & Volkmar, F. R. (2005). Clinical evaluation in autism spectrum
Multidisciplinary Training (TEACCH Component) disorders: Psychological assessment within a transdisciplinary framework. In F. R. Volkmar, R. Paul, A. Klin, & D. Cohen (Eds.), Handbook of autism and pervasive developmental disorders (pp. 772–798). Hoboken: Wiley. National Joint Committee on Learning Disabilities. (2010). Comprehensive assessment of evaluation of students with learning disabilities. Retrieved from http://www. ldanatl.org/pdf/NJCLD%20Comp%20Assess%20Pap er%206-10.pdf Noland, R. M., & Gabriels, R. L. (2004). Screening and identifying children with autism spectrum disorders in the public school system: The development of a model process. Journal of Autism and Developmental Disorders, 34(3), 265–277. Prelock, P. A. (2006). Autism spectrum disorder: Issues in assessment and intervention. Austin: Pro-Ed. Turnbull, A., Turnbull, R., & Wehmeyer, M. L. (2010). Exceptional lives: Special education in today’s schools (6th ed.). Upper Saddle River: Pearson Education. United States Department of Education. (2007). History: Twenty-five years of progress in educating children with disabilities through IDEA. Retrieved from http:// www2.ed.gov/policy/speced/leg/idea/history.html
Multidisciplinary Team ▶ Interdisciplinary Team
Multidisciplinary Training (TEACCH Component) Lee Marcus1 and Jemma Grindstaff2 1 TEACCH Autism Program, University of North Carolina, Chapel Hill, NC, USA 2 Chapel Hill TEACCH Center, Carrboro, NC, USA
Definition Training of professionals in approaches to intervention in autism has been an integral part of the TEACCH program since its inception in 1972. Training includes a wide range of topics in diagnosis, assessment, treatment, and education and occurs at TEACCH center throughout North Carolina and in multiple sites throughout North
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Carolina, other states, and across the world. The training methodologies encompass lectures with audiovisual supports, activities designed for participant experiential learning, and hands-on experience with student trainers (i.e., children, adolescents, and adults with autism). The trainings are led by TEACCH directors (typically a Ph. D. licensed psychologist) and TEACCH staff therapists who have been working in the program for many years and have been trained in the various training models. Over the years, hundreds of professionals from across the world have been trained to be trainers, working as part of a TEACCH-led team.
Historical Background When TEACCH was established as a statewide mandated program for serving individuals with autism and their families in 1972, there was virtually nothing known about service delivery, evidence-based practices, nor effective ways of preparing professionals to work with this population. There was little interest in autism as a field and more misunderstanding than accurate understanding of the condition. TEACCH grew out of a NIMH-funded project led by Eric Schopler and Robert Reichler which, on a small scale, documented effective ways of supporting children and their parents. With the legislation creating TEACCH at the University of North Carolina came the task of starting three diagnostic and treatment centers, training staff, and putting a model into practice. Simultaneously, 11 classrooms for students with autism were established, 10 of which were in public schools across North Carolina. Most, if not all, of these school districts had never educated a child with autism nor were prepared to recruit and train teachers. TEACCH took on this responsibility and organized a major training program for the new teachers as well as clinical staff at the three regional centers. The first trainings were lengthy, intensive, and complex and were based in a classroom setting with students with autism, a training team, and new teachers and assistant for 2 weeks. Over the course of the 2 weeks, along with lectures and group
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discussions, trainees observed the training staff, learned the principles and practices of TEACCH, and gradually assumed responsibility for running the classroom during the second week. Staff to trainee ratio was high, close to one to one. This first model became a template for later variations of the training approach, based on the collaboration of trainer and trainee, experiential learning, and individualization. A major development in the model occurred about a decade later when a team of three TEACCH trainers led by Dr. Gary Mesibov, Director of TEACCH, adapted the earlier model with a less intense trainer to trainee ratio and shortening the process. The curriculum remained the same, but the training methods modified to effectively train more participants. Over the next three decades, there have been other modifications to the core structure of this training model and development of briefer basic training workshops that can reach larger audiences. The efficiency and accessibility of the trainings enable the program to be disseminated across the entire United States and dozens of countries across the world. Coordination of these training programs was led for much of this period by Dr. Roger Cox and implemented by TEACCH directors and staff across North Carolina. Paralleling the professional service training models has been the center-based training of students at all levels of training and across many disciplines. The centerpiece of this training has been the Clinical Psychology Internship Program, started in 1974, and the Psychology Postdoctoral Training Program, started in the mid-1980s. The internship program is part of the UNC Department of Psychiatry’s broad Psychology Internship Program, a program accredited by the American Psychological Association since 1962. Former TEACCH interns have gone onto prominence in the field of autism as leading researchers, clinicians, and program directors. Students receive intensive and comprehensive training in the diagnostic, assessment, and intervention methods used by TEACCH and regardless of their discipline or time commitment to their training experience receive exposure to the basic philosophy and practices of TEACCH, as well as to the field of autism as a whole.
Multidisciplinary Training (TEACCH Component)
A third area of training, also center-based, is placement of professionals from countries outside the United States who are interested in learning TEACCH methods and practices that can be applied to their home country. These international interns spend anywhere from a month to a year at a TEACCH center learning about all aspects of the program and most have returned to their countries and made significant contributions to services for individuals with autism, their families, and professionals.
Rationale or Underlying Theory The rationale underlying TEACCH training is based on a key component of its mission statement: The University of North Carolina TEACCH Autism Program creates and cultivates the development of exemplary community-based services, training programs, and research to enhance the quality of life for individuals with Autism Spectrum Disorder and their families. TEACCH, while a North Carolina state agency, established to meet the needs of families in the state through direct services is also a significant part of the University of North Carolina and its School of Medicine. The dissemination of its knowledge and experience through training within North Carolina and around the world has been a primary activity since its inception in 1972.
Goals and Objectives The goals and objectives of the TEACCH multidisciplinary training model are as follows: 1. To disseminate current knowledge, principles, and practices of the comprehensive TEACCH approach to the understanding and treatment of autism. 2. To provide state of the science training through effective and relevant ways to professionals in the field of autism in North Carolina, other states, and internationally.
Multidisciplinary Training (TEACCH Component)
3. To provide preservice education and training to undergraduate, graduate, and postdoctoral students across disciplines, including, but not limited to, psychology, psychiatry, speech pathology, education, social work, occupational therapy, pediatrics, and family medicine. 4. To provide experiential learning programs to students and professionals on specific tools and strategies, developed by TEACCH, such as structured teaching and the use of visual supports, working with parents as co-therapists, Child Autism Rating Scale (CARS2), Psychoeducational Profile – Third Edition (PEP-3), and TEACCH Transitional Assessment Profile (TTAP).
Treatment Participants Historically, there have been three types of multidisciplinary training at TEACCH, as described above: in-service training of professionals, preservice training of students, and international interns. These types of training continue today, and each type tends to attract a different group of participants. The in-service training can be further divided into a five-day, intensive training model, which includes hands-on learning with student trainers, and other, more didactic workshops, typically of shorter duration (2–3 days) and with written scenarios to provide an experiential component. Thus far, two studies have examined these training models to evaluate their relative efficacy (Grindstaff 1998, 2000). In 1999, participants of the 5-day model were primarily special educators (55.4%), followed by speech language pathologists (10.9%), psychologists (8.9%), occupational therapists (5.0%), educational consultants (5.0%), parents (4.0%), teaching assistants (3.0%), school administrators (3.0%), and regular education teachers (1.0%). Participants in the comparison group were similar, with special educators in the majority (51.6%), followed by teaching assistants (16.1%), speech language pathologists (9.7%), regular education teachers (6.5%), parents (6.5%), school administrators (3.2%), psychologists (3.2%), and educational consultants (3.2%). An interesting shift has taken place in the last 12 years, such that, in the summer of 2011, the
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group of participants in the five-day training model included fewer classroom teachers and a greater number of consultants who provide support to students with autism in regular education settings. Typically, the five-day model has attracted participants from across the United States, as well as a variety of foreign countries. The workshops have tended to attract a more local audience, specific to the region of North Carolina where the workshop has been held. Preservice training includes students of all levels of training and from a variety of disciplines. Most often, these have been graduate students in psychology, speech language pathology, occupational therapy, and special education. Medical residents and fellows from pediatrics and psychiatry have also been an important subset. The greatest amount of training time and effort has been expended to develop the Clinical Psychology Internship. The TEACCH Intern spends at least 50% of his or her 12-month internship involved at TEACCH in autism-specific diagnosis, assessment, consultation, ad intervention. Competition for this internship can be fierce, as applicants come from across the US and Canada; many of them will go on to take leadership positions in the field as researchers, clinicians, and program directors. International interns have come from a variety of countries; many of them were first exposed to TEACCH principles during a five-day training held either here in the US or in their home country. Collaborations have been particularly rich with professionals from Japan, and more recently, from Sweden and Norway, leading to a greater percentage of international interns from those areas.
Treatment Procedures The five-day training model is the most intense of the in-service training programs. Participants first attend lectures about the culture of autism and structured teaching to orient them for the week. Subsequently, they spend a small portion each day in lecture; the bulk of their learning takes place in small teams as they observe the TEACCH staff, develop activities for the student trainers, implement these activities with the student trainers, and
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reflect on their collective experiences. Throughout the week, the principles of structured teaching are applied to the curriculum areas most relevant to students with autism: informal assessment, communication, independence, leisure skills, social engagement, and behavior management. Teams work with a different student trainer each day, ensuring that they have broad exposure to the diversity of the autism spectrum. The five student trainers are, of course, unique individuals, who also vary by age, cognitive ability, and severity of autism symptoms. Most training participants report that the opportunity to teach in the model classroom and interact with our student trainers is the most memorable component of this training experience. Workshops vary considerably because they cover a wide range of topics. The Fundamentals Workshop introduces participants to the culture of autism and structured teaching, similar to the first 2 days of the five-day training model. Other workshops focus on the diagnosis of autism spectrum disorders, approaches to social skills instruction, and adapting curriculum for students in regular education, early intervention, and many others. New workshops are developed as the need arises. Most recently, workshops for parents have expanded to address their need for information about upcoming transitions: preparing for kindergarten, middle school, and adulthood. In all cases, an attempt is made to include a component of experiential learning – typically some type of written exercise or thought experiment that provides an opportunity for participants to apply what they are learning to their classroom, client, or child (in the case of parents). It is much less common to be able to provide the “live” interaction with student trainers that is so powerful during the five-day training. Preservice training is often scheduled to coincide with the academic calendar. Residents or graduate students may observe for as little as 1 day; many future pediatricians, for example, spend a day observing a TEACCH diagnostic evaluation in order to increase their awareness of autism spectrum diagnoses and to facilitate appropriate referrals. Other trainees spend a month, a semester, or an academic year at their local
Multidisciplinary Training (TEACCH Component)
TEACCH center. Their experiences are tailored to meet their training goals and departmental expectations. As previously mentioned, the TEACCH psychology intern spends a 12-month year in the most intense preservice training. He or she may choose to spend an additional, fellowship year with TEACCH to develop greater independence as a clinician, participate in training efforts, and pursue research interests. Training for international interns is also individualized, based on the expertise and interest of the intern. Many of these professionals are quite experienced in their discipline and with people with autism; however, they are seeking additional training specific to TEACCH principles and procedures. Often, they hope to acquire enough skill to implement TEACCH methodology in their home country, as well as the ability to communicate TEACCH principles to their colleagues. An intern may be responsible for establishing a new program or modifying an existing one to better reflect TEACCH principles. To that end, interns typically maintain an ongoing relationship with their TEACCH center to receive consultation and, sometimes, additional training.
Efficacy Information Informal program evaluation takes place during every form of TEACCH training. Participants at all levels of training (in-service, preservice, and international interns) complete rating scales, answer written questions, and are generally encouraged to provide feedback to TEACCH staff. These responses have been universally positive. It is not uncommon for participants to report that their TEACCH training experience has proven more valuable than any other training program in which they have participated. Sometimes, participants also offer constructive criticism. Suggestions are always taken seriously and have sometimes resulted in a helpful revision to the delivery of the training model. As mentioned above, there have been two formal evaluations of TEACCH’s in-service training efforts, designed to compare the relative efficacy of the five-day training model versus the two-day
Multidisciplinary Training (TEACCH Component)
Fundamentals Workshops (Grindstaff 1998, 2000). Outcomes were compared across the participants (1) knowledge of the training content, (2) attributions of controllability with regard to the extreme behavior of students with autism, (3) self-efficacy, (4) negative affect, and (5) use of structured teaching. Measures were completed at the very start of the respective training programs and again 6 weeks post-training. Results indicated that both training models were effective in increasing the participants’ knowledge of the training content; however, neither model was associated with significant change in participants’ attributions, self-efficacy, or affect. Participants in the five-day training model reported a significant increase in their use of structured teaching strategies back in their home environments; workshop participants did not. Grindstaff (2000) concluded that either training model could effectively convey the principles and philosophy of structured teaching but that only the five-day model (with its hands-on component) achieved the more elusive goal of helping participants apply structured teaching strategies to their classroom, clinic, or other work setting. Results from a small pilot study (Grindstaff 2000) indicated that the Structured Teaching Checklist could be used by a trained observer to evaluate the degree to which a classroom has successfully implemented the principles of structured teaching; this tool could prove useful as a more objective measure of training participants’ use of structured teaching, as the initial studies relied solely on self-report.
Outcome Measurement Since its inception, the Structured Teaching Checklist (STC; Grindstaff 2000) has been adapted for use as a standardized measure of TEACCH treatment fidelity (Hume et al. 2011). The TEACCH Fidelity Form has been demonstrated to have strong psychometric properties (e.g., inter-rater agreement, test-retest reliability, and internal consistency) and reliably discriminates between classrooms that adhere to the TEACCH model versus other treatment approaches (Hume et al. 2011). The TEACCH
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Fidelity Form is specific to preschool classrooms, but perhaps, it could be elaborated to include classrooms for students at other stages of development, including a version for self-contained classrooms and one for students who are included in regular education settings. Once these additional versions are added, the TEACCH Fidelity Form will make an ideal measure for the effectiveness of TEACCH training.
Qualifications of Treatment Providers For preservice training programs, there is typically a Ph.D. level psychologist who serves as the administrator and clinical director. Other trainers are often Masters level TEACCH staff who have served with the TEACCH program for a number of years and have demonstrated mastery of the principles of TEACCH training, as well as an ability to communicate effectively. There is a multistep process for becoming a TEACCH trainer that applies to TEACCH staff, as well as interested professionals from other organizations. First, the potential trainer must attend the five-day training model as a participant. He or she then returns as a “shadow trainer” – which means that he or she is apprenticed to an experienced trainer for the five days, with a focus on working with a specific student trainer, instead of the training participants. Shadow training is often repeated two or three times, until the potential trainer feels comfortable with a variety of student trainers. In the final stage of preparation, the potential trainer returns as a “reverse shadow” – which means that he or she now takes the lead not only with the student trainer but also with the training participants, with support from an experienced TEACCH trainer “behind the scenes.” The potential trainer may continue as a “reverse shadow” until he or she is comfortable communicating about TEACCH principles and strategies, while also improvising with a student trainer. There is no specific time requirement for the completion of these stages of training, but the commitment is considerable. In-service trainees and international interns receive supervision from the staff of their assigned
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TEACCH center. This always includes the center director, a Ph.D. level psychologist, as well as the Masters level psychoeducational therapists, who come from a variety of disciplines. If there is a TEACCH staff member from the same discipline as the trainee, the two are often matched in a mentor-mentee relationship.
See Also ▶ Informal Assessment ▶ Structured Classrooms ▶ Structured Teaching ▶ Treatment Fidelity ▶ Visual Supports
References and Reading Grindstaff, J. P. (1998). Attributions and autism: An evaluation of TEACCH’s experiential training programs. Unpublished master’s thesis, University of North Carolina at Chapel Hill, Chapel Hill. Grindstaff, J. P. (2000). Further evaluation of TEACCH’s experiential training programs: Change in participants’ knowledge, attributions, and use of structure. Doctoral dissertation, University of North Carolina at Chapel Hill, Chapel Hill, NC, 2000. Dissertation Abstracts International-B, 62, 5374. Hume, K., Boyd, B., McBee, M., Coman, D., & Gutierrez, A. (2011). Assessing implementation of comprehensive treatment models for young children with ASD: Reliability and validity of two measures. Research in Autism Spectrum Disorders, 5(4), 1430–1440. Mesibov, G. B., Shea, V., & Schopler, E. (2004). Training issues. In G. B. Mesibov, V. Shea, & E. Schopler (Eds.), The TEACCH approach to autism spectrum disorders (pp. 189–201). New York: Springer.
Multihandicapped
be in the category of autism, mental retardation, hearing impairment or deafness, visual impairment or blindness, emotional disturbance, speech and language impairment, orthopedic impairment, or health impairment. When referred to as multihandicapped, the person typically has disabilities that are severe enough to require intensive support to handle the functions of daily living. In education, the term “multiple disability” is used for students who have two or more disabilities that effect their ability to access education through traditional means. Children with multiple disabilities have a combination of affected areas such as speech and language, mobility, learning, intellectual functioning, sensory losses or dysfunctions, and behavior or social problems. Children are impacted in different ways, and the disabilities will vary in level of severity and how they manifest in everyday performance. Children with autism may be identified as having multiple disabilities if diagnosed with autism and intellectual disability. Educational support is often needed in the classroom, and some children will need support in all settings throughout their lives. Programming for these children will be based on the characteristics they display which will vary widely depending on the areas involved. Typically, the educational team includes a variety of professionals such as special education and regular education teachers, physical therapists, occupational therapists, speech and language pathologist, adaptive physical education teachers, nurses, paraprofessionals, psychologist, and possibly medical professionals.
Historical Background
Multihandicapped Michelle Lestrud The Gengras Center, University of Saint Joseph, West Hartford, CT, USA
Definition Multihandicapped refers to a person that has more than one disabling condition. The disability may
The term “multi” means many or several and is used frequently in numerous contexts. The term handicap has a more involved history, some of which is not based on facts that can be substantiated. There is a story that is associated with the word “handicap” that leads people to believe it was first used when people with disabilities were allowed to beg in the streets with their “cap in hand.” There have been several attempts to dispel this myth but it seems to live on. The word does have a connection to a competitive sport dating
Multilocus Genetic Models
back to 1650s but was not used in conjunction with a person with a disability until the early 1900s. As the term “handicap” evolved, it now refers to a person with a disability. The term alone does not give information as to what type of disability a person may have. Once multi was added to handicap, it came to have the meaning we know today of more than one disabling condition which makes functioning in daily activities more difficult when compared to a person without the disabilities.
See Also ▶ Disability
References and Reading Amundson, R. (2011). About the meaning of “handicap”. Retrieved July 26, 2011, from http://www.uhh.hawaii. edu/~ronald/HandicapDefinition.htm. Klewe, L. (1993). Brief report: An empirical evaluation of spelling boards as a means of communication for the multihandicapped. Journal of Autism and Developmental Disorders, 23(3), 559–566. Miller, G. (1993). Expanding vocational options. Review, 25(1), 27. NICHCY. (2004). Disability fact sheet number 10. Washington, DC: Author. Retrieved July 2011, from http://www.accessiblesociety.org/topics/demographicsidentity/dkaplanpaper.htm. Reid, D., Phillips, J. F., & Green, C. W. (1991). Teaching persons with profound multiple handicaps: A review of the effects of behavioral research. Journal of Applied Behavior Analysis, 24(2), 319–336.
Multilocus Genetic Models John D. Murdoch Child Study Center, Yale University School of Medicine, New Haven, CT, USA
Definition A multilocus genetic model means that a given phenotypic outcome is influenced by multiple genes (an individual gene is referred to as a
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locus) – this can imply multiple disease variants acting simultaneously in each patient, or single mutations in each patient, but in different genes (unsure of this definition). Tuberous sclerosis, for example, can be caused by mutations in either TSC1 or TSC2 (EntrezGene, OMIM). Coronary artery disease, on the other hand, could be due to more than one gene working in concert (e.g., Saade et al. 2011). Some ASD cases are attributable to specific chromosomal abnormalities, or to deletions/ rearrangements known to induce autism (when these rearrangements confer other symptoms and phenotypes in addition to autism, they are known as “syndromic” autism). No one cause has been found that explains more than 1–2% of autism cases (gross chromosomal abnormalities). A recent review summarized that taken together, all gross chromosomal abnormalities, all syndromes associated with autism, disruptions to known autism loci (e.g., SHANK3), and rare variants in candidate genes account for only 12–17% of autism cases, meaning that the majority of autism cases remain unclear with regard to specific genetic etiology (Buxbaum 2009). Another estimate put it at 5–15% (Pinto et al. 2010). Additionally, even syndromic causes that associate strongly with autism, such as fragile X syndrome, are not completely sufficient to induce autism; there exist fragile X patients who do not present with autism or ASD. Though some cases are largely influenced by highly deleterious single-gene mutations, even these are likely modulated by mutations in other genes. Similar ASD phenotypes need not necessarily involve the exact same genes in each case; it is possible that many autism cases can be caused by any one of a number of combinations of different genes in neurological pathways.
Historical Background Modern understanding of genetic inheritance is widely accepted to have begun with Gregor Mendel and his experiments with trait inheritance in garden peas. What we call Mendelian inheritance is the simplest form of transmission of genetic information: one gene, two or more alleles, a
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relationship between the alleles (dominant, recessive, codominant, incompletely dominant, etc.). Sex-linked traits modify the situation somewhat; disruptive mutations on the X-chromosome that may be recessive in females, being offset by the healthy X-chromosome, can be deleterious in males, who have only one X-chromosome. Multilocus disease models, while always theoretically possible, became more discussed in the late 1970s and early 1980s, particularly in the context of autoimmune diseases (see Rotter and Landaw 1984). A consensus for autism as a primarily genetic disorder did not fully emerge until the late 1980s to mid-1990s (Bailey et al. 1995; Bolton et al. 1994; Smalley et al. 1988; Szatmari et al. 1998), and fairly quickly, it became apparent that single-gene inheritance models did not explain most autism cases (e.g., Jorde et al. 1991). An early multilocus model of family recurrence in autism determined that 3 interacting genes, in a range of 2–10, best accounted for their data. Another multilocus model was put forth by Risch et al. in 1999. They concluded that on the basis of evidence seen in 97 affected sibling pairs (ASPs; both siblings diagnosed with autism) and 51 discordant sibling pairs (DSPs; one sibling had autism and one was unaffected), as well as follow-up in 50 ASPs and 29 DSPs, one or a few mutated gene loci in a given sibling pair did not adequately explain the increase in shared DNA inheritance between the affected siblings relative to the discordant siblings. On the basis of the families they assessed, the authors determined that a model based on 15 or more interacting gene loci explained their data better than a model with 10 or fewer gene loci (Risch et al. 1999). As autism genetic models were being debated, a broader debate was emerging in the genetic field about the etiology of complex (multigenic), relatively common diseases. The common disease-common variant theory held that for a disease to remain relatively common in the population, the mutations being inherited must not be damaging enough, on their own, to reduce reproductive success of those carrying them, and therefore more severe common disease phenotypes must be due to the interaction of
Multilocus Genetic Models
many common variants, each of a small effect size (contributing a small amount to the disease phenotype, and not disruptive enough to reduce reproductive fitness on its own) but significant enough when inherited in combination to induce a disease state. In contrast, the common diseaserare variant theory held that multiple rare variants, each of larger effect (more disruptive), were sufficient to cause common diseases. These deleterious rare variants would in theory be more prone to reducing reproductive success, but if many of these rare mutations were being generated spontaneously (de novo) rather than inherited from parents, in a large enough number of genes, it could explain the baseline prevalence of even severely disruptive alleles. As evidence accumulated that methodologies driven by the common disease-common variant assumption (exemplified by genome-wide association studies) could account for only a modest proportion of common complex disorders, the common disease-rare variant theory gained traction in research on these disorders. Autism in particular was poorly accounted for by genetic methods seeking to associate common variation with the disorder (e.g., Anney et al. 2010; Curran et al. 2011; Hirschhorn and Gajdos 2011). A few implicated genes and regions were replicated in other studies (e.g., MET, CNTNAP2, and 5p14.1) (Alarcon et al. 2008; Arking et al. 2008; Campbell et al. 2010; Jackson et al. 2009; Sousa et al. 2009; Wang et al. 2009; Weiss et al. 2009); however, common variants overall likely account for a small proportion of autism heritability (Pinto et al. 2010). Rare copy number variants (Bucan et al. 2009; Pinto et al. 2010; Sanders et al. 2011) began to gain attention as several studies suggested a role in autism. With the emergence of dense genome-wide marker SNP genotyping as well as exome sequencing, and follow-ups on these studies, evidence accumulated for a multihit etiology (i.e., mutations in multiple genes in the same person) in at least some autism cases, for example, FOXP1 and CNTNAP2 (O’Roak et al. 2011), CNTNAP5 and DOCK4 (Pagnamenta et al. 2010), and DMD and TRPM3 (Pagnamenta et al. 2011). The emerging methods allow us to concretely demonstrate the multilocus
Multilocus Genetic Models
model in patient data, beyond just discussing its theoretical fit to the existing data.
Current Knowledge The current state of the field is that large-scale chromosome rearrangements, genetic syndromes, and the few known definitively autismassociated loci (e.g., SHANK3, 15.q11-13) are of large effect size, but account for a modest proportion of total autism cases, and likely in conjunction with other genes in a given case. The majority of cases are likewise caused by mutations in a variety of genes, the list of which researchers are constantly working to validate, corroborate, and expand. The challenge is no longer to demonstrate the validity of the theoretical underpinnings of the multilocus model but to use more and more precise genetic tools to establish exactly what genes, and perhaps networks of genes (e.g., gene families or biological pathways), are involved in autism.
Future Directions As the prices of whole-exome and wholegenome resequencing continue to drop, these technologies, combined with wider- and wider scale genotyping of SNP markers across the entire genome, it will be possibly to analyze patients with autism at a finer and finer scale, potentially uncovering new viable candidate genes and contributing mutations, and strengthening or weakening the cases for existing candidate genes as we examine larger numbers of people in greater detail than ever before. These thorough analyses of individuals and their families represent the emerging present and near future in autism research.
See Also ▶ C o m m o n D i s e a s e - C o m m o n Va r i a n t Hypothesis ▶ Common Disease-Rare Variant Hypothesis
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References and Reading Alarcon, M., Abrahams, B. S., Stone, J. L., Duvall, J. A., Perederiy, J. V., Bomar, J. M., et al. (2008). Linkage, association, and gene-expression analyses identify CNTNAP2 as an autism-susceptibility gene. American Journal of Human Genetics, 82(1), 150–159. Anney, R., Klei, L., Pinto, D., Regan, R., Conroy, J., Magalhaes, T. R., et al. (2010). A genome-wide scan for common alleles affecting risk for autism. Human Molecular Genetics, 19(20), 4072–4082. Arking, D. E., Cutler, D. J., Brune, C. W., Teslovich, T. M., West, K., Ikeda, M., et al. (2008). A common genetic variant in the neurexin superfamily member CNTNAP2 increases familial risk of autism. American Journal of Human Genetics, 82(1), 160–164. Bailey, A., Le Couteur, A., Gottesman, I., Bolton, P., Simonoff, E., Yuzda, E., et al. (1995). Autism as a strongly genetic disorder: Evidence from a British twin study. Psychological Medicine, 25(1), 63–77. Bolton, P., Macdonald, H., Pickles, A., Rios, P., Goode, S., Crowson, M., et al. (1994). A case-control family history study of autism. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 35(5), 877–900. Bucan, M., Abrahams, B. S., Wang, K., Glessner, J. T., Herman, E. I., Sonnenblick, L. I., et al. (2009). Genome-wide analyses of exonic copy number variants in a family-based study point to novel autism susceptibility genes. PLoS Genetics, 5(6), e1000536. Buxbaum, J. D. (2009). Multiple rare variants in the etiology of autism spectrum disorders. Dialogues in Clinical Neuroscience, 11(1), 35–43. Campbell, D. B., Warren, D., Sutcliffe, J. S., Lee, E. B., & Levitt, P. (2010). Association of MET with social and communication phenotypes in individuals with autism spectrum disorder. American Journal of Medical Genetics. Part B, Neuropsychiatric Genetics, 153B(2), 438–446. Curran, S., Bolton, P., Rozsnyai, K., Chiocchetti, A., Klauck, S. M., Duketis, E., et al. (2011). No association between a common single nucleotide polymorphism, rs4141463, in the MACROD2 gene and autism spectrum disorder. American Journal of Medical Genetics. Part B, Neuropsychiatric Genetics, 156B(6), 633–639. Hirschhorn, J. N., & Gajdos, Z. K. (2011). Genome-wide association studies: Results from the first few years and potential implications for clinical medicine. Annual Review of Medicine, 62, 11–24. Jackson, P. B., Boccuto, L., Skinner, C., Collins, J. S., Neri, G., Gurrieri, F., et al. (2009). Further evidence that the rs1858830 C variant in the promoter region of the MET gene is associated with autistic disorder. Autism Research, 2(4), 232–236. Jorde, L. B., Hasstedt, S. J., Ritvo, E. R., Mason-Brothers, A., Freeman, B. J., Pingree, C., et al. (1991). Complex segregation analysis of autism. American Journal of Human Genetics, 49(5), 932–938.
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3040 O’Roak, B. J., Derziotis, P., Lee, C., Vives, L., Schwartz, J. J., Girirajan, S., et al. (2011). Exome sequencing in sporadic autism spectrum disorders identifies severe de novo mutations. Nature Genetics, 43(6), 585–589. Pagnamenta, A. T., Bacchelli, E., de Jonge, M. V., Mirza, G., Scerri, T. S., Minopoli, F., et al. (2010). Characterization of a family with rare deletions in CNTNAP5 and DOCK4 suggests novel risk loci for autism and dyslexia. Biological Psychiatry, 68(4), 320–328. Pagnamenta, A. T., Holt, R., Yusuf, M., Pinto, D., Wing, K., Betancur, C., et al. (2011). A family with autism and rare copy number variants disrupting the Duchenne/ Becker muscular dystrophy gene DMD and TRPM3. Journal of Neurodevelopmental Disorders, 3(2), 124–131. Pinto, D., Pagnamenta, A. T., Klei, L., Anney, R., Merico, D., Regan, R., et al. (2010). Functional impact of global rare copy number variation in autism spectrum disorders. Nature, 466(7304), 368–372. Risch, N., Spiker, D., Lotspeich, L., Nouri, N., Hinds, D., Hallmayer, J., et al. (1999). A genomic screen of autism: Evidence for a multilocus etiology. American Journal of Human Genetics, 65(2), 493–507. Rotter, J. I., & Landaw, E. M. (1984). Measuring the genetic contribution of a single locus to a multilocus disease. Clinical Genetics, 26(6), 529–542. Saade, S., Cazier, J. B., Ghassibe Sabbagh, M., Youhanna, S., Badro, D. A., Kamatani, Y., et al. (2011). Large scale association analysis identifies three susceptibility loci for coronary artery disease. PLoS One, 6(12), e29427. Sanders, S. J., Ercan-Sencicek, A. G., Hus, V., Luo, R., Murtha, M. T., Moreno-De-Luca, D., et al. (2011). Multiple recurrent de novo CNVs, including duplications of the 7q11.23 Williams syndrome region, are strongly associated with autism. Neuron, 70(5), 863–885. Smalley, S. L., Asarnow, R. F., & Spence, M. A. (1988). Autism and genetics: A decade of research. Archives of General Psychiatry, 45(10), 953–961. Sousa, I., Clark, T. G., Toma, C., Kobayashi, K., Choma, M., Holt, R., et al. (2009). MET and autism susceptibility: Family and case-control studies. European Journal of Human Genetics, 17(6), 749–758. Szatmari, P., Jones, M. B., Zwaigenbaum, L., & MacLean, J. E. (1998). Genetics of autism: Overview and new directions. Journal of Autism and Developmental Disorders, 28(5), 351–368. Wang, K., Zhang, H., Ma, D., Bucan, M., Glessner, J. T., Abrahams, B. S., et al. (2009). Common genetic variants on 5p14.1 associate with autism spectrum disorders. Nature, 459(7246), 528–533. Weiss, L. A., Arking, D. E., Daly, M. J., Chakravarti, A., & Gene Discovery Project of Johns Hopkins & the Autism Consortium. (2009). A genome-wide linkage and association scan reveals novel loci for autism. Nature, 461(7265), 802–808.
Multiple Complex (or Multiplex) Developmental Disorder
Multiple Complex (or Multiplex) Developmental Disorder Jan Rutger Van der Gaag Department of Psychiatry and Karakter University Center for Child and Adolescent Psychiatry, Radboud University Medical Centre, Utrecht, Netherlands Stradina University of Riga, Riga, Latvia
Definition The greatly missed Cohen et al. (1986) thought we should try and specify subgroups within the so-called not otherwise specified areas within the broader category of pervasive developmental disorders. From cluster analyses of a big group of individuals with atypical development, within the area on the fringe of autistic disorders, two categories emerged: Asperger’s disorder (that was added to DSM IV 1994) and the group so-called multiplex developmental disorders (later renamed multiple complex developmental disorder to avoid confusion with MDD – “major depressive” disorder). In this entry, this “heuristic” category is described.
Historical Background Along with defined categories from the Diagnostic and Statistical Manual of Mental Disorders, 4th edition, text revision (DSM-IV-TR) includes so-called Not Otherwise Specified (NOS) categories meant for lesser variants or borderline conditions to the clearly defined cases. The NOS subcategory for pervasive developmental disorder (PDD-NOS) was introduced in DSM-III-R (1986). Cohen et al. (1986) were concerned that having only two categories for autism in DSM-III-R would be too few to provide a more fine-tuned description of subgroups within what is now commonly named the autistic spectrum. These subgroups had been identified by
Multiple Complex (or Multiplex) Developmental Disorder
on-cluster analyses on large groups of children with “atypical” behavior (Prior et al. 1975; Dahl et al. 1986). Cohen et al. suggested that two new categories should be introduced in DSM-IV. The first one would be Asperger’s disorder (Asperger 1944; Wing 1981), referring to individuals with developmental problems in the areas of social reciprocity, communication, and rigid and restricted patterns of behavior. These individuals differed from the classical Kanner autism in that they developed language at typical stages (yet not the adequate pragmatics), and they had motor clumsiness and intellectual preoccupations more than motor stereotypies. This category was finally included in DSM-IV, but the criteria did not include many of the features Asperger actually described (Miller and Ozonoff 1997). The second proposal (Cohen et al. 1986, 1991) on multiple complex developmental disorders (MCDD) was not included in DSM-IV. It was a heuristic proposal aimed at promoting research on a category well known in clinical practice (Green and Jones 1998) and clearly emerging as a distinct cluster in the analysis of the children with atypical developmental (Dahl et al. 1986). These children had previously been described under different diagnostic labels, including “borderline children (Bemporad et al. 1982; Pine 1974; Vela et al. 1983), schizoid personality in childhood (Wolff 2003), and schizotypal children (Nagy and Szatmari 1986). Ironically, it had been included in DSM-III (American Psychiatric Association [APA] 1980) with the label “Childhood-Onset Pervasive Developmental Disorder” but, for reasons that are not clear, was not continued into DSM-III-R and thus not considered in DSM-IV. All these labels recognized children who could be placed “in the borderlands of autism.” They share with more classically autistic children a lack of social sensitivity and empathy, but yet differ importantly from autistic children. Where children with “classic Kanner autism” lack imagination, these atypical children tend to get carried away by a far too-vivid imagination that blurs their reality testing.
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Current Knowledge The defining criteria for MCDD include (1) consistently impaired social behavior and sensitivity, (2) impaired regulation of affective state, and (3) impaired cognitive processing “thinking disorder.” The discriminative potential of these criteria in categorizing these children reaches sensitivity and specificity levels comparable to those for autistic disorder (Buitelaar and van der Gaag 1998) (Table 1). The face validity of the concept was demonstrated in a series of independent studies (Towbin et al. 1993; Van der Gaag et al. 1995). The predictive validity is high (Van der Gaag 1993). There is longitudinal persistence of symptoms in the area of development of social reciprocity and thinking disorders. The extreme problems in the regulation of affective states are less prominent in adolescents and adults. There is a marked shift toward symptoms from the schizophrenia spectrum in adults, with up to 17% of the cases meeting criteria of schizophrenia after one or several psychotic episodes and over 60% Multiple Complex (or Multiplex) Developmental Disorder, Table 1 Criteria for multiple complex developmental disorder (Cohen et al. 1994 – van der Gaag et al. 2005) 1. Impaired regulation of states of mind and anxieties A. Unusual or peculiar fears and phobias or frequent idiosyncratic or bizarre anxiety reactions B. Recurrent panic episodes or flooding with anxiety C. Episodes of behavioral disorganization punctuated by markedly immature, primitive, or violent behavior 2. Impaired social behavior A. Social disinterest, detachment, avoidance, or withdrawal B. Markedly disturbed and/or ambivalent attachments 3. The presence of thought disorder A. Irrationality, magical thinking, sudden intrusion on normal thought processes, bizarre ideas, neologisms, or repetitions of nonsense words B. Perplexity and easy confusability C. Overvalued ideas, including fantasies of omnipotence, paranoid preoccupations, overengagement with fantasy figures, and referential ideation Note. A total of five (or more) items from (1), (2), and (3), with at least one item from (1), one item from (2), and one item from (3) (Buitelaar and van der Gaag 1998)
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meeting the criteria for schizoid or schizotypal personality disorder (Van Engeland and Van der Gaag 1994). Other differences come from studies on the stress-regulation characteristics in the MCDD group (Jansen et al. 2003; Kemner et al. 1999) that show marked differences in these areas between individuals with autistic disorder and individuals with MCDD who, on these dimensions, are more similar to adults within the schizophrenia spectrum. The third characteristic of MCDD children includes “disordered thinking,” which is commonly thought to be specific to schizophrenia.
Future Directions In DSM V, the search for specific subgroups will be dropped. But research should be pursued along the dimensions of impaired regulation of affective states and thought disorder in individuals with autism spectrum disorders. This research will be important for a well-suited guidance and treatment program and to explore the boundaries between autism spectrum and schizophrenia spectrum disorders as apparently at least a portion of high functioning individuals with autism spectrum disorders are at risk of having psychotic episodes in their adult life and for developing schizophrenia and/or addictive behavior.
See Also ▶ Asperger’s Disorder ▶ Psychosis ▶ Schizophrenia
References and Reading Buitelaar, J. K., & van der Gaag, R. J. (1998). Diagnostic rules for children with PDD-NOS and multiple complex developmental disorder. Journal of Child Psychology and Psychiatry, 39(6), 911–919. Cohen, D. J., Volkmar, F. R., & Paul, R. (1986). Issues in the classification of pervasive developmental disorders: History and current status of nosology. Journal of the American Academy of Child & Adolescent Psychiatry, 25(2), 158–161.
Multiple Complex (or Multiplex) Developmental Disorder Dahl, E. K., Cohen, D. J., & Provence, S. (1986). Clinical and multivariate approaches to the nosology of pervasive developmental disorders. Journal of the American Academy of Child & Adolescent Psychiatry, 25(2), 170–180. de Bruin, E. I., de Nijs, P. F., Verheij, F., Hartman, C. A., & Ferdinand, R. F. (2007). Multiple complex developmental disorder delineated from PDD-NOS. Journal of Autism and Developmental Disorders, 37(6), 1181–1191. Herba, C. M., de Bruin, E., Althaus, M., Verheij, F., & Ferdinand, R. F. (2008). Face and emotion recognition in MCDD versus PDD-NOS. Journal of Autism and Developmental Disorders, 38(4), 706–718. Jansen, L. M., Gispen-de Wied, C. C., Van der Gaag, R. J., ten Hove, F., Willemsen-Swinkels, S. W., Harteveld, E., et al. (2000). Unresponsiveness to psychosocial stress in a subgroup of autistic-like children, multiple complex developmental disorder. Psychoneuroendocrinology, 25(8), 753–764. Jansen, L. M., Gispen-de Wied, C. C., van der Gaag, R. J., & van Engeland, H. (2003). Differentiation between autism and multiple complex developmental disorder in response to psychosocial stress. Neuropsychopharmacology, 28(3), 582–590. Klin, A., Mayes, L. C., Volkmar, F. R., & Cohen, D. J. (1995). Multiplex developmental disorder. Journal of Developmental & Behavioral Pediatrics, 16(Suppl. 3), S7–S11. Lahuis, B. E., Durston, S., Nederveen, H., Zeegers, M., Palmen, S. J., & Van Engeland, H. (2008). MRI-based morphometry in children with multiple complex developmental disorder, a phenotypically defined subtype of pervasive developmental disorder not otherwise specified. Psychological Medicine, 38(9), 1361–1367. Lahuis, B. E., Van Engeland, H., Cahn, W., Caspers, E., Van der Geest, J. N., Van der Gaag, R. J., et al. (2009). Smooth pursuit eye movement (SPEM) in patients with multiple complex developmental disorder (MCDD), a subtype of the pervasive developmental disorder. World Journal of Biological Psychiatry, 10(4) Pt. 3), 905–912. Lincoln, A. J., Bloom, D., Katz, M., & Boksenbaum, N. (1998). Neuropsychological and neurophysiological indices of auditory processing impairment in children with multiple complex developmental disorder. Journal of the American Academy of Child & Adolescent Psychiatry, 37(1), 100–112. Sprong, M., Becker, H. E., Schothorst, P. F., Swaab, H., Ziermans, T. B., Dingemans, P. M., et al. (2008). Pathways to psychosis: A comparison of the pervasive developmental disorder subtype multiple complex developmental disorder and the “at risk mental state”. Schizophrenia Research, 99(1–3), 38–47. Towbin, K. E., Dykens, E. M., Pearson, G. S., & Cohen, D. J. (1993). Conceptualizing “borderline syndrome of childhood” and “childhood schizophrenia” as a developmental disorder. Journal of the American Academy of Child & Adolescent Psychiatry, 32(4), 775–782. Van der Gaag, R. J., Buitelaar, J., Van den Ban, E., Bezemer, M., Njio, L., & Van Engeland, H. (1995).
Multiple Exemplar Training A controlled multivariate chart review of multiple complex developmental disorder. Journal of the American Academy of Child & Adolescent Psychiatry, 34(8), 1096–1106. van der Gaag, R. J., Caplan, R., van Engeland, H., Loman, F., & Buitelaar, J. K. (2005). A controlled study of formal thought disorder in children with autism and multiple complex developmental disorders. Journal of Child and Adolescent Psychopharmacology, 15(3), 465–476.
Multiple Exemplar Instruction (MEI) ▶ Multiple Exemplar Training
Multiple Exemplar Training Patricio Erhard1, Rob El Fattal2 and Nissa Van Etten2 1 University of Texas at Austin, Austin, TX, USA 2 Cultivate Behavioral Health and Education, Bee Cave, TX, USA
Synonyms Multiple exemplar instruction (MEI); General case teaching, general case instruction
Definition Multiple exemplar training (MET), which is also known as multiple exemplar instruction or general case teaching, is a term in applied behavior analysis (ABA) for the use of related stimuli samples during instruction to increase a person’s ability to respond to novel, untrained stimuli (Greer et al. 2005). For example, teaching a child to say “lime” when shown various examples of limes (i.e., photographs, illustrations, physical objects) increases the likelihood that the child will be able to say “lime” when he/she is later shown an untrained image of a lime that possesses some characteristics related to previously taught exemplars. The ability
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for an individual to deduce that two similar stimuli are related or the same, such as labeling untrained images of limes, is crucial to engage in generative behavior (the ability to “generate” novel behaviors based on previously learned stimuli). It’s worth noting that learning both physical and arbitrary relations may contribute to generative behaviors. That is, like learning to label a “lime,” one can also learn to label a lime with arbitrary labels, such that when asked, “what shape is this?” or “what color is this fruit?”, one answers “round/green.” Successful generative responses may occur when interpretation of context-specific stimuli occurs, in which many cases involve a combination of both physical and arbitrary conditions such that when one is asked, “give me a round, green, fruit,” one can deduce that the speaker is likely asking for a lime (Holth 2017). The term “multiple exemplar training” was first coined by Hughes and Rusch (1989) in a study that taught people with intellectual disabilities to solve work-related problems using MET and selfinstruction (Holth 2017). Although the term MET did not appear until 1989, there are multiple records in which MET teaching strategies were implemented before the term was conceived. For instance, a study in 1967 noted an increase in correct responding to other, untrained imitations after teaching and reinforcing various models of imitation (Baer et al. 1967). The use of MET to teach children with autism spectrum disorder (ASD) first emerged in a study that taught four high school students with ASD to initiate and sustain social interactions with neurotypical developing peers through a “multiple exemplar strategy” (Breen et al. 1985). People with autism often have difficulty engaging in generative behaviors which tends to result in limited responses for a wide variety of situations (Schnell et al. 2018). Because the use of generative behaviors is imperative for adapting to an environment with varying stimuli, and given that people with ASD often have difficulty naturally learning to respond to varying stimuli, MET is often used as a teaching strategy in ABA to assist people with ASD learn to emit generative behaviors and promote the maintenance of skills (Greer et al. 2005). MET studies have noted the emergence of two MET teaching variations, serial MET (S-MET), in
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Multiple Exemplar Video Modeling and Unscripted Social Communication
which exemplars are introduced one at a time, and concurrent MET (C-MET), in which a group of exemplars are all introduced at the same time (Schnell et al. 2018). Although MET studies have successfully taught a variety of skills and related generative responses, such as expressive labeling, imitation, listener responding, behavioral variability, matching, social behavior, and responding to wh-questions (Holth 2017; Schnell et al. 2018), contrasting findings exist between studies regarding which type of MET strategy is more efficient (Schnell et al. 2018). A study by Schnell, Vladescu, Kodak, and Nottingham compared the mean training time, total training time, exposure to mastery, and training sessions to mastery between S-MET and C-MET when teaching expressive labeling and observed that S-MET had the lowest mean training time and total training time, which differed from previous studies that indicated C-MET is a more efficient strategy (Schnell et al. 2018). Be that as it may, more research should be conducted to replicate the efficacy of MET teaching variations since there are currently two studies that have examined the efficacy of MET regarding expressive labeling tasks. Researchers also stressed the importance of expanding MET efficacy research for teaching other tasks, such as comparing efficacy of S-MET and C-MET when applied to imitation tasks, listener responding tasks, and other types of behaviors. In addition, future studies should focus on evaluating whether MET teaching strategies are more effective when individualized to the students. For example, Schnell et al. (2018) noted that, despite S-MET had the lowest mean training time, efficiency varied between students and across teaching strategies. In other words, all the participants were able to master exemplars the fastest with the use of S-MET, but each participant’s performance varied in both time and number of exposures to mastery.
References and Reading Baer, D. M., Peterson, R. F., & Sherman, J. A. (1967). The development of imitation by reinforcing behavioral similarity to a model. Journal of the Experimental Analysis of Behavior, 10, 405–416. https://doi.org/10. 1901/jeab.1967.10-405. Breen, C., Haring, T., Pitts-Conway, V., & Gaylord-Ross, R. (1985). The training and generalization of social interaction during breaktime at two job sites in the natural environment. The Association for Persons with Severe Handicaps, 10, 41–50. https://doi.org/10. 1177/154079698501000105. Greer, R. D., Yaun, L., & Gautreaux, G. (2005). Novel dictation and Intraverbal responses as a function of a multiple exemplar instructional history. The Analysis of Verbal Behavior, 21, 99–116. https://doi.org/10.1007/ bf03393012. Holth, P. (2017). Multiple exemplar training: Some strengths and limitations. Behavior Analyst, 40, 225–241. https://doi.org/10.1007/s40614-017-0083-z. Hughes, C., & Rusch, F. R. (1989). Teaching supported employees with severe mental retardation to solve problems. Journal of Applied Behavior Analysis, 22, 365–372. https://doi.org/10.1901/jaba.1989.22-365. Schnell, L. K., Vladescu, J. C., Kodak, T., & Nottingham, C. L. (2018). Comparing procedures on the acquisition and generalization of tacts for children with autism spectrum disorder. Journal of Applied Behavior Analysis, 51, 769–783. https://doi.org/10.1002/jaba.480.
Multiple Exemplar Video Modeling and Unscripted Social Communication Ana D. Dueñas1, Joshua B. Plavnick2 and Savana M. Y. Bak3 1 Education and Human Services, Lehigh University, Bethlehem, PA, USA 2 Michigan State University, East Lansing, MI, USA 3 University of Minnesota, Twin Cities, Educational Psychology, Minneapolis, MN, USA
Definition See Also ▶ Applied Behavior Analysis (ABA) ▶ Generalization and Maintenance ▶ Stimulus Control
Multiple exemplar video modeling involves the creation and presentation of several videos depicting multiple response options and varied stimuli (e.g., setting, materials) of desired
Multiple Exemplar Video Modeling and Unscripted Social Communication
behaviors to promote response and stimulus generalization.
Historical Background Based on social learning theory (Bandura 1977), video modeling (VM) is a highly effective intervention used to teach new behaviors through imitation of a model presented via video technology. VM is considered an evidence-based practice by the National Autism Center (NAC 2015) and the National Professional Development Center on Autism Spectrum Disorder (NPDC; Wong et al. 2015). Video modeling researchers have reported the following inherent factors in VM that may produce positive feasibility and acceptability: (a) although recording and editing videos can take time, once these materials are made, their repeated use is infinite; (b) the increasing availability and usability of digital video equipment increase their probable use (Ayres and Langone 2005); (c) videos are often presented in socially acceptable technologies such as iPods and iPads that do not limit the individual’s opportunities to interact with peers (Shane et al. 2012); and (d) researchers have also reported that VM is cost- and time-effective (Charlop-Christy et al. 2000). Though none of these factors have been empirically evaluated, they may influence the likelihood that VM interventions are delivered in natural settings (e.g., school, home, and community), which may contribute to the long-term effectiveness of VM. Researchers and practitioners have evaluated and implemented VM to teach scripted play verbalizations to children with autism spectrum disorder (D’Ateno et al. 2003; MacDonald et al. 2005; MacManus et al. 2015) and have involved typically developing peers as part of the intervention (Joint Video Modeling; Dueñas et al. 2019; MacDonald et al. 2009; Maione and Mirenda 2006). Despite positive social communication outcomes for children with ASD reported in the extant VM literature, little is known about the generality of social communication repertoires acquired following brief periods of VM exposure, as few studies measure generalization effects
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(Bellini and Akullian 2007; Goldstein et al. 2014; Sutton et al. 2018). This is an important direction for future research as the aim of social communication interventions for children with ASD is the generalization of acquired social communication behaviors across people, settings, and contexts that have social and applied value. That is, the degree to which a skill generalizes can have crucial implications over the utility of the skill for a child with ASD. For individuals with ASD who struggle to generalize acquired social communication, it is necessary to program generalization in instructional or treatment arrangements. There are numerous recommended strategies for promoting generalization, primarily stemming from reviews of behavior-analytic literature (Haring 1988; Stokes and Baer 1977; Stokes and Osnes 1989). One of these strategies is to program multiple exemplars. Programming various exemplars involves providing various examples for responding until untrained responses emerge.
Current Knowledge Preliminary research in VM has shown that varying the social communication behaviors modeled in videos during social interactions may evoke several potential responses when presented with similar contexts (see Dueñas et al. 2019; MacManus et al. 2015; Maione and Mirenda 2006; Plavnick and Dueñas 2018). Programming multiple exemplars is an instructional strategy that may promote the acquisition of repertoires as opposed to rote responses (Stokes and Baer 1977). This focus may help children with ASD to demonstrate more natural social communication across varied contexts (Kasari et al. 2016). Novel social communication skills are a desired focus of social interactions between children with ASD and their peers, as predictability and rote responding are less typical among children who are not diagnosed with ASD. Video modeling is an intervention that can be used for programming several generalization strategies that promote response and stimulus generalization. For example, recording several
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videos to model multiple response options for promoting response generalization (i.e., multiple exemplar video modeling). Dueñas et al. (2019) demonstrated that multiple exemplar video modeling was a promising intervention for teaching scripted and unscripted social communication during pretend play with typically developing peers in an inclusive early childhood setting. This multicomponent intervention combined video modeling with peer-mediated intervention. Video models were presented to both children prior to play sessions as a way to prompt children to engage in verbalizations during pretend play interactions. The procedure involved an initial invitation from the typically developing peer to invite the child with ASD to play. The children were seated at a small table side by side according to the roles they were imitating in the video. The iPad was presented on the table directly in the middle of both children, and they were exposed to the audio in the video using headphones with a splitter. The video was shown twice, and the researcher ensured their bodies were positioned in front of the screen and their faces were directed to the screen. Three video exemplars were rotated across sessions to ensure equal number of presentations for each video exemplar. The children were instructed to “Do and say what you saw in the video” and were given 3 min to play with the toys that were set up at the table. For a sample of video scripts, see Table 1. Although children with ASD increased scripted and unscripted verbalizations toward peers, participants with ASD in the study all responded differently to the intervention. Dueñas et al. (2019) hypothesized that as children with ASD began to deviate from scripts, typically developing peers struggled to respond consistently. Researchers can also record various people to model social behaviors (e.g., peers, teachers, and parents), using portable electronic devices that optimize implementation across settings (e.g., home, community, classrooms). For example, in Plavnick and Dueñas (2018), researchers evaluated the effects of video-based group instruction on spontaneous social questions and comments of four adolescents with ASD. The researchers created three variations of video clips for five
Multiple Exemplar Video Modeling and Unscripted Social Communication, Table 1 Sample video script Objects Mom bunny Sister bunny Mom bunny Sister bunny Mom bunny Sister bunny Mom bunny Sister bunny
Scripted play actions Holds bunny and jumps up Holds bunny and moves downstairs Moves bunny to table Sits bunny on chair Has bunny put two plates on table Has bunny knock down table Has bunny set table Moves bunny to chair
Scripted verbalizations “Dinner’s ready” “I’m coming” “Made your favorite” “What is it?” “It’s pizza” “No, my pizza” “I got it” “Thanks, mom”
different social behaviors. The videos varied in terms of models, materials used, and the vocal statements modeled in the videos. All participants demonstrated an increase in spontaneous social questions and comments.
Future Directions Although preliminary research shows promise in promoting generalized responding, future research is needed to determine whether a relationship exists between multiple exemplar video modeling and novel social communication. That is, precise procedures that lead to novel social communication continue to be unknown. Specifically, whether typically developing peer involvement, multiple exemplar videos, or both led to children with ASD’s deviation from scripts. In addition, when designing multiple exemplar video modeling procedures, several questions remain, for example, What is the ideal number of video exemplars?, What is the amount of video exposure needed and is this skills- and/or student-dependent?, and Are there prerequisite or language skills of children with ASD that make them ideal candidates for this intervention?.
Multiple Phenotypes Associated with Disruption of a Single Gene
See Also ▶ Communication Interventions ▶ Generalization and Maintenance ▶ Video Modeling/Video Self-Modeling
References and Reading Ayres, K. M., & Langone, J. (2005). Intervention and instruction with video for students with autism: A review of the literature. Education and Training in Developmental Disabilities, 40, 183–196. Bandura, A. (1977). Social learning theory. Englewood: Prentice Hall. Bellini, S., & Akullian, J. (2007). A meta-analysis of video modeling and video self-modeling interventions for children and adolescents with autism spectrum disorders. Exceptional Children, 73, 264–287. https://doi. org/10.1177/001440290707300301. Bellini, S., Peters, J. K., Benner, L., & Hopf, A. (2007). A meta-analysis of school-based social skills interventions for children with autism spectrum disorders. Remedial and Special Education, 28, 153–162. Charlop-Christy, M. H., Le, L., & Freeman, K. A. (2000). A comparison of video modeling with in vivo modeling for teaching children with autism. Journal of Autism and Developmental Disorders, 30, 537–552. https:// doi.org/10.1023/A:1005635326276 D’Ateno, P., Mangiapanello, K., & Taylor, B. (2003). Using video modeling to teach complex play sequences to a preschooler with autism. Journal of Positive Behavior Interventions, 5, 5–11. https://doi.org/10. 1177/10983007030050010801. Dueñas, A. D., Plavnick, J. B., & Bak, M. Y. S. (2019). Effects of joint video modeling on unscripted play behavior of children with autism spectrum disorder. Journal of Autism and Developmental Disorders, 49, 236–247. https://doi.org/10.1007/s10803-018-3719-2. Goldstein, H., Lackey, K. C., & Schneider, M. J. B. (2014). A new framework for systematic reviews: Application to social skills interventions for preschoolers with autism. Exceptional Children, 80, 262–286. https:// doi.org/10.1177/0014402914522423. Haring, N. G. (1988). Generalization for students with severe handicaps: Strategies and solutions. Seattle: University of Washington Press. Kasari, C., Dean, M., Kretzmann, M., Shih, W., Orlich, F., Whitney, R., Landa, R. . . . King, B. (2016). Children with autism spectrum disorder and social skills groups at school: A randomized trial comparing intervention approach and peer composition. The Journal of Child Psychology and Psychiatry, 57, 171–179. https://doi. org/10.1111/jcpp.12460. MacDonald, R., Clark, M., Garrigan, E., & Vangala, M. (2005). Using video modeling to teach pretend play to children with autism. Behavioral Interventions, 20, 225–238. https://doi.org/10.1002/bin.197.
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MacDonald, R., Sacramone, S., Mansfield, R., Wiltz, K., & Ahearn, W. (2009). Using video modeling to teach reciprocal pretend play to children with autism. Journal of Applied Behavior Analysis, 42, 43–55. https://doi. org/10.1901/jaba.2009.42-43. MacManus, C., MacDonald, R. P. F., & Ahearn, W. H. (2015). Teaching and generalizing pretend play in children with autism using video modeling and matrix training. Behavioral Interventions, 30, 191–218. https://doi.org/10.1002/bin.1406. Maione, L., & Mirenda, P. (2006). Effects of video modeling and video feedback on peer-directed social language skills of a child with autism. Journal of Positive Behavior Interventions, 8, 106–118. https:// doi.org/10.1177/10983007060080020201. National Autism Center. (2015). National standards project findings and conclusions. Randolph: National Autism Center. Plavnick, J. B., & Dueñas, A. D. (2018). Brief report: Effects of video-based group instruction on spontaneous social interaction of adolescents with autism spectrum disorders. Journal of Autism and Developmental Disorders, 48, 2231–2236. https://doi.org/10.1007/ s10803-018-3481-5. Shane, H. C., Laubscher, E. H., Schlosser, R. W., Flynn, S., Sorce, J. F., & Abramson, J. (2012). Applying technology to visually support language and communication in individuals with autism spectrum disorders. Journal of Autism and Developmental Disorders, 42, 1228–1235. https://doi.org/10.1007/s10803-011-1304-z. Stokes, T. F., & Baer, D. M. (1977). An implicit technology of generalization. Journal of Applied Behavioral Analysis, 10, 349–367. https://doi.org/10.1901/jaba.1977. 10-349. Stokes, T. F., & Osnes, P. G. (1989). An operant pursuit of generalization. Behavior Therapy, 20, 337–355. Sutton, B., Webster, A. A., & Westerveld, M. F. (2018). A systematic review of school-based interventions targeting social communication behaviors for students with autism. Autism, 1–13. https://doi.org/10.1177/ 1362361317753564. Wong, C., Odom, S. L., Hume, K. A., Cox. A. W., Fettig, A., Kucharczyk, S., Brock, M. E. . . . Schultz, T. R. (2015). Evidence-based practices for children, youth, and young adults with autism spectrum disorder: A comprehensive review. Journal of Autism and Developmental Disorders, 45, 1951–1966. https://doi.org/ 10.1007/s10803-014-2351-z.
Multiple Phenotypes Associated with Disruption of a Single Gene ▶ Pleiotropy
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Multiple Regression
Stephan Sanders Child Study Center, Yale University, New Haven, CT, USA
Simons Simplex Collection (SSC) with exclusively simplex families. Categorizing families as being multiplex or simplex can be difficult. For example, the presence of only a single affected individual among siblings may not truly reflect the underlying genetic architecture of that family. For example, an unaffected sibling could simply not have inherited a transmitted risk factor that is still present in the parents (and might be transmitted to the next sibling to be born). Alternatively, it is common to observe individuals carrying known genetic risk variants for ASD without showing clinical evidence of autism. The objective of making the distinction is to determine whether shared or unique risk factors are most likely to account for disease. Since ASD is likely to be the combination of many genetic and environmental risk factors, it is probable that many individuals with ASD have a combination of both shared and unique risk factors. The distinction between multiplex and simplex families is therefore best considered as a continuous spectrum that enriches for shared or unique risk factors at each respective extreme. The distinction between multiplex and simplex families also has bearing on the risk of a further child having ASD. This risk is considerably higher in families with a previous child with ASD, especially if more than one child is affected (Ozonoff et al. 2011).
Definition
See Also
“Multiplex” refers to families in which multiple individuals are affected by a specific disease, while “simplex” refers to families in which only a single individual has a specific disease. This distinction is important when designing a study to identify risk factors for ASD, as multiplex families are thought to be more likely to have shared risk factors (i.e., inherited risk), while simplex families are more likely to show de novo genetic risks. Accordingly major collections of ASD samples for genetic analysis are often designed to enrich for specific types of families, such as the Autism Genetic Research Exchange (AGRE) with many multiplex families and the
▶ American Board of Genetic Counseling ▶ Common Disease-Common Variant Hypothesis ▶ Common Disease-Rare Variant Hypothesis ▶ DNA ▶ Genetics
Multiple Regression ▶ Regression Analysis
Multiple Stimulus with Replacement (MSW) Preference Assessment ▶ Preference and Reinforcer Assessment
Multiple Stimulus Without Replacement (MWSO) Preference Assessment ▶ Preference and Reinforcer Assessment
Multiplex-Simplex Comparisons
References and Reading Ozonoff, S., Young, G. S., Carter, A., Messinger, D., Yirmiya, N., Zwaigenbaum, L., et al. (2011). Recurrence risk for autism spectrum disorders: A Baby Siblings Research Consortium study. Pediatrics, 128(3), e488–e495 .peds.2010-2825[pii]. https://doi.org/10. 1542/peds.2010-2825.
Music Therapy
Multi-skilling ▶ Role Release
MUSAD ▶ Music-Based Scale for Autism Diagnostics (MUSAD) Assessing Adults with Intellectual Disability
Music Therapy Jinah Kim Department of Creative Arts Therapy, College of Cultural Convergence, Jeonju University, Jeonju, Jeollabukdo, Republic of Korea
Definition Music therapy uses musical activities such as singing, playing instruments, improvisation, and music listening to promote interaction between the therapist and the client and to meet the client’s therapeutic needs. American Music Therapy Association (2005) defined music therapy as “the clinical and evidence-based use of music interventions to accomplish individualized goals within a therapeutic relationship by a credentialed professional who has completed an approved music therapy program.” There are many different approaches in music therapy. In a broad sense, music therapy methods can be divided into either active or receptive music therapy, depending on whether the client is actively involved in live music-making processes with the therapist or the client is involved in a preselected music listening using either a recorded music or a live performance. In active, especially the improvisational music therapy intervention, interactive use of live and spontaneous music-making process between the therapist and the client, the client is encouraged and facilitated.
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Therefore, musical-therapeutic context evolves through musical interaction between the therapist and the client here and now and moment by moment. In receptive music therapy intervention, music is selected or precomposed based on the preference and needs of the client to accomplish certain therapeutic outcomes for individuals with autism spectrum disorders (ASD). Research evidences have indicated that both active and receptive music therapy methods are found to be effective at facilitating and improving communication and social interaction skills and to modify certain behaviors of individuals with ASD (Accordino et al. 2007; Geretsegger et al. 2014; Simpson and Keen 2011). In clinical practice, music therapists tend to use the method that serves their client’s individual needs, sometimes resulting in eclectic approaches.
Historical Background There have been various approaches in music therapy, depending on the different philosophies and principles of the founder of each music therapy approach in different clinical settings. Early music therapy pioneers such as Juliette Alvin, Paul Nordoff, and Clive Robbins worked with children with special needs including autism from the 1950s. These early pioneers used live interactive music (improvisation) to engage the children with ASD and to address their difficulties in communication and social interaction (Alvin 1978; Nordoff and Robbins 1971). Now, there are many music therapists working within the improvisational model of music therapy worldwide, especially in the UK and Europe. Following are four internationally known improvisational models of music therapy: 1. Free improvisation therapy founded and developed by Juliette Alvin 2. Creative music therapy founded and developed by Paul Nordoff and Clive Robbins 3. Analytically oriented music therapy founded and developed by Mary Priestley 4. Interactive music therapy founded and developed by Amelia Oldfield
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All four approaches above have been mainly developed in the UK. Another significant approach in music therapy has been behavioral modification model of music therapy that was chiefly developed in the USA and is still the primary music therapy intervention in the USA. The influence of behavioral modification model of music therapy is also prevalent worldwide especially for children with special needs including autism. Simpson and Keen (2011) has identified composed songs and improvisation as the predominant music therapy techniques used for children with ASD. Research evidence indicated that behavioral music therapy and improvisational music therapy all work well with individuals with ASD (Geretsegger et al. 2014; Kaplan and Steele 2005; Simpson and Keen 2011).
Rationale or Underlying Theory Music is often described as a universal or emotive language. Music therapists have attributed this mainly due to its power to reach and affect human emotions. No matter how disabled a person can be, anyone can respond to music, and musical responses are innate ability deeply engrained in human nature (Alvin 1978; Kim et al. 2009; Nordoff and Robbins 1971). Moreover, musical experiences are often intrinsically joyful and interesting. Music often inspires, motivates, and brings people together even when they are total strangers. Kim and her colleagues (2009) claim: “Music acts as an essentially emotional, relational and motivational medium when, in music therapy, it is purposefully created to engender ‘interpersonal relatedness’ by employing a well-measured systematic intervention.” Thaut (1987) found that children with autism engaged significantly more time with the music than agematched typically developing children. He also analyzed musical responsiveness of children with autism, and that of children without autism through music improvisation they played, and found no significant difference in musical responsiveness between them. The results indicated
Music Therapy
intact musical responsiveness and musical aptitude in children with ASD. Clinical implication of these findings suggests significant therapeutic potential in music that can be used in helping individuals with ASD in everyday life. Music can be an excellent medium for emotional communication and social interaction for individuals who cannot easily assimilate the conventional ways of human communication and interaction. Instead of verbal exchange, nonverbal aspects of music such as playing simple instruments, vocalizing, and listening to music can provide an alternative way of communicating and engaging the individuals with ASD and others without ASD. Music can be utilized to teach new skills (Buday 1995; Kern and Aldridge 2006), to help children with ASD perceive and recognize specific emotions (Katagiri 2009), and to modify children’s behaviors (Brownell 2002; Kern et al. 2007) in a certain direction that can be helpful and adaptable in everyday life. As children with ASD are reported to respond better to songs than spoken words, a music therapist might create original songs (Brownell 2002; Katagiri 2009; Kern and Aldridge 2006), or lyrics with familiar melody (Kern et al. 2007) about a specific behavior, and use these songs with children with ASD with specific therapeutic goals in mind including speech and language development.
Goals and Objectives Goals and objectives of music therapy intervention for individuals with ASD are usually established through music therapy assessment. Music therapists assess the individual’s needs, especially the domains of emotional, communicative, behavioral, and social responses and cognitive skills for individuals with ASD. Systematic review of music therapy literature has indicated primary goal areas in music therapy intervention: (1) to promote socialization, (2) to increase communication and language skills, and (3) to modify maladaptive behaviors (Accordino et al. 2007; Geretsegger et al. 2014; Simpson and Keen 2011). Once broad goals are set up, the therapist
Music Therapy
identifies specific objectives that are observable and measurable such as “when therapist sings a ‘hello song’ in the beginning of the session, child will look at the direction of therapist 30% of the time.” Objectives of music therapy will change in time as therapy progresses. As autism spectrum disorders are complex neurodevelopmental disorders showing variety and individuality across all ages and abilities, therapeutic objectives for individuals with ASD varies a lot from person to person and stage of each therapy process. Musical responses of the brain is not localized in one area, but known to be widespread. Therefore music has the strong potential to strengthen and optimize brain function that is known to be damaged and/or weak. In the improvisational music therapy, the main goal of the therapy with individuals with ASD is to establish, maintain, and develop interaction between the therapist and the client through active music-making processes and to deal with developmental and therapeutic issues that are presented by the client during each stage of therapy process.
Treatment Participants Music therapy has been used for preschool and school-age children, adolescents, and adults with wide range of different abilities and symptom severity. The most common participants are preschool and school-age children with ASD in music therapy (Geretsegger et al. 2014).
Treatment Procedures When referral is made to a music therapist, the music therapist assesses the individual’s needs, plans either individual or group music therapy sessions based on the needs of the individual, and implements and evaluates music therapy sessions where appropriate. Following are the typical music therapy procedures: 1. Referral: Referral to music therapy can be made by the parents, doctors, speech therapists, teachers, social workers, etc.
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2. Assessment: The music therapist assesses the individual’s needs in terms of his/her strengths and weaknesses through musical responses using various musical activities. 3. Goal setting: Treatment goals and objectives are developed based on the findings of assessment. Therefore, assessment will lead to setting up appropriate treatment plan for individuals with ASD. 4. Treatment implementation: Music therapy sessions can be one-on-one or in a group, which consist of musical activities that are designed to meet the individual’s needs and therapeutic goals. 5. Evaluation: Music therapy sessions are regularly evaluated by the music therapist and often within the interdisciplinary team. 6. Termination of music therapy: The duration of music therapy varies from person to person, depending on the needs of the individual. Short-term therapy typically consists of 10–12 sessions. Long-term therapy can be up to 2 or 3 years. For young children with ASD, music therapy sessions are typically more than 3 months. Children with ASD typically have once, twice, or thrice weekly music therapy sessions for about 20–50 min, depending on attention span and needs of the child. Music therapy is typically offered in an individual (one-to-one), group, and/or family-based (family members such as parents and other siblings) setting. Kaplan and Steele (2005) investigated 40 music therapy clients with ages ranging from 2 to 49 years with ASD in Cleveland, USA. They found individual music therapy as the most common session type, followed by partner, small group (3–5 subjects), large group (more than 6 subjects), etc.
Efficacy Information In the last few decades, there have been accumulating research evidences indicating the efficacy and the effectiveness of music therapy for individuals with ASD, with some promising results in
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the domains of social, communicative, and behavioral skills (Bieleninik et al. 2017a; Brownell 2002; Buday 1995; Geretsegger et al. 2014; Katagiri 2009; Kern and Aldridge 2006; Kim et al. 2008, 2009). Recently, there have been some systematic or narrative reviews on effects of music therapy for individuals with ASD. It was found that majority of published articles in music therapy for individuals with ASD were case studies with anecdotal evidences, and there have been limited numbers of well-designed empirical investigations typically with small samples (Accordino et al. 2007; Geretsegger et al. 2014; Simpson and Keen 2011). In the recently updated Cochrane meta-analysis of music therapy for ASD, Geretsegger and associates (2014) reported sufficient evidences for music therapy as effective intervention in improving core symptoms of ASD that is in the areas of social communication, social emotional reciprocity, and parent-child relationship. Effect sizes were moderate-to-large. Included ten studies (nine RCTs, 165 children aged between two to nine) of Cochrane review examined short- to medium-term effects of music therapy compared to either standard care (SC) or similar intervention without music. Seven out of ten studies were short-term trials (1–5 weeks) employing a highly structured therapist-directed approach largely using receptive techniques. Remaining three studies were medium-term trials (3–7 months) utilizing a child-centered and interactive approach using improvisation as means of communication between the therapist and the child. In contrast to the recent Cochrane findings, Bieleninik and associates (2017a) conducted a large-scale, well-designed, multicenter randomized clinical trial (364 children from nine countries) of improvisational music therapy (IMT) for young children with ASD (4–7 years-old), comparing IMT plus standard care (SC) with SC to determine effects of IMT on the core symptoms of ASD. They found no significant difference between IMT and SC on the reduction of core symptoms of ASD. They employed the ADOS (Autism Diagnostic Observation Schedule) social affect domain as primary outcome and SRS (Social Responsiveness Scale) as secondary
Music Therapy
outcome measures. The children in their study showed slight reduction of core symptoms over time in both conditions from the ADOS social affect domain. Although there were no significant differences in the primary outcome measure, post hoc exploratory analyses revealed that the children in IMT showed higher proportion of improvement in social communication at 5 months (95/182 [52%]) than the children in SC (76/182 [42%]). They also found significant effects in several SRS subscales. For example, music therapy was found to be more effective at improving social motivation than SC over 5 months. There could be different levels of explanation and debates on how to understand the contradictory results between the Cochrane review and the most recent RCT as these two types of studies are considered the most rigorous clinical evidence. Bieleninik et al. (2017b) conducted metaanalysis of 5771 children with ASD aged 7 months to 16.5 years to determine temporal stability of ASD diagnosis and core symptom severity over time. The meta-analysis of their study indicated core symptoms of ASD were markedly stable over time during childhood of these children. Bieleninik and associates (2017b) stated that clinical outcome studies should focus on “other areas, such as quality of life and adaptive functioning.” In Kaplan and Steele’s investigation (2005) on 40 music therapy clients with ages ranging from 2 to 49 years with ASD, 100% of parents and caregivers of these clients reported that these individuals with ASD generalized skills and responses obtained in music therapy to other environments.
Outcome Measurement Abovementioned clinical outcome studies used generalized (intervention outcome measured outside of music therapy setting) and nongeneralized (intervention outcome measured within music therapy) outcome measurements to examine effects of music therapy on people with ASD. Four studies (Bieleninik et al. 2017; Gattino et al. 2011; Kim et al. 2008; Thompson 2012) used standardized and published scales such as
Music Therapy
the ADOS/the SRS; the Childhood Autism Rating Scale (CARS); the Early Social Communication Scale (ESCS)/the Pervasive Developmental Disorder Behavior Inventory (PDDBI); and the Vineland Social-Emotional Early Childhood Scales (SEEC)/the SRS.. The ADOS and the CARS are primarily developed as diagnostic tool adapted to intervention studies whereas the ESCS, the PDDBI, the Vineland-EEC, and the SRS are developed and/or used to examine responsiveness to interventions in individuals with ASD. These four studies measured generalized social communication skills utilizing these standardized scales. However, nongeneralized outcome measurements are still the most common assessments in music therapy, mostly based on either direct or through video recording observations, and/or substantial clinical documentation. For observational outcome measurements, specific target behaviors are defined prior to conducting outcome evaluation. Some music therapy researchers measured specific target behaviors through direct observation using event recordings (Brownell 2002; Kern et al. 2007). Others used video recordings to evaluate behavioral outcomes for children with ASD (Buday 1995; Kern and Aldridge 2006; Kim et al. 2008, 2009). While it is important to use observational outcome measurements in music therapy, it is also pertinent to develop standardized outcome measurement in music therapy capturing unique benefit of music therapy for the individuals with ASD that is valid, reliable, and comparable to other standardized measurements.
Qualifications of Treatment Providers Music therapy services are provided by registered music therapists who have completed an accredited music therapy degree program by the music therapy association in their respective country. In the USA, Australia, many countries in Europe, and South Korea, music therapists typically hold either undergraduate or master’s degree in music therapy. In the UK, music therapists typically hold a master’s degree in music therapy and have state registration with Health Professions Council. In
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the USA, there are large numbers of American Music Therapy Association-approved programs providing equivalency or certificate degrees in music therapy for people that have already obtained a degree in related fields. Individuals who have completed an approved music therapy degree program are qualified to take the national board certification examination and can earn the credential of MT-BC (Music Therapist-Board Certified) after passing the exam. South Korea has benchmarked the American system and has set up the national music therapy certification examination for music therapists.
See Also American Music Therapy Association: www. musictherapy.org Australian Music Therapy Association: www. austmta.org.au British Society for Music Therapy/Association of Professional Music Therapists: www.apmt.org Canadian Association for Music Therapy: www. musictherapy.ca National Association of Korean Music Therapists: www.nakmt.or.kr Nordoff-Robbins Center in the UK: www. nordoff-robbins.org.uk The European Music Therapy Confederation: www.emtc-eu.com World Federation of Music Therapy: http://wfmt. info/WFMT/Home.html
References and Reading Accordino, R., Comer, R., & Heller, W. B. (2007). Searching for music’s potential: A critical examination of research on music therapy with individuals with autism. Research in Autism Spectrum Disorders, 1, 101–115. Alvin, J. (1978). Music therapy for the autistic child. London: Oxford University Press. American Music Therapy Association. (2005). What is music therapy?: Definition and quotes about music therapy. Retrieved May 2012 from www.musictherapy. org/about/musictherapy/. Bieleninik, L., Geretsegger, G., Mössler, K., Assmus, J., . . . Gold, C. (2017a). Effects of improvisational music
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therapy vs enhanced standard care on symptom severity among children with autism spectrum disorder. The TIME-A randomized clinical trial. Journal of American Medical Association, 318(6): 525–535. Bieleninik, Ł., Posserud, M. B., Geretsegger, M., Thompson, G., Elefant, C., & Gold, C. (2017b). Tracing the temporal stability of autism spectrum diagnosis and severity as measured by the Autism Diagnostic Observation Schedule: A systematic review and metaanalysis. PLoS One, 12(9), e0183160. https://doi.org/ 10.1371/journal.pone.0183160. Brownell, M. K. (2002). Musically adapted social stories to modify behavior in students with autism: Four case studies. Journal of Music Therapy, 39, 117–144. Buday, E. M. (1995). The effects of signed and spoken words taught with music on sing and speech imitation by children with autism. Journal of Music Therapy, 32, 189–202. Gattino, G. S., Riesgo, R. D. S., Longo, D., Leite, J. C. L., & Faccini, L. S. (2011). Effects of relational music therapy on communication of children with autism: A randomized controlled study. Nordic Journal of Music Therapy, 20(2), 142–154. Geretsegger, G., Elefant, C., Mössler, K. A., & Gold, C. (2014). Music therapy for people with autism spectrum disorder. Cochrane Database Systematic Reviews, (6), CD004381. https://doi.org/10.1002/14651858.CD004 381.pub3. Kaplan, R. S., & Steele, A. L. (2005). An analysis of music therapy program goals and outcomes for clients with diagnoses on the autism spectrum. Journal of Music Therapy, 42, 2–19. Katagiri, J. (2009). The effect of background music and song texts on the emotional understanding of children with autism. Journal of Music Therapy, 46(1), 15–31. Kern, P., & Aldridge, D. (2006). Using embedded music therapy interventions to support outdoor play of young children with autism in an inclusive community-based child care program. Journal of Music Therapy, 43(4), 270–294. Kern, P., Wolery, M., & Aldridge, D. (2007). Use of songs to promote independence in morning greeting routines for young children with autism. Journal of Autism and Developmental Disorders, 37, 1264–1271. Kim, J., Wigram, T., & Gold, C. (2008). The effects of improvisational music therapy on joint attention behaviors in autistic children: A randomized controlled study. Journal of Autism and Developmental Disorders, 38, 1758–1766. Kim, J., Wigram, T., & Gold, C. (2009). Emotional, motivational and interpersonal responsiveness of children with autism in improvisational music therapy. Autism, 13(4), 389–409. Mössler, K., Gold, C., Assmus, J., Schumacher, K., Calvet, C., Reimer, S., Iversen, G., & Schmid, W. (2017). The therapeutic relationship as predictor of change in music therapy with young children with autistic spectrum disorder. Journal of Autism Developmental Disorder. https://doi.org/10. 1007/s108 03-017-3306-y.
Nordoff, P., & Robbins, C. (1971). Therapy in music for handicapped children. London: Gollancz. Simpson, K., & Keen, D. (2011). Music interventions for children with autism: Narrative review of the literature. Journal of Autism and Developmental Disorders, 41, 1507–1514. Thaut, M. (1987). Visual versus auditory (musical) stimulus preferences in autistic children: A pilot study. Journal of Autism and Developmental Disorders, 17, 425–432. Thompson, G. (2012). Family-centered music therapy in the home environment: Promoting interpersonal engagement between children with autism spectrum disorder and their parents. Music Therapy Perspectives, 30(2), 109–116.
Music-Based Scale for Autism Diagnostics (MUSAD) Assessing Adults with Intellectual Disability Thomas Bergmann Berlin Treatment Center for Mental Health in Developmental Disabilities, Ev. Krankenhaus Königin Elisabeth Herzberge, Berlin, Germany
Synonyms MUSAD
Description The MUSAD is a standardized observational instrument to assess autistic traits in adults with intellectual disabilities (ID) suspected of having an additional autism spectrum disorder (ASD). The nonverbal and age-independent quality of musical interaction is used as a framework to assess people with speech impairments. The assessment comprises 11 structured musical-interactional situations to prompt diagnostically relevant behaviors. Detailed step-bystep instructions are given to ensure stability of the application across time and between clients and different raters. First of all, the assessor attempts to engage the client in musical activity,
Music-Based Scale for Autism Diagnostics (MUSAD) Assessing Adults with Intellectual Disability
e.g., joint drumming. In this particular situation, several prompts – tempo change, alternating of hands (right-left stroke), invitation to dialogue by playing a short motif, and a joyful crescendo with a final blow – are set to elicit client reactions in diagnostic relevant behaviors. Secondly, observation priorities are given to recapitulate the assessment with note-taking in free text. In the drumming situation, focus is placed on interpersonal attunement in synchronizing to tempo changes, motor coordination in right-left strokes, nonverbal social reciprocity in back-and-forth playing, and social affect in sharing musical dynamics and intensity. Video recording is recommended to support the assessor in changing roles from being in playful contact with the client to being a distant observer, closely assessing and monitoring client behavior. Thirdly, each of 46 items is coded on a 4-point Likert scale according to ascending symptom severity. Each item is assigned to a situation/prompt (e.g., drumming ! “reciprocity in musical interaction”) or even overall (e.g., “eye contact”), and each point is described by quantitative or qualitative behavioral markers. The diagnostic algorithm is formed by 26 items representing the dyadic ASDconstruct of social impairments and stereotyped behaviors, including sensory issues, according to DSM-5. A total score is formed by summing up the values of the marked algorithm items. A cutoff point of 30 is proposed to indicate the presence vs. absence of ASD. Especially in borderline cases around 30 points, the diagnostic indication may be discussed, considering ID level, comorbidities including sensory and motor impairments not associated with ASD, or score inconsistencies across domains.
Historical Background Going far back in the history of ASD, Kanner (1943) described pronounced musical interest and skills in 6 of 11 children in his groundbreaking case series. Up to now, music has played an important role in the therapy and education of children with ASD (Geretsegger et al. 2014, Srinivasan and Bhat 2013). Alongside several
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music-based approaches in fostering people with ASD, there have been some semi-structured attempts using the music-therapy setting as a framework to observe and assess autism-related behaviors in children for diagnostic purposes (Oldfield 2006; Wigram 1999). For decades, the main focus in developing scales and instruments for proper ASD diagnosis was on children and youths, resulting in numerous screening tools and a diagnostic gold standard for this age group. The fact of frequently undiagnosed ASD in adulthood was only recognized relatively recently (Brugha et al. 2011), as was the high prevalence of ASD in the ID population (Saemundsen et al. 2010). Within the low-functioning ASD group, a significantly larger number of comorbid conditions were found as compared with people diagnosed only with ID, demanding individually tailored treatment and support (Cervantes and Matson 2015). However, especially in adults with ID, the symptom overlap of ASD and severe cognitive impairment combined with comorbid conditions represents a major diagnostic challenge. Certain symptoms may be misinterpreted or not recognized, as they may be biased by ▶ diagnostic overshadowing and a lack of information about early childhood. This led to the development of various ID-specific ASD screeners that capture an adult ASD-phenotype (e.g., DIBAS-R). Even if the diagnostic gold standard represented by the combination of ▶ ADOS and ▶ ADI-R is principally applicable in adults with ID, its usefulness is limited owing to reduced feasibility, imbalanced algorithms, and age-inappropriate materials (Sappok et al. 2013). This unsatisfactory situation was the starting point for modifying a semi-structured music-therapy approach for ASD-diagnostic purpose and developing the MUSAD (Bergmann et al. 2009). The concept of assessing interactional skills and stereotyped behaviors in structured but naturalistic situations in direct contact with the client was oriented toward the ADOS. In addition to using a musical-interactional setting to prompt ASDrelated behaviors, the MUSAD differs with its concept of increasing demands throughout the entire examination to increase feasibility in
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cases of high irritability, along with including motor coordination as a potential ASD marker. Items were created based on DSM-5 ASD criteria, a review of scales and literature, and clinical experience of the test developers in the field of the targeted population and music-based interventions. The framework for developing the MUSAD was a specialized treatment center with both in- and outpatient clinics, for adults with ID and mental health problems. A guideline-compliant diagnostic work-up has been established on the basis of an expert consensus conference supported by various ASD measures (Bergmann et al. 2016). This allowed the evaluation and validation of the MUSAD assessment based on consecutive samples and an external criterion for diagnostic classification reflecting clinical reality. The future direction will be to develop a brief version of the instrument (MUSAD-Short) aiming to improve economy and user-friendliness and allowing ASD screening in people with ID including children and adolescents.
Psychometric Data Psychometric properties of the MUSAD have been evaluated in a pilot study assessing feasibility, user-friendliness, and construct validity (N ¼ 80; Bergmann et al. 2015a, b) and a validation study focusing mainly on inter-rater reliability and criterion validity (N ¼ 124; Bergmann et al. 2019). Feasibility was 95% (N ¼ 80). Compared with the play-based ADOS applied in parallel, the MUSAD showed considerably lower dropout rates (5% vs. 15%) in n ¼ 40 cases. Practicability of the test draft was supported by the successful video-based coding of one case by 12 raters from different professional backgrounds who were unfamiliar with the instrument. Plausibility of the items and coding descriptions were judged as good by questionnaire, even if the substantial coding effort was criticized. A confirmatory factor analysis supported a three-dimensional construct (F1 ¼ social interaction, F2 ¼ stereotyped behaviors including sensory issues, F3 ¼ motor coordination). Testing
the MUSAD construct fit indices indicated a good model fit (CFI ¼ 0.97, RMSEA ¼ 0.06, 90% CI [0.05–0.07] and WRMR ¼ 1.02), even if the chi-squared test of exact model fit suggested significance (w2 (df ¼ 626) ¼ 822.8, p < 0.001). Concurrent validity was supported by substantial and significant correlation with the convergent ASD screener DiBAS-R (r(111) ¼ 0.62, p < 0.001) and a negligible nonsignificant correlation with the discriminant aggression scale MOAS (r(56) ¼ 0.15, p ¼ 0.252). Scale reliability was high (α ¼ 0.95). Inter-rater agreement based on the sum scores across 4 raters in n ¼ 22 cases was excellent (ICC ¼ 0.92). Test-retest reliability in n ¼ 9 ad hoc test repetitions was moderate (ICC ¼ 0.73). Criterion validity was calculated by applying an ROC analysis. With a cutoff score of 30 points, 76.6% of all individuals assessed were classified correctly (k ¼ 0.52; sensitivity ¼ 0.79, 95% CI [0.68–0.87] and specificity ¼ 0.74, 95% CI [0.59–0.85]. The positive predictive value was 0.82, 95% CI [0.74–0.88], and the negative predictive value was 0.69, 95% CI [0.59–0.78]. The AUC values were 0.81, 95% CI [0.73–0.88] for the overall score, 0.79, 95% CI [0.71–0.88] for the social interaction score, and 0.78, 95% CI [0.70–0.87] for stereotyped behaviors including sensory and motor issues.
Clinical Uses The MUSAD is used clinically as one part in a multidimensional team-based diagnostic work-up as proposed by current guidelines (NICE 2012). The targeted population is adults (age >18 years) with ID and impaired speech. The implementation requires approximately 90 min, including videobased coding. Because of this relatively high effort, comparable to the ADOS and ADI-R, it might be useful to apply the MUSAD in a twostage diagnostic work-up after ASD screening in case of diagnostic uncertainty. The assessment can be performed by a music therapist or other professionals (psychologists, pedagogues) with basic musical skills. For all users a specialized training is required. This includes correct implementation as well as behavioral observation and
Music-Based Scale for Autism Diagnostics (MUSAD) Assessing Adults with Intellectual Disability
coding supporting inter-rater agreement. A special feature of the MUSAD is the inclusion of motor coordination, considered a cardinal feature of ASD throughout the entire spectrum (Fournier et al. 2010). This may support differential diagnostic discrimination, e.g., over against psychosis or trauma-related disorders/hospitalization. As with other ASD scales, the MUSAD should not be used as a stand-alone instrument for diagnostic classification, but it may be a useful resource in team-based diagnostic procedures.
See Also ▶ Autism Diagnostic Observation Schedule ▶ Barriers to Formal Diagnosis of Autism Spectrum Disorder in Adults ▶ Diagnostic Overshadowing ▶ DIBAS-R Validation ▶ Intellectual Disability ▶ Music Therapy
References and Reading Bergmann, T., Sappok, T., Schumacher, K., & Diefenbacher, A. (2009). Musiktherapeutischer Ansatz in der Behandlung von Erwachsenen mit Autismus und geistiger Behinderung [Music-therapeutic aproach in the treatment of adults with autism and intellectual disability]. In F. Schneider & M. Groezinger (Eds.), Psychische Erkrankungen in der Lebensspanne: Abstractband zum DGPPN Kongress 2009. Berlin: Deutsche Gesellschaft für Psychiatrie, Psychotherapie und Nervenheilkunde. Bergmann, T., Sappok, T., Diefenbacher, A., Dames, S., Heinrich, M., Ziegler, M., & Dziobek, I. (2015a). Music-based Autism Diagnostics (MUSAD): A newly developed diagnostic measure for adults with intellectual developmental disabilities suspected of autism. Research in Developmental Disabilities, 43–44, 123–135. https://doi.org/10.1016/j.ridd.2015.05.011. Bergmann, T., Sappok, T., Diefenbacher, A., & Dziobek, I. (2015b). Music in diagnostics: Using musical interactional settings for diagnosing autism in adults with intellectual developmental disabilities. Nordic Journal of Music Therapy, 25(4), 319–351. https://doi.org/10. 1080/08098131.2015.1039567. Bergmann, T., Diefenbacher, A., Heinrich, M., & Sappok, T. (2016). Perspektivenverschränkung: Multiprofessionelle Autismusdiagnostik bei erwachsenen Menschen mit Intelligenzminderung und Autismusverdacht [Entangled perspectives. A multiprofessional
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approach in diagnosing autism in adults with intellectual disability]. Zeitschrift für Psychiatrie, Psychologie und Psychotherapie, 64, 257–267. https://doi.org/10. 1024/1661-4747/a000287. Bergmann, T., Heinrich, M., Ziegler, M., Dziobek, I., Diefenbacher, A., & Sappok, T. (2019). Developing a diagnostic algorithm for the music-based scale for autism diagnostics (MUSAD) assessing adults with intellectual disability. Journal of Autism and Developmental Disorders, 49(9), 3732–3752. https:// doi.org/10.1007/s10803-019-04069-y. Brugha, T. S., McManus, S., Bankart, J., Scott, F. J., Purdon, S., Smith, J., . . . Meltzer, H. (2011). Epidemiology of autism spectrum disorders in adults in the community in England. Archives of General Psychiatry, 68(5), 459–465. https://doi.org/10.1001/ archgenpsychiatry.2011.38 Cervantes, P. E., & Matson, J. L. (2015). Comorbid symptomology in adults with autism spectrum disorder and intellectual disability. Journal of Autism and Developmental Disorders, 45(12), 3961–3970. https:// doi.org/10.1007/s10803-015-2553-z. Fournier, K. A., Hass, C. J., Naik, S. K., Lodha, N., & Cauraugh, J. H. (2010). Motor coordination in autism spectrum disorders: A synthesis and meta-analysis. Journal of Autism and Developmental Disorders, 40(10), 1227–1240. https://doi.org/10.1007/s10803010-0981-3. Geretsegger, M., Elefant, C., Kim, J., & Gold, C. (2014). Music therapy for people with autism spectrum disorder. The Cochrane Database of Systematic Reviews, 6, CD004381. https://doi.org/10.1002/ 14651858.CD004381.pub3. Kanner, L. (1943). Autistic disturbance of affective contact. The Nervous Child, 2, 217–250. National Institute for Health and Clinical Excellence. (2012). Autism in adults: Diagnosis and management. Retrieved from https://www.nice.org.uk/guidance/ cg142/chapter/1-guidance Oldfield, A. (2006). Interactive music therapy in child and family psychiatry: Clinical practice, research, and teaching. London/Philadelphia: Jessica Kingsley. Saemundsen, E., Juliusson, H., Hjaltested, S., Gunnarsdottir, T., Halldorsdottir, T., Hreidarsson, S., & Magnusson, P. (2010). Prevalence of autism in an urban population of adults with severe intellectual disabilities: A preliminary study. Journal of Intellectual Disability Research, 54(8), 727–735. https://doi.org/ 10.1111/j.1365-2788.2010.01300.x. Sappok, T., Diefenbacher, A., Budczies, J., Schade, C., Grubich, C., Bergmann, T., . . . Dziobek, I. (2013). Diagnosing autism in a clinical sample of adults with intellectual disabilities: How useful are the ADOS and the ADI-R? Research in Developmental Disabilities, 34(5), 1642–1655. https://doi.org/10. 1016/j.ridd.2013.01.028. Srinivasan, S. M., & Bhat, A. N. (2013). A review of “music and movement” therapies for children with autism: Embodied interventions for multisystem development. Frontiers in Integrative Neuroscience, 7, 22. https://doi.org/10.3389/fnint.2013.00022.
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3058 Wigram, T. (1999). Contact in music: The analysis of musical behaviour in children with communication disorder and pervasive developmental disability for differential diagnosis. In T. Wigram & J. de Backer (Eds.), Clinical applications of music therapy in developmental disability, paediatrics and neurology (pp. 69–118). London: Jessica Kingsley.
Sample References Book American Psychiatric Association. (2000). Diagnostic and statistical manual (4th ed., Text Rev.). Washington, DC: APA Press.
Chapter in a Book Howlin, P. (2005). Outcomes in autism spectrum disorders. In F. R. Volkmar, A. Klin, R. Paul, & D. J. Cohen (Eds.), Handbook of autism and pervasive developmental disorders (Vol. I, pp. 640–649). Hoboken: Wiley. Mesibov, G. B., Shea, V., & Schopler, E. (2004). The teacch approach to autism spectrum disorders. New York: Springer.
Article in a Journal Bachevalier, J. (1996). Brief report: Medical temporal love and autism: A putative animal model in primates. Journal of Autism and Developmental Disorders, 26 (2), 217–220. Kanner, L. (1943). Autistic disturbances of affective contact. The Nervous Child, 2, 217–250.
Music-Evoked Emotion Kevin G. Stephenson1, Mikle South2 and E. M. Quintin3 1 Department of Psychology, Nationwide Children’s Hospital and The Ohio State University, Columbus, OH, USA 2 Departments of Psychology and Neuroscience, Brigham Young University, Provo, UT, USA 3 Department of Educational and Counselling Psychology, McGill University, Montreal, QC, Canada
Definition “Emotion elicited through musical stimuli, including subjective, cognitive, and physiological responses”.
Music-Evoked Emotion
Historical Background Considerable research and study has been dedicated to understanding emotional processing in autism spectrum disorder (ASD). Differences in emotion recognition and emotional responsiveness are commonly noted in clinical presentations (Begeer et al. 2008). As a result, considerable attention has also been dedicated to better understand emotional processing in the research literature. Historically, these investigations focused on social stimuli such as viewing emotional faces. Results from multiple meta-analyses indicate that, despite considerable heterogeneity, overall impairments in facial emotion recognition exist and cannot be completely accounted for by intellectual ability of people with ASD (Lozier et al. 2014; Uljarevic and Hamilton 2013). These differences seem to be related, at least in part, to different ways of processing faces such as reduced eye fixation (Jones et al. 2008; Klin et al. 2002; Pelphrey et al. 2002) and appear to be strongest for the emotions of anger and fear (Lozier et al. 2014). The question then arises if these patterns are present for other emotional material, such as music. There is a long history of people with ASD showing an interest in music. Pioneering work by Heaton and colleagues highlighted strengths in musical processing of ASD children compared to children without ASD. Their work and that from other labs have shown musical strengths including memory of musical information and pitch perception (e.g., discrimination, detection, and memory) (see Quintin (2019) for a review). Additionally, the experience of music may be largely similar for people with ASD with evidence showing similar levels of interest in, time spent listening to, and reasons for listening to music as the typical listener (Allen et al. 2009; Bhatara et al. 2013). Music has a powerful influence on emotional states of humans, and there is a wealth of information highlighting how music can activate neural pathways similar to other nonmusical forms of emotional stimuli and people are consistently able to identify specific emotions depicted through music (see Koelsch (2014) for a review; Schaefer 2017). The actual musical elements that tend to
Music-Evoked Emotion
elicit a particular emotion is varied and complex; they include pitch variation, rhythm, tempo, tonality, timbre, articulation, musical mode, harmony, and melodic direction and motion, to name a few (Gabrielsson and Lindström 2011). The scientific interest in music-evoked emotion and ASD has direct applications, most notably with regards to the use of music therapy. Music therapy, or complementing traditional therapy with music, is a promising avenue for improving outcomes (see reviews by Geretsegger et al. 2014; James et al. 2015) including verbal (Chenausky et al. 2016), general communication (Sharda et al. 2018), and social skills of children with ASD such as socialemotional reciprocity, quality of social interactions, and initiation of social exchanges (LaGasse 2017).
Current Knowledge Identification of Music-Evoked Emotion There have been several studies that have directly investigated the question of “can people with ASD identify emotions in instrumental music as well as their peers without ASD?” Thus far, the evidence suggests that the answer to that question is likely “yes.” There are studies that have clearly showed intact identification of specific emotions in music including happy, sad (Heaton et al. 1999; Whipple et al. 2015), fear (Quintin et al. 2011; Stephenson et al. 2016), disgust, and anger (Whipple et al. 2015). Other studies give evidence for intact ability to match emotional social scenes with corresponding musical excerpts (Heaton et al. 2008) and assigning emotional valence (i.e., positive vs. negative) to happy and sad music (Gebauer et al. 2014). One study found that children and adolescents with ASD did not differ in their emotional labeling skills, but this was the case only after statistically controlling for differences in verbal intellectual ability (Quintin et al. 2011). Another study found possible agerelated differences between youth with and without ASD, specifically for scary music, in that younger autistic children tended to be slightly more accurate in identifying scary music compared to their older autistic peers, the opposite
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age-related pattern was found for children and adolescents without autism (Stephenson et al. 2016). Thus, emotion identification of people with ASD appears to be largely intact, although there may be nuanced effects of age and verbal ability that will need to be explored and clarified with future research.
Response to Music-Evoked Emotion In addition to the behavioral aspect of recognizing emotional characteristics of music, there is also the psychological and physiological responsiveness to music-evoked emotion. Parents of children with ASD rate their children’s emotional responsiveness to music similarly to those of their nonASD peers (Bhatara et al. 2013) and children themselves do not differ in the their self-reported perception of intensity of music-evoked emotion in laboratory-based studies using simple rating scales (Quintin et al. 2011; Stephenson et al. 2016), with similar results appearing for adults (Caria et al. 2011). However, according to studies with autistic adults, they tend to describe emotional reactions to music using different words than non-autistic adults, which may be related to difficulties understanding one’s internal emotion states and struggling to put words to emotional experiences, often referred to as alexithymia, (Allen et al. 2013; Allen et al. 2009), which is often associated with ASD (Bird and Cook 2013; Poquérusse et al. 2018). Given the intact ability of autistic people to recognize and match emotional music to expressed emotions, music has potential to be used as a tool in developing emotional understanding and expression outside of music for people with ASD (Allen and Heaton 2010). Evidence on physiological responsiveness to music is just emerging. Adults with ASD showed no difference in their galvanic skin response compared to non-autistic adults (Allen et al. 2013), whereas autistic children and adolescents had reduced physiological response compared to their neurotypical peers in another study when listening to the same instrumental music stimuli (Stephenson et al. 2016). Neuroimaging studies have shown that brain networks typically involved in the processing of music-evoked emotion, including reward circuitry, are intact for
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people with ASD, at least for happy and sad music (Caria et al. 2011; Gebauer et al. 2014). Although some results indicate that listening to happy music evoked somewhat reduced activation in certain areas including insula (Caria et al. 2011), other results show increased activation for happy vs. sad music in insula as well as dorsolateral prefrontal cortex, possibly related to increased arousal and cognitive processing (Gebauer et al. 2014). Notably, current neuroimaging studies have focused exclusively on autistic adults and a fairly narrow range of music-evoked emotions (i.e., happy and sad). Investigating a wider range of emotions is needed, especially given the propensity towards anxiety and fear in autistic individuals including differences in sensory and cognitive processing of fear (South and Rodgers 2017). Despite heterogeneity in research findings within ASD samples, which is not uncommon, the growing literature paints a picture of overall intact psychological and physiological responsiveness to music-evoked emotion. However, similar to results from emotion identification, there may exist nuanced differences in emotional experience, particularly when different emotions are considered.
Future Directions Despite the current knowledge of the processing of music-evoked emotion of people with ASD, there are still many unanswered questions. Additional research is needed using a wider range of emotions, especially in neurophysiological studies. In particular, research is needed regarding response to anger and fear, which are important for both emotional processing and emotional regulation of people with ASD. Another potential avenue of further research is variations in musical stimuli used in studies. The majority of studies have relied on existing music from classic repertory and film, with only a few selections crafted specifically for research use (Whipple et al. 2015). Additionally, the use of lyrics in emotional music is also understudied as well as the impact of pairing emotional music to other social-emotional
Music-Evoked Emotion
contexts such as movies. Many studies show relatively high levels of accuracy in emotion identification among participants possibly suggesting that the completion of the tasks is too easy. Finding ways to increase the difficulty of musical tasks may provide more variability from which group differences may arise. Only one study has investigated music-evoked emotions using stimuli which vary in level of emotional intensity (Bhatara et al. 2010). Bhatara et al. found that participants with ASD did not provide ratings with as much variability as those without ASD, but the participants with ASD also had lower verbal intellectual ability than those in the comparison group. Additional work varying level of intensity or study designs using congruent vs. incongruent emotional information (e.g., happy music with angry lyrics) may be possible ways to achieve different levels of difficulty to increase the variability within emotional identification accuracy. The vast majority of research investigations up to this point have included people with intact intellectual ability. Similar to other areas of research, music-evoked emotion studies need to include people at all levels of the autism spectrum in order to understand the musical experience among those with minimal verbal ability. Preliminary results indicate that children and adolescents with ASD with verbal and cognitive skills ranging from the lower to higher ranges can accurately recognize music-evoked emotions (Dahary et al. 2018). Additionally, relatively little is known about the developmental trajectory of musical experience and ASD, although some research points to possible age-related differences (Stephenson et al. 2016). Longitudinal studies investigating music-evoked emotion at all points of development, from early childhood into older adulthood, would shed valuable light into this area. With a better understanding of the differences and similarities of processing of and responses to music-evoked emotion, we may have access to additional tools for the diagnosis and treatment of autism. One music-based autism diagnostic assessment tool for adults with developmental disabilities has already been developed (Bergmann et al. 2015), and there is potential for other applications of music therapy including
Music-Evoked Emotion
development of executive functioning skills in young children (Frischen et al. 2019). Furthermore, with improving technology of wearable physiological measurement devices, neural imagining, and behavioral strategies, the prospect of including a wide range of individuals is better than ever. Many of these promising avenues are already being pursued (Dahary et al. 2018; Sivathasan et al. 2019), although there is room for more research to be done in this area. As with much autism-related research, studies of music-evoked emotion and ASD is lacking in larger, high statistical power studies. Multisite collaborations can help with this as well as researchers considering including musical stimuli and paradigms in larger clinical trials and lab-based research of emotion processing. In sum, people with autism display generally intact accuracy and processing of music-evoked emotion. The use of music-evoked emotion has great potential for scientific and clinical benefit in autism spectrum disorder and remains a promising field for future endeavors.
References and Reading Allen, R., & Heaton, P. (2010). Autism, music, and the therapeutic potential of music in alexithymia. Music Perception: An Interdisciplinary Journal, 27(4), 251–261. https://doi.org/10.1525/mp.2010.27.4.251. Allen, R., Hill, E., & Heaton, P. (2009). ‘Hath charms to soothe . . .’: An exploratory study of how highfunctioning adults with ASD experience music. Autism, 13(1), 21–41. https://doi.org/10.1177/ 1362361307098511. Allen, R., Davis, R., & Hill, E. (2013). The effects of autism and alexithymia on physiological and verbal responsiveness to music. Journal of Autism and Developmental Disorders, 43(2), 432–444. https://doi.org/ 10.1007/s10803-012-1587-8. Begeer, S., Koot, H. M., Rieffe, C., Meerum Terwogt, M., & Stegge, H. (2008). Emotional competence in children with autism: Diagnostic criteria and empirical evidence. Developmental Review, 28(3), 342–369. https://doi.org/10.1016/j.dr.2007.09.001. Bergmann, T., Sappok, T., Diefenbacher, A., Dames, S., Heinrich, M., Ziegler, M., & Dziobek, I. (2015). Musicbased Autism Diagnostics (MUSAD) – A newly developed diagnostic measure for adults with intellectual developmental disabilities suspected of autism.
3061 Research in Developmental Disabilities, 43–44, 123–135. https://doi.org/10.1016/j.ridd.2015.05.011. Bhatara, A., Quintin, E. M., Levy, B., Bellugi, U., Fombonne, E., & Levitin, D. J. (2010). Perception of emotion in musical performance in adolescents with autism spectrum disorders. Autism Research, 3(5), 214–225. https://doi.org/10.1002/aur.147. Bhatara, A., Quintin, E. M., Fombonne, E., & Levitin, D. J. (2013). Early sensitivity to sound and musical preferences and enjoyment in adolescents with autism spectrum disorders. Psychomusicology: Music, Mind, and Brain, 23(2), 100–108. https://doi.org/10.1037/ a0033754. Bird, G., & Cook, R. (2013). Mixed emotions: The contribution of alexithymia to the emotional symptoms of autism. Translational Psychiatry, 3, e285. https://doi. org/10.1038/tp.2013.61. Caria, A., Venuti, P., & de Falco, S. (2011). Functional and dysfunctional brain circuits underlying emotional processing of music in autism spectrum disorders. Cerebral Cortex, 21(12), 2838–2849. https://doi.org/10. 1093/cercor/bhr084. Chenausky, K., Norton, A., Tager-Flusberg, H., & Schlaug, G. (2016). Auditory-motor mapping training: Comparing the effects of a novel speech treatment to a control treatment for minimally verbal children with autism. PLoS One, 11(11), e0164930. https://doi.org/10.1371/ journal.pone.0164930. Dahary, H., Sivathasan, S., & Quintin, E. M. (2018). Comparing intensity ratings of emotions in music and faces by adolescents with autism spectrum disorder. Presented at the International Society for Autism Research (INSAR) Annual Meeting, Rotterdam, Netherlands. Frischen, U., Schwarzer, G., & Degé, F. (2019). Comparing the effects of rhythm-based music training and pitch-based music training on executive functions in preschoolers. Frontiers in Integrative Neuroscience, 13. https://doi.org/10.3389/fnint.2019.00041. Gabrielsson, A., & Lindström, E. (2011). The role of structure in the musical expression of emotions. In P. N. Juslin & J. Sloboda (Eds.), Handbook of music and emotion: Theory, research, applications. Oxford: Oxford University Press. Gebauer, L., Skewes, J., Westphael, G., Heaton, P., & Vuust, P. (2014). Intact brain processing of musical emotions in autism spectrum disorder, but more cognitive load and arousal in happy vs. sad music. Frontiers in Neuroscience, 8. https://doi.org/10.3389/fnins.2014. 00192. Geretsegger, M., Elefant, C., Mössler, K. A., & Gold, C. (2014). Music therapy for people with autism spectrum disorder. Cochrane Database of Systematic Reviews, 6. https://doi.org/10.1002/14651858. CD004381.pub3. Heaton, P., Hermelin, B., & Pring, L. (1999). Can children with autistic spectrum disorders perceive affect in music? An experimental investigation. Psychological Medicine, 29(6), 1405–1410. https://doi.org/10.1017/ s0033291799001221.
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3062 Heaton, P., Allen, R., Williams, K., Cummins, O., & Happé, F. (2008). Do social and cognitive deficits curtail musical understanding? Evidence from autism and down syndrome. British Journal of Developmental Psychology, 26(2), 171–182. https://doi.org/10.1348/ 026151007X206776. James, R., Sigafoos, J., Green, V. A., Lancioni, G. E., O’Reilly, M. F., Lang, R., . . . Marschik, P. B. (2015). Music therapy for individuals with autism spectrum disorder: A systematic review. Review Journal of Autism and Developmental Disorders, 2(1), 39–54. https://doi.org/10.1007/s40489-014-0035-4. Jones, W., Carr, K., & Klin, A. (2008). Absence of preferential looking to the eyes of approaching adults predicts level of social disability in 2-year-old toddlers with autism spectrum disorder. Archives of General Psychiatry, 65(8), 946–954. https://doi.org/10.1001/archpsyc. 65.8.946. Kanner, L. (1943). Autistic disturbances of affective contact. Nervous Child, 2, 217–250. Klin, A., Jones, W., Schultz, R., Volkmar, F., & Cohen, D. (2002). Visual fixation patterns during viewing of naturalistic social situations as predictors of social competence in individuals with autism. Archives of General Psychiatry, 59(9), 809–816. https://doi.org/10.1001/ archpsyc.59.9.809. Koelsch, S. (2014). Brain correlates of music-evoked emotions. Nature Reviews Neuroscience, 15(3), 170–180. https://doi.org/10.1038/nrn3666. LaGasse, A. B. (2017, February 20). Social outcomes in children with autism spectrum disorder: A review of music therapy outcomes. https://doi.org/10.2147/ PROM.S106267. Lozier, L. M., Vanmeter, J. W., & Marsh, A. A. (2014). Impairments in facial affect recognition associated with autism spectrum disorders: A meta-analysis. Development and Psychopathology, 26(4pt1), 933–945. https:// doi.org/10.1017/S0954579414000479. Pelphrey, K. A., Sasson, N. J., Reznick, J. S., Paul, G., Goldman, B. D., & Piven, J. (2002). Visual scanning of faces in autism. Journal of Autism and Developmental Disorders, 32(4), 249–261. https://doi.org/10.1023/ A:1016374617369. Poquérusse, J., Pastore, L., Dellantonio, S., & Esposito, G. (2018). Alexithymia and autism Spectrum disorder: A complex relationship. Frontiers in Psychology, 9. https://doi.org/10.3389/fpsyg.2018.01196. Quintin, E.-M. (2019). Music-evoked reward and emotion: Relative strengths and response to intervention of people with ASD. Frontiers in Neural Circuits, 13. https:// doi.org/10.3389/fncir.2019.00049. Quintin, E.-M., Bhatara, A., Poissant, H., Fombonne, E., & Levitin, D. J. (2011). Emotion perception in music in high-functioning adolescents with autism spectrum disorders. Journal of Autism and Developmental Disorders, 41(9), 1240–1255. https://doi.org/10.1007/ s10803-010-1146-0. Schaefer, H.-E. (2017). Music-evoked emotions—Current studies. Frontiers in Neuroscience, 11. https://doi.org/ 10.3389/fnins.2017.00600.
Mutual Gaze Sharda, M., Tuerk, C., Chowdhury, R., Jamey, K., Foster, N., Custo-Blanch, M., . . . Hyde, K. (2018). Music improves social communication and auditory–motor connectivity in children with autism. Translational Psychiatry, 8. https://doi.org/10.1038/s41398-0180287-3. Sivathasan, S., Burack, J. A., & Quintin, E.-M. (2019). Multimodal emotion recognition in children with autism spectrum disorders: Vocalizations are more informative than faces or music. Presented at the International Society for Autism Research (INSAR) Annual Meeting, Montreal, Quebec, Canada. South, M., & Rodgers, J. (2017). Sensory, emotional and cognitive contributions to anxiety in autism spectrum disorders. Frontiers in Human Neuroscience, 11. https://doi.org/10.3389/fnhum.2017.00020. Stephenson, K. G., Quintin, E. M., & South, M. (2016). Age-related differences in response to music-evoked emotion among children and adolescents with autism spectrum disorders. Journal of Autism and Developmental Disorders, 46(4), 1142–1151. https://doi.org/ 10.1007/s10803-015-2624-1. Uljarevic, M., & Hamilton, A. (2013). Recognition of emotions in autism: A formal meta-analysis. Journal of Autism and Developmental Disorders, 43(7), 1517–1526. https://doi.org/10.1007/s10803-0121695-5. Whipple, C. M., Gfeller, K., Driscoll, V., Oleson, J., & McGregor, K. (2015). Do communication disorders extend to musical messages? An answer from children with hearing loss or autism spectrum disorders. Journal of Music Therapy, 52(1), 78–116. https://doi.org/10. 1093/jmt/thu039.
Mutual Gaze Sally J. Rogers Department of Psychiatry and Behavioral Sciences, UC Davis M.I.N.D. Institute, Sacramento, CA, USA
Synonyms Eye contact; Eye-to-eye gaze; Social gaze; Visual attention to eyes; Visual orienting to eyes
Definition What Is It? Mutual gaze occurs when two people make eye contact or look into each other’s eyes. Mutual
Mutual Gaze
gaze is an important part of social communication and perception of others’ emotion states and is the one of the foundational skills necessary in the development of joint attention (George and Conty 2008; Morales et al. 2005; Saito et al. 2010; Senju and Johnson 2009). Mutual gaze has been described as “the most powerful mode of establishing a communicative link between humans” (Farroni et al. 2002). When Does It Occur? From birth, infants are innately motivated to gaze into their caregivers’ eyes. In fact, the field of vision of newborns is approximately the distance required to make eye contact when held by an adult (Stern et al. 1985). Infants prefer to look at faces over other stimuli, especially faces that engage them in mutual gaze (Farroni et al. 2002). Eye-tracking studies have revealed that when looking at faces, even infants preferentially fixate on the eyes over other facial features (Mauer and Salapatek 1976).
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1. The affective hyperarousal model which posits that gaze avoidance is an adaptive response for individuals with ASD who find focusing on the face and eyes of others to be strongly adversive 2. The affective hypoarousal model which posits that individuals with ASD experience hypoactivation of the amygdala, which interferes in the association of a positive social reward with making eye contact, resulting in reduced levels of eye contact 3. The communicative intention detector model that proposes that individuals with ASD engage in less eye contact because they do not understand the social salience of eye contact in understanding others’ states 4. The fast-track modulator model which states that atypical eye contact in ASD could be caused either by an impairment in the subcortical face and eye contacting detecting route or in the network of cortical and subcortical structures specializing in the processing of social information (Senju et al. 2009)
See Also Mutual Gaze and ASD ▶ Eye Gaze One of the most prominent features of autism spectrum disorder (ASD) is an atypical pattern of eye contact and reduced levels of mutual gaze with social partners (Klienmann et al. 2010). Although reduced levels of gaze behavior at 6 months was not found to be predictive of autism diagnosis at 24 months (Young et al. 2009), toddlers with autism do show abnormal gaze to the eye region and decreased shared affect (Chawarska et al. 2010). Eye-tracking data for children with ASD have shown active avoidance of direct eye contact through increased gaze shifts away from the eye area (Klienmann et al. 2010). The DSM-IV-TR includes reduced eye contact as one of the symptoms of ASD: “marked impairment in the use of multiple nonverbal behaviors (e.g. eye-to-eye gaze,. . .) to regulate social interaction and communication” (American Psychiatric Association 2000, p. 70). There are currently four models that are used to explain the decreased levels of mutual gaze or eye contact in children with ASD:
References and Reading American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders, DSM-IV-TR (4th ed.). Washington, DC: Author. Text revision. Chawarska, K., Volkmar, F., & Klin, A. (2010). Limited attentional bias for faces in toddlers with autism. Archives of General Psychiatry, 67, 178–185. Dawson, G., Hill, D., Spencer, A., Galpert, L., & Watson, L. (1990). Affective exchanges between young autistic children and their mothers. Journal of Abnormal Child Psychology, 18, 335–345. Elsabbagh, M., Volein, A., Csibra, G., Homboe, K., Garwood, H., Tucker, L., et al. (2009). Neural correlates of eye gaze processing in the infant broader autism phenotype. Biological Psychiatry, 65, 31–38. Farran, D. C., & Kasari, C. (1990). A longitudinal analysis of the development of synchrony in mutual gaze in mother-child dyads. Journal of Applied Developmental Psychology, 11, 419–430. Farroni, T., Csibra, G., Simion, F., & Johnson, M. H. (2002). Eye contact detection in humans from birth. Proceedings of the National Academy of Sciences of the United States of America, 99, 9602–9605.
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3064 George, N., & Conty, L. (2008). Facing the gaze of others. Clinical Neurophysiology, 38, 197–207. Gibson, J. J., & Pick, A. D. (1963). Perception of another’s looking behavior. The American Journal of Psychology, 76, 386–394. Johnson, M. H., Bziurawiec, S., Ellis, H., & Morton, J. (1992). The effects of movement of internal features on infants’ preferences for face-like stimuli. Cognition, 40, 1–19. Klienmann, D., Dziobek, I., Hatri, A., Steimke, R., & Heekeren, H. R. (2010). Atypical reflexive gaze patterns on emotional faces in autism spectrum disorders. Journal of Neuroscience, 30, 12281–12287. Kylliainen, A., & Hietanen, J. K. (2006). Skin conductance responses to another person’s gaze in children with autism. Journal of Autism and Developmental Disorders, 36, 517–525. Mauer, D., & Salapatek, P. (1976). Developmental changes in the scanning of faces by young infants. Child Development, 47, 523–527. Morales, M., Mundy, P., Crowson, M. M., Neal, A. R., & Delgado, C. E. F. (2005). Individual differences in infant attention skills, joint attention, and emotion regulation behaviour. International Journal of Behavioral Development, 29, 259–263. Ozonoff, S., Iosif, A., Baguio, F., Cook, I. C., Hill, M. M., Hutman, T., et al. (2010). A prospective study of the emergence of early behavioral signs of autism. Journal of the American Academy of Child and Adolescent Psychiatry, 49, 256–266. Saito, D. N., Tanabe, H. C., Izuma, K., Hayashi, M. J., Morito, Y., & Komeda, H. (2010). “Stay tuned”: Interindividual neural synchronization during mutual gaze and joint attention. Frontiers in Integrative Neuroscience, 4, 1–12. Senju, A., & Johnson, M. H. (2009). Atypical eye contact in autism: Models, mechanisms, and development. Neuroscience and Biobehavioral Reviews, 33, 1204–1214. Senju, A., Yaguchi, K., Tojo, Y., & Hasegawa, T. (2003). Eye contact does not facilitate detection in children with autism. Cognition, 89, B43–B51. Senju, A., Yaguchi, K., Tojo, Y., & Hasegawa, T. (2005). Deviant gaze processing in children with autism: An ERP study. Neuropsychologia, 43, 1297–1306. Stern, D. N., Hofer, L., Haft, W., & Dore, J. (1985). Affect attunement: The sharing of feeling states between mother and infant by means of inter-model fluency. In T. M. Field & N. A. Fox (Eds.), Social perception in infants (pp. 323–378). Norwood: Ablex. Volkmar, F. R., & Mayes, L. C. (1990). Gaze behavior in autism. Development and Psychopathology, 2, 61–69. Young, G. S., Merin, N., Rogers, S. J., & Ozonoff, S. (2009). Gaze behavior and affect at 6 months: Predicting clinical outcomes and language development in typically developing infants and infants at risk for autism. Developmental Science, 12, 798–814.
Mutual Regulation
Mutual Regulation Amanda C. Gulsrud UCLA Semel Institute for Neuroscience and Human Behavior, Los Angeles, CA, USA
Synonyms Co-regulation; Emotion regulation; Emotional synchrony; Self-regulation
Definition The construct of emotion regulation refers to “the extrinsic and intrinsic processes responsible for monitoring, evaluating and modifying emotional reactions” (Thompson 1994). Important to this definition is the emphasis on both intrinsic and extrinsic processes, which may include both internal or self-driven processes and external or mother-driven support. This mother-driven support of the child’s emerging emotional processes is co-regulation. The development of emotion regulation is a process that occurs over the first years of life. Beginning in infancy, children learn to regulate emotions, attention, and behaviors through the external scaffold of mothers. The process gradually becomes an internal, self-driven process (Kopp 1989). As children gain a greater ability to regulate these processes, they also show a greater ability to remain flexible while facing the ongoing social demands of their environment. Mother-child interaction research has contributed to our knowledge of emotion co-regulation by emphasizing the dynamic and interpersonal nature of the construct. These studies have found that typical mothers and children are sensitive to each other’s emotional states and that mothers regulate child emotional states by reading emotion signals and modulating child arousal (Field 1994). In addition, work in this area has found that the quality of interpersonal regulation between
Mutually Acceptable Written Agreement (MAWA)
mother and child predicts later social-emotional outcomes in the child, including self-control and social competence (Feldman et al. 1999). Several studies have begun to examine the development of emotion regulation in children with autism. Bieberich and Morgan (2004) employed parent report measures to examine emotion regulation outcomes. In another study, emotion regulation and temperament in children with autism spectrum disorder was measured by behavioral coding of maladaptive and adaptive regulation strategies during a mildly frustrating delay task. Results showed that children with autism employed less adaptive strategies and a greater range of strategies compared to typical controls (Konstantareas and Stewart 2006). These findings are among the first empirical support for a specific disturbance in emotion selfregulation in children with autism. A recent study explored the development of emotion co-regulation in young children with autism and their mothers within the context of an early social-communication intervention (Gulsrud et al. 2010). Results showed that children displayed significant distress in almost half of their interactions with their mothers, and these episodes of distress lasted on average 20% of the total length of the interaction. Children most frequently engaged in active strategies (tension release, avoidance, and distraction). Similarly, mothers most frequently engaged in active strategies (redirection of attention, prompting/helping, and physical strategies) during child negativity. These results suggest that both mothers and children are actively working together to regulate the child’s emotions and that co-regulation is a viable construct for study in this population.
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References and Reading Bieberich, A., & Morgan, S. B. (2004). Self-regulation and affective expression during play in children with autism or Down syndrome: A short-term longitudinal study. Journal of Autism and Developmental Disorders, 34, 439–448. Feldman, R., Greenbaum, C. W., & Yirmiya, N. (1999). Mother-infant affect synchrony as an antecedent to the emergence of self-control. Developmental Psychology, 35, 223–231. Field, T. (1994). The effects of mother’s physical and emotional unavailability on emotion regulation. In N. A. Fox (Ed.), The development of emotion regulation: Biological and behavioral considerations (Monographs of the society for research in child development, Vol. 59, pp. 208–227). Chicago: University of Chicago Press. Gulsrud, A., Jahromi, L. B., & Kasari, C. (2010). The co-regulation of emotions between mothers and their children with autism. JADD, 34, 439–448. Konstantareas, M., & Stewart, K. (2006). Affect regulation and temperament in children with autism spectrum disorder. Journal of Autism and Developmental Disorders, 36, 143–154. Kopp, C. B. (1982). Antecedents of self-regulation: A developmental perspective. Developmental Psychology, 18, 199–214. Kopp, C. B. (1989). Regulation of distress and negative emotions: A developmental view. Developmental Psychology, 25, 343–354. Thompson, R. (1994). Emotion regulation: A theme in search of definition. Monographs of the Society for Research in Child Development, 59(2–3, Serial No. 240), 25–52
Mutually Acceptable Written Agreement (MAWA) Lindsey Capece Quinnipiac University, Hamden, CT, USA
Definition See Also ▶ Co-regulation ▶ Emotion Regulation ▶ Self-regulation ▶ Temperament
A “mutually acceptable written agreement” is a binding document that is the result of a mediation session. Mediation is a negotiation session, typically used as an informal alternative to the adversary system, facilitated by a neutral third party who tries to help the parties in the dispute to reach a mutually acceptable written agreement.
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The agreement can cover a wide range of issues including issues that would not be recognized in a court of law. There is some uncertainty as to whether a mutually acceptable written agreement is legally enforceable. Most states require that these agreements be reduced to writing and be signed by both parties, and many states require that the agreement be filed in court. Furthermore, there are often statutes that provide requirements for an agreement reached in a particular type of mediation setting. In the absence of legislation, one must look to common law contract principles as authority on whether the agreement is enforceable. These agreements may also be subject to agency law for the purpose of determining whether those entering into the agreement have the authority to do so.
See Also ▶ Consent ▶ Liability
References and Reading Bush, B., & Richard, A. (2008). Staying in orbit or breaking free: The relationship of mediation to the courts
Myoclonic Spasms of Infancy over four decades. Grand Forks: North Dakota Law Review. D’Alo, G. E. (2003). Accountability in special education mediation: Many a slip ‘twixt vision and practice? Cambridge, MA: Harvard Negotiation Law Review. Feinberg, E., Breyer, J., & Moses, P. (2002). Beyond mediation: Strategies for appropriate early dispute resolution in special education. Eugene: The Consortium for Appropriate Dispute Resolution in Special Education (CADRE). Parker, B. R. (1992). What can be done to enforce mediation agreements. Chicago: IADC. Defense Counsel Journal. Thorpe, R. W., & Boyens, J. (1998). Mediation settlement agreements: Legal, ethical, and practical issues. New York: The International Institute for Conflict Prevention and Resolution. Alternatives to the High Cost of Litigation.
Myoclonic Spasms of Infancy ▶ Infantile Spasms/West Syndrome
Myo-inositol ▶ Inositol: Definition
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Naltrexone Evdokia Anagnostou1 and Deepali Mankad2 1 Department of Peadiatrics, University of Toronto, Clinician Scientist, Bloorview Research Institute, Toronto, ON, Canada 2 Holland Bloorview Kids Rehabilitation Hospital, Bloorview Research Institute, Toronto, ON, Canada
Synonyms Depade; Naltrexone hydrochloride; Revia
Indications Alcohol dependence
Mechanisms of Action Naltrexone is an opiate antagonist with minimal agonist activity.
Clinical Use (Including Side Effects) Naltrexone is not currently approved for the treatment of ASD-related symptoms but enjoys some off-label use.
A series of early open-label studies had suggested significant improvements in selfinjurious behaviors (SIB). The side effect profile was relatively benign. Following early data, there have been a few small controlled studies (max sample size 41, Campbell et al. 1993) treating children between the ages of 2.8 and 19 years with naltrexone. A nice systematic review by Elchaar et al. in 2006 has summarized available data in ASD. Overall, the data does not support the use of naltrexone for the treatment of core symptom domains of ASD, despite original hypothesis regarding abnormalities in the endogenous opioid system in ASD (Panksepp 1979). However, preliminary data suggests that at least a subgroup of children with ASD may show improvements in self-injurious behaviors with naltrexone. Still there is no clarity regarding the dose (doses used vary from 0.5 mg/kg weekly to 2.35 mg/kg single dose) and/or duration of treatment. There is also lack of clarity about whether higher doses are more or less effective, with some studies suggesting that doses of 2 mg/kg/day or higher may have less selectivity for the opiate receptor, therefore competing with the opiate antagonism effect. The most commonly reported side effect in ASD studies is sedation. The bitter taste of naltrexone may interfere with compliance. Naltrexone carries an FDA black box warning regarding hepatic insufficiency, but no elevation in liver enzymes has been reported so far in patients
© Springer Nature Switzerland AG 2021 F. R. Volkmar (ed.), Encyclopedia of Autism Spectrum Disorders, https://doi.org/10.1007/978-3-319-91280-6
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with ASD. Gastrointestinal upset and two cases of panic attacks have also been reported. At this point, it is hard to make a recommendation regarding naltrexone. If it were to be used, a reasonable dose range is between 0.5 and 2 mg/kg/day. Safety needs to be monitored, especially regarding the FDA black box warning. A large randomized controlled trial of naltrexone targeting SIB and exploring predictors of response is likely warranted.
Naltrexone Hydrochloride
Narrative Analysis ▶ Narrative Assessment ▶ Story Structure Decision Tree
Narrative Assessment
▶ Trexan
Joshua J. Diehl Autism Services, LOGAN Community Resources, Inc., South Bend, IN, USA Department of Psychology, University of Notre Dame, Notre Dame, IN, USA
References and Reading
Synonyms
Campbell, M., Anderson, L. T., Small, A. M., Adams, P., Gonzalez, N. M., & Ernst, M. (1993). Naltrexone in autistic children: Behavioral symptoms and attentional learning. Journal of the American Academy of Child & Adolescent Psychiatry, 32, 1283–1291. Elchaar, G. M., Maisch, N. M., Augusto, L. M., & Wehring, H. J. (2006). Efficacy and safety of naltrexone use in pediatric patients with autistic disorder. Annals of Pharmacotherapy, 40, 1086–1095. Pansepp, J. (1979). A neurochemical theory of autism. Trends in Biochemical Sciences, 2, 174–177.
Narrative analysis
See Also
Naltrexone Hydrochloride ▶ Naltrexone
Definition A narrative assessment is the analysis of a story told by an individual to assess language and communication. A narrative can be used to measure pragmatic language use because it involves recounting a personal experience or retelling a story to another individual. Narratives can be examined for their structure (e.g., sentence complexity, story components), content (e.g., use of mental state terms), and/or style of presentation (e.g., use of prosody, gestures, facial expressions).
Naming
See Also
▶ Label
▶ Communication Assessment ▶ Pragmatic Communication ▶ Pragmatic Language Impairment ▶ Pragmatics ▶ Story Structure Decision Tree ▶ Strong Narrative Assessment Procedure (SNAP), Second Edition
Nanocephaly ▶ Microcephaly
Narrative Language in ASD
References and Reading Baixauli, I., Colomer, C., Roselló, B., & Miranda, A. (2016). Narratives of children with high-functioning autism spectrum disorder: A meta-analysis. Research in Developmental Disabilities, 59, 234–254. Diehl, J. J., Bennetto, L., & Young, E. C. (2006). Story recall and narrative coherence of high-functioning children with autism spectrum disorders. Journal of Abnormal Child Psychology, 34, 87–102. Losh, M., & Capps, L. (2003). Narrative ability in highfunctioning children with autism or Asperger’s syndrome. Journal of Autism and Developmental Disorders, 33, 239–251. Loveland, K., McEvoy, R., Tunali, B., & Kelley, M. L. (1990). Narrative story telling in autism and Down’s syndrome. British Journal of Developmental Psychology, 8, 9–23. Tager-Flusberg, H. (1995). ‘Once upon a ribbit’: Stories narrated by autistic children. British Journal of Developmental Psychology, 13, 45–59. Young, E. C., Diehl, J. J., Morris, D., Hyman, S. L., & Bennetto, L. (2005). Pragmatic language disorders in children with autism: The use of two formal tests to distinguish affected children from controls. Language, Speech, and Hearing Services in Schools, 36, 62–72.
Narrative Comprehension ▶ Visual Narrative Comprehension
Narrative Language in ASD Michelle Lee1,2 and Molly Losh3 1 NYU School of Medicine, New York, NY, USA 2 Department of Communication Sciences and Disorders, Northwestern University, Evanston, IL, USA 3 The Roxelyn and Richard Pepper Department of Communication Sciences and Disorders, Northwestern University, Evanston, IL, USA
Definition Narrative (i.e., storytelling) is a ubiquitous form of communication and primary means of sharing experiences with others. Organizing experiences
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through narrative imposes temporal and causal order to events, providing meaning and interpersonal significance to experiences. Narrative is the primary means of sharing one’s world of experience with others and plays a major role in forging interpersonal connections. In autism spectrum disorder (ASD), narrative abilities are impaired, particularly in open-ended conversational contexts that pervade daily interactions. Given the centrality of narrative to social interactions, characterizing the narrative profile in ASD is critical to understanding the origins of this group’s difficulties with more complex social language use and intervention development. The development of narrative competence draws on a number of linguistic, cognitive, and social cognitive skills that are impacted in ASD. Observed narrative impairment in ASD has been linked to deficits in structural language and social cognition (e.g., failure to identify the needs of one’s listener) and may also be associated with executive function deficits (e.g., difficulties with attention, inhibition, and working memory) and a weak central coherence cognitive style (e.g., a focus on irrelevant details at the expense of the whole). Difficulties across these domains likely can impact both microstructural (e.g., length, complexity of word use, grammatical markers) and macrostructural (e.g., overall form and content) elements of narrative. For instance, impairments in structural language may result in reduced grammatical complexity, limiting one’s ability to communicate temporality of events and to establish causal relationships between events. Further, social cognition is important for recognizing the emotional experiences of those involved in the story and adapting narrative style and content based on listener responses. Although narrative abilities vary within individuals with ASD, particularly as a function of comorbid cognitive and language impairment, studies suggest a pattern or relative strengths in highly structured contexts (e.g., wordless picture books) and weaknesses in more open-ended conversational contexts that are typical of daily interactions. Generally, in more structured narrative tasks, individuals with ASD produce narratives
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of similar length, with comparable rates of grammatical markers, emotion-state words, and basic narrative elements (e.g., main characters or setting) as those of typically developing peers. However, individuals with ASD demonstrate relative challenges in many aspects of narrative, with the most consistent difficulties observed in the use of evaluative devices, or statements that infuse psychological perspective within a narrative, such as a causal explanation of a character’s emotion. Narratives of individuals with ASD have also been characterized by greater frequency of off-topic remarks, overly formal language, and reduced overall coherence. These difficulties have been observed across the lifespan of individuals with ASD, although research has primarily focused on childhood and early adolescence. Given that difficulties with narration are most prominent in conversational contexts, it will be critical for future research to focus on the assessment of this form of narrative in order to elucidate difficulties most relevant to daily interactions. Similarly, a targeted intervention approach that focuses on developing conversational narratives has the potential to improve the social and communicative experiences of individuals with ASD.
Narrative Understanding Tager-Flusberg, H., & Sullivan, K. (1995). Attributing mental states to story characters: A comparison of narratives produced by autistic and mentally retarded individuals. Applied Psycholinguistics, 16(3), 241–256.
Narrative Understanding ▶ Visual Narrative Comprehension
National Guideline for the Assessment and Diagnosis of Autism Spectrum Disorders in Australia Giacomo Vivanti1 and Andrew Whitehouse2,3 1 A.J. Drexel Autism Institute, Drexel University, Philadelphia, PA, USA 2 Telethon Kids Institute, University of Western Australia, Nedlands, WA, Australia 3 Research Section (Psychology), University of Western Australia, Crawley, WA, Australia
Definition See Also ▶ Narrative Assessment ▶ Pragmatics
References and Reading Capps, L., Losh, M., & Thurber, C. (2000). The frog ate a bug and made his mouth sad: Narrative competence in children with autism. Journal of Abnormal Child Psychology, 28, 193–204. Colle, L., Baron-Cohen, S., Wheelwright, S., & van der Lely, H. K. (2008). Narrative discourse in adults with high functioning autism or Asperger syndrome. Journal of Autism and Developmental Disorders, 38, 28–40. Losh, M., & Capps, L. (2003). Narrative ability in highfunctioning children with autism or Asperger’s syndrome. Journal of Autism and Developmental Disorders, 33(3), 239–251. Ochs, E., & Capps, L. (2001). Living narrative: Creating lives in everyday storytelling. Cambridge: Harvard University Press.
The Australian National Guideline for the Assessment and Diagnosis of Autism Spectrum Disorders is a clinical Guideline comprising 70 recommendations designed to “create greater consistency in diagnostic practices across the country to ensure individuals on the autism spectrum and their families can receive the optimal clinical care.”
Historical Background Australia is a high-income country with a population of approximately 25 million people spread over a geographical area around the same size as the USA or continental Europe. The prevalence of ASD in Australia is estimated between 1 and 2.5% (Randall et al. 2016). The Australian system of government includes a central federal government and six states which,
National Guideline for the Assessment and Diagnosis of ASD
together with two self-governing territories, have their own constitutions, parliaments, governments, law, as well as systems for health, education, and community services, including those impacting individuals with disability and autism spectrum disorder (ASD). Local government is the third tier of government in Australia, with each city or municipal area providing services for individuals with disability, including those with ASD. As previous report commissioned by the Cooperative Research Centre for Living with Autism (Autism CRC) and the Commonwealth Department of Social Services, variability between states and territories in diagnostic practices and standards complicates the provision of ASD diagnosis in Australia (Taylor et al. 2016). For example, some states require diagnostic evaluations to be conducted by multidisciplinary teams, while in other states, diagnostic decisions can be made by a sole clinician. Disparity in resources and in the availability of trained clinicians between urban and rural or remote areas was highlighted as a further challenge. An additional source of fragmentation is the difference in diagnostic standards between the health, education, and disability public services at the state and federal level, resulting in the risk that diagnostic decisions provided by one service (e.g., health system) would not be recognized by another system (e.g., the school system). As highlighted in the report, uneven diagnostic standards involve the risk of unequal service provision.
Current Knowledge In response to the challenges highlighted in the 2014 report, the National Disability Insurance Agency (an agency of the Australian federal government) sponsored the creation of the first Australian National Guideline for the Assessment and Diagnosis of Autism Spectrum Disorders, which was developed by the Autism CRC with the stated purpose to “create greater consistency in diagnostic practices across the country to ensure individuals on the autism spectrum and their
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families can receive the optimal clinical care.” The specific objectives were to develop a guideline that: 1. Describes a rigorous framework for accurately determining whether an individual meets diagnostic criteria for ASD 2. Outlines a comprehensive approach to identify related support needs 3. Contains sufficient flexibility to apply to the assessment of a child, adolescent, or adult of any age, gender, cultural or language background, communication or intellectual capacity, and medical complexity, living anywhere in Australia 4. Describes a feasible process for clinical service providers to administer across the full breadth of community settings in Australia, including public and private healthcare settings 5. Meets the needs and expectations of individuals being assessed and their caregivers Published in 2018, the guideline content was developed by an expert committee chaired by Andrew Whitehouse (Whitehouse et al. 2018). The expert committee was responsible for conducting a detailed research process that collected and collated new and existing scientific, clinical, and consumer knowledge and generating clinical recommendation that would best apply to the local context. The guideline is notable for the extensive consultation with a range of stakeholders, including family advocates and self-advocates, and the extensive research to assess the evidence. The development process included a steering committee, which comprised members of the national peak bodies of professions commonly involved in the diagnosis and management of individuals on the autism spectrum, as well as members of service providers and advocacy groups (including adults with a diagnosis of autism). Face-to-face consultation workshops were also held in each state of Australia, as well as a “virtual” workshop held via videoconference that focused on issues for rural consumers. An open feedback process also allowed any individual in Australia to comment on a draft version of the guideline.
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National Guideline for the Assessment and Diagnosis of ASD
The guideline was developed in accordance with six guiding principles: 1. Evidence based: Clinical diagnosis is most effective and safe when it is based on rigorous scientific evidence. 2. Individual and family centered: The primary sources of information required during an assessment of ASD concerns are the individual undergoing assessment and their family members (most notably, caregivers and support people). 3. Holistic framework: An individual is far more than the autistic behaviors, and an exclusive focus on the evaluation of ASD behaviors during the diagnostic process will fail to provide an adequate clinical appraisal of the individual. 4. Strengths focused: The strengths of an individual and their family are as important for service delivery as identifying their challenges. 5. Equity: All individuals, regardless of age, gender, cultural background, socioeconomic status, or geographical location, must be able to access a timely and rigorous assessment. 6. Lifespan perspective: People continue to grow and change throughout their lives as they are faced with new tasks, challenges, and opportunities, and consideration in clinical decisionmaking of the lifetime of a client is critical to providing optimal clinical care. There are several key features of the guideline. First, the guideline is notable for the adoption of a person-centered approach, including the indication that the child’s profile of strengths and needs, rather than the diagnostic status alone, should be used to make decisions on treatment and services. Second, the guideline adopts a multistage approach to diagnosis. This includes a comprehensive needs assessment (including an assessment of functioning and medical evaluation), followed by a diagnostic evaluation by a single clinician and the involvement of a larger diagnostic team for more complex cases. Third, the guideline provides recommendations around how, where, and when each stage of the diagnostic process should occur and also the competencies for professionals involved in the diagnostic process.
Finally, the guideline provides a detailed summary of the latest scientific and clinical knowledge about how the behaviors used to diagnose autism can vary according to certain factors. These factors include age, intellectual and/or communication capacity, sex and/or gender, culturally and linguistically diverse backgrounds, and other complex psychosocial factors. Each of the 70 clinical recommendations is linked to an individual table that describes the evidence underpinning a given recommendation from various scientific, clinical, or community sources. In July 2018, the guideline was endorsed by the peak organization in Australia for health evidence, the National Health and Medical Research Council, as representing optimal clinical care.
Future Directions The Australian government will release a nationwide implementation plan for the guideline in the second half of 2019. The guideline is to be updated every 5 years, with the next update scheduled in 2023.
See Also ▶ Australia and Autism
References and Reading Randall, M., Sciberras, E., Brignell, A., Ihsen, E., Efron, D., Dissanayake, C., & Williams, K. (2016). Autism spectrum disorder: Presentation and prevalence in a nationally representative Australian sample. Australian and New Zealand Journal of Psychiatry, 50(3), 243–253. Taylor, L. J., Eapen, V., Maybery, M. T., Midford, S., Paynter, J., Quarmby, L., . . . Whitehouse, A. J. O. (2016). Diagnostic evaluation for autism spectrum disorder: A survey of health professionals in Australia. BMJ Open, 6(9). https://doi.org/10.1136/ bmjopen-2016-012517. Whitehouse, A. J. O., Evans, K., Eapen, V., & Wray, J. (2018). A national guideline for the assessment and diagnosis of autism spectrum disorders in Australia. Brisbane: Cooperative Research Centre for Living with Autism.
National Research Council
National Research Council Catherine Lord1,2 and Sarah Butler1 1 Center for Autism and the Developing Brain, New York-Presbyterian Hospital/Westchester Division, White Plains, NY, USA 2 UCLA, Los Angeles, CA, USA
Major Areas or Mission Statement The National Research Council (NRC) is part of the National Academy of Sciences in the United States. It is a private, nonprofit society of distinguished scholars engaged in scientific research, mandated by Congress to advise the US federal government on scientific matters. The relevance to autism is that the NRC published a report in 2001 integrating scientific evidence concerning the effects and features of early intervention programs for children with autism. This report was written by an interdisciplinary committee of experts from education, psychology, psychiatry, neurology, speech-language pathology, and augmentative communication convened by the NRC for several years specifically for this project. The charge to the committee was to review the scholarly literature, including research, theory, and policies, to create a framework for evaluating the scientific evidence concerning educational interventions for children under age 8 who have autism. The charge also included specific suggestions to consider the role of early intervention, diagnosis and classification, inclusion, assistive technology, and the rights of children with autism within the United States (Individuals with Disabilities Education Act).
Landmark Contributions The NRC report is similar to clinical guidelines provided by organizations in other countries (such as by the National Health Service in the United Kingdom) and from particular groups within the United States (e.g., National Autism Standards project; Wilczynski et al. 2009; American
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Academy of Neurology; Filipek et al. 2000) but differed from some other reports in that the NRC committee members were well-known scholars from a range of disciplines and theoretical perspectives appointed by a panel of eminent scientists at the Academy, as opposed to members of a specific professional organization (e.g., American Academy of Pediatrics 2007) or a particular bureaucratic entity (e.g., National Health Service or New York Board of Health; New York State Department of Health Early Intervention Program 2005) or a self-nominated group (National Autism Standards; Wilczynski et al. 2009). The committee agreed on standards with which they would evaluate research. Public meetings were held in which researchers and agencies presented information; much of this information is summarized in a series of papers published in the Journal of Autism and Developmental Disorders in 2002 (Baranek 2002; Goldstein 2002; Horner et al. 2002; Kasari 2002; Mandlawitz 2002; McConnell 2002; Turnbull et al. 2002; Wolery and Garfinkle 2002). The committee achieved consensus for a number of recommendations which helped to shape educational services for young children with autism in the United States. The NRC report has been used by school systems and state agencies as well as the federal government and families as a guideline for scientifically based approaches to diagnosis, education, treatment, and policies. Major recommendations include (see National Research Council [NRC] 2001): 1. Children with any autism spectrum disorder (autism, Asperger syndrome, atypical autism, PDD-NOS, childhood disintegrative disorder, Rett syndrome), regardless of level of severity or function, should be eligible for special education services within the category of autism, as appropriate to the child’s needs. 2. Early identification and screening for autism, as is done for vision and hearing, should be promoted through research and training of first-line professionals. 3. Children should receive multidisciplinary assessments as soon as possible after identification. Follow-up assessments 1–2 years after the initial evaluation should be considered
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standard because of the many changes that occur during preschool years in autism. Educational approaches to autism must include provision of information to families as well as ongoing consultation and individualized problem-solving and an opportunity to learn techniques for teaching their children new skills and reducing difficult behaviors. Families’ concerns and perspectives should help shape educational planning. Ongoing measurement of treatment objectives and progress must be documented frequently and interventions adjusted accordingly. Objectives should be accomplished within a year, and interventions or objectives should be changed if there is little progress in 3 months. Objectives should be behaviors that are anticipated to affect a child’s participation in education, the community, and/or the family and include generalization across environments. Educational services should begin as soon as a child is suspected to have an autism spectrum disorder. Services should include a minimum of 25 h a week, 12 months a year in which a child is engaged in systematically planned, developmentally appropriate educational activities directed toward identified objectives. What constitutes these hours and how structured the interventions are may vary dramatically depending on the child’s age and abilities, the priorities, and supports available to a family within a community but should be specified individually for each child. Priorities of focus are functional spontaneous communication, social instruction delivered throughout the day in various settings, cognitive development, play, and academics (when appropriate). As much as is appropriate for an individual child, specialized instruction should take place in a setting in which ongoing positive social interactions can occur with typically developing children. Coordinated, systematic strategies should be developed to fund interventions in local communities and schools so that the cost is
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not borne by parents, caregivers, or local school systems and so that there is continuity of care. 9. A federal joint agency task force should be created to address policy issues related to autism spectrum disorders (see US Interagency Autism Coordinating Council; http://iacc.hhs.gov/). It was recommended to the Office of Special Education Programs that personnel preparation programs for teaching of children with autism be accelerated. 10. Standards for adequate description and design for intervention studies were recommended in order to allow more useful interpretation of results, particularly given the range of skills of children with autism. A call was made for stricter requirements to tie measures of efficacy to program development in autism. 11. Comparisons of different interventions were prioritized as well as the development of research designs that identify the “active ingredients” of interventions and that consider interactions between child and family variables and the effectiveness of different approaches.
Major Activities The NRC Committee on the Effectiveness of Early Intervention was a committee convened only for this specific project and so is no longer in effect. The National Research Council, along with the Institute of Medicine and National Academy of Engineering, continues to provide reports and makes recommendations as charged by the National Academy of Science and the US federal government on other issues.
References and Reading American Academy of Pediatrics. (2007). Autism: Caring for children with autism spectrum disorders: A resource toolkit for clinicians. ElkGrove Village: Author.
Natural History Baranek, G. T. (2002). Efficacy of sensory and motor interventions for children with autism. Journal of Autism and Developmental Disorders, 32(5), 397–422. Filipek, P. A., Accardo, P. J., Ashwal, S., Baranek, G. T., Cook, E. H., Jr., Dawson, G., et al. (2000). Practice parameter: Screening and diagnosis of autistic spectrum disorders – Report of the Quality Standards Subcommittee of the American Academy of Neurology and the Child Neurology Society. Neurology, 55(4), 468–479. Goldstein, H. (2002). Communication intervention for children with autism: A review of treatment efficacy. Journal of Autism and Developmental Disorders, 32(5), 373–396. Horner, R. H., Carr, E. G., Strain, P. S., Todd, A. W., & Reed, H. K. (2002). Problem behavior interventions for young children with autism: A research synthesis. Journal of Autism and Developmental Disorders, 32(5), 423–446. Kasari, C. (2002). Assessing change in early intervention programs for children with autism. Journal of Autism and Developmental Disorders, 32(5), 447–461. Koegel, L. K., LaZebnik, C., Stone, W. L., DiGeronimo, T. F., Smerling, K., & Notboh, E. (2008). Autism speaks first 100 days kit. Retrieved Accessed 8 June 2012 from http://www.autismspeaks.org/family-services/toolkits/100-day-kit Mandlawitz, M. R. (2002). The impact of the legal system on educational programming for young children with autism spectrum disorder. Journal of Autism and Developmental Disorders, 32(5), 495–508. McConnell, S. R. (2002). Interventions to facilitate social interaction for young children with autism: Review of available research and recommendations for educational intervention and future research. Journal of Autism and Developmental Disorders, 32(5), 351–372. National Research Council. (2001). Educating children with autism. Committee on Educational Interventions for Children with Autism (C. Lord & J. P. McGee, Eds.). Washington, DC: National Academy Press. New York State Department of Health Early Intervention Program. (2005). Report of the guideline recommendations: Autism/pervasive developmental disorders, assessment and intervention for young children (age 0–3 years). Retrieved Accessed 8 June 2012 from http://www.health.ny.gov/community/infants_chil dren/early_intervention/disorders/autism/ Turnbull, H. R., III, Wilcox, B. L., & Stowe, M. J. (2002). A brief overview of special education law with focus on autism. Journal of Autism and Developmental Disorders, 32(5), 479–493. Wilczynski, S., Green, G., Ricciardi, J., Boyd, B., Hume, A., Ladd, M., et al. (2009). National Standards Report: The national standards project – addressing the need for evidence- based practice guidelines for autism spectrum disorders). Randolph: The National Autism Center. Retrieved Accessed 8 June 2012 from http://
3075 www.nationalautismcenter.org/pdf/NAC%20Stan dards%20Report.pdf Wolery, M., & Garfinkle, A. N. (2002). Measures in intervention research with young children who have autism. Journal of Autism and Developmental Disorders, 32(5), 463–478.
Natural Environment Andrea McDuffie MIND Institute University of California-Davis, Sacramento, CA, USA
Definition Natural Environment Intervention (NEI) or Natural Environment Training (NET) is a language intervention approach for children with autism that was developed by Sundberg and Partington (1999) based upon Skinner’s Analysis of Verbal Behavior (1957). NET represents an extension of ABA and discrete trial training approaches. In a manner similar to Pivotal Response Training and Milieu Language Teaching, NET emphasizes the use of intrinsically motivating materials, focuses on the child’s immediate interests (following the child’s lead), and teaches in the child’s everyday environment. This type of approach addresses child motivation and generalization issues by using materials that are reinforcing for the child and teaching within the same contexts that skills will be used. In addition, by making available materials that are reinforcing to the child, NET focuses on allowing the child to initiate teaching episodes to improve manding (requesting) and to increase the initiation of spontaneous communication attempts.
Natural History ▶ Course of Development ▶ Longitudinal Research in Autism
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Natural Language Paradigm Lisa Shull Division of Neurodevelopmental and Behavioral Pediatrics, Golisano Children’s Hospital, University of Rochester School of Medicine, Jamaica, NY, USA Clinical Psychology, Long Island University, Brooklyn, NY, USA
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in functional speech. In an effort to address this inadequacy, researchers started to explore other ways to teach communication, such as sign language and symbol language. This shortcoming also served as the impetus for NLP. As the treatment evolved, the developers focused increasingly on enhancing children’s motivation and later recast NLP as Pivotal Response Treatment or PRT (Koegel et al. 1989, 1999).
Rationale or Underlying Theory Definition The Natural Language Paradigm (Koegel et al. 1987) is a teaching approach targeting verbal language acquisition for children with severe language deficits associated with autism. Compared to more structured behavioral treatments (e.g. discrete trial teaching), this loosely structured intervention aims to create a learning environment for the child that more closely resembles a natural language speaking situation and apply teaching approaches based on operant conditioning in that environment. This combination is intended to increase and generalize the child’s use of communicative speech across settings.
Historical Background NLP has its origins in work on incidental teaching conducted in the 1960s (e.g., Hart and Risley 1968, 1974). In the 1980s, Risley and McGee (McGee et al. 1983, 1985) began studying ways to adapt incidental teaching for children with autism. Combining this work and research by Koegel, Schreibman, and others on the unique learning styles of these children (e.g., a tendency to focus on only one component of a complex stimulus), Koegel and colleagues developed the Natural Language Paradigm. They presented it as an alternative to more structured operant teaching procedures (e.g. Hewett 1965; Lovaas 1966, 1977). Although there was evidence that these structured procedures were effective in teaching vocabulary and other language skills, many argued that they did not demonstrate sufficient generalized improvement
The rationale for the Natural Language Paradigm is largely derived from past research indicating that children with autism spectrum disorders often lack the motivation necessary to communicate or learn new skills (e.g. Dunlap 1984; Dunlap and Egel 1982). The intervention strategies, such as the use of reinforcers directly related to the communication (e.g., giving a child access to an item that he or she has requested), the reinforcement of all attempts to communicate, the frequent variation and sharing of tasks and stimulus materials, and the use of incidental teaching all are designed to contribute to increasing the child’s motivation to interact and communicate with the therapist. The presumption is that, once the child understands, he or she is able to control access to a desired item or situation through initiating and responding to verbal communication, his or her motivation to use language increases. Additionally, the developers of NLP regard each of these strategies as a key component of the language acquisition process that typically developing children go through. By refocusing the treatment on more naturally occurring speech interactions, rather than structured or didactic behavioral teaching methods, NLP aims to increase the generalization of language skills to settings outside of the clinic (Koegel et al. 1987).
Goals and Objectives The intervention is designed to increase the child’s use of spontaneous and appropriate language, encourage the generalization of these skills
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to other settings, and increase social interactions and child-initiation of activities.
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Ideal treatment participants meet criteria for autism as defined in the Diagnostic and Statistical Manual of Mental Disorders (American Psychiatric Association 1994) and demonstrate severe language deficits and delays associated with the disorder. These children are generally classified as nonverbal, meaning they produce no spontaneous words or utterances, but may produce other types of vocalizations (Koegel et al.). The participants in the few studies specifically examining the NLP have been children between 4 and10 years of age (Koegel et al.; Delprato 2001).
and durable increases in both immediate and deferred utterances,” while these same subjects showed less, if any, improvement in these areas while in the analog teaching condition. More importantly, these gains were maintained and generalized to situations outside of the clinical setting by subjects treated with the NLP, but not by those in the analog teaching condition. Later studies comparing naturalistic language approaches to traditional behavior treatments reported similar results (Delprato 2001), which served to confirm the findings from the initial study by Koegel et al. However, because the studies on NLP have involved on a small number of subjects and focused on a very narrow range of communication skills, general conclusions cannot be drawn regarding the relative efficacy of NLP compared to other teaching approaches (Goldstein 2002, JADD).
Treatment Procedures
Outcome Measurement
NLP is designed as an individualized therapy that closely approximates the interactions and components present in a natural speaking situation. To accomplish this, the therapist presents a series of play trials using several stimulus items that have been previously selected by the child. These items are varied between trials and should be familiar to the child from his or her everyday environment. At the beginning of each trial, the therapist presents an item and then models appropriate verbal responses and play actions with the stimulus items to prompt the child to use speech. All of the child’s attempts at verbal communication are repeated and expanded upon by the therapist and reinforced with praise and opportunities to play with the stimulus item.
Treatment outcomes for the NLP intervention have most often been measured using spontaneous language samples, or “probes,” throughout the course of treatment. Probes assess the total number of utterances emitted by the child over a specified period of time and are conducted in a variety of settings, including intervention sessions, break periods, and during free play. The child’s utterances are coded and categorized as one three types: imitative, deferred imitation, or spontaneous (Koegel et al.).
Treatment Participants
Efficacy Information Due to the relatively brief existence of the NLP in its original form, efficacy information is somewhat limited. In the original comparative study, each subject participated in both the analog teaching condition and the NLP condition consecutively. Koegel and colleagues reported that subjects, after treatment with the NLP, demonstrated “steady
Qualifications of Treatment Providers In Koegel and colleagues’ initial study, the clinicians administering the NLP intervention were supervised hearing and speech students with advanced training in behavior modification and language treatment.
See Also ▶ Incidental Teaching ▶ Pivotal Response Training
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References and Reading American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (4th ed.). Washington, DC: American Psychiatric Association. Delprato, D. (2001). Comparisons of discrete-trial and normalized behavioral language intervention for young children with autism. Journal of Autism and Developmental Disorders, 31(3), 315–325. Dunlap, G. (1984). The influence of task variation and maintenance tasks on the learning and affect of autistic children. Journal of Experimental Child Psychology, 37, 41–46. Dunlap, G., & Egel, A. L. (1982). Motivational techniques. In R. L. Koegel, A. Rincover, & A. L. Egel (Eds.), Educating and understanding autistic children. San Diego: College-Hill Press. Goldstein, H. (2002). Communication intervention for children with autism: A review of treatment efficacy. Journal of Autism and Developmental Disorders, 32(5), 373–396. Hart, B., & Risley, T. (1968). Establishing use of descriptive adjectives in the spontaneous speech of disadvantaged preschool children. Journal of Applied Behavior Analysis, 1, 109–120. Hart, B., & Risley, T. (1974). Using preschool materials to modify the language of disadvantaged children. Journal of Applied Behavior Analysis, 7(2), 243–256. Hewett, F. M. (1965). Teaching speech to an autistic child through operant conditioning. Journal of Orthopsychiatry, 35, 927–936. Koegel, R. L., O’Dell, M. C., & Koegel, L. K. (1987). A natural language teaching paradigm for nonverbal autistic children. Journal of Autism and Developmental Disorders, 17(2), 187–200. Koegel, R. L., Schriebman, L., Good, A., Cernigla, L., Murphy, C., & Koegel, L. K. (1989). How to teach pivotal behaviors to children with autism: A training manual. Santa Barbara: California. Koegel, L. K., Koegel, R. L., Harrower, J. K., & Carter, C. M. (1999). Pivotal response intervention I: Overview of approach. Journal of the Association for Persons with Severe Handicaps, 24(3), 174–185. Laski, K. E., Charlop, M. H., & Schreibman, L. (1988). Training parents to use the Natural Language Paradigm to increase their autistic children’s speech. Journal of Applied Behaviour Analysis, 21(4), 391–400. Lovaas, O. I. (1966). A program for the establishment of speech in psychotic children. In J. K. Wing (Ed.), Early childhood autism. Boston: Houghton Mifflin. Lovaas, O. I. (1977). The autistic child: Language development through behaviour modification. New York: Irvington. McGee, G. G., Krantz, P. J., Mason, D., & McClannahan, L. E. (1983). A modified incidental-teaching procedure for autistic youth: Acquisition and generalization of receptive object labels. Journal of Applied Behavior Analysis, 16(3), 329–338. McGee, G. G., Krantz, P. J., & McClannahan, L. E. (1985). The facilitative effects of incidental teaching on preposition use by autistic children. Journal of Applied Behavior Analysis, 18(1), 17–31.
Naturalistic Behavioral Intervention ▶ Pivotal Response Training
Naturalistic Interventions Kristen Ashbaugh1 and Robert L. Koegel2,3 1 Koegel Autism Center, University of California, Santa Barbara, CA, USA 2 Koegel Autism Center/Clinical Psychology, Gevirtz Graduate School of Education, University of California, Santa Barbara, CA, USA 3 Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
Definition Naturalistic interventions are behavior teaching procedures that occur in the context of naturally occurring activities (Koegel et al. 1987; McGee et al. 1984; Vismara and Rogers 2010). Intervention procedures embed teaching opportunities within the environment where the skills will be needed (Vismara & Rogers).
Historical Background Individuals with an autism spectrum disorder exhibit deficits in communication, social interaction, and restricted and repetitive behavior (American Psychiatric Association [APA] 2000). Intensive early intervention programs have been shown to produce positive outcomes in a large percentage of the children diagnosed as having autism (Paul 2008; Harper et al. 2008). However, Carr (1983) reviewed behavior modification procedures for language instruction of children with autism and identified a weakness in many operant language programs. In intervention strategies employed in highly structured environments, treatment gains that were observed in the clinic
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rarely generalized to other settings and behaviors were not often maintained (Mirenda-Linne and Melin 1992). Many operant language programs also frequently resulted in cue dependency, rote responding, and little spontaneity of the target behavior (Miranda-Linne & Melin). Furthermore, treatment programs that did succeed in improving language skills required a great amount of time for even a minimal vocabulary (Koegel et al. 1987). In response to these difficulties, many researchers investigated new behavioral interventions. In a study by Koegel et al. (1987), the effects of a traditional analog approach were compared to a natural language teaching condition (Koegel and Koegel 2006). The analog approach consisted of many successive trials, with each item chosen and presented by the clinician until the child reached a specific criterion (Koegel et al. 1987). In the natural language paradigm, procedures were modified to follow a more natural language-speaking situation and incorporated child choice, maintenance tasks, rewarding attempts to respond, and natural reinforcers directly related to the task (Koegel et al.). It was shown that children in the natural language condition had a higher rate and accuracy of correct responding, a greater increase in spontaneous utterances, and a greater generalization of language skills outside of the clinic setting (Koegel and Koegel 2006). In 1986, the Congress issued a federal mandate PL 99-457 to provide early intervention for young children with disabilities in the least restrictive and most typical environments of the children (Kaiser and Trent 2007). Subsequent research has expanded the benefits of naturalistic interventions and has addressed the limitations of analog procedures that occur in highly structured settings (Koegel et al. 1987; Warren and Gazdag 1990). Data has shown that naturalistic interventions result in a higher generalization and maintenance of target behaviors (Barnet et al. 1993; Koegel et al. 1987, 1998; Mirenda-Linne and Melin 1992; Vismara and Rogers 2010). These results have influenced current treatment programs to include procedures across multiple settings and within the child’s natural environment in order to
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effectively increase language, socialization, and behavioral skills for children with autism (Vismara & Rogers).
Rationale or Underlying Theory Naturalistic interventions embed procedures in the context of naturally occurring activities and have shown to increase generalization, create more spontaneity, and improve the maintenance of the target behavior (Barnet et al. 1993; Ingersoll et al. 2007; Koegel et al. 1987, 1998; MirendaLinne and Melin 1992; Vismara and Rogers 2010). As opposed to analog procedures that are directed by the clinician, involve drilling of the target behavior until acquisition is reached, and incorporate arbitrary reinforcers, natural interventions manipulate traditional variables (Koegel et al. 1987; Koegel and Koegel 2006). In naturalistic interventions, stimulus items are functional and chosen by the child, communicative attempts are rewarded, and natural reinforcers that directly relate to the task are employed (Koegel et al.; Koegel and Williams 1980; Miranda-Linne and Melin 1992). Procedures are implemented in the environment where the skills will be needed (i.e., the home, school, or community), so gains are rapidly produced, able to be generalized, and maintained after intervention (Brown and Odom 1995; Koegel and Koegel 2006; McGee et al. 1984).
Goals and Objectives Naturalistic intervention procedures were developed with the following goals: 1. Embed treatment procedures in the context of naturally occurring activities and incorporate child choice, task variation, rewarding attempts, and direct and natural reinforcers to increase acquisition of language skills (Kaiser and Trent 2007; Koegel et al. 1987). 2. Improve the generalization and spontaneous use of target language skills across various settings and with typical peers and individuals
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in the child’s daily routine (Hart and Risley 1975; McGee et al. 1984). 3. Increase the maintenance of treatment gains following intervention (Mirenda-Linne and Melin 1992). 4. Create a nonaversive treatment program and improve the affect of children with autism and their treatment providers (Vismara and Rogers 2010).
Treatment Participants Children with autism receive naturalistic interventions for deficits in social interaction, communication skills, and appropriate behavior (Williams and Williams 2010). A unique aspect of naturalistic interventions is the high level of participation of parents, paraprofessionals, and typical peers in the child’s natural environment (Kaiser and Trent 2007; Kalyva and Avramidis 2005; Koegel and Koegel 2006). The procedures are designed for children with autism to receive treatment through interactions with individuals in their everyday life. Research shows that intensive intervention throughout the day is effective to increase the social, communication, and behavioral skills of children with autism; however, resources can be limited (Paul 2008). In order for children with autism to receive “round-the-clock” intervention services, the individuals that they interact with on a daily basis must be trained to implement treatment procedures (Barnet et al. 1993). In naturalistic intervention programs, parents, educators, and typical peers are trained in order to increase the quantity and availability of intervention across natural settings (Brown and Odom 1995; Koegel et al. 1996). Parents are active participants in naturalistic interventions (Koegel et al. 1978, 1996). Research has shown that parents of children with autism can effectively implement behavioral, social, and communication procedures (Albert and Kaiser 1992; Koegel and Koegel 2006). Naturalistic treatment programs often incorporate a parent education component to teach caregivers how to employ intervention procedures and embed opportunities into the child’s daily living routine
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(Koegel and Koegel 2006). Effective parent participation can increase positive outcomes as well as have a collateral effect on family interactions (Koegel et al. 1996). Paraprofessionals and typical peers are also significant contributors to natural intervention procedures. Increasing the inclusion of children with autism in regular classrooms and creating opportunities to engage in interactions with typical peers have been shown to increase the child’s social development (Brown and Odom 1995; Fisher and Meyer 2002). In naturalistic intervention programs, teachers and counselors are trained in the treatment procedures and prompt typically developing children to engage in verbal interaction and social activities with children with autism (Koegel and Koegel 2006). Parents, paraprofessionals, teachers, and typical peers all collaborate as key participants in naturalistic intervention procedures for children with autism.
Treatment Procedures Naturalistic interventions use treatment procedures employed in the context of naturally occurring activities (McGee et al. 1984). Procedures are implemented in inclusive environments, and treatment strategies are embedded into everyday activities across multiple settings (Vismara and Rogers 2010). There are numerous teaching programs that use naturalistic interventions, and all incorporate the following procedural components or variations thereof. Treatment protocols take place in the child’s natural environment (i.e., home, school, and community), and procedures are based on the foundation of child choice and the use of direct and natural reinforcers (Gillum et al. 2003; Kaiser and Trent 2007; Koegel et al. 1987, 1998; Koegel and Williams 1980). Common naturalistic methods to increase language acquisition are milieu therapy, incidental teaching, and pivotal response treatment (Hart and Risley 1975; Kaiser and Trent 1992; Koegel and Koegel 2006). Naturalistic treatment programs are designed to fit each child’s individual routine, and procedures incorporate child choice of stimulus
Naturalistic Interventions
materials (Hart and Risley 1975; Kaiser and Trent 1992; Koegel et al. 1998). Child choice refers to following the child’s lead and using childpreferred stimulus items, topics, toys, and interests (Koegel et al. 1999; Thiemann and Warren 2004; Yoder et al. 1995). Instead of the traditional approach in which the clinician arbitrarily selects a stimulus item, naturalistic procedures involve the clinician presenting stimulus items that the child selected from a pool of items (Koegel et al. 1987). The treatment provider uses the child’s eye gaze, reaching, touching, etc., to determine the preferred items and uses those items in the intervention. Compared to the teacher selecting stimulus items, child choice of toys results in higher levels of engagement with toys, which creates a foundation from which more complex play behaviors can be taught (Reinhartsen et al. 2002). Furthermore, child choice has been shown to decrease problem behavior as well as increase autonomy (Reinhartsen et al.). In addition to following the child’s lead, naturalistic interventions incorporate direct and natural rewards (Koegel and Williams 1980; Williams et al. 1981). This means that the child is immediately reinforced with a reward that is directly related to the language opportunity (Koegel and Koegel 2006). For example, if the child makes an attempt to say “milk,” then he or she will be immediately be given the milk so that a connection is made between their verbalization and the desired item (Koegel and Koegel 2006). This varies from the traditional approach which rewarded the child with an arbitrary item, for example, the child would receive an M&M’s for any correct response that he or she provided (Koegel et al. 1987). This procedural component strengthens the connection between verbalizations and consequences (Williams et al. 1981). Several natural interventions have been developed around the basic naturalistic treatment procedures for children with autism. Milieu therapy, incidental teaching, and pivotal response treatment all use naturalistic procedures to improve symptoms of autism (Hart and Risley 1968; Kaiser and Trent 1992; Koegel and Koegel 1996). Milieu language intervention is a common naturalistic treatment that uses the procedures of
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incidental teaching, modeling, mand-modeling, and time delay (Albert and Kaiser 1992; Warren and Gazdag 1990). Hart and Risley (1968a) developed the incidental teaching procedure to demonstrate the effectiveness of child-initiated interactions in treatment. Incidental teaching uses occasions when the child is verbally or nonverbally requesting materials or assistance as beneficial therapy opportunities (Thiemann and Warren 2004). Teachers, parents, or other children wait to prompt the child until he or she shows an interest by requesting or reaching for an object in the natural environment (Hart and Risley 1975). Once the child is interested, the opportunity exists for a model procedure, mand-model procedure, or time delay procedure (Albert and Kaiser 1992). A model procedure involves the adult producing a syllable, word, phrase, or sentence with the intention that the child will imitate the production (Albert and Kaiser 1992). Milieu therapy also uses the mand-model technique in which the adult presents a question or instruction for the child to verbalize within a naturally occurring context (Warren and Gazdag 1990; Yoder et al. 1995). Time delay is another procedure often incorporated into milieu therapy (Albert and Kaiser 1992; Yoder et al. 1995). Time delay involves the adult presenting a stimulus and then waiting anywhere between 5 and 30 s for the child’s response (Mancil 2009). Pivotal response treatment (PRT) is another naturalistic intervention model that targets pivotal areas of a child’s development, such as motivation, responsivity to multiple cues, selfmanagement, and social initiations (Koegel et al. 1999). Instead of targeting each individual behavior, PRT focuses on pivotal areas that when changed generally produce large collateral improvements in other areas (Koegel and Koegel 2006). Research has shown that improving the child’s motivation level during treatment sessions can accelerate language learning, decrease problem behavior such as aggression and tantrums, and increase the affect of the child and the treatment provider (Koegel et al. 1997, 1987, 1998). PRT uses naturalistic and motivational procedures of child choice, task variation, interspersing maintenance tasks, rewarding attempts, and the use of
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direct and natural reinforcers, with the overall goal of providing individuals with autism the social and educational skills to succeed in inclusive settings (Koegel and Koegel 2006). As opposed to the traditional analog approach that reinforces only correct responses, PRT broadens the reinforcement contingency so that the child is rewarded if they make a clear and direct attempt at the target behavior (Koegel et al. 1987). For example, if a child says “ba” instead of “ball,” he or she will be given the ball as a natural reward for their verbal attempt to communicate. PRT treatment procedures are used to teach language, decrease disruptive/self-stimulatory behaviors, and increase social, communication, and academic skills (Koegel and Koegel 2006). In addition, floortime therapy, reciprocal imitation training, and positive behavioral support incorporate components of naturalistic procedures in their intervention techniques (Gillum et al. 2003; Ingersoll et al. 2007). Common treatment procedures used in these naturalistic intervention programs may include in vivo modeling, turn taking, shared control, priming, conversational recast, and self-management (Brown and Odom 1995; Camarata 1993; Gillum et al. 2003; Koegel and Koegel 1996; McGee et al. 1984).
Efficacy Information Naturalistic interventions are effective in improving generalization across settings, increasing spontaneous speech, improving maintenance following intervention, and incorporating parents and teachers into the treatment program (Ingersoll et al. 2007; Koegel et al. 1987, 1998; Koegel and Koegel 1996; Koegel and Williams 1980; McGee et al. 1984; Miranda-Linne and Melin 1992). Traditional analog procedures succeed in increasing language, social, play, and behavioral skills of children with autism, but the effectiveness is limited in regard to generalization and maintenance of treatment gains (Vismara and Rogers 2010). Subsequent research has led to the formation of naturalistic interventions, which diminish the limitations of traditional approaches
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and expand treatment procedures to the natural environment (Koegel et al. 1987). A key benefit in implementing naturalistic interventions is the high level of generalization that occurs in the absence of special programming (Ingersoll et al. 2007; McGee et al. 1984; Warren and Gazdag 1990). Although analog procedures can create positive outcomes, the treatment gains frequently do not carry over to natural settings (Brown and Odom 1995; Camarata 1993). In naturalistic interventions, treatment takes place in already existing activities, so the child learns how to utilize skills in the environment in which they are needed (McGee et al. 1984). Procedures can be embedded into everyday activities such as the home, the classroom, and the community, and generalization of language gains does not require additional teaching programs (Hart and Risley 1975; Vismara and Rogers 2010). For example, Hart and Risley (1968) showed that traditional teaching procedures were effective in generating adjective-noun combinations in that restricted setting, but the child’s spontaneous vocabularies increased with the use of environmental contingencies. Furthermore, results from a study by Brown and Odom (1995) show that naturalistic peer interventions can easily be incorporated into a school setting and can lead to gains in socialization that can be applied across multiple settings. Naturalistic interventions are also effective in improving the maintenance of treatment gains following intervention (Koegel et al. 1987). Miranda-Linne and Melin (1992) found that traditional methods can produce positive outcomes during home generalization probes, but the gains dissipated 1 week after intervention was completed. Incidental methods led to slower treatment gains, but the skills acquired were more permanent and continued to increase after teaching had been discontinued (Mirenda-Linne and Melin 1992). A similar study found that generalization from trained to spontaneous language skills was maintained during a 4.5-month follow-up probe when teaching was provided in naturally occurring environments (Koegel and Koegel 1996; McGee et al. 1984).
Naturalistic Interventions
Parents and families have found naturalistic interventions useful in expanding existing resources and reducing parental stress (Albert and Kaiser 1992; Koegel and Koegel 1996). According to Thiemann and Warren (2004), one of the primary challenges in treatment focused on communication is effectively implementing the intervention procedures into everyday practice. Including individuals in the child’s natural environment (i.e., parents and teachers) can increase the intervention opportunities and the child’s rate of progress (Barnet et al. 1993). In addition to improving and maintaining the child’s treatment gains with the parent education component, families who implement naturalistic treatment procedures report higher amounts of leisure time spent together (Koegel and Koegel 1996). When parents are incorporated into naturalistic interventions, they show increased affect, more interest, lower levels of stress, and a positive engagement of communication with their child with autism (Koegel et al. 1996). Naturalistic interventions are effective in increasing language acquisition, improving generalization and maintenance, and creating positive interactions for parents and family members with a child with autism.
Outcome Measurement The National Standards Project has found sufficient evidence to show that naturalistic teaching strategies produce favorable outcomes for individuals on the autism spectrum. Through a systematic review of 32 studies, it was found that naturalistic strategies increase communication, interpersonal skills, learning readiness, and play behavior (National Autism Center 2009). With respect to age, naturalistic interventions demonstrate favorable outcomes for children aged zero to nine that have a diagnosis of autism or pervasive developmental disorder-not otherwise specified (PDD-NOS) (National Autism Center 2009). A main diagnostic criterion of children with autism is deficits in communication and language skills (Mancil 2009). In 2009, the National Autism Center evaluated the literature and found
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empirical support that naturalistic intervention procedures improve nonverbal and verbal methods of sharing emotions and information. Outcome measures have shown that children receiving naturalistic treatments can improve their speech intelligibility and functional use of target sounds during conversation (Camarata 1993; Koegel et al. 1998). Additionally, there is evidence that the intervention techniques are effective at increasing the level of responding and initiating communication in children with autism (Koegel et al. 2003). Outcome measures also show that naturalistic treatment procedures improve interpersonal skills (National Autism Center 2009). Intervention strategies lead to increased play, social interaction, and assertions with peers (McGee et al. 1984; Stahmer and Ingersoll 2009; Vismara and Rogers 2010). Comprehensive social programs that embed natural procedures increase the level of appropriate conversation and peer interactions (Koegel and Frea 1993; Koegel et al. 2009). The National Autism Center also found sufficient evidence for support of treatment procedures as a method to increase learning success in complex skills. Studies regarding natural intervention were rated high in experimental rigor, quality of the dependent variable, evidence of treatment fidelity, demonstration of participant ascertainment, and generalization of data collected. Thorough examination of naturalistic strategies by the National Autism Center has found empirical evidence to show that the intervention procedures produce positive gains in many core areas of autism and can be successfully implemented for children of many ages on the autism spectrum.
Qualifications of Treatment Providers One key benefit of naturalistic interventions is the ease with which treatment providers can be taught to embed procedures into existing activities (Vismara and Rogers 2010). Treatment providers can be specialized therapists, members of the regular staff, or family members that are involved in
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the child’s daily life (Kaiser and Trent 1992; Kalyva and Avramidis 2005; Koegel and Koegel 2006). Due to the fact that intervention procedures are typically employed in a setting outside the clinic, it is especially important that treatment providers are well trained in intervention strategies. Interventionists, whether they are students, parents, or consultants, must be proficient in the protocols and routinely be given feedback on their performance. In order to successfully implement intervention procedures, treatment providers typically receive training in the specific intervention that includes lectures, observations, role-play practice, and a written exam on policies and procedures (National Research Council 2001). Furthermore, some intervention programs require audiotaped or videotaped interactions that show the provider implementing the intervention with children (Albert and Kaiser 1992). Treatment providers must meet fidelity of implementation in the intervention procedures in order for them to effectively employ the protocol. Treatment providers are typically trained in the intervention and then scored in vivo or through videotape by an observer. Fidelity of implementation is obtained when intervention procedures are performed correctly for a specified amount of time during the scoring period (Koegel and Koegel 2006). If intervention procedures do not meet the specified criterion level, then the trainer can identify areas that need improvement and may provide training on those specific components. Once the treatment providers are able to employ each intervention component at the specified level of fidelity, then they will be able to successfully perform the intervention with a child. Feedback should be given on a routine basis for ongoing tracking of child progress and treatment fidelity. Supervision can take place by direct observation of the treatment provider interacting with the child, teacher-collected data, and videotaped samples of intervention sessions (National Research Council 2001). In summary, naturalistic interventions reduce problems of generalization and maintenance. Further, they reduce family stress when compared to more structure intervention procedures. As such,
Naturalistic Interventions
research suggests the importance of providing naturalistic interventions for children with autism.
See Also ▶ Generalization and Maintenance ▶ Incidental Teaching ▶ Natural Language Paradigm ▶ Pivotal Response Training
References and Reading Albert, C., & Kaiser, A. (1992). Training parents as milieu language teachers. Journal of Early Intervention, 16(1), 31–52. American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders (4th ed., text rev.). Washington, DC: American Psychiatric Association. Barnet, D., Carey, K., & Hall, J. (1993). Naturalistic intervention design for young children: Foundations, rationales, and strategies. Topics in Early Childhood Special Education, 13(4), 430–444. Brown, W., & Odom, S. (1995). Naturalistic peer interventions for promoting preschool children’s social interactions. Preventing School Failure, 39(4), 38–43. Camarata, S. (1993). The application of naturalistic conversation training to speech production in children with speech disabilities. Journal of Applied Behavior Analysis, 26, 172–182. Carr, E. G. (1983). Behavioral approaches to language and communication. In E. Schopler & G. Mesibov (Eds.), Current issues in Autism (Communication problems in Autism, Vol. III). New York: Plenum. Fisher, M., & Meyer, L. H. (2002). Development and social competence after two years for students enrolled in inclusive and self-contained educational programs. Research & Practice for Persons with Severe Disabilities, 27(3), 165–174. Gillum, H., Camarata, S., Nelson, K., & Camarata, M. (2003). A comparison of naturalistic and analog treatment effects in children with expressive language disorder and poor preintervention imitation skills. Journal of Positive Behavior Interventions, 5(3), 171–178. Harper, C., Symon, J., & Frea, W. (2008). Recess is timein: using peers to improve social skills of children with autism. Journal of Autism and Developmental Disorders, 38(5), 815–826. Hart, B., & Risley, T. (1968a). Developing correspondence between the non-verbal and verbal behavior of preschool children. Journal of Applied Behavior Analysis, 1(4), 267–281. Hart, B., & Risley, T. (1968b). Establishing use of descriptive adjectives in the spontaneous speech of disadvantaged preschool children. Journal of Applied Behavior Analysis, 1, 109–120.
Naturalistic Interventions Hart, B., & Risley, T. (1975). Incidental teaching of language in the preschool. Journal of Applied Behavior Analysis, 8, 411–420. Ingersoll, B., Lewis, E., & Kroman, E. (2007). Teaching the imitation and spontaneous use of descriptive gestures in young children with Autism using a naturalistic behavioral intervention. Journal of Autism and Developmental Disorders, 37, 1446–1456. Kaiser, A. P., & Trent, J. A. (2007). Communication intervention for young children with disabilities: Naturalistic approaches to promoting development. In S. Odom, R. Horner, M. Snell, & J. Blacher (Eds.), Handbook of developmental disabilities. New York: Guilford Press. Kalyva, E., & Avramidis, E. (2005). Improving communication between children with Autism and their peers through the “circle of friends”: A small-scale intervention study. Journal of Applied Research in Intellectual Disabilities, 18(3), 253–261. Koegel, R. L., & Frea, W. D. (1993). Treatment of social behavior in Autism through the modification of pivotal social skills. Journal of Applied Behavior Analysis, 26, 369–377. Koegel, R. L., & Koegel, L. K. (2006). Pivotal response treatments for Autism. Baltimore: Paul H. Brookes. Koegel, R., & Williams, J. (1980). Direct versus indirect response-reinforcer relationships in teaching Autistic children. Journal of Abnormal Child Psychology, 8(4), 537–547. Koegel, R., Glahn, T., & Nieminen, G. (1978). Generalization of parent-training results. Journal of Applied Behavior Analysis, 11, 95–109. Koegel, R., O’Dell, M., & Koegel, L. (1987). A natural language paradigm for nonverbal Autistic children. Journal of Autism and Developmental Disorders, 17(2), 187–200. Koegel, R., O’Dell, M., & Dunlap, G. (1988). Producing speech use in nonverbal Autistic children by reinforcing attempts. Journal of Autism and Developmental Disorders, 18(4), 525–538. Koegel, R., Bimbela, A., & Schreibman, L. (1996). Collateral effects of parent training on family interactions. Journal of Autism and Developmental Disabilities, 26(3), 347–359. Koegel, L. K., Koegel, R. L., & Smith, A. (1997). Variables related to differences in standardized test outcomes for children with Autism. Journal of Autism and Developmental Disorders, 27, 233–243. Koegel, R., Camarata, S., Koegel, L., Ben-Tall, A., & Smith, A. (1998). Increasing speech intelligibility in children with Autism. Journal of Autism and Developmental Disorders, 28, 3. Koegel, L. K., Koegel, R. L., Harrower, J. K., & Carter, C. M. (1999). Pivotal response intervention I: Overview of approach. The Journal of the Association for Persons With Severe Handicaps, 24(3), 174–185. Koegel, R. L., Koegel, L. K., & Brookman, L. I. (2003). Pivotal response training for Autistic disorder. In A. E. Kazdin & J. Weisz (Eds.), Evidence-based psychotherapies for children and adolescents. New York: Guilford Publications.
3085 Koegel, L. K., Robinson, S., & Koegel, R. L. (2009). Empirically supported intervention practices for Autism spectrum disorders in school and community settings. In W. Sailor, G. Dunlap, G. Sugai, & R. Horner (Eds.), Handbook of positive behavior support (Issues and practices, pp. 149–176). New York: Springer. McGee, G., Krantz, P., & McClannahan, L. (1984). Conversational skills for Autistic adolescents: Teaching assertiveness in naturalistic game settings. Journal of Autism and Developmental Disorders, 14(3), 319–330. Mirenda-Linne, F., & Melin, L. (1992). Acquisition, generalization, and spontaneous use of color adjectives: A comparison of incidental teaching and traditional discrete-trial procedures for children with Autism. Research in Developmental Disabilities, 13, 191–210. National Autism Center. (2009). National standards project – addressing the need for evidence-based practice guidelines for Autism spectrum disorders. From http:// www.nationalautismcenter.org/about/national.php National Research Council. (2001). Educating children with Autism. Committee on educational interventions for children with Autism. In C. Lord & J. P. McGee (Eds.), Division of behavioral and social sciences and education. Washington, DC: National Academy Press. Paul, R. (2008). Interventions to improve communication in Autism. Child and Adolescent Psychiatric Clinics of North America, 17, 835–856. Reinhartsen, D., Garfinkle, A., & Wolery, M. (2002). Engagement with toys in two-year-old children with Autism: Teacher selection versus child choice. Research & Practice for Persons with Severe Disabilities, 27(3), 175–187. Stahmer, A., & Ingersoll, B. (2009). Inclusive programming for toddlers with Autism spectrum disorders: Outcomes from the children’s Toddler school. Journal of Positive Behavior Interventions, 6(2), 67–82. Thiemann, K., & Warren, S. F. (2004). Programs supporting young children’s language development. In R. E. Tremblay, R. G. Barr, & R. Peters (Eds.), Encyclopedia on early childhood development (online) (pp. 1–11). Montreal: Centre of Excellence for Early Childhood Development. Vismara, L., & Rogers, S. (2010). Behavioral treatments in Autism spectrum disorder: What do we know? Annual Review of Clinical Psychology, 6, 447–468. Warren, S., & Gazdag, G. (1990). Facilitating early language development with Milieu intervention procedures. Journal of Early Intervention, 14(1), 62–85. Williams, B., & Williams, L. (2010). Effective programs for treating Autism spectrum disorder: Applied behavior analysis models. New York: Routledge. Williams, J., Koegel, R., & Egel, A. (1981). Responsereinforcer relationships and improved learning in Autistic children. Journal of Applied Behavior Analysis, 14, 53–60. Yoder, P., Kaiser, A., Goldstein, H., Alpert, C., Mousetis, L., Kaczmarek, L., et al. (1995). An exploratory comparison of Milieu teaching and responsive interaction in classroom application. Journal of Early Intervention, 19(3), 218–242.
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References and Reading
Navane Fred R. Volkmar Child Study Center, Irving B. Harris Professor of Child Psychiatry, Pediatrics and Psychology, Yale Child Study Center, School of Medicine, Yale University, New Haven, CT, USA
Synonyms Thiothixene
Scahill, L. (2008). How do I decide whether or not to use medication for my child with autism? Should I try behavior therapy first? Journal of Autism & Developmental Disorders, 38(6), 1197–1198. Scahill, L., & Martin, A. (2005). Psychopharmacology. In F. R. Volkmar, A. Klin, R. Paul, & D. J. Cohen (Eds.), Handbook of autism and pervasive developmental disorders (Vol. 2, pp. 1102–1122). Hoboken: Wiley. Scheffer, R. E. (2006). Psychopharmacology: Clinical implications of brain neurochemistry. Pediatric Clinics of North America, 53(4), 767–775. White, S., Scahill, L., Klin, A., Koenig, K., & Volkmar, F. (2007). Educational placements and service use patterns of individuals with autism spectrum disorders. Journal of Autism & Developmental Disorders, 37(8), 1403–1412.
Definition One of the first-generation neuroleptic (major tranquilizer) medications, thiothixene, is of medium potency. Like other agents in this class, its primary mode of action is through the dopamine neurotransmitter system. Compared to the newer, atypical neuroleptics (several of which have been approved for treatment of autism), these earlier agents were more likely to cause various side effects. These have been widely used for decades in the treatment of psychosis (particularly schizophrenia) but also have been used to treat maladaptive behaviors in autism (stereotyped movements, agitation, and self-injurious behaviors). Side effects of this medication can include muscle stiffness, dry mouth, weight gain, and a cluster of features often referred to as extrapyramidal symptoms (restlessness and movement problems that resemble some aspects of Parkinson’s disease). Some of the side effects can be treated by anticholinergic agents like diphenhydramine. The most serious long-term side effect is tardive dyskinesia. Neuroleptic malignant syndrome is a rare but serious (and potentially fatal) side effect associated with fever, movement problems, and changes in mental state.
See Also ▶ Diphenhydramine ▶ Dopamine ▶ Thiothixene
Need for Caregiver Support for Families of Children with ASD, The Kimberly M. Bean1 and Karen Meers2 1 Department of Special Education, Center of Excellence on Autism Spectrum Disorders, Southern Connecticut State University, New Haven, CT, USA 2 Center of Excellence on Autism Spectrum Disorders, Southern Connecticut State University, New Haven, CT, USA
Definition The need for in-home support for caregivers of children with ASD.
Historical Background Prevalence rates of ASD have been steadily increasing for a number of years, and currently it is estimated that 1:59 children in the United States are impacted by ASD (Center for Disease Control 2019). Individuals with this disability have varying social-communication and behavior challenges. These challenges impact a person from infancy into adulthood and not only impact the quality of life of the individuals
Need for Caregiver Support for Families of Children with ASD, The
themselves, these impairments also affect their caregivers and other family members (Gupta and Singhal 2004). Caregivers of children with disabilities have been widely studied in research, with a more recent intense focus on understanding how parenting a child with ASD specifically impacts the family (i.e., Ekas and Whitman 2011; Freedman et al. 2012). Parents and guardians of individuals with autism spectrum disorders have significantly high levels of burden and stress in caring for their child (Goedeke et al. 2019). Research suggests that parents of individuals with autism experience both emotional and financial burdens (Krakovich et al. 2016). The financial burdens may vary across the lifespan, but for toddlers and school-age children, these financial hardships may be attributed to the cost of early intervention services, child care, medical expenses, or additional services not provided by the school district (i.e., private therapy services). Increased stress levels can be attributed to the level of severity of behavior and social-communication challenges of the child (Estes et al. 2009) and the financial costs associated with their care (Krakovich et al. 2016). Historically, parents of individuals with ASD have reported higher levels of stress when compared with parents of children without disabilities or of children with other disabilities (Paynter et al. 2013). The stress of caring for a child with ASD has been found to trigger a number of adverse psychological reactions. The profound changes in families resulting from having a child with autism often lead to tension and stress. There are often anxious and depressive impacts on parents (Ben Soussia et al. 2017). Mothers of children with ASD have been studied more frequently than fathers, with reports indicating more stress, depression, and anxiety in mothers of individuals with ASD as compared to mothers of individuals with other disabilities (Davis and Carter 2008). Parents have reported that this heightened tension has a direct impact on the entire family (Carbone et al. 2010). Freedman et al. (2012) identifies speculated reports that have indicated that parents of individuals with autism have a higher divorce rate (nearly 80%) than parents of
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typically developing children. It is important to note that this statistic has not been substantiated in empirical research. Although an accurate divorce rate has not been identified, it is clear that marital relationships are impacted by raising a child with ASD (Freedman et al. 2012). While there is limited validated research on divorce rates of parents, it is important to acknowledge that parents and caregivers still require additional supports. Krakovich et al. (2016) identify examples of external supports as respite and formal ASD services and examples of internal supports such as education level and income.
Current Knowledge A survey conducted by the State Department of Education in 2015 in Connecticut aimed to better understand the perspectives of parents of children with disabilities educated in CT schools. When looking at questions related to parent involvement and supports, 32% of parents did not know whether parent training opportunities existed within their district; more specifically, only 41% of parents with children with autism reported that they knew whether parent training opportunities existed within their district (Department of Special Education 2015). Additionally, only 39% of families of children with autism reported they were involved in a support network for families of children with disabilities, and only 44% of families of children with ASD reported they knew that parent support opportunities even existed (Department of Special Education). These findings indicate that services for parents do exist, but accessing the services is limited, which may be attributed to a variety of factors (i.e., location of supports, awareness of needs, finances, etc.). In another study (Carbone et al. 2010), parents report that although family supports are challenging to find, they were found to have positive results with these services (i.e., mental health services, parent and sibling support groups, and respite care). Additionally, parents reported that they had more positive experiences with informal parent-parent interactions (Carbone et al.). These findings indicate that there is a need to help parents identify quality
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Need for Caregiver Support for Families of Children with ASD, The
resources and supports that are available to them. Families rely on these informal and formal supports in order to help cope with caring for their child (Searing 2014). Using education and training to empower families of children with ASD is best practice (Dawson-Squibb et al. 2019). The definition of parent training in ASD, however, is ambiguous due to the complexity of ASD and the number of areas that are targeted for intervention. After a review of the literature, Bearrs et al. (2015) suggest that parent training has a broad application in areas such as psychoeducation, care coordination, parent-mediated interventions for maladaptive behaviors, and core symptoms.
Future Directions There is existing research exploring parent perspective of what their children need in terms of services and programming and findings pertaining to the increased levels of stress of families of children with ASD (Estes et al. 2009). However, there is still limited knowledge on the specific needs of the parents of children with autism. More research needs to be conducted to determine exactly what parents feel they need, what interventions are beneficial for parents, and how parents can gain access to available resources. Historical data and current knowledge indicate that there is a need for caregiver support for families of children with ASD. It is imperative that other professionals help parents find the right support (Vernhet et al. 2019). For instance, local education agencies should understand the role that they have in supporting families. School districts can assist in identifying the specific needs of parents of students in their schools. Then districts can offer a variety of services and trainings for parents. Parent training can be offered as a related service in a child’s individualized education plan (IDEA 2004). Offering training to meet the specific needs of parents can help with consistency of implementation of evidencebased practices used in school settings. Training
should also be provided through a variety of modalities (face to face, online webinars, presented in multiple languages, etc.). By having a variety of options, these trainings may reach a more diverse audience of caregivers (i.e., parents, grandparents, community members, babysitters, etc.). Additionally, if parents understand how to use these evidence-based practices, they can share these strategies with other caregivers. Training has been found to positively increase knowledge and skills of parents. Parent education of ASD and strategies can have a positive impact on stress levels (Tonge et al. 2006) and also improve family relationships. Additionally, districts may need to market these services to parents in various ways in order to make sure families know what supports are available to them. These districts could collaborate with parent advocacy groups. Some research indicates that parents supporting other parents can help reduce stress levels (Dykens et al. 2014). Parent advocacy groups can help to offer assistance, programs, and support groups for other parents. Additionally, it will be vital for parent advocacy groups to continue to reach out in a variety of different ways to help support parents. These parent advocacy groups can reach out to parents within their network to inform them of trainings and programs being offered. Social media and webinars are also ways for parents to learn more about specific interventions, support services, and trainings. Parents of children with ASD report that they do not have enough time for parental responsibilities or enough time for their romantic relationships (Hirsch and Paquin 2019). Programs should be more available to help caregivers find more personal time (Ekas and Whitman 2011). Respite care includes temporary caregiving services that can occur in the home setting. This may be an option for some families and can enable a parent to take a needed break from the daily responsibilities of raising a child with autism. However, it is important that people providing these caregiving services understand the challenges associated with autism (Goedeke et al. 2019).
Need for Caregiver Support for Families of Children with ASD, The
Additionally, challenges associated with the degree of ASD symptoms the child exhibits are related to obstacles to engaging in family activities (i.e., eating out, vacations, etc.) (Hirsch and Paquin 2019). There is a need to help support families with parent training on strategies (e.g., Bearss et al. 2015). Additionally, these resources that are offered should be researched to determine if in fact they help reduce levels of stress within the home (Krakovich et al. 2016). Although predictors of stress have been identified, and recommendations have been suggested to help support parents, there is limited knowledge on which specific interventions have been reported to reduce parent stress levels (Krakovich et al.; Ekas and Whitman 2011) and improve the quality of lives of families with ASD. Further research should be conducted in this area in order to validate specific programs, training, or support that positively impact families of children with ASD (Goedeke et al. 2019).
See Also ▶ Family-Centered Care, Second Edition ▶ Medical Home and ASD ▶ Parent Training
References and Reading Bearss, K., Burrell, T. L., Stewart, L., & Scahill, L. (2015). Parent training in autism spectrum disorder: What’s in a name? Clinical Child and Family Psychology Review, 18(2), 170–182. https://doi.org/10.1007/s10567-0150179-5. Ben Soussia, R., Bouallagui, A., Khouadja, S., Marrag, I., & Nasr, M. (2017). Psychoaffectives repercussions of autism on parents. European Psychiatry, 41, S123. https://doi.org/10.1016/j.eurpsy.2017.01.1922. Carbone, P. S., Behl, D. D., Azor, V., & Murphy, N. A. (2010). The medical home for children with autism spectrum disorders: Parent and pediatrician perspectives. Journal of Autism & Developmental Disorders, 40(3), 317–324. https://doi.org/10.1007/ s10803-009-0874-5. Center for Disease Control and Prevention. (2019). Autism spectrum disorders. Retrieved from https://www.cdc. gov/ncbddd/autism/data.html
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Davis, N. O., & Carter, A. S. (2008). Parenting stress in mothers and fathers of toddlers with autism spectrum disorders: Associations with child characteristics. Journal of Autism and Developmental Disorders, 38, 1278–1291. Dawson-Squibb, J.-J., Davids, E. L., Harrison, A. J., Molony, M. A., & de Vries, P. J. (2019). Parent education and training for autism spectrum disorders: Scoping the evidence. Autism. https://doi.org/10.1177/ 1362361319841739 Department of Special Education. (2015). Connecticut special education parent survey: Summary report. https://portal.ct.gov/-/media/SDE/Special-Education/ Parent_Survey_Summary_Report_2015.pdf Dykens, E. M., Fisher, M. H., Taylor, J. L., Lambert, W., & Miodrag, N. (2014). Reducing distress in mothers of children with autism and other disabilities: A randomized trial. Pediatrics, 134(2), e454–e463. https://doi.org/10.1542/peds.2013-3164. Ekas, N. V., & Whitman, T. L. (2011). Adaptation to daily stress among mothers of children with an autism spectrum disorder: The role of daily positive affect. Journal of Autism & Developmental Disorders, 41(9), 1202–1213. https://doi.org/10.1007/s10803010-1142-4. Estes, A., Munson, J., Dawson, G., Koehler, E., Zhou, X. H., & Abbott, R. (2009). Parenting stress and psychological functioning among mothers of preschool children with autism and developmental delay. Autism, 13(4), 375–387. Freedman, B., Kalb, L., Zablotsky, B., & Stuart, E. (2012). Relationship status among parents of children with autism spectrum disorders: A population-based study. Journal of Autism & Developmental Disorders, 42(4), 539–548. https://doi.org/10.1007/s10803-011-1269-y. Goedeke, S., Shepherd, D., Landon, J., & Taylor, S. (2019). How perceived support relates to child autism symptoms and care-related stress in parents caring for a child with autism. Research in Autism Spectrum Disorders, 60, 36–47. https://doi. org/10.1016/j.rasd.2019.01.005. Gupta, A., & Singhal, N. (2004). Positive perceptions of parents with disabilities. Asia Pacific Disability Rehabilitation Journal, 15(1), 22–25. Hirsch, K. H., & Paquin, J. D. (2019). The stress of the situation has changed us both: A grounded theory analysis of the romantic relationship of parents raising children with autism. Journal of Child and Family Studies, 28(10), 2673–2689. https://doi.org/10.1007/ s10826-019-01448-y. Individuals With Disabilities Education Act, 20 U.S.C. § 1400 (2004). Krakovich, T., McGrew, J., Yu, Y., & Ruble, L. (2016). Stress in parents of children with autism spectrum disorder: An exploration of demands and resources. Journal of Autism & Developmental Disorders, 46(6), 2042–2053. https://doi.org/10.1007/s10803016-2728-2.
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3090 Paynter, J., Riley, E., Beamish, W., Davies, M., & Milford, T. (2013). The double ABCX model of family adaptation in families of a child with an autism spectrum disorder attending an Australian early intervention service. Research in Autism Spectrum Disorders, 7(10), 1183–1195. https://doi.org/10.1016/ j.rasd.2013.07.006. Searing, B. (2014). Support needs of families living with children with autism spectrum disorder in New Zealand (Unpublished Master’s Degree), University of Otago, Dunedin, New Zealand Statistics New Zealand. (2006). New Zealand: An urban/rural update. New Zealand: Wellington. Tonge, B., Brereton, A., Kiomall, M., MacKinnon, A., King, N., & Rinehart, N. (2006). Effects on parental mental health of an education and skills training program for parents of young children with autism: A randomized controlled trial. Journal of the American Academy of Child and Adolescent Psychiatry, 45(5), 561–569. Vernhet, C., Dellapiaza, F., Blanc, N., Cousson-Gelie, F., Miot, S., Roeyers, H., & Baghdadli, A. (2019). Coping strategies of parents of children with autism spectrum disorder: A systematic review. European Child & Adolescent Psychiatry, 28, 747–758. Retrieved from https://doi.org/10.1007/s00787-018-1183-3.
Negative Priming Effect Adam Naples Yale Child Study Center, Yale University, New Haven, CT, USA
Negative Priming Effect
response to a target stimulus in the presence of distracters. For example, participants may see two letters on a computer screen and be instructed to name the upper case letter (e.g., from May et al. 1995), e.g., “H” and “p.” In this trial, the participant would respond with “H.” Importantly, on a later trial, the letters might be “P” and “r.” The inhibition applied to “p” as a distracter on the earlier trial carries over into the current trial, when the “P” is now a target. This “negative priming” results in slower response times, i.e., people are slower to respond to targets that were previously distracters. The effect need not be driven by exactly the same stimulus; it can be caused by targets occupying the same location as a prior distracter, targets sharing attributes or category membership with earlier distracters, or targets that require responses that were inhibited by earlier distracters. The negative priming effect is robust across a wide range of tasks.
See Also ▶ Attention ▶ Executive Function (EF)
References and Reading Definition Negative priming is an experimental phenomenon hypothesized to reflect inhibitory aspects of executive function. Executive function encompasses complex processes such as planning, organization, reflection, and metacognition and more basic processes such as engaging and disengaging attention and initiating or inhibiting responses. Inhibition is a fundamental component of executive function and is thought to be captured by the negative priming effect on simple cognitive tasks. The negative priming effect occurs in experiments where participants are required to make a
Hill, E. (2004). Executive dysfunction in autism. Trends in Cognitive Sciences, 8(1), 26–32. May, C., Kane, M., & Hasher, L. (1995). Determinants of negative priming. Psychological Bulletin, 118(1), 35–54. Ozonoff, S., & Strayer, D. (1997). Inhibitory function in nonretarded children with autism. Journal of Autism and Developmental Disorders, 27(1), 59–78.
Negative Punishment ▶ Omission Training ▶ Type II Punishment, Second Edition
NEPSY-II
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References and Reading
Negative Reinforcement 1
2,3
Rebecca Doggett and Robert L. Koegel 1 Koegel Autism Center, Gevirtz Graduate School of Education University of California, Santa Barbara, Santa Barbara, CA, USA 2 Koegel Autism Center/Clinical Psychology, Gevirtz Graduate School of Education, University of California, Santa Barbara, CA, USA 3 Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
Devlin, S., Healy, O., Leader, G., & Reed, P. (2008). The analysis and treatment of problem behavior evoked by auditory stimulation. Research in Autism Spectrum Disorders, 2(4), 671–680. Iwata, B. A. (1987). Negative reinforcement in applied behavior analysis: An emerging technology. Journal of Applied Behavior Analysis, 20, 361–378. Iwata, B. A. (2006). On the distinction between positive and negative reinforcement. Behavior Analyst, 29, 121–123. Michael, J. (1975). Positive and negative reinforcement, a distinction that is no longer necessary; or better ways to talk about bad things. Behavior, 3, 33–45. Skinner, B. F. (1953). Science and human behavior. New York: Macmillan.
Definition Negative reinforcement is a key principle in the behavioral theory of learning. Negative reinforcement is defined as the removal, reduction, or avoidance of a stimulus contingent upon a behavior, which results in an increased frequency of that behavior in the future. For example, a child who screams until an adult gives them a preferred item may negatively reinforce the adult’s behavior. The termination of the screaming may serve as negative reinforcement for the adult’s behavior of giving the child a preferred item when he/she is screaming. Negative reinforcement often involves the removal of an aversive stimulus, but it is not a requirement that the stimulus be undesirable. Negative reinforcement is similar to positive reinforcement in that both lead to increased responding in the future, but are dissimilar in that negative reinforcement involves the termination of a stimulus, and positive reinforcement requires the presentation of a stimulus. Negative reinforcement is typically confused with punishment; however, punishment results in a decrease in responding, not an increase.
Neostriatum
See Also
Synonyms
▶ Positive Reinforcement
NEPS; NEPS-U
▶ Caudate Nucleus
NEPS ▶ NEPSY-II
NEPS-U ▶ NEPSY-II
NEPSY-II Stephen R. Hooper Department of Allied Health Sciences, School of Medicine, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA
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Definition The title of this test, “NEPSY,” is not a true acronym but, rather, a combination of letters, across countries and languages that reflect the term “neuropsychology.”
Description The NEPSY-II is a comprehensive neuropsychological battery for children aged 3–12. The test provides measures of sensor-motor, language, visuospatial processing, memory and learning, attention/executive functions, and social cognition. It is one of the few well-normed and standardized neuropsychological batteries for children and adolescents. Similar to its predecessor, the NEPSY-II continues to be based on the theoretical foundation of Luria (1966) wherein designated brain functions correspond with selected assessment tasks. The neuropsychological domains covered by the NEPSY-II are clearly multidimensional and representative of the broad range of neurocognitive functions espoused by most neuropsychological models. Consistent with the NEPSY, the NEPSYII includes the domains of Attention and Executive Functioning, Language, Memory and Learning, Sensorimotor, and Visuospatial Processing. Further, the NEPSY-II contains a new domain, Social Perception. This latter addition is innovative with respect to its inclusion in such assessment batteries, and provides an additional avenue for assessment from the neuroscience literature that has not been routinely available to most clinicians. These tasks also will be relevant for professionals conducting neuropsychological assessments for children and adolescents with autism spectrum disorder (ASD). It is important to note that the neuropsychological domains of the NEPSY-II are not empirically derived, or even statistically independent. Although this is clearly noted in the manual, the user is left to determine how specific subtests relate within and across domains and, importantly, how these relationships may change over the 3.0–16.11 age range proposed by the NEPSY-II. Specific subtests and their age range are provided below.
NEPSY-II Domain/subtest Sensorimotor Fingertip tapping Imitating hand positions Manual motor sequences Visuomotor precision Language Body part naming and identification Comprehension of instructions Oromotor sequences Phonological processing Repetition of nonsense words Speeded naming Word generation Visuospatial processing Arrows Block construction Design copying Geometric puzzles Picture puzzles Route finding Memory and learning List memory List memory delayed Memory for design Memory for designs delayed Memory for faces Memory for faces delayed Memory for names Memory for names delayed Narrative memory Sentence repetition Word list interference Auditory attention and executive functioning Animal sorting Auditory attention Auditory attention response set Clocks Design fluency Inhibition Statue Social perception Affect recognition Theory of mind
Age range 5–16 3–12 3–12 3–12 3–4 3–16 3–12 3–16 5–12 3–16 3–16 5–16 3–16 3–16 3–16 7–16 5–12 7–12 7–12 3–16 5–16 5–16 5–16 5–16 5–16 3–16 3–6 7–16 7–16 5–16 7–16 7–16 5–12 5–16 3–6 3–16 3–16
The NEPSY and the NEPSY-II represent significant efforts to extend neuropsychological testing into the preschool years. Outside of selected intellectual batteries and single test approaches, there are few batteries that have attempted to
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provide neuropsychological measurement for such a young population. This version of the test should facilitate increased precision with respect to description of neurocognitive functions for a wide variety of preschool children with both neurological and neurodevelopmental disorders and, subsequently, prescription of early intervention strategies. Administration and Scoring For the NEPSY-II, there is a separate Administration Manual that provides detailed, manualized procedures for administration and scoring of each subtest. The domain, description, materials, starting points, discontinue rule, timing, recording, general guidelines, and specific administration and scoring details are provided for each subtest. These details are particularly important for several subtests, such as Clocks, Design Copying, Visuomotor Precision, and Word Generation, where increased attention to detail in administration and scoring is required. The materials provided in the NEPSY-II kit are easy to use, colorful and attractive, and appear to be relatively durable. Separate test forms are provided for the preschool battery (ages 3–4) and the school-age battery (ages 5–16). There also are additional teaching items, which should facilitate assessment to children with ASD. One modification asserted for the NEPSY-II was to provide increased flexibility in the order of subtest administration. Although this was present in the 1998 version of the NEPSY, it was not clear how different orders of subtest administration would affect scores in a larger test profile. For the NEPSY-II, the test developers examined the flexibility systematically via four different administration orders during the standardization process. By not showing any order effects, they were successful in being able to document that the subtests in the NEPSY-II could be administered in a variety of orders without affecting results. This should facilitate subtest administration, particularly in difficult-to-test samples of children (e.g., child psychiatric disorders, ADHD-Hyperactive Type), and this benefit will extend into both the clinical and research realms.
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Scoring procedures for the NEPSY-II subtests are clearly presented in the Administration Manual. While most of these can be calculated manually, the efficiency of their use likely will increase with use of the NEPSY-II computer scoring program. To obtain the various scores on the NEPSYII, however, the Clinical and Interpretive Manual also will be needed as this is where the normative tables are located. If the computer scoring program is utilized, it will be important for the examiner to consult the Clinical and Interpretive Manual to determine how to use these scores, especially when employing the various contrast scores and behavioral observations that can be calculated. The deletion of the NEPSY domain scores should facilitate the scoring process, but it will require additional knowledge with respect to how various subtests interrelate for a selected clinical population of interest. Unlike many intelligence tests, an overall score is not calculated. Interpretation Just like other neuropsychological assessment approaches, the interpretation of the NEPSY-II requires a certain amount of examiner knowledge with respect to brain functioning and, at a minimum, a knowledge base in the underlying neurocognitive functions of selected conditions. So, while this battery can be administered by many different psychological examiners, its interpretation does require additional knowledge that will facilitate its use and clinical utility across settings and patient populations. This will be especially pertinent to the nonneuropsychologist who may be working with the complexities inherent in a population of children and adolescents with ASD.
Historical Background The American version of the NEPSY was published in 1998, with approximately 10 years of development going into its evolution. This version of the NEPSY included 36 subtests arranged across five major neuropsychological domains based on Lurian Theory: Attention/ Executive Functions, Sensorimotor, Language,
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Visuospatial, and Memory/Learning. The test had normative data that extended from ages 3.0 through age 12.11, with selected tasks being devoted to preschoolers (3.0–5.11) and schoolage children (ages 6.0–12.11), with an array of different scores being available to assist with interpretation (e.g., standard scores, percentile ranks, age equivalents). This version of the NEPSY, along with its Scandinavian counterparts, provided one of the first well-normed neuropsychological batteries for children. The NEPSY was developed out of a specific professional need; that is, the lack of adequate, developmentally appropriate neuropsychological assessment tools for children. Dr. Marit Korkman, a Finnish pediatric neuropsychologist, assembled a variety of tasks into a brief neuropsychological assessment for children 5 through 6 years of age – the NEPS. This seminal version of the NEPSY was subsequently revised by expanding the scoring from a pass/fail criterion assessment measure to allowing for standardized age-based scores for children from ages 3.6 to 9.5. This set of tasks was called the NEPS-U in Finland and the NEPSY in English. Similar versions of the NEPSY were published in Sweden and Denmark, with the current version, the NEPSY-II, appearing in 2010.
Psychometric Data Standardization The normative sample was extracted from the most recently available census data, with excellent concurrence with the available census figures with respect to race/ethnicity, parent education, and geographic region of the country. The minority representation was noteworthy across nearly all of the stratification cells. The NEPSY-II also was stratified by chronological age and evenly divided by gender. More specifically, the NEPSY-II was standardized on 1200 children across the ages of 3 through 16 years. Of the 29 possible subtests included in the standardization, 17 were administered to children ages 3–4 years; children 5–6 years of age were administered 22 subtests and 2 delay tasks; children ages 6–12 years were given 23 subtests
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and 2 delay tasks; and the adolescent children ages 13–16 years received 24 subtests and 3 delay tasks. Despite these efforts, a number of NEPSY subtests were not included in the current standardization, but were still included in the NEPSY-II. These subtests were not included in an effort to shorten the time required for a child to complete the standardization battery, but also because the development team determined that the targeted tasks required no modifications and likely would show little change in normative values. Consequently, these subtests (i.e., Design Fluency, Oromotor Sequences, Repetition of Nonsense Words, Manual Motor Sequences, Route Finding, and Imitative Hand Positions) were not included in the standardization version and, for the NEPSY-II, retained the normative scores from the 1998 version of the NEPSY. Reliability The area of reliability is critical in test construction as not only does it determine the test’s capabilities of replicating results, but it also sets the upper limit on validity. For the NEPSY-II, most subtests have adequate-to-high internal consistency estimates, and the standard error of measure for all of the subtests ranged from 0.85 to 2.18 across all of the age ranges. Temporal stability of the NEPSY-II subtests across six age groups (i.e., ages 3–4; 5–6; 7–8; 9–10; 11–12; and 13–16) revealed little change in scores over an average of a 3-week period (range 12–51 days), suggesting that there was little practice effect across a relatively short time frame. This is critical in a test where alternative form reliability is not available, and provides an evidence-based foundation to use many of the subtests in various types of intervention studies. Given the potential application of the NEPSY-II to children with ASD, it is important to note that the reliability estimates of the subtests were relatively stable for both typical and clinical samples. Although not directly related to reliability, the issue of tests floors and ceilings can have a direct effect on reliability by potentially restricting the range of scores available. This is especially important for both lower and higher functioning
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individuals on the autism spectrum. For the NEPSY-II the test developers focused significant resources on improving the test floors. This is absolutely critical for a test that is designed to uncover neurocognitive weaknesses across different presenting problems and disorders. In this regard, nearly all of the NEPSY-II subtests across the age bands have at least a two standard deviations limit; that is, a child or adolescent can continue to show test variability even when the items begin to become too challenging for them. An adequate floor is essential for differentiating the nature and severity of weaknesses. There is a difference across the age range, however, with all or nearly all of the subtests having an adequate floor from ages 9.0 to 16.9, but 5 to 13 of these subtests not having an adequate floor for ages 5–13 years. In general, the number of subtests having at least a two standard deviation floor increases with age. Conversely, the test developers were not as focused on having at least a two standard deviations ceiling on the subtests. Although this is consistent with the notion of assessment via the NEPSY-II, this is unfortunate as it may place a limit on determining the presence of neurocognitive strengths; consequently, the concept of utilizing neurocognitive strengths to facilitate intervention may be limited. This notion is apparent in the NEPSY-II normative data as from ages 3.0 to 5.9, few subtests meet this criterion (0–5 subtests); from ages 6.0 to 9.9, only 3–8 subtests meet this criterion; and from ages 10.0 to 16.9, only 11–17 subtests meet this criterion. Validity Content validity procedures produced a battery of tasks that adequately sample the targeted constructs of interest. For construct validity, the NEPSY-II provides an interesting challenge, in that while there appears to be an overarching set of neuropsychological domains (e.g., Language, Visuospatial Processing, and Social Perception), the test is not designed to provide scores for these domains. As such, and in accordance with the guidance in the Clinical and Interpretive Manual, the administration and interpretation of the NEPSY-II should be guided by the subtests. Consistent with this philosophy, the NEPSY-II
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continues to focus on the subtests and their interrelationships. For the NEPSY-II, a multitraitmultimethod correlational model demonstrated support for construct validity across both typical and clinical samples. Although previous efforts to examine the factor structure of the 1998 version of the NEPSY (Mosconi et al. 2008; Stinnett et al. 2002) provided mixed support for the NEPSY, it is unfortunate that these additional analyses were not explored. In support of the test developers’ contentions, however, it is likely that different factor structures would be present across different clinical groups, much as is seen in the pattern of intercorrelations of subtests between clinical samples and the normative sample, and this question will require ongoing examination. Further, with an ongoing emphasis on test interpretation at the subtest level, it would have been beneficial for each task to have accompanying subtest specificity estimates (i.e., an index for what proportion of a subtest’s variance is unique to the subtest and reliable). This would have facilitated interpretation and utilization of the subtests in the way they were intended, but with empirical evidence as to their ability to measure a specific function. Criterion-related validity was determined primarily by using concurrent validity studies (i.e., the relationship of the NEPSY-II with other tests measuring similar constructs) wherein good correlations were obtained (e.g., NEPSY-II Memory and Learning subtests correlated most highly with selected subtests from the Children’s Memory Scale). Taken together, these findings support a convergent validity line of evidence for the NEPSYII; however, it is important to note that these patterns of correlation may change depending on the age of the child, the presenting clinical condition, and the method of assessment (e.g., parent rater, clinician ratings). Additionally, few empirical data are available with respect to the utility of the preschool battery for the NEPSY – either from the publisher or independent investigators, and the same seems to be true with respect to the release of the NEPSY-II. The field will need to determine the ultimate clinical utility of the preschool version of this test, particularly for very young children suspected of being on the autism
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spectrum. This issue will require additional examination as the NEPSY-II begins to be employed in a variety of clinical settings, particularly with the wide spectrum of cognitive and behavioral manifestations seen in ASD and the movement toward earlier diagnosis.
Clinical Uses Clinical Application to ASD Similar to its neurocognitive predecessors, including the NEPSY, the NEPSY-II utilized available data to showcase its utility with both neurological and neurodevelopmental disorders. The utility with neurological disorders continues the longstanding standard of use of such tests and procedures with frank neurological conditions, but the inclusion of neurodevelopmental disorders – conditions that presume neurological involvement (e.g., ASD) – clearly expands the overall application of this test to a wide variety of potential clients. To facilitate this process, the manual proposes eight different referral batteries from which to facilitate subtest selection. One of those suggested batteries pertains to children with autism, or suspected of being on the autism spectrum. Suggested tasks span all six of the NEPSY-II neurocognitive domains and include Attention/Executive Functioning (Statue, Design Fluency, Auditory Attention and Response Set, Inhibition, and Animal Sorting); Language (Comprehension of Instructions, Speeded Naming, Word Generation, and Phonological Processing); Memory and Learning (Memory for Designs and Delayed Recall, Narrative Memory, Memory for Faces and Delayed Recall, and Word List Interference); Sensorimotor (Imitating Hand Positions, Visuomotor Precision, Manual Motor Sequences, and Fingertip Tapping); Social Perception (Affect Recognition and Theory of Mind); and Visuospatial Processing (Block Construction, Geometric Puzzles, Design Copying, Geometric Puzzles, Arrows, and Picture Puzzles). Special Group Studies The special group studies conducted with the NEPSY-II are noteworthy in that the test developers employed 10 different clinical conditions.
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Comparison groups were derived from the normative sample and matched on chronological age, gender, race, and parent education levels. These studies have promised to determine the differential sensitivity of the NEPSY-II to the neuropsychological profiles that can be manifested by specific disorders. Findings from these special group studies generally support the clinical utility of the NEPSY-II in the assessment of children referred for different conditions and disorders. More specifically, the special groups typically differed from the typical groups on variables where they would be expected to deviate, as well as in a wide range of other variables. For example, children with intellectual disabilities and ASD performed more poorly than the typical group on nearly all of the NEPSY-II subtests. The separate examination of the newly added Theory of Mind Subtest for the ASD versus Controls, and for the Asperger’s Disorder versus Controls, also provided support for the use of this subtest with these types of clinical referrals. Although useful, these group comparisons should be carefully considered, given their limitations (e.g., small sample sizes, inclusion/exclusion criteria, comorbidity, etc.) until more independent, contemporary data on the use of the NEPSY-II emerge over time. Empirical Findings in ASD With the original NEPSY, there were a number of studies that were conducted with samples of children and adolescents with ASD to demonstrate its ability to differentiate between various neurocognitive functions as well as between different diagnostic groups (Hooper et al., 2006; Joseph et al. 2005; Kjelgaard & Tager-Flusberg, 2001; Planche & Lemonnier, 2012). Since the emergence of the NEPSY-II in 2010, there have been a number of studies that have utilized this revised set of procedures for children with ASD. Several studies have demonstrated the utility of the NEPSY-II in differentiating the cognitive abilities of children with high functioning ASD. Narzisi et al. (2012) compared the neurocognitive performance of 22 children with high functioning to 44 healthy controls matched by age, race, gender, and maternal education. Results indicated the presence of significant deficits in Attention and Executive Functions, Language, Learning and
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Memory, and Senorimotor Processing, with only Visuospatial Processing being similar to healthy controls. Further, Narzisi et al. found the expected Theory of Mind deficits in the ASD sample, but this was noted only in the verbally-based tasks. Barron-Linnankoski et al. (2015) conducted a similar study for children, ages 6 to 11 years, and reported the high functioning ASD group to show relative strengths in their verbal reasoning and the expected weaknesses in tasks reflecting cognitive flexibility, verbal fluency, and narrative memory. In addition to these emergent studies showing the utility of the NEPSY-II with children with ASD, another important trend that is surfacing is the use of the NEPSY-II subtests in interventional science as targeted outcome measures. Specifically, Young and Posselt (2012) used the Affect Recognition Subtest as one of the outcome measures of their examination of The Transporters DVD as a learning tool for children with ASD, aged 4–8 years of age. In addition to their positive findings overall, the NEPSY-II Affect Recognition Test performed well in this intervention, showing positive changes from pre- to posttest over the course of a 3-week trial. Using the same intervention, Williams et al. (2012) used the Affect Recognition and Theory of Mind subtests as outcome measures for their trial. Both NEPSYII tasks performed well in this 4-week intervention, which also included a 1-month postintervention follow-up.
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surface. These findings have demonstrated its clinical utility in detailing relative strengths and weaknesses in neurocognitive functions in individuals with ASD as well as in differentiating between different clinical groups. Importantly, subtests of the NEPSY-II have begun to be used in interventions, and the selected tasks have performed well with respect to the obtainability of reliable data and being sensitive to changes secondary to the targeted intervention. It is important to note that while the NEPSY-II will not provide information on the specific diagnostic symptoms of ASD, it will compliment ongoing diagnostic efforts by characterizing the neurocognitive manifestations of ASD in an individual child, and the emergent empirical literature has begun to show this to be the case. This neurocognitive profile should prove useful in educational planning and cognitive remediation efforts, assisting in how to structure other interventions (e.g., specific therapies, social interactions, etc.), and using the NEPSY-II and its subtests as targeted outcome measures for interventional science.
N See Also ▶ Executive Function (EF) ▶ Wisconsin Card Sorting Test (WCST)
References and Reading Conclusion The NEPSY-II is one of the few well-standardized and normed neuropsychological batteries for children. The NEPSY-II has benefited from a long developmental history, with the most recent revision capitalizing on the most contemporary findings in child neuropsychology. Changes to the NEPSY-II in terms of its content and administration should provide a fruitful avenue for neuropsychological assessment of children with an existing ASD diagnosis and those suspected as being on the spectrum. The NEPSY-II maintains good psychometric properties, and its use with children and adolescents with ASD has begun to
Barron-Linnankoski, S., Reinvall, O., Lahervuori, A., Voutilainen, A., Lahti-Nuuttila, P., & Korkman, M. (2015). Neurocognitive performance of children with higher functioning autism spectrum disorders on the NEPSY-II. Child Neuropsychology, 21, 55–77. Hooper, S. R., Poon, K. K., Fine, C., & Marcus, L. (2006). Neuropsychological characteristics of school-age children with high-functioning autism: Performance on the NEPSY. Child Neuropsychology, 12, 299–305. Joseph, R. M., McGrath, L. M., & Tager-Flusberg, H. (2005). Executive dysfunction and its relation to language ability in verbal school-age children with autism. Developmental Neuropsychology, 27, 361–378. Kemp, S. L., & Korkman, M. (2010). Essentials of NEPSYII assessment. Hoboken: Wiley. Kjelgaard, M. M., & Tager-Flusberg, H. (2001). An investigation of language impairment in autism: Implications
3098 for genetic subgroups. Language and Cognitive Processes, 16, 287–308. Korkman, M., Kirk, U., & Kemp, S. (1998). NEPSY. A developmental neuropsychological assessment. San Antonio: The Psychological Corporation. Korkman, M., Kirk, U., & Kemp, S. (2007). NEPSY-II. San Antonio: The Psychological Corporation. Luria, A. R. (1966). The working brain. New York: Penguin Press. Mosconi, M., Nelson, L., & Hooper, S. R. (2008). Confirmatory factor analysis of the NEPSY for younger and older school-age children. Psychological Reports, 102, 861–866. Narzisi, A., Muratori, F., Calderoni, S., Fabbro, F., & Urgesi, C. (2013). Neuropsychological profile in high functioning autism spectrum disorders. Journal of Autism and Developmental Disorders, 43, 1895–1909. Planche, P., & Lemonnier, E. (2012). Children with highfunctioning autism and Asperger’s syndrome: Can we differentiate their cognitive profiles? Research in Autism Spectrum Disorders, 6, 939–948. Stinnett, T. A., Oehler-Stinnett, J. O., Fuqua, D. R., & Palmer, L. S. (2002). Examination of the underlying structure of the NEPSY: A developmental neuropsychological assessment. Journal of Psychoeducational Assessment, 20, 66–82. Williams, B. T., Gray, K. M., & Tonge, B. J. (2012). Teaching emotion recognition skills to young children with autism: a randomized controlled trial of an emotion training programme. The Journal of Child Psychology and Psychiatry, 53, 1268–1276. Young, R. L., & Posselt, M. (2012). Using The Transporters DVD as a learning Tool for children with autism spectrum disorders (ASD). Journal of Autism and Developmental Disorders, 42, 984–991.
Netherlands and Autism, The Jan Rutger Van der Gaag Department of Psychiatry and Karakter University Center for Child and Adolescent Psychiatry, Radboud University Medical Centre, Utrecht, Netherlands Stradina University of Riga, Riga, Latvia
Historical Background – Legal Aspects – Current Practice Van Krefeld introduced the concept of autism in the Netherlands in the 1950s from Leiden who, interestingly, referred both to the work of Kanner as well as to that of Asperger! And
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though the work of the Creak party and Lotter and Rutter was known in this country, most of the research and clinical practice focused on what was then referred to as “developmental psychosis” (Utrecht: Profs Kamp, Van Engeland). At the end of the 1970s, the primacy of psychoanalysis in child and adolescent psychiatry was replaced by a far more scientific and empirical and pragmatic approach. The introduction of DSM III had a great impulse on the scientific approach spread over the country: Rotterdam Epidemiology (Frank Verhulst) Utrecht Developmental Disorders Autism and ADHD and Multiplex Developmental Disorders (Herman van Engeland) and PDD-NOS Groningen (Ruud Minderaa). The greatly inspiring and dynamic Donald Cohen closely linked the two latter centers with Yale Child Study Center, thus encouraging a fruitful transatlantic cooperation. At the same time, parents of children with autism joined forces and in the late 1970s started the Netherlands Autism Association (NVA). At the start is was only a small group of parents of severely impaired children. They did not rest before the 1984 Autism Bill was passed in parliament acknowledging that autism is a serious developmental disorder and a great burden on families. The bill states that individuals with autism and their families should be entitled access to relevant services and remuneration of necessary care and educational services. The Bill on Autism assumed that the prevalence of autism would hover around 5/10,000 as the epidemiological finding by Lotter suggested, though the prevalence in the Camberwell Study (1979) adding up to 30/10,000 was precluded. The “passport” to access special education, specialized services, or social benefits for handicapped people was set as a DSM diagnosis of autism or PDD-NOS. The social benefits include a so-called “Personalized Budget” (PGB). This is community funded and may add up to € 80,000 per year. The patient/parents may submit a budget including the salaries for those that will provide the support they/their child requires. To date no systematic epidemiological studies have been undertaken in the Netherlands, and it is assumed that the prevalence will be in line with
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those of our neighbors with an estimate of around 1% in the total population (Baird et al. 2010). Yet figures from primary education show that nearly 1.4% of the pupils are coined as “leaners with special needs” having received a diagnosis of “autism spectrum disorder” according to DMS V. Likewise, the prevalence of autism in Eindhoven (cradle of high-tech industry) is much higher (2.3%) than in other Dutch towns like Harlem (0.84%) and the university town Utrecht (0.57%). This finding or skewed repartition is in line with findings of higher prevalence of ASD in, e.g., Silicon Valley and Cambridge. The National Health Council produced two cardinal reports: the first one in 2009 “Autism: a lifelong different” (https://www.gezondheidsraad.nl/nl/ taak-werkwijze/werkterrein/optimale-gezondheids zorg/autismespectrumstoornissen-een-leven-langanders with a summary in English.) pointed attention to the increasing amount of adults and also the underdiagnosed group of girls and women along with the observation that as modern society speeds up and requires a great flexibility, individuals with ASD are more at risk in schools, universities, and the workplace. This report was followed in 2012 by the report “Knowledge Infra-structure for Autism Spectrum Disorders” (https://www. gezondheidsraad.nl/sites/default/files/201209_1.pdf with a summary in English.) notifying the Minister of Health that expertise in the field of Autism should be present in centers countrywide and knowledge on autism spectrum disorders should become available to experts as well as to the general public. Since 2012, multidisciplinary Guidelines are available for professionals and the general public. These address the diagnostic issues as well as the evidence of stimulation, treatment, and support programs. In 2014, the secretary of State for (Mental)Health launched a working group called: “Autism: insiders views.” In this working group, {adult}patients and parents of children and adolescents with autism spectrum disorder addressed sixty bottlenecks within the homecare, educational system, leisure time, respite care, working-place offering comments from hand-on experts, and making suggestions for improvement, changes, and innovation. The working group after termination of its advices to the government has pursued its activities in a
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coalition “Taking the perspective of the person with autism” including more than a hundred patients with specific expertise on one of the sixty issues that from their point of view merit investment and improvement.
Early Detection and Intervention Programs Many parents with a child with a developmental disorder, e.g., autism, have endured a painful endeavor, being referred from one professional to the other most often with the meant as reassuring news “we cannot tell precisely yet. . . so let us wait and see how your child develops, before getting a proper diagnosis and adequate help.” Extremely frustrating for parents and detrimental for their child as precious time when adequate stimulation could have made a difference. So, very understandably, parents that have had that frustrating experience call for early detection in order to prevent others going through the same nightmare as they themselves. Three different approaches have been set up to date. SOSO (translated: Screening and Assessment of Social Development) (Publications between 2006 and 2007.) was conducted by the department of Child and Adolescent Psychiatry from the University Medical Centre in Utrecht. It was ambitious in the sense that it aimed at diagnosing children with autism as early in life as possible. The population was a year birth cohort including 30,000 newborn children all screened at the age of 14 months at the community pediatric consultations attended by 99% of all newborns in our country. Parents were asked to fill in a questionnaire, the ESAT (Early Screening for Autistic Traits). When enough “red flags” pointed at a deviant development, the parents were offered a home visit by a trained psychologist and if through direct observation the suspicion was confirmed a thorough assessment with ADOS, ADI, and neuropsychological and neurological/psychiatric assessment followed. The lessons learned were that many parents were reluctant to accept the invitation (though at a later stage they all sought help for their child). A second project
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built on these experiences and was called the DIANE (Publications between 2008 and 2011.) (DIagnosing Autism in the NEtherlands) project (Nijmegen area). The team here took a slightly different approach. Community pediatricians were trained to enhance their awareness for autism in very young children. When they detected such a child in their Baby-Wellbeing clinics (attended by 98% of all the children born in the Netherlands), they signaled this. The ESAT was now performed during a home visit where the psychologist observed the child while interviewing the parents. After the formal assessment in the university clinic, the parents were offered a home-based intervention: an interactive program in which parents were trained in better acknowledging the child’s messages and needs and to meet them of to discipline them in a far more effective manner. Yet the program was not successfully implemented as the essence lies in the continuous training of the grassroots workers and the involvement and motivation of parents. The third attempt is on its way with yet a more elaborated approach. The Papageno Foundation (Jaap & Aaltje van Zweden) has joined forces to develop a nationwide early detection and intervention network for parents that have concerns about the development of their child. Twenty-two teams of professionals involved in early intervention in autism have joined forces and coordinate their assessment programs in order to offer the same program through the whole country. Moreover, experts in different fields of autism are prepared to give videoconference consultation anywhere in the country to the parents at professionals request. This is aimed at making sure children and their families get the best possible treatment near to their homes. The parents manage the child’s file and provide access to all professionals involved. The file is part of a secured electronic environment where forum conversations can take place addressing any questions relevant for any of the participants, thus making parents less dependent on their professional support team. Finally, the network includes a database where all that involved have access to all the relevant literature (with annotations of evidence and relevance). Finally, every referral leads to the home visit, as soon as possible, by a trained
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professional who provides a home-based basic intervention to help parents cope with the basics of helping their child given its peculiarities. At the same time, this intervention provides opportunity to observe the child and thus determine which assessment of medical examination would prove most valuable for this child in its social and family context. The first two pilot regions are currently active; further implementation will follow in 2018.
Current Treatment and Centers Throughout the whole country alongside all university clinics, both mental health institutions and psychologists and psychiatrists in private practice are well knowledgeable of the diagnostic and treatment/guidance needs of individuals with autism spectrum disorders and their families and close dear ones. There are guidelines for all phases of development. And just like the network for early detection and intervention, there are networks for adults with autism (CASS 18+) and a special network for autism in women and girls (Women Inc. and FANN). Finally, adults with autism spectrum disorder are well organized in an own organization (PAS) that offers advice and combats stigmatization in the workplace.
Education and Autism Spectrum Disorders A DSM classification of ASD qualifies those children as a learner with special needs. Extra support can be given in the classroom in a regular school both for remedial teaching or addressing challenging behavior or anxieties in the classroom/ playground situation. But if necessary, the Dutch education system includes special schools in different categories. Learners with autism can be helped in so-called Cluster 2 schools for learners with hearing, speech, and communication problems; Cluster 3 schools for learners with developmental delays; and finally cluster 4 for learners with emotional and/or conduct problems. The beauty of the system is that it offers specific
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support or a secure environment for children and adolescents with autism spectrum disorders regardless their intellectual capacities. The vulnerability of a system is much adapted to the peculiarities that it makes integration in the “real world” where individuals with ASD will to a certain degree have to adapt to less ideal situation, more difficult.
Autism and Comorbidity Specialized Centers for Autism (e.g., Centrum Autisme Leiden (https://www.rivierduinen.nl/ autisme) and/or Centrum OntwikkelingsStoornissen (Centre for Developmental Disorders) Dimence GGz Deventer (https://centrumontwikkelings stoornissen.dimence.nl/)) and the University Clinics offer countrywide consultation for (Mental Health) professionals encountering difficulties in coping with and offering adequate treatment for individuals with autism with comorbid conditions ranging from learning disability, cerebral palsy, psychosis, depression, addiction, etc.
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are responsible for the well-being of their inhabitants. Care for people with a handicap or chronic condition is offered in various manners: they can receive support given according to national guidelines either from healthcare agencies of welfare providers. They can also opt for a “Personalized Health Budget.” The individual, his parents, or legal mentors present a budget plan including the cost of help and support they estimate required for the well-being of the individual concerned and for which budget they are accountable year after year. The national report “Autism: a lifelong different” also looks into the requirements for education, meaningful daytime activities, and preferably integration in paid labor. Finally, individuals with autism spectrum disorder, can from their 18th birthday on, benefit from an invalidity pension provided by the state (WAJONG: “invalidity bill for youth”) that will support them lifelong, and is interrupted if the individual finds paid work.
Tentative Conclusions Overview of Research Directions The Netherlands has a solid track record in autism research in a great variety of areas. These include taxonomy, neuroimaging, and electrophysiology as well as neuropsychological studies. Strangely no countrywide epidemiological studies have been conducted in the Netherlands, though some studies suggest that the prevalence of autism spectrum disorders may be quite high.
Autism spectrum disorders are quite a common problem in the Netherlands. Despite the absence of any systematic epidemiological data, the figure of 1.4% learners with ASD in primary education reflects a high prevalence of autism spectrum disorders in this country. Along with a nationwide network for early detection, services for individuals with ASD and their families are well organized. Social welfare and health insurance provides the necessary support.
Social Policies Healthcare in the Netherlands is undergoing a profound change. More and more healthcare is integrated with (social) welfare. Healthcare is supported by a national insurance scheme in which all citizens are enrolled for reimbursement of basic healthcare. If one cannot afford it, the social welfare provides assistance. Social welfare is provided by the municipalities, which
References Oosterling IJ, Wensing M, Swinkels SH, van der Gaag RJ, Visser JC, Woudenberg T, Minderaa R, Steenhuis MP, Buitelaar JK (2010) Advancing early detection of autism spectrum disorder by applying an integrated two-stage screening approach. J Child Psychol Psychiatry 51(3):250–8. Salomone E, Beranová Š, Bonnet-Brilhault F, Briciet Lauritsen M, Budisteanu M, Buitelaar J, Canal-Bedia
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3102 R, Felhosi G, Fletcher-Watson S, Freitag C, Fuentes J, Gallagher L, Garcia Primo P, Gliga F, Gomot M, Green J, Heimann M, Jónsdóttir SL, Kaale A, Kawa R, Kylliainen A, Lemcke S, Markovska-Simoska S, Marschik PB, McConachie H, Moilanen I, Muratori F, Narzisi A, Noterdaeme M, Oliveira G, Oosterling I, Pijl M, Pop-Jordanova N, Poustka L, Roeyers H, Rogé B, Sinzig J, Vicente A, Warreyn P, Charman T (2016) Use of early intervention for young children with autism spectrum disorder across Europe. Autism 20 (2):233–49.
Network Coherence ▶ Brain Connectivity Theories of Autism
Neural Mechanisms in Autism Manuel Casanova Department of Psychiatry, University of Louisville, Louisville, KY, USA
Definition Minicolumns are elemental anatomical and functional modules of the neocortex that serve as cytoarchitectural templates for the arrangement of cells and their connections. A chain of excitatory neurons, along with their axonal and dendritic bundles, constitutes its core. These excitatory neurons extend radially, i.e., perpendicular to the neocortex, through layers II to VI. Among other anatomical elements present at the periphery of the minicolumn are (a) synaptic elements which integrate translaminar connections and (b) interneurons that provide for a vertical sheath of surround inhibition to the information being processed by the core. The minicolumn exhibits variability between different brain regions, across hemispheres within a given brain, as well as among individuals within and across species. Nevertheless, the basic organization of microcircuitry is maintained throughout the cortex of all mammals (Buxhoeveden and Casanova 2005; Douglas and Martin 2004).
Network Coherence
Historical Background The neocortex covers the massive network of myelinated fibers in the white matter like the crust on a camembert, yet the vast majority of connections in the brain are not provided by corticocortical projections in the white matter. The majority of cortical connections take place between neighboring cells in a dimensional scale spanning only a few microns. These connections are typically contained within stereotyped microcircuits which are distributed iteratively throughout the cortex. Early neuroanatomists, like Cajal and Meynert, were among the first to describe the anatomical skeleton of these circuits when they noted the distribution of pyramidal cells in recurring arrays perpendicular to the pial surface (DeFelipe 2005). von Economo and Koskinas called them “radii” as they paralleled the radial arrangement of white matter bundles within the cortex (von Economo and Koskinas 1925). Other distinguished neuroscientists further defined the anatomy of these modules as consisting of a pyramidal cell column core with synaptic elements and inhibitory interneurons at its periphery (Peters and Sethares 1991, 1996; Szentágothai 1978). It is now known that these circuit arrangements, termed “minicolumns” by the pioneering neurophysiologist Vernon Mountcastle, feature a polarity in which information ordered by evolution and development is represented in the topographical pattern of minicolumnar inputs directed vertically toward inputs from across the cortex (Casanova 2008a, 2010; Casanova and Tillquist 2008; Mountcastle 1998). Although the presence of these vertical arrangements cannot be questioned, a functional role was never suggested or described by early researchers. It was not until the late 1930s that these anatomical units acquired physiological significance as elementary units of information processing. Based on his experience with the Golgi silver impregnation technique, Rafael Lorente de Nó (1938) proposed the existence of clusters of cells arranged in vertical modules that applied canonical operations on thalamocortical and corticocortical inputs. This idea receives some anatomical support from the fact that the
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neocortex unvaryingly uses layers II and III for association (e.g., corticocortical integration), layer IV for reception, and layers V and VI for efferent connectivity. The work of Lorente de Nó antedated our current understanding of computer science wherein a single logic gate (i.e., NOR or the minicolumn in our case) in different configurations can provide for higher-level functions capable of implementing any computer program (Casanova et al. 2003). During the last few decades, many authors have revived and expanded de Nó’s idea of a canonical neocortical circuit (Creutzfeldt 1995; Douglas and Martin 2004).
Current Knowledge The advantage of short, stereotyped microcircuits contained within minicolumns is that patterns of information can be represented by synchronous outputs coordinated among units. The white matter connections linking distant cortical areas are sparse thus providing a “small-world” architecture which minimizes wiring. The end result is a balance between neural clusters and wiring that minimizes connection steps between local circuits across the neocortex. In effect, most neurons maintain short connection lengths within clusters that themselves are linked by longer range projections. The preferred clustering configuration for neurons within the neocortex is the minicolumn (Casanova 2010).
Ontogeny Pyramidal cell arrays are derived from the primordial neuroepithelium, i.e., progenitor cells that attach themselves both to the ependymal (ventricular) and pial surfaces. Their epithelial origin is betrayed by the presence of tight junctions within their end feet which together constitute a pia externa glia limiting membrane. The dividing neuroepithelium provides for migrating neuroblasts that wedge themselves between the first (primordial) lamina and the subplate. Neurons fated for the infragranular layers are thought,
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during asymmetric division, to inherit the pial fiber from the radial glial cell, which then allows the neuroblast to translocate into the lower layers of the cortical plate. In contrast, neural progeny meant for superficial cortical layers attaches and locomotes along the radial glial scaffolding until the neuroblast’s pial-directed extension reaches the marginal zone, at which point it disengages from the radial glial scaffolding and finishes its journey via somal translocation (Casanova et al. 2009; Williams and Casanova 2011a). As neuroblasts loose their glial attachment, they begin to mature into pyramidal cells having as a common characteristic their attachment by their apical dendrites to the first lamina. In humans, the cortical plate starts to form at approximately the 7th week of gestation and is almost completed by the 15th week of gestation (Marin-Padilla 2011). Interneurons are later on coupled to the pyramidal cell arrays following an inside out pattern juxtaposed on concomitant lamination, vascularization, and the introduction of protoplasmic astrocytes (Marin-Padilla 2011). Four excitatoryinhibitory systems have been described in the literature: pyramidal Martinotti, pyramidal basket, pyramidal chandelier, and pyramidal double bouquet cells. The end result of this complex radial glia-mediated migration (pyramidal cells) and coupling to horizontal transversing neuronal precursors (interneurons) is a cortical plate that is a unique mammalian innovation (Gressens and Evrard 1993). Although minicolumns are readily recognized in fetal/embryonic material, the characteristic radial configuration is less readily ascertained with aging. This may be due to the translation of pyramidal cells around the central axis of the minicolumn, growth or atrophy of anatomical elements, and the introduction of glial elements. At least two studies have looked at the continuity between the ontogenetic minicolumn and its putative postnatal counterpart. Krmpotic-Nemanic et al. (1984) using a limited series of human fetal material from the auditory cortex traced the developmental transformation of ontogenetic cell columns into mature minicolumns. Similar results were observed in a larger series (n ¼ 67) of human postmortem specimens that modeled the
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rate of change in neuropil space (Casanova et al. 2007). The latter study used Nissl-stained sections and computerized image analysis to measure the median-free path through the neuropil in the radial and tangential directions. No significant change was noted in the ratio of both directions in either the prenatal or postnatal sample population. The maintenance of constant ratios of radial to tangential median neuropil space over the course of development suggests that these morphometric relations reflect an intrinsic organization of circuitry present from the earliest stages of cortical development. Results from the study also suggested that the total cell numbers within a minicolumnar lamina and the corresponding minicolumnar dimensions are constrained by packing limits imposed by their associated vertically oriented processes (Casanova et al. 2007). In effect, corticalization may be determined by the total number of radial units (i.e., radial unit hypothesis of Rakic) only as they ultimately define the total number of minicolumns in the neocortex (Rakic 2003). Recent studies have given credence to Rakic’s radial unit hypothesis (Rakic 1995). It is now commonly accepted that changes in the number of germinal cell divisions giving rise to minicolumns provide for corticalization and brain parcellation (Casanova and Tillquist 2008). Thus, a single microinjection of fibroblast growth factor (FGF) 2 into the cerebral ventricles of rat embryos doubles the number of pyramidal cells while opposite results are noted in a knockout mouse of the Fgf2 gene (Vaccarino et al. 1999). Similarly, preliminary work on the mouse model for velocardiofacial syndrome suggests that deletion of regulatory genes that control the cell cycle for germinal cells provides for changes in both brain volume and the specification of cells within different cortical layers (Tucker et al. 2008).
Morphometry (Lateralization and Minicolumnar Width) Comparative neuroanatomy has long studied the existence and meaning of asymmetries in various parts of the human and nonhuman primate brains.
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In areas of the human brain associated with language, such as the planum temporale, MRI studies reported essentially identical asymmetries in humans and in African great apes but not in the Old or New World monkey (Gannon et al. 1998; Hopkins et al. 1998). However, an examination of minicolumns between the two hemispheres revealed that minicolumns in homologous regions of the cortex can be lateralized. Perhaps Seldon (1981a) was the first researcher to report an example of minicolumnar asymmetry in his studies of the human auditory cortex. Seldon (1981a) found that cell columns and neuropil space were wider in the left hemisphere as compared to the right. Buxhoeveden et al. (2001) examined cell columns in both hemispheres of humans, chimpanzees (Pan troglodytes), and monkeys (Macaca mulatta). They reported that minicolumns were lateralized in humans but not in the chimpanzee or monkey brain. The asymmetry consisted of wider cell columns in the left hemisphere. The asymmetrical morphology of cell columns within a brain is highly suggestive of organizational differences between hemispheres (Seldon 1981a, b). Still, despite these interhemispheric differences, gross variability in minicolumnar width appears to be constrained within a brain and within particular species. In a sample population of autopsy specimens from 67 human patients (vide supra; Casanova et al. 2007), five brain regions (areas 4, 17, 21, 22, and 40) were selected to assess minicolumnar width. The width of minicolumns averaged between 40 and 50 mm. The mean minicolumnar width for association cortex (area 40) was 46.8 mm and 31.6 mm for primary visual cortex (area 17). One might conclude that the mean size of minicolumns in human brain falls within a specific range that is generally greater than that of other primates but seemingly equal or smaller than that reported for many other smallbrained mammals such as the cat, rabbit, or rat (Buxhoeveden and Casanova 2005; for a review see Casanova 2008a). Comparisons of independent studies in human cortex confirm these numbers (Buldyrev et al. 2000; Del Río and DeFelipe 1997a, b; Seldon 1981a; Schlaug et al. 1995). A study of minicolumns in humans was done by
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creating a GLI-profile analysis wherein a linear combination of parameters provided a verticality index (a measure of columnarization) (Schlaug et al. 1995). Regions of the cingulate cortex were examined in normal humans using digitized images of Nissl-stained material. The authors reported a mean minicolumnar spacing of 41.7 mm. Of the five areas examined, area 7 (pericingulate) displayed the smallest column width of 39.9 mm and area 31 was the largest with 46.6 mm.
Molecular Biology The genetics underlying neocortical development in autism is complex, reflective of the intricacies of stem cell proliferation, differentiation, migration, neurite outgrowth, and synaptic development. Particular to a significant number of cases of autism is an increase in total minicolumnar number within the neocortex, indicative of an increase of the neural stem cell population (Casanova et al. 2002a). Subject to genetic and epigenetic influences are numerous pathways involved in cell cycle regulation, cellular proliferation, and cell survival. Activation or inhibition of key signaling molecules along these pathways has the potential to upregulate proliferation of the neural germinal population, thereby increasing total minicolumnar number. Targeting of even one or a few molecules can affect activation of related pathways due to crosstalk among them. In addition, upregulation of these pathways may alter migration, neuritogenesis, and synaptogenesis as well because these pathways have been exapted for a variety of forms of growth and development within the cell, linking aberration in cellular proliferation and later minicolumnar morphometry to abnormalities in cell-to-cell connectivity (for review, see Williams and Casanova 2011b). Genetic and epigenetic animal models of autism can illustrate how varying genotypes lead to a common neuroanatomic phenotype. For example, in a subtype of autism spectrum disorders known as Rett syndrome, approximately 80% of cases display a germline mutation of the methyl-CpG-binding protein 2 (MECP2) gene on the X chromosome. Due to paternal parent of
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origin effects, Rett syndrome mainly targets females. Knockout mice heterozygous for Mecp2 expression are now used to study the condition at the molecular, neuroanatomical, neurophysiological, and behavioral levels. MECP2 is a regulatory molecule intimately involved in methylation and deacetylation of DNA within the central nervous system, forming complexes which frequently repress promoter activation. Researchers have found that DNA which is not both methylated and deacetylated remains available for transcription, indicating the vital regulatory position this complex occupies. Even though MECP2 is ubiquitously transcribed, it exhibits temporal heterogeneous expression in frontal cortex, as MECP2hi levels increase with postnatal age and MECP2lo levels simultaneously decrease with age (Samaco et al. 2004). This increase in MECP2hi levels may help to explain the timing of regressive events seen in Rett syndrome and may offer some insight into regression in idiopathic autism as well. While idiopathic autism rarely presents with MECP2 mutations, the conditions often exhibit abnormal MECP2 activation, indicating aberrant regulation upstream of this molecular complex (Samaco et al. 2004). Another intracellular signaling molecule, phosphatase and tensin homolog (PTEN), is encoded on chromosome 10q23 and regulates cellular growth in part by inhibiting PI3K/Akt pathway activation. While autism is a highly heterogenetic group of conditions with a wide variety of associated mutations, approximately 7% of the autistic population harbors PTEN germline mutations (McBride et al. 2010). Within this subpopulation, autism is almost always associated with co-occurring macrocephaly, and in heterozygous Pten knockout mice, this macrocephaly is especially apparent. Therefore, given the high rates of PTEN mutations within the autistic population, these mice have been proposed as a viable animal model for autism (Kwon et al. 2006). Tuberous sclerosis is a single gene condition in which 25–50% of those affected also exhibit autistic-like symptoms. The mutations which give rise to the condition target the gene products, tuberous sclerosis complex (TSC) 1 or 2, although
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mutations in TSC2 are more frequently associated with co-occurring autism (Jones et al. 1999). A mutation within either TSC 1 or 2 can significantly alter its functionality, decreasing inhibition of the mTOR pathway, which is a sensitive detector of growth factors, insulin, and oxidative stress and which also maintains a prime position to either upregulate or downregulate growth based on this incoming information. The resultant phenotype of the condition presents with global or localized forms of megalencephaly, tubers, subependymal nodules, and other proliferative and migratory malformations. Of interest, similar malformations have also been noted within idiopathic autism (Bailey et al. 1998; Wegiel et al. 2010), illustrating how genetic heterogeneity can still lead to a common phenotype. Recently, the Autism Genome Project Consortium published work comparing 996 autistics to 1,287 matched controls and found significant copy number variations across three functional domains: (1) cellular proliferation, (2) cell projection and motility, and (3) GTPase/Ras signaling (Pinto et al. 2010). While GTPase/Ras signaling may be subsumed under the previous two functions, neuroscientists have had a tendency to view proliferation and projection/motility as distinct cellular processes, and their research have subsequently “split” them by their differences rather than “lump” them by their inherent similarities. But the molecular parsimony which exists between cellular proliferation, neurite outgrowth, synaptic development, and cell movement is considerable. Molecular parsimony, otherwise known as the “small toolkit” of molecular biology, suggests that evolution consistently exapts the same types of molecules and signaling pathways to perform new and related functions, such that conserved “families” of genes emerge within and across species. Therefore, the division of a cell into two, the podlike projection of a neuritic arm, the budding of a new synapse, or the slinking movement of a cell along its path have each been refined from a baser process so that they now serve different purposes. And the baser process from which these functions have been borrowed is the general process of growth and division: a common thread tying many, if not all, forms of autism
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together. Viewing these conditions in a broader molecular light may help us understand how individuals with such seemingly disparate genetic backgrounds, and arising from complex ecologies, can each be at risk for the development of autism.
Minicolumnar Networks: Electrophysiology and Circuitry The minicolumn is defined by cytoarchitectural arrangements of cell elements aligned with the core of pyramidal cells and the apical dendrite and myelinated axon bundles arising from them. Apical dendrite bundles and myelinated axon bundles vary in number and laminar origin and cell type of their constituents, according to species and area. They are spaced in register with pyramidal cell columns and interneurons; the distribution and structure of these constituents provide complementary parameters for morphometric analysis. The basis for the computational flexibility of microcircuit functions in the minicolumn lies in the regulation of the core pyramidal cells’ electrotonic properties by interneuron classspecific networks and their synaptic distributions in the various membrane compartments of pyramidal cells. Coordinated inhibition on proximal dendritic segments results in the functional segregation of the soma and distal dendrite compartments. Tonic inhibition applied to basal dendrites or proximal to the apical dendrite tuft in layers I and II disrupts excitatory postsynaptic potentials (EPSPs) arising from cells in neighboring minicolumns and limits their input. Moreover, the disposition of interneuron connections modulates activity in the recurrent pyramidal cell circuits whose positive feedback properties render them unstable. Interneuron regulation of the reverberant properties and gain of these circuits allow for the maintenance of information in each minicolumnar circuit in a metastable state. In this way, a signal input could allow for the mutual relaxation of minicolumn subsets into a stable state, providing a basis for pattern completion or matching. Thus, each subclass of interneuron plays critical functional roles in control of translaminar circuits.
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In the canonical cortical microcircuit proposed by Douglas and Martin (2004), the flow of excitatory information from upstream areas or thalamocortical inputs is directed toward middle layers. As was noted earlier, most circuits in cortex are recurrent and local. In cat visual cortex, thalamocortical synapses were calculated to constitute less than 7% of the total number of inputs to layer IVc spiny stellate cells (Ahmed et al. 1994). While sparse, this input is highly divergent and convergent both on other spiny stellate stars and on target pyramidal neurons in layer II/III (Lübke et al. 2000, 2003). Thalamocortical inputs also provide direct input to GABA neurons, primarily parvalbumin-expressing (PV+) basket cells or vasoactive intestinal peptide (VIP+) bipolar cells (Freund et al. 1985; Hájos et al. 1997; Staiger et al. 1996, 1997). Subpopulations of spiny pyramidal and stellate neurons in layer IV form precise retinotopic patterns in primary visual cortex (VI) of cats and rodents (Burkhalter 1989; Parnavelas et al. 1977). What is notable is that these precise patterns of inputs feed ordered arrangements of fine-scale microcircuits. Double cell intracellular recordings of pyramidal cell pairs in layer II/III (Yoshimura et al. 2005) revealed that pyramidal cell pairs which received common afferents were significantly more likely to be reciprocally connected. This specificity was also established for connections between fast-spiking interneurons and pyramidal neurons, with fast-spiking interneurons preferentially forming reciprocal rather than one-way connections with pyramidal cells, providing a dedicated feedback circuit (Yoshimura and Callaway 2005). In addition, these interneurons only received input from pyramidal neurons which were already interconnected or from strong layer 4 afferents. Fast-spiking interneurons were dedicated to finescale pyramidal cell circuits, thereby providing a strong source of feature-tuned feed-forward inhibition to these circuits (Yoshimura and Callaway). The brain’s limited long-range wiring cannot directly sustain coordinated activity across arbitrary cortical locations, but it can convey patterns of synchronous activity as oscillatory neuronal fluxes, represented by local field potentials measured by electroencephalography (EEG).
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Coordination of oscillations at varying interacting frequencies allows for relatively efficient and unconstrained segregation or integration of cognitive representations in varying forms and across hierarchical levels. Disrupted patterns of coordinated oscillatory output in distributed minicolumnar networks might be associated with cortical disconnection in autism. Likewise, altered oscillatory activity in developing cortical circuits may contribute to impaired development of intraareal and transcortical connections (Casanova and Trippe 2009).
Minicolumnar Networks: Neurochemistry Oscillations of pyramidal cells in minicolumns and across assemblies of minicolumns are maintained by networks of different species of inhibitory, GABA-expressing, interneurons. GABA neurons are classified according to characteristic combinations of morphological, physiological, or molecular phenotypes (Ascoli 2008). Cell morphologies are conventionally classified as basket cells (interneurons having a large proportion of terminals on soma or proximal dendrites), chandelier cells (defined by vertical arrays of cartridge terminals aligned on pyramidal cell initial segments), and double bouquet cells (interneurons having predominantly radially aligned axodendritic arbors). Most interneurons express only one of the calcium-binding proteins parvalbumin or calretinin, or the neuropeptide somatostatin but not the others. Radially oriented interneurons containing calretinin commonly coexpress vasointestinal peptide (VIP). Basket cells and chandelier cells containing parvalbumin have a fast-spiking synaptic dynamics with high amplitude. Other basket cells containing the neuropeptide cholecystokinin, and never parvalbumin, have irregular-spiking profiles. Radially oriented cells typically have an accommodating broader synaptic profile which varies according to cell type and connections. While tangentially arrayed basket cells function, in part, to coordinate activity among remote neuronal ensembles, by contrast, radially oriented
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inhibitory interneurons prominently located in the peripheral neuropil space surrounding pyramidal cell columns likely function to segregate columns from interference, both from other minicolumns within an array and from fields of activity or inhibition in neighboring minicolumnar arrays. Cholinergic inputs to cells of either cholecystokinin (CCK+) or PV + basket cell subclasses are complementary and nonoverlapping (Fig. 1). Cholinergic activation of alpha-7 subunitcontaining nicotinic receptor expressed in CCK + basket cells results in depolarization and increased GABAergic output. In rodent hippocampus, CCK + cell depolarization profiles vary according to interneuron size or molecular features (Kawaguchi 1997). By contrast, PV + interneurons express presynaptic muscarinic M2 receptors (Hájos et al. 1998) which serve to inhibit GABA release from PV + basket cell and GABAergic drive on pyramidal neurons. Given acetylcholine’s well-established role in refining neuronal circuits and in increasing attention and perceptual feature selectivity (Hájos et al. 1998;
Neural Mechanisms in Autism, Fig. 1 Role of CCK cells within minicolumnar cell architecture (see text for explanation). Circles represent interneurons, triangles represent pyramidal cells. Abbreviations: cholecystokinin (CCK), calretinin (CR), parvalbumin (PV), and somatostatin (SST) Right: laminae; dotted, vertical lines: minicolumnar boundaries
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Kawaguchi 1997), this complementary expression pattern suggests a mechanism whereby increased presynaptic activation of CCK + interneurons and modulation of their gamma-band activity facilitates dynamic restructuring of connections between established networks in response to environmental or state-dependent cholinergic inputs; “clocklike” (Freund 2003) oscillatory drive of PV + neurons is suspended, and long-term plasticity of these interneuron networks will depend on the circuit remodeling driven by CCK + cells. Disruptions of the dynamic GABAergic balance synergistically regulated by cholinergic pathways, endocannabinoids, monoamines, and other neuroactive molecules would therefore affect the integration of local microcircuits into wider networks, a pathogenetic trajectory consistent with our current understanding of the behavioral pathology of autism as a “disconnection syndrome” (Casanova et al. 2006a; Just et al. 2004). CCK + basket cells are notable for the restricted axodendritic arbor field to within
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minicolumnar dimensions, together with radial interneurons expressing varying combinations of calretinin (CR), calbindin (CB), VIP, or somatostatin (SOM). As recently shown in monkey prefrontal cortex (Zaitsev et al. 2009), some CR + radial interneurons extending across most cortical layers or into white matter give rise to perisomatic baskets in superficial layers which surround small cell profiles resembling interneurons. Moreover, CBR1 mRNA and protein expression are distributed along a vertical translaminar gradient, with mRNA expression most intense in superficial layer II and CBR1 immunoreactivity increasing to a maximum in deep layer III (Eggan and Lewis 2007; Zaitsev et al. 2009). One implication of this finding is that CBR1/CCK + basket cell terminals may have a vertical distribution extending from their cell body. Radially oriented interneurons may therefore serve to coordinate activity of CCK + basket cells across layers of a minicolumn thereby regulating development of its recurrent circuits during development. CCK + interneuron circuits are more susceptible to disruption than are PV + interneurons and that disruption has greater pathological impact (Freund 2003). This may indicate that they, along with CR + or VIP + radial interneurons, serve as “keystones” in interneuronal networks driving synchronous interneuron activity across supra- and infragranular layers of a minicolumn.
Minicolumnopathy of Autism Recent postmortem studies have found areaspecific alterations in minicolumnarity. All of these studies have been carried out targeting pyramidal cell arrays in Nissl stains as immunocytochemical markers may prove unreliable given the long postmortem intervals before autopsy and the variability in agonal conditions between patients and normative individuals. The first study was provided by Casanova et al. (2002a) using celloidin-embedded Nissl material from nine autistic patients and an equal number of controls. Three brain areas (9, 21, and 22) were studied. In this study, the brains of autistic individuals were found to have smaller minicolumns (p ¼ 0.034)
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with most of the decrease attributable to a reduction in the peripheral neuropil space. Results were later on validated when the same series was analyzed according to the gray level index (GLI) (Casanova et al. 2002b). A more recent study used an independent sample to analyze minicolumnarity within representative primary sensory, motor, and prefrontal association cortices (areas 17, S1, 4, and 9) (Casanova et al. 2006a). Differences in tissue preparation and section thickness prevented direct comparisons with earlier studies. However, minicolumnar width was significantly diminished in autistic individuals as compared to controls (p ¼ 0.023) (Fig. 2). The finding was also corroborated by using GLI measures of distance between amplitude peaks. Nucleolar area was thresholded from the background and found to be smaller in autistic individuals. A Boolean model found greater density of Nissl-stained elements (neurons). A Delaunay triangulation method suggested a diminution in the distance between minicolumns but an otherwise normal complement of neurons within minicolumns. The authors concluded that the decrease in neuronal and nucleolar size noted in autistic subjects could constrain the length of corticocortical projections thereby biasing their circuitry toward shorter connections (arcuate fibers) at the expense of longer ones (i.e., commissural projections) (Casanova et al.). A study of minicolumnar abnormalities in postmortem samples of brains of autistic individuals and controls was expanded on previous findings by exploring nine cortical areas that included paralimbic, heteromodal association, unimodal association, and primary or idiotypic areas (Casanova et al. 2006b). Computerized image analysis clustered neurons into minicolumnar fragments. The authors found an interaction of diagnosis and region for neuropil space (p ¼ 0.041). Post hoc analysis showed significant differences for the frontopolar region (area 10) and the anterior cingulate gyrus (area 24). The smaller minicolumns in autism translate into more of them per unit of length. The assumption of an increase total number of minicolumns was modeled to true columnar parameters (three dimensions) with a strong correlation (Casanova and Switala 2005).
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Neural Mechanisms in Autism
Neural Mechanisms in Autism, Fig. 2 Minicolumns in a section of primary motor cortex (Brodmann area 4), lamina III, in an autistic patient (bottom) and an agematched control case (top). Insets highlight the cores of minicolumn fragments. Scale bars measure 200 mm on left and 50 mm on right
Estimates of cortical interconnectivity suggest that each module (minicolumn) is connected to 103 other modules (Casanova 2004). If connectivity is fixed as brain size increases, the number of connections would scale as the number of modular units squared. Most of the addition in white matter takes the form of short corticocortical fibers as longer connections incur a penalty of increased conduction time and metabolic demands. Not surprisingly, MRI studies parcellating the white matter have found a disproportionate increase in the outer white matter compartment (short late myelinating fibers) as opposed to inner compartment containing longer bridging connections (Herbert et al. 2004).
Future Directions Genome-wide linkage screen have refuted the concept of autism as having a Mendelian inheritance and rather sustain the condition as a multifactorial disorder. The author has proposed a “triple hit hypothesis” where manifestations supervene whenever three factors converge on a particular infant: (a) a critical period of brain development, (b) an underlying genetic vulnerability, and (c) exogenous stressor(s) (Casanova 2008b) (Fig. 3). While it is important to investigate the genetics of autism, it is also important to remain aware that phenotype is a combination of genetic and epigenetic influences. And therefore, environmental agents which target pathways
Neural Mechanisms in Autism
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Neural Mechanisms in Autism, Fig. 3 Autism is a multifactorial disorder where manifestations supervene when three factors impinge to various degrees upon a particular infant: (1) a critical period of brain development, (2) an underlying vulnerability (genetics), and (3) exogenous stressors (Casanova 2008a)
common to autism may lead to comparable phenotype yet not be reflected within the genotype. A variety of teratogenic agents have already been associated with the conditions, and more epigenetic influences will likely come to light with further research (Williams and Casanova 2011a). The overriding theme of autism is that overactivation of key growth-related pathways during vulnerable time periods of development may lead to the conditions (Levitt and Campbell 2009; Williams and Casanova 2011a). The value of understanding the putative minicolumnopathy of autism lies in its explanatory powers and translational possibilities. Early results predicted changes in brain volume, alterations in the ratio of gray to white matter, bias in white matter parcellation (inner vs. outer white matter), and abnormalities in gamma-binding frequencies (Casanova et al. 2002c). More recently, the idea that autistic individuals have defects in their minicolumnar peripheral neuropil space has provided for a possible clinical intervention using rTMS to strengthen the inhibitory surround of
these modules (Sokhadze et al. 2009a, b, c). Other therapeutic attempts based on the minicolumnopathy of autism include the shifting of the terminal fields of sensory stimuli as a way of compensating for the bias in short connections observed in this condition.
See Also ▶ Cerebral Cortex ▶ Pyramidal System ▶ Rett Syndrome ▶ Tuberous Sclerosis Complex
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Neural Mechanisms of Emotional Dysregulation Karim Ibrahim1, Gregory McCarthy2 and Denis G. Sukhodolsky1 1 Child Study Center, Yale School of Medicine, Yale University, New Haven, CT, USA 2 Department of Psychology, Yale University, New Haven, CT, USA
Synonyms Amygdala-prefrontal network; Cognitive control circuitry
Definition Circuitry Implicated in the Cognitive Control of Emotion Emotion regulation refers broadly to the implementation of automatic or intentional strategies to modulate the trajectory of an emotion or emotional state in order to promote adaptive, goaldirected behavior. Further, the use of functional MRI (fMRI) has provided insight into the neural systems of cognitive control that support regulatory strategies and the modulation of emotional reactivity. Examples of cognitive-based strategies that are amenable to functional imaging paradigms of cognitive control include attentional deployment, which involves controlling the gating of emotional stimuli (e.g., selective attention, distraction); response modulation, which involves strategies to modulate expression of negative emotions (e.g., suppression); and cognitive change, which involves altering the interpretation or meaning of a stimulus (e.g., reappraisal).
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Several cognitive processes and neural systems are involved in regulating emotions. In particular, prefrontal regulatory regions including the ventromedial and ventrolateral prefrontal cortices (PFC), ventral anterior cingulate cortex, dorsolateral prefrontal, and parietal regions are primarily involved in modulating the emotional reactivity of regions such as the amygdala, insula, ventral striatum, and periaqueductal gray (Dolcos and McCarthy 2006; Etkin et al. 2015). For instance, the ventromedial PFC, a key region implicated in emotion regulation, downregulates amygdala reactivity in response to threat and other negative stimuli (Ochsner et al. 2012; Silvers et al. 2016). Thus, intact emotion regulation (e.g., cognitive reappraisal) can be characterized by the activation of regions involved in emotional reactivity in response to aversive or negative stimuli, such as threat and fear, and this response is then dampened by ventral prefrontal regions involved in the cognitive control of emotion (Ochsner et al. 2012). Neural Bases of Emotion Dysregulation in ASD Deficits in emotion regulation are postulated as a mechanism underlying several psychiatric disorders including internalizing disorders (Jazaieri et al. 2015), disruptive behavior disorders, and autism spectrum disorder (ASD) (Mazefsky et al. 2014). On a neural-systems level, models of emotion dysregulation implicate perturbations in functional connectivity, a measure of the synchronization in activity in between two brain regions, between the amygdala and ventral prefrontal cortex (vPFC) as a possible shared underlying mechanism across several psychiatric disorders characterized by impairments in cognitive control (Etkin et al. 2015; Ochsner et al. 2012). For instance, hyperactive circuitry of emotional reactivity, such as the amygdala, paired with reduced vPFC response to emotional stimuli is associated with anxiety disorders (Goldin et al. 2013), disruptive behavior disorders (Coccaro et al. 2011), and ASD particularly in the presence of co-occurring disorders including anxiety (Herrington et al. 2017) and disruptive behaviors (Ibrahim et al. 2019). Emotion dysregulation in ASD may be characterized by reduced functional connectivity
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between the amygdala and vPFC during emotional perception and cognitive control tasks. For instance, decreased connectivity between the amygdala and ventrolateral PFC was shown during cognitive reappraisal in response to disgustinducing images in children with ASD (Pitskel et al. 2014). Similarly, reduced prefrontal response during cognitive reappraisal was also shown in adults with ASD, suggesting a shared mechanism of emotion dysregulation among children as well as adults with ASD (Richey et al. 2015). Recent work also implicates differential patterns of reduced connectivity between the amygdala and ventrolateral PFC in children with ASD and co-occurring disruptive behavior in comparison to children with ASD without disruptive behavior (Ibrahim et al. 2019). Similar to findings from fMRI studies in non-ASD youths with conduct problems (Lozier et al. 2014), distinct relationships between callous-unemotional traits and severity of disruptive behaviors with amygdala reactivity have been shown in children with ASD. In particular, greater levels of callousunemotional traits were found to be related to reduced amygdala reactivity, while the opposite pattern of greater levels of disruptive behavior was found to be related to increased amygdala reactivity to emotional faces (e.g., fearful) in children with ASD when controlling for the variance of the other variable (Ibrahim et al. 2019). Thus, it is possible that emotion dysregulation in ASD, particularly in the presence of co-occurring disorders, could be associated with connectivity patterns that are shared across children without ASD neural systems. Given that face processing impairments are common in ASD, it is also important to note that perturbed responses in cognitive control circuitry, particularly the amygdala and vPFC circuit, have been shown during social perception including emotion processing tasks such as viewing emotionally expressive faces (Davies et al. 2011; Swartz et al. 2013) and social cognitive tasks such as viewing biological motion (Di Martino et al. 2009). Therefore, it is possible that aberrant recruitment of emotional reactivity and control circuitry during social perception could indicate misinterpretation or reduced
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saliency of social cues in individuals with ASD. Conversely, amygdala hyper-reactivity as well as amygdala-vPFC hyper-connectivity was also shown in individuals with ASD during face perception tasks (Kleinhans et al. 2011; Monk et al. 2010; Tottenham et al. 2013), which could indicate heightened sensitivity to threat or lack of differentiation of threat versus safe social cues (Top et al. 2016). In terms of future work, it is not clear which neural components underlying emotion regulation are most affected in ASD in the presence of a co-occurring disorders when different cognitive control processes are engaged (e.g., reappraisal vs. suppression vs. selective attention). Likewise, more work is needed to elucidate whether emotion regulatory impairments in ASD are due to a failure to activate specific cognitive control regions or abnormalities in the synchronization or connectivity between large-scale networks. Few studies have also examined the neural response to multiple sensory stimuli in ASD based on an emotion regulation framework (Green et al. 2015). Given that sensory over-responsivity including severe negative responses to stimuli (e.g., sounds, noisy environments, textures) is prevalent among youths with ASD, future studies are needed to understand possible dysfunction in emotion regulation circuitry during exposure to sensory stimuli. Further, the use of a dimensional approach to model cognitive control along a continuum in ASD, as well as in comparison to other clinical comparison groups, could reveal new insights into the relationship between emotion regulation circuitry and severity of associated symptoms, supporting the identification of possible neuroendophenotypes that could serve as mechanism-based targets for personalized treatments in ASD.
See Also ▶ Amygdala ▶ Emotional Regulation ▶ Prefrontal Cortex ▶ RDoC and Autism
Neural Reorganization
References Coccaro, E. F., Sripada, C. S., Yanowitch, R. N., & Phan, K. L. (2011). Corticolimbic function in impulsive aggressive behavior. Biological Psychiatry, 69(12), 1153–1159. Davies, M. S., Dapretto, M., Sigman, M., Sepeta, L., & Bookheimer, S. Y. (2011). Neural bases of gaze and emotion processing in children with autism spectrum disorders. Brain and Behavior: A Cognitive Neuroscience Perspective, 1(1), 1–11. https://doi.org/10.1002/ brb3.6. Di Martino, A., Ross, K., Uddin, L. Q., Sklar, A. B., Castellanos, F. X., & Milham, M. P. (2009). Functional brain correlates of social and nonsocial processes in autism spectrum disorders: An activation likelihood estimation meta-analysis. Biological Psychiatry, 65(1), 63–74. https://doi.org/10.1016/j. biopsych.2008.09.022. Dolcos, F., & McCarthy, G. (2006). Brain systems mediating cognitive interference by emotional distraction. Journal of Neuroscience, 26(7), 2072–2079. Etkin, A., Büchel, C., & Gross, J. J. (2015). The neural bases of emotion regulation. Nature Reviews Neuroscience, 16(11), 693. Goldin, P. R., Ziv, M., Jazaieri, H., Hahn, K., Heimberg, R., & Gross, J. J. (2013). Impact of cognitive behavioral therapy for social anxiety disorder on the neural dynamics of cognitive reappraisal of negative selfbeliefs: Randomized clinical trial. JAMA Psychiatry, 70(10), 1048–1056. Green, S. A., Hernandez, L., Tottenham, N., Krasileva, K., Bookheimer, S. Y., & Dapretto, M. (2015). Neurobiology of sensory overresponsivity in youth with autism spectrum disorders. JAMA Psychiatry, 72(8), 778–786. Herrington, J. D., Maddox, B. B., McVey, A. J., Franklin, M. E., Yerys, B. E., Miller, J. S., & Schultz, R. T. (2017). Negative valence in autism spectrum disorder: The relationship between amygdala activity, selective attention, and co-occurring anxiety. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 2(6), 510–517. Ibrahim, K., Eilbott, J., Ventola, P., He, G., Pelphrey, K. A., McCarthy, G., & Sukhodolsky, D. G. (2019). Reduced amygdala-prefrontal functional connectivity in children with autism spectrum disorder and co-occurring disruptive behavior. Biological Psychiatry: Cognitive Neurosience and Neuroimaging, 4(12), 1031–1041. Jazaieri, H., Morrison, A. S., Goldin, P. R., & Gross, J. J. (2015). The role of emotion and emotion regulation in social anxiety disorder. Current Psychiatry Reports, 17(1), 531. https://doi.org/10.1007/s11920-014-0531-3. Kleinhans, N. M., Richards, T., Johnson, L. C., Weaver, K. E., Greenson, J., Dawson, G., & Aylward, E. (2011). fMRI evidence of neural abnormalities in the subcortical face processing system in ASD. NeuroImage, 54(1), 697–704. Lozier, L. M., Cardinale, E. M., VanMeter, J. W., & Marsh, A. A. (2014). Mediation of the relationship
3117 between callous-unemotional traits and proactive aggression by amygdala response to fear among children with conduct problems. JAMA Psychiatry, 71(6), 627–636. https://doi.org/10.1001/jamapsychiatry.2013.4540. Mazefsky, C. A., Borue, X., Day, T. N., & Minshew, N. J. (2014). Emotion regulation patterns in adolescents with high-functioning autism spectrum disorder: Comparison to typically developing adolescents and association with psychiatric symptoms. Autism Research, 7(3), 344–354. https://doi.org/10.1002/aur.1366. Monk, C. S., Weng, S.-J., Wiggins, J. L., Kurapati, N., Louro, H. M., Carrasco, M., . . . Lord, C. (2010). Neural circuitry of emotional face processing in autism spectrum disorders. Journal of Psychiatry & Neuroscience, 35(2), 105. Ochsner, K. N., Silvers, J. A., & Buhle, J. T. (2012). Functional imaging studies of emotion regulation: A synthetic review and evolving model of the cognitive control of emotion. Annals of the New York Academy of Sciences, 1251, E1–E24. https://doi.org/10.1111/j. 1749-6632.2012.06751.x. Pitskel, N. B., Bolling, D. Z., Kaiser, M. D., Pelphrey, K. A., & Crowley, M. J. (2014). Neural systems for cognitive reappraisal in children and adolescents with autism spectrum disorder. Developmental Cognitive Neuroscience, 10, 117–128. Richey, J. A., Damiano, C. R., Sabatino, A., Rittenberg, A., Petty, C., Bizzell, J., . . . Dichter, G. S. (2015). Neural mechanisms of emotion regulation in autism spectrum disorder. Journal of Autism and Developmental Disorders, 45, 3409. https://doi.org/10.1007/s10803015-2359-z Silvers, J. A., Insel, C., Powers, A., Franz, P., Helion, C., Martin, R. E., . . . Ochsner, K. N. (2016). vlPFC– vmPFC–amygdala interactions underlie age-related differences in cognitive regulation of emotion. Cerebral Cortex, 27(7), 3502–3514. Swartz, J. R., Wiggins, J. L., Carrasco, M., Lord, C., & Monk, C. S. (2013). Amygdala habituation and prefrontal functional connectivity in youth with autism spectrum disorders. Journal of the American Academy of Child and Adolescent Psychiatry, 52(1), 84–93. Top, D., Stephenson, K. G., Doxey, C. R., Crowley, M. J., Kirwan, C. B., & South, M. (2016). Atypical amygdala response to fear conditioning in autism spectrum disorder. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 1(4), 308–315. Tottenham, N., Hertzig, M. E., Gillespie-Lynch, K., Gilhooly, T., Millner, A. J., & Casey, B. (2013). Elevated amygdala response to faces and gaze aversion in autism spectrum disorder. Social Cognitive and Affective Neuroscience, 9(1), 106–117.
Neural Reorganization ▶ Plasticity, Neural
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Neural Science ▶ Neuroscience
Neural Signatures of Treatment Response Jiedi Lei1,2 and Pamela E. Ventola2 1 Centre for Applied Autism Research, Department of Psychology, University of Bath, Bath, UK 2 Yale Child Study Center, School of Medicine, Yale University, New Haven, CT, USA
Definition As a field, clinical research is increasingly moving toward the goal of precision medicine (Insel 2014), an initiative that highlights the importance of taking into consideration individual differences from the genetic to behavioral level when formulating effective treatment plans. Recent advancements have increasingly adopted an interdisciplinary approach to overcome two major barriers: (1) to better characterize how a treatment can elicit change at the individual level, and (2) to predict who will most likely respond to the intervention. Successful integration of neuroimaging techniques and behavioral measures have the potential to guide identification of sensitive and objective biomarkers that can reflect and predict response to specific evidencebased treatment models in individuals with autism spectrum disorder (ASD). By integrating knowledge from social and cognitive neuroscience that have shown atypical neural response among individuals with ASD when processing social stimuli compared to typically developing (TD) individuals (Adolphs 2009), as well as established neural signatures of ASD (Kaiser et al. 2010), neuroimaging techniques can facilitate the progression toward precision medicine in two ways.
Neural Science
First, clinical research relying on behavioral measures alone are not sufficient for overcoming the challenge of equifinality, where similar behavioral gains observed across participants following treatment might mask differential underlying mechanisms of change. Integrating neuroimaging techniques such as functional magnetic resonance imaging (fMRI) can help characterize the heterogeneous neural mechanisms of change underlying similar behavioral gains observed across participants. fMRI examines functional changes in brain response to social stimuli that are both common at the group pathology level of ASD, and unique changes at the individual level (Venkataraman et al. 2016; Ventola et al. 2015). Therefore, the utilization of neuroimaging tools can help clinicians to better understand not only how a treatment can elicit change, but also how change is occurring at the individual level, which can inform future treatment decisions. Second, tracking clinically sensitive biomarkers can enable clinicians to both objectively quantify and qualitatively assess trajectories of change over the course of treatment (Ventola et al. 2015; Voos et al. 2012), and data collected over time can help generate predictive models of responder profiles to treatment (Yang et al. 2016), assisting the understanding of who will most likely respond to intervention. One evidence-based intervention that has seen a recent expansion in identifying neural signatures of treatment response is pivotal response treatment (PRT), a behavioral-based intervention primarily aimed to target social communication skills in children with ASD (Koegel et al. 1987; Koegel and Egel 1979; Koegel and Koegel 2012; Verschuur et al. 2013). Based on principles of applied behavior analysis (ABA), PRT adopts a more naturalistic approach and employs a variety of strategies to increase children’s motivation, such as following the child’s choice, interspersing maintenance and acquisition tasks, and reinforcing both correct responses and appropriate attempts to increase the frequency of children’s exposure to naturally contingent reinforcements (Koegel et al. 1987; Koegel and Egel 1979). Building upon its large empirical evidence base from behavioral studies (Verschuur et al.
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2013), recent advancements have began to integrate neuroimaging techniques to identify both neural signatures of treatment response (Ventola et al. 2015; Voos et al. 2012), as well as neural biomarkers to help predict responder profiles to PRT (Yang et al. 2016). This entry will first outline the current state of science behind the identification of brain areas of interest to serve as potential neural signatures of treatment response in individuals with ASD, as well as the evidence supporting the use of functional paradigms sensitive to detect clinically meaningful outcomes in terms of neural response to social stimuli. Next, a critical evaluation of recent advancements in identifying neural signatures of treatment response to PRT for individuals with ASD is offered. Finally, future directions for expanding research to help uncover neural signatures of treatment response can increase the use of other noninvasive and more costeffective techniques such as electroencephalography (EEG), as well as eye-tracking are discussed, alongside limitations to take into consideration for future research.
Background: Examining the Current State of Science Overview of Neural Signatures of ASD Recent advancements in the identification of neural signatures of treatment response for individuals with ASD have relied on the assimilation of knowledge from social and cognitive neuroscience research, to first establish a robust profile of neural correlates that can reliably detect differences in social cognition among individuals with ASD compared to their TD peers. One wellestablished paradigm known to reliably elicit differential patterns of neural response in brain regions associated with social cognition among individuals with and without ASD is a biological motion paradigm (Klin et al. 2009; Pavlova 2012; Pelphrey et al. 2003), which depicts a series of either biologically coherent (BIO) or scrambled (SCR) motion by using a series of point-light displays (Fig. Fig. 1). For the biologically coherent displays, the point-light displays depict social
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Neural Signatures of Treatment Response, Fig. 1 Stimuli showing biological and scrambled motion paradigm
movements such as a person dancing, walking, or playing games that are familiar to children such as pat-a-cake (Pelphrey et al. 2003). The ability to perceive form from biological motion capitalizes on the intrinsic visual sensitivity toward kinematics that is congruent with the spatiotemporal dynamics of movements elicited by biological agents (Johansson 1973; Pavlova 2012; Puce and Perrett 2003). Biological motion paradigms have been widely used in both primate and human neuroimaging research to study social cognition, and have generated convergent findings that highlight the role of superior temporal sulcus (STS) in the recognition of socially salient information (Oram and Perrett 1994, 1996; Puce and Perrett 2003; Vaina et al. 2001). Primate findings have shown that sensory integration occurs when motionrelated information from the dorsal pathway and form-related information from the ventral pathway derived from the same biological object
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converge at the superior temporal polysensory area (STP; found within the STS) within 100 ms, enabling perception of coherent biological motion (Oram and Perrett 1994, 1996). In humans, similar response profiles are seen in typically developing adults, where biological motion have been shown to activate both the ventral and dorsal visual pathways as well as the point of confluence at the superior temporal gyrus (STG), the homologue region to the STP found in primates (Pelphrey et al. 2003; Vaina et al. 2001), as well as the fusiform gyrus (FG) known for facial recognition. In TDs, preferential looking toward biologically coherent motion emerges during early infancy, and serves to bias one’s attention toward socially salient stimuli (Klin et al. 2009), which is an important behavioral precursor to later-emerging social cognition and affect regulation skills (Baron-Cohen 1997; Frith and Frith 1999). This is further supported by neurobiological findings, where STS projects to both the amygdala and orbitofrontal cortex (OFC) which are involved in processing emotionally salient information and affect regulation, and together form part of the social cognition network associated with theory of mind (Adolphs 2009; Baron-Cohen 1997; Brothers 2001). Therefore, biological motion is a well-validated paradigm that is known to elicit robust response profiles in brain areas involved in social information processing, and atypical profiles of neural response to BIO versus SCR motion may be indicative of pathology that impacts social cognitive processing (Kaiser et al. 2010; Ventola et al. 2015; Voos et al. 2012). To better characterize the distinct neural profile associated with ASD that might underlie differences in social cognition when compared to TD peers, Kaiser et al. (2010) compared and contrasted the neural response to BIO versus SCR motion in children with ASD (n ¼ 25) with their unaffected siblings (US; n ¼ 20) and TD children (n ¼ 17). The authors conducted wholebrain analysis at voxel-wise uncorrected threshold level of p < 0.05, to evaluate brain regions that showed increased activation during BIO versus SCR motion (BIO>SCR) in TD>SD, TD>US, US>ASD, and US>TD group comparisons. To ensure that the false positive discovery rate of
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active voxels is 20 contiguous voxels at α < 0.05 within each group level comparison were selected. The further determine α value for each cluster, the relative occurrence of each cluster during 5000 iterations of Monte Carlo simulation was noted. In order to determine distinct neural profiles of (a) “state,” (b) “trait,” and (c) “compensatory” patterns of activation in response to biological motion, higher-level conjunction analyses were conducted using an uncorrected voxel-wise threshold level of p < 0.0025, and a cluster threshold of k > 20 to further control for the rate of false positives (Fig. 2). The “state,” “trait,” and “compensatory” regions served as unique neural signatures that characterized the differential response to biological motion across ASD, US, and TD children. “State” regions refer to brain areas that showed reduced activation during BIO>SCR in children with ASD only, thus indicative of atypical neural response to social stimuli unique to the state of ASD pathology. Such regions included ventromedial and left ventrolateral prefrontal cortex (vmPFC; vlPFC), right amygdala (rAMG), bilateral fusiform gyrus (FG), and right posterior superior temporal sulcus (rpSTS). “Trait” regions refer to brain areas that showed reduced activation during BIO>SCR in both children with ASD and US relative to TD children, thus indicative of an endophenotype underlying atypical neural response to social stimuli that may be associated with shared genetic vulnerability between ASD and TD. Such regions included right inferior temporal gyrus (ITG), left dorsolateral prefrontal cortex (dlPFC), and anterior bilateral fusiform gyri (FG). “Compensatory” regions refer to differential neural response to BIO>SCR found in US group only, suggesting possible compensatory effects to overcome their shared vulnerability with the ASD group that affects their ability to process social stimuli. Such regions included anterior rostral vmPFC and caudal rpSTS. Taken together, the “state,” “trait,” and “compensatory” profiles guide the selection of sensitive biomarkers that can detect atypical brain response to social stimuli both specifically related to ASD pathology, and
Neural Signatures of Treatment Response Neural Signatures of Treatment Response, Fig. 2 Brain areas showing activation differences in response to biological motion among children with autism spectrum disorders (ASD), as identified from 1 Kaiser et al. (2010), 2 Voos et al. (2012), 3 Ventola et al. (2015). Note: dlPFC dorsolateral prefrontal cortex, vlPFC ventrolateral prefrontal cortex, FFG fusiform gyrus, pSTS posterior superior temporal sulcus, AMG amygdala
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those that are related to the broader autism phenotype, providing a more comprehensive overview of the range of phenotypic abnormalities associated with disrupted social information processing related to ASD. Evaluating Sensitivity of Neural Signatures of ASD as Clinical Biomarkers Building upon the empirical data that have characterized biomarkers indicative of atypical response to social stimuli among children with ASD (Kaiser et al. 2010), such biomarkers may be clinically informative when monitored over the course treatment to help objectively quantify both the quality and magnitude of change in brain responsiveness to social stimuli. Furthermore, the demarcation of whether changes occur in areas that correspond to “state” or “compensatory” neural profiles can inform clinicians if following treatment, children with ASD may show increased normalization of brain response to social stimuli within the BIO>SCR contrast compared to TD children (by increased activation in “state” and “trait” regions), or if there are compensatory effects analogous to that observed in their unaffected siblings (by increased activation in “compensatory” regions), or both. To assess the clinical sensitivity of the identified neural signatures as potential biomarkers of treatment response, Voos et al. (2012) conducted the first fMRI study (case study) to evaluate whether behavioral changes in increased social functioning following 16 weeks of PRT is accompanied by simultaneous increased brain activation to biological motion in two high-functioning children with ASD. Primarily targeting social communication skills in children with ASD, PRT embeds learning opportunities naturally within games that are intrinsically motivating to each child, and delivers natural reinforcements contingent upon each child’s appropriate social attempts and responses to increase social initiations, use of functional language, and overall social reciprocity (Koegel et al. 1998; Koegel et al. 1987; Koegel and Egel 1979). Individualized treatment goals were established for each participant at the onset of treatment, which included lower-level social skills such as appropriate body positioning and voice modulation for participant one, and higher-
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level social skills such as social reciprocity, perspective taking, and use of functional language to initiate and make requests for participant two. Following PRT, clinical assessments by using the Autism Diagnostic Observation Schedule (ADOS) and Clinical Evaluation of Language Fundamentals (CELF-4) showed significant improvements in social functioning and use of adaptive language for both children. In response to biological motion, significant increases in activation were only observed in “state” and “trait” regions, where both children showed gains in bilateral FG. In addition, participant two showed more widespread collateral gains in other “state” areas including rpSTS and vlPFC, reflective of higher-order social processing skills targeted during PRT. The overlapping, yet distinct neural profiles of change across both participants, is important to note, as it helped to partition differential mechanisms of change underlying similar behavioral gains assessed by clinical measures, and helps to overcome the problem of equifinality by noting how treatment response might differ at the individual level. The use of fMRI also helped to elucidate the mechanism underlying how PRT may be inducing social gains in children with ASD, as the lack of change in activation of “compensatory” regions highlights that PRT elicited social gains in both children were driven by an increased normalization of neural response to social stimuli that is more comparable to the developmental trajectory of their TD peers. However, there are several limitations in Voos et al. (2012)’s findings. First, it was a case study with two participants so not generalizable to the wider clinical population. Second, no control groups were included, thus making it challenging to conclude whether the changes in brain activation to BIO>SCR were specific to PRT per se, or reflective of broader developmental changes that naturally occurred over the course of 16 weeks for both participants. Nonetheless, the Voos et al. (2012) study is important to note as it showed that the neural signatures of ASD identified by Kaiser et al. (2010) can potentially serve as clinically sensitive biomarkers that can track changes in social functioning at the brain level, within the timeframe of 16 weeks of PRT for children with ASD.
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Current Knowledge: Neural Signatures of Pivotal Response Treatment Overcoming the Challenge of Equifinality Based on exploratory results from Voos et al. (2012) that demonstrated the clinical sensitivity of neural signatures of ASD in detecting change in social functioning at the brain level in individuals with ASD, it is important to further examine how individual variances in neural profiles presented at baseline might further influence the trajectories of functional changes observed over the course of PRT. Identifying changes in functional neural pathways might indicate possible heterogeneous mechanisms of biological change underlying behavioral gains in social functioning observed in children with ASD, and help to address not only how a treatment might be eliciting positive social outcomes, but also how such outcomes are occurring at the individual level. To capture heterogeneity that might underlie similar behavioral manifestations of reduced social responsiveness such as inattention to faces and poor eye contact in children with ASD, Ventola et al. (2015) examined the brain response profile to BIO>SCR in the rpSTS in ten high-functioning children with ASD compared to five TD children. Relative to the mean activation level in rpSTS at the group level to biological motion at baseline found in TD children, five children with ASD demonstrated relatively hyperactive rpSTS activation (ASD>TD), and five children demonstrated relatively hypoactive rpSTS activation (TD>ASD). The authors hypothesized that the hyper- and hypoactive groups at baseline might experience different challenges that disrupted their response to social stimuli at baseline. Specifically, the hyperactive group might experience reduced sensory gating and poor attentional control, resulting in hyperarousal when exposed to socially salient information at baseline (Markram and Markram 2010), and reflected by increased activation of thalamus, amygdala, and temporal cortical regions. In comparison, the hypoactive group might experience reduced social motivation when attending to social stimuli, reflected by reduced activation in reward processing regions such as the ventral striatum (VS), nucleus accumbens (NAcc), and amygdala (Chevallier et al. 2012).
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Following 16 weeks of PRT, both hyper- and hypoactive groups showed significant improvements in social functioning assessed by behavioral measures, though distinct patterns of underlying neural mechanisms of change emerged. For the hyperactive group, the authors found reduced activation in rpSTS and other brain areas related to sensory gating and attention control (thalamus, amygdala, and hippocampus) during biological motion, suggesting improvements in regulating sensory gating and arousal levels in response to social stimuli following PRT. For the hypoactive group, the authors found increased activation and functional connectivity between both the rpSTS and the VS, a major region involved in reward processing, as well as other regions of the mirror neuron system (Fig. 2), suggesting improvements in social reward processing. Therefore, Ventola et al. (2015) successfully showed that not only does PRT elicit normalized neural response profiles to socially meaningful stimuli that accompanies behavioral social gains in children with ASD, but that changes occur differentially at the individual level, suggesting each child with ASD may be benefitting from PRT in individualized ways. Unmasking the heterogeneous neural mechanisms of change underlying similar behavioral gains can help clinicians to better identify how different active ingredients of PRT may be elicit therapeutic potentials for each child with ASD, and help to develop more individualized goals matched with appropriate intervention techniques to more effectively address each individual’s social deficits, thus moving toward the goal of precision medicine. Finally, given that functional changes in response to social stimuli can be seen at the regional level following PRT, it is interesting to conjecture whether co-occurring changes in functional connectivity between brain regions involved in lower- and higher-order social information processing may be altered at the brain circuitry level. This is of particular interest given that PRT simultaneously targets many social skills ranging from lower-level skills, such as increasing frequency of social initiations, to higher-level perspective-taking and selfmonitoring. Investigating how such co-occurring behavioral social gains may elicit functional
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rewiring of the brain in response to social stimuli can further examine whether PRT may be beneficial in increasing the brain’s overall efficiency at processing meaningful social information. In a sample of 19 high-functioning school-aged children with ASD, Venkataraman et al. (2016) evaluated co-occurring regions that showed changes in functional connectivity that accompanied aggregated functional change in response to biological motion in the posterior cingulate cortex (PCC) following 16 weeks of PRT. The authors found notable changes in brain circuitry associated with processing and assignment of reward values to external stimuli implicated by reduced functional connectivity to orbitofrontal cortex (OFC), as well as circuitry associated with facial recognition implicated by increased functional connectivity to occipital-temporal cortex. Taken together, PRT is shown to elicit both normalized trajectories of brain response to social stimuli both at the regional and brain circuitry level, suggesting there may be improved neural efficiency in processing socially salient information in children with ASD. Predictors of Responder Profile In addition to addressing how treatment may elicit change at the individual level, incorporating neuroimaging tools can also help to characterize neural profiles that might predict, at baseline, an individual’s response to treatment. Identifying neural predictors of treatment response can guide clinicians to select more appropriate treatments for individuals with ASD, and to maximize the cost and time efficacy for eliciting positive therapeutic gains. Furthermore, identifying neural predictors of treatment response and the corresponding behavioral profiles can increase clinicians’ awareness of areas that may be more treatment resistant, and require greater care when formulating treatment plans to develop effective strategies targeting such areas without breaching fidelity of treatment protocol. From a neuroimaging perspective, it is possible to conduct a retrospective regression analysis to assess how brain activation in response to social stimuli at baseline might predict the magnitude of improvement in social functioning following treatment, thus identifying possible neural predictors of treatment response.
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Monitoring changes in brain response to biolgoical motion, Yang et al. (2016) conducted regression-based multivariate pattern analyses (MVPA) to decode how unique distributions of voxel activation pattern at baseline within neural networks can be associated with the efficiency of social cognitive processing. The authors found that response to BIO>SCR in four neural clusters at baseline subsequently predicted the magnitude of increase in social competency following 16 weeks of PRT in 20 children with ASD, which included areas such as the STS, superior parietal lobule, FG, vmPFC, VS, and putamen. Functionally, the identified brain areas corresponded to a range of social and cognitive functions including motion and object recognition, emotion regulation and response inhibition, visuospatial attention, and reward processing. Furthermore, the authors utilized a crossvalidation design and found that the MVPA model of pretreatment response trained using one cohort of patient data was able to successfully predict changes in social functioning in a second independent cohort of patient data, suggesting that neural predictors identified may be generalizable across children with ASD. However, treatment specificity to PRT of the biomarkers identified could not be assessed, as the authors did not include an alternative treatment group for comparison, which warrants further attention. Despite the limitation, the successful implementation of MVPA by integrating fMRI and behavioral data has shown a promising first step for moving toward the goal of identifying who will most likely benefit from PRT among children with ASD, a major obstacle en route to establishing precision medicine in psychiatry.
Implications and Future Directions In summary, research evaluating neural signatures of treatment response has many valuable clinical implications. Characterizing neural signatures that can both objectively quantify and show qualitative differences in trajectories of change over the course of treatment can help clinicians to better monitor progression toward treatment goals for each patient, as well as more flexibly
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adapt the dosage, intensity, and treatment targets tailored to meet individuals’ needs in a more time-efficient manner. Furthermore, determining responder neural profiles to treatment at baseline is important, especially in children where brain plasticity and windows of opportunity become increasingly sparse over the course of development, further enhancing the difficulty for interventions to effectively elicit therapeutic changes. Expanding clinical research to better characterize neural signatures that predict response to treatment can help guide clinicians when selecting an appropriate intervention type for the individual in question, and therefore serve as an important clinical tool in future pediatric psychiatry. To further widen the evidence base for neural signatures of treatment response, as well as to increase the dissemination of affordable ways to identify reliable neural signatures of treatment response to the community, future directions and considerations are outlined. Current advancements in identifying neural signatures of PRT have solely relied on the use of fMRI, which provides good spatial resolution to help identify brain regions of interest. However, fMRI is very expensive and lacks good temporal resolution to resolve the sequence of subprocesses underlying any neural response. A practical implication to consider is the relatively low success rate for young children with ASD to complete fMRI scans, even after extensive training and preparation, as they often show elevated levels of head motion that affect the quality of images collected for analyses. Therefore, future research should continue to look to integrate other more cost-effective neuroimaging tools with enhanced temporal resolution and higher success rates for completion, such as EEG and eyetracking, to gain insight into not only which areas might serve as neural signatures of treatment response, but how these atypical neural activations might sequentially interact to affect overall neural efficiency in the processing of social stimuli. One limitation of the current published findings is that participants have been highfunctioning children with ASD (IQ > 70). Future studies should use larger samples with more heterogeneous symptomatology and a wider range of cognitive levels, to examine the generalizability of
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results to lower-functioning children with ASD. Another major limitation is the lack of inclusion of either a waitlist control group, or a group that receives an alternative form of intervention. Inclusion of a waitlist control group of children wellmatched on ASD symptomatology, age, IQ, and gender is critical to ensure that neural signatures showing changes in response to social stimuli are truly reflective of the therapeutic effects of the target intervention, rather than associated with general effects of development over the passage of time. Future studies should also include stringent control groups to better address the question of specificity of neural signatures of treatment response in children with ASD. Furthermore, inclusion of an alternative form of intervention will address the question of specificity of the neural signatures identified, to determine whether they are predictors of treatment response specific to PRT, or whether they may be neural signatures more broadly reflective of improved social cognition following interventions that target social skills in children with ASD. All of these future developments will further advance the field of clinical research toward the goal of developing precision medicine in pediatric psychiatry.
See Also ▶ Behavior Therapy ▶ Biological Motion ▶ Event-Related Functional Magnetic Resonance Imaging (MRI) ▶ Social Cognition ▶ Social Interventions
References and Reading Adolphs, R. (2009). The social brain: Neural basis of social knowledge. Annual Review of Psychology, 60, 693–716. https://doi.org/10.1146/annurev.psych. 60.110707.163514. Baron-Cohen, S. (1997). Mindblindness: An essay on autism and theory of mind. Cambridge: MIT Press. Brothers, L. (2001). Friday’s footprint: How society shapes the human mind. Oxford, New York: Oxford University Press. Chevallier, C., Kohls, G., Troiani, V., Brodkin, E. S., & Schultz, R. T. (2012). The social motivation theory of
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3126 autism. Trends in Cognitive Sciences, 16(4), 231–239. https://doi.org/10.1016/j.tics.2012.02.007. Frith, C. D., & Frith, U. (1999). Interacting minds – A biological basis. Science, 286(5445), 1692–1695. Insel, T. R. (2014). The NIMH research domain criteria (RDoC) project: Precision medicine for psychiatry. American Journal of Psychiatry, 171(4), 395–397. https://doi.org/10.1176/appi.ajp.2014.14020138. Johansson, G. (1973). Visual perception of biological motion and a model for its analysis. Perception & Psychophysics, 14(2), 201–211. https://doi.org/10. 3758/BF03212378. Kaiser, M. D., Hudac, C. M., Shultz, S., Lee, S. M., Cheung, C., Berken, A. M., et al. (2010). Neural signatures of autism. Proceedings of the National Academy of Sciences of the United States of America, 107(49), 21223–21228. https://doi.org/10. 1073/pnas.1010412107. Klin, A., Lin, D. J., Gorrindo, P., Ramsay, G., & Jones, W. (2009). Two-year-olds with autism orient to non-social contingencies rather than biological motion. Nature, 459(7244), 257–261. https://doi.org/ 10.1038/nature07868. Koegel, L. K., Camarata, S. M., Valdez-Menchaca, M., & Koegel, R. L. (1998). Setting generalization of question-asking by children with autism. American Journal of Mental Retardation, 102(4), 346–357. Koegel, R. L., Dyer, K., & Bell, L. K. (1987). The influence of child-preferred activities on autistic children’s social behavior. Journal of Applied Behavior Analysis, 20(3), 243–252. https://doi.org/10.1901/jaba.1987. 20-243. Koegel, R. L., & Egel, A. L. (1979). Motivating autistic children. Journal of Abnormal Psychology, 88(4), 418–426. Koegel, R. L., & Koegel, L. K. (2012). Treatment of pivotal areas. In R. L. Koegel & R. L. Koegel (Eds.), The PRT pocket guide: Pivotal response treatment for autism spectrum disorders (pp. 13–38). Baltimore: Paul H. Brookes Publishing. Markram, K., & Markram, H. (2010). The intense world theory – A unifying theory of the neurobiology of autism. Frontiers in Human Neuroscience, 4. https://doi.org/10.3389/fnhum.2010.00224. Oram, M. W., & Perrett, D. I. (1994). Responses of anterior superior temporal polysensory (STPa) neurons to “biological motion” stimuli. Journal of Cognitive Neuroscience, 6(2), 99–116. https://doi.org/10.1162/jocn. 1994.6.2.99. Oram, M. W., & Perrett, D. I. (1996). Integration of form and motion in the anterior superior temporal polysensory area (STPa) of the macaque monkey. Journal of Neurophysiology, 76(1), 109–129. Pavlova, M. A. (2012). Biological motion processing as a hallmark of social cognition. Cerebral Cortex, 22(5), 981–995. https://doi.org/10.1093/cercor/bhr156. Pelphrey, K. A., Mitchell, T. V., McKeown, M. J., Goldstein, J., Allison, T., & McCarthy, G. (2003). Brain activity evoked by the perception of human walking: Controlling for meaningful coherent motion.
Neurexin 1 The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 23(17), 6819–6825. Puce, A., & Perrett, D. (2003). Electrophysiology and brain imaging of biological motion. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 358(1431), 435–445. https://doi. org/10.1098/rstb.2002.1221. Vaina, L. M., Solomon, J., Chowdhury, S., Sinha, P., & Belliveau, J. W. (2001). Functional neuroanatomy of biological motion perception in humans. Proceedings of the National Academy of Sciences, 98(20), 11656–11661. https://doi.org/10.1073/pnas. 191374198. Venkataraman, A., Yang, D. Y.-J., Dvornek, N., Staib, L. H., Duncan, J. S., Pelphrey, K. A., & Ventola, P. (2016). Pivotal response treatment prompts a functional rewiring of the brain among individuals with autism spectrum disorder. Neuroreport, 27(14), 1081–1085. https://doi.org/10.1097/WNR. 0000000000000662. Ventola, P., Yang, D. Y., Friedman, H. E., et al. (2015). Heterogeneity of neural mechanisms of response to pivotal response treatment. Brain Imaging and Behavior, 9(1), 74–88. Verschuur, R., Didden, R., Lang, R., Sigafoos, J., & Huskens, B. (2013). Pivotal response treatment for children with autism Spectrum disorders: A systematic review. Review Journal of Autism and Developmental Disorders, 1(1), 34–61. https://doi. org/10.1007/s40489-013-0008-z. Voos, A. C., Pelphrey, K. A., Tirrell, J., Bolling, D. Z., Wyk, B. V., Kaiser, M. D., et al. (2012). Neural mechanisms of improvements in social motivation after pivotal response treatment: Two case studies. Journal of Autism and Developmental Disorders, 43(1), 1–10. https://doi.org/10.1007/s10803-012-1683-9. Yang, D., Pelphrey, K. A., Sukhodolsky, D. G., Crowley, M. J., Dayan, E., Dvornek, N. C., et al. (2016). Brain responses to biological motion predict treatment outcome in young children with autism. Translational Psychiatry, 6(11), e948. https://doi.org/10.1038/tp. 2016.213.
Neurexin 1 Ellen J. Hoffman Albert J. Solnit Integrated Training Program, Yale Child Study Center, Program on Neurogenetics, Yale School of Medicine, New Haven, CT, USA
Synonyms NRXN1
Neurexin 1
Definition Neurexin 1 (NRXN1) is a candidate gene in autism spectrum disorders (ASD). NRXN1 is one of three members of the neurexin gene family, which encodes a group of cell adhesion molecules that are found at the synapses of nerve cells. Each neurexin gene can produce two types of neurexin proteins, both a long and a short version, which are referred to as α- and β-neurexins, respectively. Interestingly, the structure of neurexin genes is such that it is possible for one gene to generate many different protein isoforms due to the alternative splicing of RNA transcripts. This means that combinations of neurexin isoforms can result in tremendous diversity at synapses at the molecular level (Sudhof 2008). There is growing interest in neurexins because they bind to neuroligins, another family of cell adhesion molecules that have been strongly implicated in ASD. Specifically, neurexins are present in the presynaptic region, while neuroligins are found postsynaptically, i.e., on the opposite side of the synapse. The interaction of neurexins and neuroligins is likely to play an important role in the formation of synapses. Moreover, neuroligins interact within the postsynaptic cell with members of the SHANK family of proteins, which have also been found in independent studies to be candidate genes in ASD (e.g., SHANK3) (Sudhof 2008). Taken together, the identification of NRXN1 and neuroligins, such as Neuroligin 4, X-linked (NLGN4X), as candidate genes indicates that cell adhesion molecules found at the synapse, and synaptic functioning in general, may represent common pathways in the biology of ASD. A number of studies have implicated NRXN1 in ASD. These studies identified rare variants disrupting NRXN1 in affected individuals at level of the DNA sequence and at the structural level in chromosomes (Feng et al. 2006; Glessner et al. 2009; Marshall et al. 2008; Szatmari et al. 2007). Three of these studies used genome-wide, arraybased cytogenetics to identify copy number variants (CNVs) associated with ASD. One study found a 300 kilobase deletion, which occurred de novo, disrupting NRXN1 in two affected female siblings (Szatmari et al. 2007), and other identified deletions disrupting NRXN1 that were
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inherited by affected individuals (Glessner et al. 2009). CNVs disrupting NRXN1 were not found in unaffected individuals (Glessner et al. 2010; Szatmari et al. 2007). Another study identified CNVs at NRXN1 in individuals with ASD that were de novo and inherited (Marshall et al. 2008). However, this study also reported finding structural variation at NRXN1 in a control population (Marshall et al. 2008). The strength of the rare variant approach taken by these studies is that finding outliers which occur at a frequency of posterior) and, so far, the excess neurons reported in the prefrontal cortex of young males with autism (Courchesne et al. 2011). Again, while probably not detectable at birth, the presence of even a small proportion of excess neurons could conceivably cause rapid postnatal growth in frontal regions. In a recent study, (Zikopoulos and Barbas 2010) stereologically investigated the fine structure by light and electron microscopy (EM) of myelinated axons in the white matter below the ACC, orbitofrontal cortex (OFC), and lateral prefrontal cortex (LPFC). They found similar overall axonal density between normal and ASD groups below all prefrontal areas. However, the ASD group had significantly fewer large axons (many presumably representing long-range corticocortical connections) in the deep white matter below the ACC associated with a significantly greater density of smaller axons (thought to connect adjacent cortical areas) in the corresponding superficial white matter. There were no discernable differences in neuronal densities or distributions in the overlying gray matter. The white matter below ACC also exhibited a significantly higher proportion of branched axons of medium caliber. This corresponded to higher levels of GAP-43 expression. Axons in the superficial white matter below the OFC exhibited significantly thinner myelin sheaths when controlled for axon diameter despite similar numbers of oligodendrocytes (the glia responsible for myelination). Limbic System As in the ACC, Kemper and Bauman (1993) reported smaller, more packed neurons with attenuated dendritic arbors in many other structures of the limbic system (particularly the amygdala, hippocampus, entorhinal cortex, and mammillary bodies) in autistic individuals relative to agematched controls. In contrast, septal neurons were unusually large in young autistic children but relatively smaller and depleted in numbers in autistic adults. Bailey et al. (1998) found increased cell packing in the hippocampus
Neuropathology
of one out of five cases. The findings in the amygdala may correspond to MRI reports of early enlargement of the amygdala; however, (Schumann and Amaral 2006) found a 15% decrease in the number of neurons in the amygdala of adolescents and adults with autism in their stereologic study, thereby indicating the possibility that neuron number is either not responsible for the increase in amygdala size in young autistic children or that significant neuron loss occurs starting in adolescence. Given the trends seen in the septum, the latter may be plausible. Wegiel et al. (2010) additionally found dysplastic laminar irregularities in the hippocampus of four brains, again implicating subtle irregularities in neuronal migration. Cerebellum By far, the most consistent finding in neuropathologic studies of postmortem ASD brain samples is a reduction in the density of cerebellar Purkinje neurons of the cerebellar cortex (Bailey et al. 1998; Kemper and Bauman 1993; Palmen et al. 2004, and many others), although there is no discernable clinical correlate of this abnormality. Kemper and Bauman first described a decrease in the number of Purkinje cells in all the autistic brains they analyzed regardless of age. This was particularly marked in the posterior-lateral part of the lateral lobes. The neurons of the deep cerebellar nuclei (the targets of Purkinje cell projections) were abnormally large in younger individuals and abnormally small, and sometimes reduced in number, in older individuals. It must be noted that these studies have not been strictly stereologic and not always replicated. For instance, Fatemi et al. (2002) reported no difference in Purkinje neuron density but did note an approximately 25% reduction in cell size. Whitney et al. (2009) found that only a proportion of their cases of ASD showed reduced Purkinje cell densities as well as decreases in basket and stellate cell densities. Wegiel et al. (2010) reported variable occurrences of cerebellar flocculonodular dysplasia, focal vermal dysplasia, cerebellar hypoplasia, and cerebellar heterotopias, indicating that evidence of altered neuronal migration could also be found in this structure.
Neuropathology
Brain Stem Kemper and Bauman (1993) reported similar alterations in the inferior olive as detected in the septum and deep cerebellar nuclei. Younger ASD brains exhibited unusually large neurons in the inferior olive, whereas in the adult cases, these neurons were small and pale. The overall numbers of inferior olivary neurons were similar to controls in both instances but tended to be concentrated at the nuclear periphery – the part of the complex that projects to the posterior-lateral part of the lateral lobes of the cerebellum. The synaptic relationship between the olivary and Purkinje neurons is established at approximately 28 weeks of gestation and is tight to the point that if Purkinje cells die for some reason, there is retrograde loss of the corresponding olivary neurons. Therefore, they reason, the “loss” of Purkinje neurons in ASD predates the formation of this relationship as olivary neuronal numbers do not appear to be reduced. While the mechanism for this is not understood, the age-related changes in neuronal size in the cerebellar nuclei, inferior olive, and septum are evidence of ongoing alterations in developmental processes, at least before adulthood. Bailey et al. (1998) reported olivary dysplasia or ectopic olivary neurons in five cases of ASD.
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For this promise to be fulfilled, future studies will have to be increasingly systematic – employing rigorous stereologic and molecular neuropathologic methods, e.g., large-scale as well as single-cell gene and protein expression analyses. This will require larger samples of ASD brain tissue, derived from a broader age range, in order to provide the necessary statistical power to contend with distinct clinical and genetic subphenotypes as well as dynamic changes observed over the developmental lifespan by structural MRI. Fortunately, significant trends toward this goal are already evident in recent literature, fueled by noteworthy improvements in the availability and quality of ASD postmortem brain tissue through efforts such as the Autism Tissue Project.
See Also ▶ Amygdala ▶ Cerebellar Abnormalities in Autism ▶ Corpus Callosum Abnormalities in Autism ▶ Prefrontal Cortex ▶ Purkinje Cells ▶ Temporal Lobes
References and Reading Future Directions In conclusion, relatively consistent neuropathologic observations in autism have included early increased brain size associated with increased numbers of prefrontal neurons, more densely packed smaller neurons in the limbic system, decreased numbers of cerebellar Purkinje neurons, and features of neuronal lamination/migration disturbances – all of which may be traced to prenatal disturbances in brain development. The identification of substantial trends is actually quite remarkable given the staggering heterogeneity in core and comorbid clinical features of ASD and demonstrates the great promise neuropathologic studies hold for identifying the elusive developmental neurobiological underpinnings of altered connectivity in ASD.
Amaral, D., Schumann, C., & Nordahl, C. (2008). Neuroanatomy of autism. Trends in Neurosciences, 31, 137–145. Avino, T., & Hutsler, J. (2011). Abnormal cell patterning at the cortical gray-white boundary in autism spectrum disorders. Brain Research, 1360, 138–146. Bailey, A., Luthert, P., Dean, A., Harding, B., Janota, I., Montgomery, M., Rutter, M., & Lantos, P. (1998). A clinicopathological study of autism. Brain, 121, 889–905. Bauman, M., & Kemper, T. (1985). Histoanatomic observations of the brain in early infantile autism. Neurology, 35, 866–867. Buxhoeveden, D. P., Semendeferi, K., Buckwalter, J., Schenker, N., Switzer, R., & Courchesne, E. (2006). Reduced minicolumns in the frontal cortex of patients with autism. Neuropathology and Applied Neurobiology, 32(5), 483–491. Casanova, M., Buxhoeveden, D., Switala, A., & Roy, E. (2002). Minicolumnar pathology in autism. Neurology, 58, 428–432.
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3144 Casanova, M. F., van Kooten, I. A. J., Switala, A. E., van Engeland, H., Heinsen, H., Steinbusch, H. W. M., et al. (2006). Minicolumnar abnormalities in autism. Acta Neuropathologica, 112, 287–303. Courchesne, E., Mouton, P. R., Clahoun, M. E., Semendeferi, k., Ahrens-Barbeau, C., Hallet, M. J., Barnes, C. C., & Pierce, K. (2011). Neuron number and size in prefrontal cortex of children with autism. Journal of the American Medical Association, 9, 2001–2010. Fatemi, S. H., Halt, A. R., Stary, J. M., Kanodia, R., Schulz, S. C., & Realmuto, G. R. (2002). Glutamic acid decarboxylase 65 and 67 kDa proteins are reduced in autistic parietal and cerebellar cortices. Biological Psychiatry, 52, 805–810. Herbert, M. R., Ziegler, D. A., Makris, N., Filipek, P. A., Kemper, T. L., Normandin, J. J., et al. (2004). Localization of white matter volume increase in autism and developmental language disorder. Annals of Neurology, 55(4), 530–540. Hutsler, J. J., & Zhang, H. (2010). Increased dendritic spine densities on cortical projection neurons in autism spectrum disorders. Brain Research, 1309, 83–94. Kemper, T., & Bauman, M. (1993). The contribution of neuropathologic studies to the understanding of autism. Behavioral Neurology, 11, 175–187. Kemper, T., & Bauman, M. (2002). Neuropathology of infantile autism. Molecular Psychiatry, 7, S12–S13. Lainhart, J. E., Bigler, E. D., Bocian, M., Coon, H., Dinh, E., Dawson, G., et al. (2006). Head circumference and height in autism: A study by the collaborative program of excellence in autism. American Journal of Medical Genetics Part A, 140A(21), 2257–2274. Palmen, S. J. M. C., van Engeland, H., Hof, P. R., & Schmitz, C. (2004). Neuropathological findings in autism. Brain, 127(12), 2572–2583. Schmitz, C., & Rezaie, P. (2008). The neuropathology of autism: Where do we stand? Neuropathology and Applied Neurobiology, 34, 4–11. Schumann, C. M., & Amaral, D. G. (2006). Stereological analysis of amygdala neuron number in autism. The Journal of Neuroscience, 26(29), 7674–7679. https:// doi.org/10.1523/JNEUROSCI.1285-06.2006. Schumann, C., & Nordahl, C. (2011). Bridging the gap between MRI and postmortem research in autism. Brain Research, 1380, 175–186. Simms, M., Kemper, T., Timbie, C., Bauman, M., & Blatt, G. (2009). The anterior cingulate cortex in autism: Heterogeneity of qualitative and quantitative cytoarchitectonic features suggests possible subgroups. Acta Neuropathologica, 118, 673–684. van Kooten, I. A. J., Palmen, S. J. M. C., von Cappeln, P., Steinbusch, H. W. M., Korr, H., Heinsen, H., et al. (2008). Neurons in the fusiform gyrus are fewer and smaller in autism. Brain, 131(4), 987–999. Wegiel, J., Kuchna, I., Krzysztof Nowicki, K., Imaki, H., Jarek Wegiel, J., Elaine Marchi, E., et al. (2010). The neuropathology of autism: Defects of neurogenesis and neuronal migration, and dysplastic changes. Acta Neuropathologica, 119, 755–770. Whitney, E. R., Kemper, T. L., Rosene, D. L., Bauman, M. L., & Blatt, G. J. (2009). Density of cerebellar
Neurophysiology basket and stellate cells in autism: Evidence for a late developmental loss of Purkinje cells. Journal of Neuroscience Research, 87(10), 2245–2254. Zikopoulos, B., & Barbas, H. (2010). Changes in prefrontal axons may disrupt the network in autism. The Journal of Neuroscience, 30(44), 14595–14609.
Neurophysiology Karen Burner1 and Raphael Bernier2 1 Department of Psychology, University of Washington, Seattle, WA, USA 2 Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA
Synonyms Electrophysiology; Psychophysiology
Structure Neural Structures Implicated Through Neurophysiological Assessment in ASD The study of neurophysiology in ASD has implicated specific brain structures, such as the superior temporal sulcus in association with processing biological movement, the fusiform gyrus in relation to face processing, the anterior cingulate cortex in relation to joint attention abilities, and the cerebellum in attention and engagement, among others. Neurophysiological studies have also implicated brain regions such as the limbic system, including the amygdala and hippocampus in relation to affective processing and memory, the mirror neuron system in relation to action understanding and imitation, and the ventromedial prefrontal cortex in association with stimulus-reward associations.
Function Neurophysiological Tools and Measurements Neurophysiology concerns the study of the functions of the nervous system. Neurophysiological measures are utilized to examine both brain
Neurophysiology
structures and neural pathways. Methods such as electroencephalography, electromyography, and assessment of skin conductance provide information about various aspects of the nervous system including sensory, motor, and cognitive functioning. Neurophysiological studies provide important information about the connections between brain structures and systems as well as what areas and networks of the brain may be impacted in individuals with disabilities such as autism spectrum disorder. Electroencephalography (EEG) One of the primary methods for studying neural pathways in the brain is electroencephalography (EEG), which is the measurement of electrical activity produced by the brain. EEG is recorded from the scalp and is a noninvasive method of measuring postsynaptic neuronal activity. EEG recordings reflect the propagation of electrical activity to the scalp from the synchronous activation of a specific population of neurons. EEG can provide important information about the functioning of neural networks. EEG oscillatory activity, measured by the frequency and amplitude of synchronized neural activity, reveals information about the state of the brain. Evoke potentials (EPs) and event-related potentials (ERPs) are the result of averaging EEG activity to examine time-locked responses to the presentation of a stimulus (visual, somatosensory, or auditory). EEG coherence refers to the functional connectivity of neural networks measured by the relation of signals over distal neuronal regions. EEG is a noninvasive technique in which the individual wears an electrode net on the scalp while observing/listening to stimuli or simply resting. EEG has excellent temporal resolution but relatively poorer spatial resolution compared to other imaging modalities. EEG studies of individuals with ASD allow us to better understand the timing of brain functioning, alterations in resting and active brain states, and potential underand overconnectivity of the brain. EEG studies can be designed such that individuals do not need to make a behavioral, motor, or vocal response, allowing for the use of the method across age groups, cognitive and verbal functioning, and the autism spectrum. In addition, it has
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the added benefit of being able to examine neuronal processing over time to better understand the stages of processing of a phenomenon. Electrooculogram (EOG) By recording activity from electrodes placed around the eye, eye movements can be carefully characterized and studied. Eye movement studies provide information about the cortical and subcortical regions of the brain that control eye movement activity. For instance, studies that examine saccadic (e.g., fast) eye movements are able to assess frontal brain regions as well as the brain stem and cerebellum, whereas voluntary eye movements and pursuit eye movements (used for visual tracking) can determine if anterior and posterior regions are involved. These basic visual processes can also be studied using eye tracking. Electromyography (EMG) EMG is a noninvasive technique used to measure and record the electrical activity of skeletal muscles. This has implications for neurophysiological functioning through the use of experimental paradigms. Fear potential startle response (FPS) is a noninvasive psychophysiological methodology that has been widely utilized in the non-ASD population to facilitate better understanding of amygdala functioning. In FPS, a sudden onset of an acoustic, visual, or tactile stimulus elicits a startle response. Acoustic startle amplitude can be enhanced by exposure to a conditioned stimulus that was paired with an aversive stimulus. This conditioned stimulus or signal produces a state of fear that potentiates the startle response and increases reflexive behavior. The threat cue primes an individual for response, creating a fear state, manifested by an exaggerated startle reflex to any suddenly imposed stimulus (Lang et al. 2000). The startle response is characterized by a fast series of muscle contractions around the head, neck, and shoulders; the first, fastest, and most stable component in this reaction is a sudden closure of the eyelid (Minshew et al. 1999). Intensity of the startle response is measured by electromyographic activity of the orbicularis muscle during the eyeblink, or blink magnitude (Lang et al. 1990).
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Skin Conductance The assessment of the electrical conductance of the skin, which varies with its moisture level, is called skin conductance or the galvanic skin response (GSR). The measurement of GSR provides insight into physiological arousal levels and the sympathetic nervous system as the sweat glands are controlled by the sympathetic nervous system. Electrocardiography (ECG) ECG is the measurement of heart rate and blood pressure. Magnetoencephalography (MEG) MEG is a method of recording neural activity by recording magnetic fields produced by electrical currents in the brain. MEG records neuronal activity directly and has high temporal ( 97th percentile) with advanced bone age, macrocephaly, characteristic facial appearance, and intellectual disability. Although these features are consistent within the Sotos syndrome population, the severity of the phenotype is variable. The majority of individuals with Sotos syndrome have mild-moderate intellectual disability or borderline intellectual functioning. In addition, increased likelihood of autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), anxiety and aggression/tantrums have been reported in individuals with Sotos syndrome.
Categorization The identification of a genetic abnormality responsible for Sotos syndrome was first established in a Japanese sample (Kurotaki et al. 2002). The authors identified that Sotos syndrome is caused by haploinsufficiency of the NSD1 (nuclear receptor binding SET domain protein 1) gene. The NSD1 gene encodes SET domaincontaining histone methyltransferases and is located at chromosome 5q35 (Tatton-Brown and Rahman 2013). Sotos syndrome is caused by intragenic mutations of the NSD1 gene or 5q35 microdeletions encompassing NSD1 and these abnormalities result in loss of function. Subsequent research investigated the prevalence of NSD1 abnormalities in a sample of 266 individuals with a clinical diagnosis of Sotos syndrome. The findings from this study identified that abnormalities of the NSD1 gene were present in more than 90% of individuals with a clinical diagnosis of Sotos syndrome (Tatton-Brown et al. 2005). As well as having macrocephaly, individuals with Sotos syndrome typically display distinctive neurological abnormalities. Schaefer et al. (1997) investigated structural brain abnormalities in 40 participants with Sotos syndrome, using magnetic resonance imaging (MRI). The findings
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indicated that participants displayed a characteristic pattern of abnormalities. Specifically, participants displayed abnormality of the corpus callosum, ventricular abnormalities, midline abnormalities, and delayed or disturbed maturation of the brain.
Epidemiology The estimated incidence of Sotos syndrome is approximately 1 in 14,000 live births (TattonBrown and Rahman 2004). In the majority of cases, the NSD1 abnormalities which cause Sotos syndrome are de novo, meaning that they occur spontaneously (Kurotaki et al. 2002). However, the syndrome has an autosomal dominant inheritance pattern, meaning that a child of an individual with Sotos syndrome has a 50% chance of also having the syndrome. A small number of familial cases arising from autosomal dominant transmission have been reported in the literature (Tatton-Brown et al. 2005; Tatton-Brown and Rahman 2007). The syndrome is one of several single-gene disorders associated with overgrowth and intellectual disability and has recently been identified as by far the most prevalent of this category of disorder (Tatton-Brown et al. 2017).
Natural History, Prognostic Factors, Outcomes Sotos syndrome was initially identified by Sotos et al. (1964) who observed five patients with similar clinical features: excessively rapid growth, acromegalic features, and a nonprogressive cerebral disorder with mental retardation. This combination of features was considered to be attributable to a specific syndrome which was termed “Sotos syndrome.” Subsequent research has confirmed these cardinal features (overgrowth, macrocephaly, characteristic facial appearance, and intellectual disability) in larger samples of individuals with Sotos syndrome (Cole and Hughes 1994; Tatton-Brown et al. 2005). As Sotos syndrome is associated with macrocephaly, initial research often used the terms “cerebral gigantism” and “Sotos syndrome” interchangeably
S
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to refer to the same condition but “cerebral gigantism” is no longer used to describe individuals with Sotos syndrome. Overgrowth (defined as height and/or head circumference > 97th percentile) is an easily identifiable feature as children with Sotos syndrome are large for their age and tend to be considerably taller than their peers. However, increased height usually becomes less apparent after puberty and in many cases, adults with Sotos syndrome are not significantly taller than those in the general population (Tatton-Brown and Rahman 2007). In contrast, macrocephaly is a stable feature of the syndrome and usually persists throughout adulthood (Tatton-Brown and Rahman 2007). The facial appearance is characterized by a prominent forehead, high anterior hairline, prominent chin, and downslanting palpebral fissures (Dodge et al. 1983). The characteristic facial appearance is most recognizable between the ages of 1 and 6 years, and the facial features often become less apparent in adulthood (Tatton-Brown and Rahman 2007). As the clinical features tend to be more prominent and recognizable in childhood, it is important for individuals displaying this combination of features to be referred for evaluation as early as possible. A number of medical issues are associated with Sotos syndrome. Approximately 70% of infants with Sotos syndrome develop neonatal jaundice and/or have difficulty with feeding. In addition, cardiac and renal anomalies, seizures, and scoliosis occur in 15–30% of individuals with Sotos syndrome but the type and severity of these issues is variable (Tatton-Brown and Rahman 2007). To date, most of the published literature reporting data on cognition and behavior in Sotos syndrome has focused on children with the syndrome, and therefore, there is limited understanding of outcomes in adulthood for individuals with Sotos syndrome (Lane et al. 2016). However, research investigating cognition in a sample of 52 adults and children with Sotos syndrome indicated that intellectual disability persists throughout adulthood and that the cognitive profile is
Sotos Syndrome and ASD
consistent in both adults and children with Sotos syndrome (Lane et al. under review).
Clinical Expression and Pathophysiology of Sotos Syndrome Intellectual disability is one of the cardinal features of Sotos syndrome and the majority of individuals have mild-moderate intellectual disability or are in the borderline range (Lane et al. 2016). However, significant variability in intellectual functioning has been reported within the literature, ranging from severe intellectual disability to average intellectual ability. Broadly, the syndrome is associated with relative strength in verbal IQ and relative weakness in performance IQ (Lane et al. 2016). Research investigating the cognitive profile in more detail has identified that individuals with Sotos syndrome display a clear and consistent profile of relative cognitive strengths and weaknesses. Specifically, the Sotos syndrome cognitive profile is characterized by relative strength in verbal ability and visuospatial memory but relative weakness in nonverbal reasoning ability and quantitative reasoning (Lane et al. under review). Although language and communication delays have been reported in several studies, in general, language abilities have been found to be consistent with overall level of intellectual functioning (Finegan et al. 1994). Fine and gross motor skills are often delayed in childhood and this can be attributable to the large size of the child, as well as the associated hypotonia and poor coordination which are common in children with Sotos syndrome (Tatton-Brown and Rahman 2007). In terms of the behavioral phenotype, ASD is the most researched behavioral issue within the Sotos syndrome population. Social communication impairment and difficulties with restricted interests and repetitive behaviors have been reported in 68% (Sheth et al. 2015) and 83% (Lane et al. 2017) of individuals with Sotos syndrome. Other behavioral issues such as ADHD, anxiety, and aggression/tantrums have been
Sotos Syndrome and ASD
reported in the literature, but to date, these behaviors have not been systematically investigated in large samples of individuals with Sotos syndrome so the prevalence of these behaviors is unknown (Lane et al. 2016). As children with Sotos syndrome are often large for their age, they may be mistaken as older and more able than their actual developmental level and this assumption may result in frustration and temper outbursts (Cole and Hughes 1994). This may account for the aggression/tantrums that have been reported in the literature.
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less severe behaviors observed in young children (2.5–5 years) and adults (20+ years), when compared to children (5–19 years) with Sotos syndrome (Lane et al. 2017). This indicates that ASD symptomatology may be more apparent in children with Sotos syndrome and that severity of ASD symptomatology may decrease in adulthood. Therefore, individuals with a diagnosis of Sotos syndrome who experience social communication impairment and difficulties with repetitive behaviors may require a comprehensive assessment for ASD. A comorbid diagnosis of ASD may be warranted for some individuals with Sotos syndrome.
Evaluation and Differential Diagnosis Until the identification of the genetic abnormality associated with Sotos syndrome was identified in 2002 (Kurotaki et al. 2002), the syndrome was diagnosed on the basis of clinical features. However, genetic screening is now widely available. Therefore, individuals who meet the clinical criteria and are suspected of having Sotos syndrome can be screened to check for abnormality of the NSD1 gene. Other examples of single-gene disorders associated with overgrowth and intellectual disability include Weaver syndrome (Weaver et al. 1974) and Tatton-Brown Rahman syndrome (TattonBrown et al. 2014). As these syndromes are also associated with overgrowth and intellectual disability, there is some overlap in the phenotypes of these syndromes. However, each of these disorders is caused by a distinct genetic abnormality and therefore molecular testing is crucial for differentiating between these overgrowth syndromes and for ascertaining the accurate diagnosis for an individual. Research has identified that the majority of individuals with Sotos syndrome display clinically significant behavioral symptomatology associated with ASD (Lane et al. 2017). In addition, the severity of ASD symptomatology does not differ between males and females with Sotos syndrome. However, severity of ASD symptomatology has been found to be affected by age, with
Treatment Sotos syndrome is a congenital disorder and is therefore a lifelong condition, which does not require treatment. Within the Sotos syndrome population, there is significant variability in the severity of the phenotype. It is therefore important to consider the needs of the individual when considering appropriate support and issues should be treated symptomatically. To date, syndromespecific support and interventions have not been established for individuals with Sotos syndrome. Within the Sotos syndrome population, individuals should be monitored for medical issues such as cardiac and genitourinary anomalies, neonatal jaundice, neonatal hypotonia, seizures and scoliosis, which are common in Sotos syndrome (Tatton-Brown and Rahman 2004) and appropriate treatment should be provided for these issues if they arise. For example, children may develop scoliosis and require a back brace or in some cases, surgery. In addition, seizures are common and some individuals may need to take medication to control the seizures (Tatton-Brown and Rahman 2004). A cognitive assessment may be useful to ascertain the intellectual ability of a child with Sotos syndrome and to consider the appropriate educational setting for the child. Approximately 50% of children with Sotos syndrome attend special
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educational needs schools, while the remaining 50% attend mainstream schools but often require additional support. As children with Sotos syndrome have developmental delay, speech and language therapy may be useful in early childhood to support language development. Similarly, children with Sotos syndrome often have fine and gross motor delays so occupational therapy might be appropriate in early childhood. As the phenotype of Sotos syndrome has not yet been clearly defined, a comprehensive assessment of each individual with Sotos syndrome will inform effective and appropriate strategies and support which can be targeted to the needs of the individual.
See Also ▶ Developmental Delay ▶ Intellectual Disability
References and Reading Cole, T. R. P., & Hughes, H. E. (1994). Sotos syndrome – A study of the diagnostic criteria and natural history. Journal of Medical Genetics, 31(1), 20–32. https://doi. org/10.1136/jmg.31.1.20. Dodge, P. R., Holmes, S. J., & Sotos, J. F. (1983). Cerebral gigantism. Developmental Medicine and Child Neurology, 25(2), 248–252. Finegan, J. A. K., Cole, T. R. P., Kingwell, E., Smith, M. L., Smith, M., & Sitarenios, G. (1994). Language and behaviour in children with Sotos syndrome. Journal of the American Academy of Child and Adolescent Psychiatry, 33(9), 1307–1315. https://doi.org/10.1097/ 00004583-199411000-00013. Kurotaki, N., Imaizumi, K., Harada, N., Masuno, M., Kondoh, T., Nagai, T., . . ., Matsumoto, N. (2002). Haploinsufficiency of NSD1 causes Sotos syndrome. Nature Genetics, 30(4), 365–366. Lane, C., Milne, E., & Freeth, M. (2016). Cognition and behaviour in sotos syndrome: A systematic review. PloS One, 11(2), e0149189. Lane, C., Milne, E., & Freeth, M. (2017). Characteristics of autism spectrum disorder in Sotos syndrome. Journal of Autism and Developmental Disorders, 47(1), 135–143. Lane, C., Milne, E., & Freeth, M. (under review). The cognitive profile of Sotos syndrome.
Sound Sensitivity Schaefer, G. B., Bodensteiner, J. B., Buehler, B. A., Lin, A., & Cole, T. R. P. (1997). The neuroimaging findings in Sotos syndrome. American Journal of Medical Genetics, 68(4), 462–465. https://doi.org/10.1002/ (sici)1096-8628(19970211)68:43.0.co;2-q. Sheth, K., Moss, J., Hyland, S., Stinton, C., Cole, T., & Oliver, C. (2015). The behavioral characteristics of Sotos syndrome. American Journal of Medical Genetics Part A, 167(12), 2945–2956. Sotos, J. F., Dodge, P. R., Muirhead, D., Crawford, J. D., & Talbot, N. B. (1964). Cerebral gigantism in childhood. A syndrome of excessively rapid growth. The New England Journal of Medicine, 271, 109–116. Tatton-Brown, K., & Rahman, N. (2004). Features of NSD1-positive Sotos syndrome. Clinical Dysmorphology, 13(4), 199–204. https://doi.org/10. 1097/00019605-200410000-00001. Tatton-Brown, K., & Rahman, N. (2007). Sotos syndrome. European Journal of Human Genetics, 15(3), 264–271. https://doi.org/10.1038/sj.ejhg.5201686. Tatton-Brown, K., & Rahman, N. (2013). The NSD1 and EZH2 overgrowth genes, similarities and differences. American Journal of Medical Genetics Part C-Seminars in Medical Genetics, 163C(2), 86–91. https://doi.org/10.1002/ajmg.c.31359. Tatton-Brown, K., Douglas, J., Coleman, K., Baujat, G., Cole, T. R. P., Das, S., . . ., Childhood Overgrowth, C. (2005). Genotype-phenotype associations in Sotos syndrome: An analysis of 266 individuals with NSD1 aberrations. American Journal of Human Genetics, 77(2), 193–204. https://doi.org/10.1086/432082. Tatton-Brown, K., Seal, S., Ruark, E., Harmer, J., Ramsay, E., del Vecchio Duarte, S., . . ., Aksglaede, L. (2014). Mutations in the DNA methyltransferase gene DNMT3A cause an overgrowth syndrome with intellectual disability. Nature Genetics, 46(4), 385–388. Tatton-Brown, K., Loveday, C., Yost, S., Clarke, M., Ramsay, E., Zachariou, A., . . ., Rittinger, O. (2017). Mutations in epigenetic regulation genes are a major cause of overgrowth with intellectual disability. The American Journal of Human Genetics, 100(5), 725–736. Weaver, D. D., Graham, C. B., Thomas, I., & Smith, D. W. (1974). A new overgrowth syndrome with accelerated skeletal maturation, unusual facies, and camptodactyly. The Journal of Pediatrics, 84(4), 547–552.
Sound Sensitivity ▶ Misophonia
South Africa and Autism
South Africa and Autism Gerrit Ian van Schalkwyk1,2, Chad Beyer3 and Petrus J. de Vries4 1 Department of Psychiatry, Yale School of Medicine, Yale Child Study Center, Yale University, New Haven, CT, USA 2 Butler Hospital, Brown University, Providence, RI, USA 3 Faculty of Medicine and Health Sciences, Stellenbosch University, Parow, South Africa 4 Division of Child & Adolescent Psychiatry, University of Cape Town, Rondebosch, South Africa
Introduction In this entry, an attempt is made to provide an overview of autism spectrum disorders (ASD) in South Africa. To this end, attention will be drawn to the historical context, current treatment options, legal mandates for service, research efforts, and relevant controversies. Compiling such a description presents a methodologically complex task – very little systematic data are available about ASD in South Africa, at least in part attributable to the absence of centralized bodies tasked with the monitoring of available services. There are even less data available in peer-reviewed journals, in line with the findings of Tomlinson and Swartz (2003), who demonstrate that in a survey of 764 journal articles related to ASD, only 4 % were outside the USA or Europe. A systematic review of “autism” and “Africa” in the Web of Science over the 10-year period of 2002–2012 revealed 33 papers about or from Africa, in rather stark contrast to >18,000 peer-reviewed papers on autism over the same period in the rest of the world (personal communication, Prof P J de Vries). This entry therefore represents a careful balance between relying on robust data sources and providing sufficient detail such that a helpful picture can be drawn. Our data sources therefore include websites of specific organizations,
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estimates of service delivery based on available databases, and perspectives of key role players – including managers of NGOs, clinicians in public and private practice, and academic psychiatrists. Every effort has made to be as transparent as possible in describing data sources, and when no citation is presented, the perspective can be assumed to be the product of the accumulated experience of the authors.
Historical Background As it is for much of sub-Saharan Africa, the history of autism in South Africa is brief and poorly documented. A survey of six African countries conducted in 1978 by Viktor Lotter (1978), a pediatrician working in the UK at the time, was probably the first systematic study to show that there were children in Africa (Ghana, Nigeria, Kenya, Zimbabwe, Zambia, and South Africa) who displayed patterns of behavior consistent with the construct of autism as understood at the time. Lotter was particularly interested in looking for autism in “indigenous African children,” defined by him as “black African children raised by black African parents.” He therefore took great care to exclude so-called mixed race or non-African black children. He also compared his African sample to a group of children from Middlesex in London identified with autism. Out of 1300 African children with intellectual disability, he identified 30 with autism-related behaviors; nine children met the “western criteria” for autism of the time. Interestingly, he found verbal and nonverbal children with autism and rates of epilepsy, gender ratios, and social backgrounds to be relatively similar to those of his Middlesex sample. On direct comparison of the African and Middlesex samples, he described differences in specific autistic characteristics. For instance, he found no flapping, ritualistic play, or self-injury in the African sample, but did report an overrepresentation of “other marked movements” and “carrying, banging, or twisting of objects.” He argued that there may be less autism in Africa
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and that some of the phenomena typically associated with autism may be uncommon in African settings (Lotter 1978). Subsequent studies have confirmed that autism has similar gender ratios in Africa as the rest of the world (Ametepee and Chitiyo 2009), and at least one subsequent study has highlighted the comparative rarity of certain movements (like rocking) that occur more commonly in western populations (Mankoski et al. 2006). Additionally, it has been noted that as in Kanner’s original description of children with autism, published cases in Africa tend to be of children from higher socioeconomic backgrounds (Ametepee and Chitiyo 2009; Kanner 1943). The most likely explanation is that wealthier individuals live in closer proximity to medical centers and are therefore more likely to be able to access services. This finding of SES as a correlate for access to services maps well onto similar observations in the USA (Durkin et al. 2010). It has now become accepted that autism occurs across Africa. To date there have been no systematic investigations into the prevalence of autism in Africa. There may be many potential reasons for this lack of epidemiological data on autism in Africa, including the fact that communicable diseases have dominated health priorities in Africa over the last century, combined with the lack of systematic research programs in autism over the last number of decades, and the lack of appropriate screening and diagnostic tools for autism in African languages (Grinker et al. 2012). In response to a growing recognition of the existence of autism, combined with very limited available services, the nongovernmental organization (NGO) Autism South Africa was founded in 1989 (Autism South Africa 2014). The history of autism in South Africa has been influenced in a significant way by the growth and the development of this organization. The group initially consisted of concerned parents and professionals that were primarily engaged in lobbying government, but, given increasing requests for the provision of support and assistance to families, the group expanded in scope and established permanent, full-time offices in 1997. Today, Autism South Africa serves as a resource for parents and families, providing regular parent training and
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facilitating referral of both families and individuals with autism to relevant educational and supportive resources. Autism South Africa has branches and staff in a number of South African Provinces. There are also other ASD NGOs, including Autism Western Cape, based in Cape Town, and Action in Autism, based in KwaZuluNatal. Despite the growth of organizations like Autism South Africa and other NGOs, there continues to be a significant shortage in services for both family members and individuals with autism, with an estimated 135,000 individuals with ASD in South Africa not receiving appropriate services (Bateman 2013).
Legal Issues: Mandates for Service There are currently no legal mandates for ASD-specific services in South Africa. Although the South African constitution is internationally renowned for enshrining equal rights for all, significant resource shortages prevent implementation of this ideal. For example, although all children in South Africa have a right to education, with only five government-funded schools tailored to ASD individuals, many children are left underserved. As a member of the WHO, South Africa adopted the resolution on autism spectrum disorders (World Health Organization 2013) (http://www.who.int/mental_health/action_plan_ 2013/eb_resolution_childhood/en/), which urges member states to expand capacity for services, develop and implement policies, promote research and awareness, and provide focus on community-based (rather than long-term institutional) treatment along with several other goals. The resolution is focused on an implementation period from 2013 to 2020. To date there are no published reports of progress currently available for South Africa.
Overview of Current Treatments and Centers The South African healthcare system is for the most part sharply divided between a wellresourced private sector, serving around 20 % of
South Africa and Autism
the population, and a relatively resource-limited state sector, which serves the remaining 80 %. However, even in the private sector, services for ASD are of variable quality, being for the most part unmonitored and not always employing evidence-based practices. In addition to being non-standardized, current services are remarkable for being of greatly insufficient capacity. Representatives from the University of Cape Town’s Autism Research Group note that ASD-specific schools in South Africa have several year waiting lists, often leading to children being accepted to these facilities and receiving appropriate intervention 3 or 4 years after the time of diagnosis (UCT Department of Psychology Autism Research 2014). These children are nevertheless more fortunate than a majority who are unable to receive specialized education altogether (Bateman 2013). Available schools, diagnostic centers, and residential treatment facilities are discussed below. Schools There are only nine schools specifically set up for individuals with ASD in South Africa (Autism South Africa 2014). Four of these are private schools that offer very comprehensive programs, including pre- and after-school care, holiday programs, and access to a wide range of recreational facilities. The remaining five are state funded, often with support from NGOs. The first government school for children with ASD was the Vera School, in Cape Town, founded in 1970. It is regarded as a specialist resource center in the management of autism spectrum disorder. The school has 145 pupils and can accommodate 35 on site in hostels. It has 74 members of staff and is publicly funded through the Western Cape Education Department (Charity SA 2014). The Alpha School was established in 1974 by what was then known as the Society for the Autistic Children. It was originally set up in a living room of a private home in Athlone, Cape Town, and after various relocations, it moved to its current premises in Woodstock, Cape Town, in 1995. It has traditionally served the less privileged sectors of the Western Cape population. At present it has approximately 76 learners up to the age of 17, in ten classrooms with a variety of volunteers and students assisting in the activities of the
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teachers and supplementary staff – which includes speech therapy, occupational therapy, and psychology. The children are occupied with classwork, swimming, and various other activities including a peace garden, which is an initiative that involves the community and the children taking ownership in cultivating a patch of a vegetable garden (Scanza 2014). As an example in the private sector, “The DreamTree School” in the relatively wealthy Western Cape Province of South Africa is a well-resourced center with occupational and speech therapists, experienced special education teachers, and psychologists on site (The Dream Tree School 2014). This facility cites evidencedbased practices such as applied behavioral analysis (ABA) and social skills training as a basis for their approach. Although there are fewer schools in other provinces, one exemplar in the non-private sector is the UNICA School in Menlo Park, Gauteng. This facility is operated as a public benefit organization (PBO), in partnership with the government, donors, and nominal fees payable by the parents of attendees (around $1000 per year in 2014 for tuition). The UNICA School attracts students from across South Africa, Swaziland, and neighboring countries and cites the TEACCH curriculum as being central to their approach (The Unica School for Autism 2014). Six of the ASD-specific schools are in the Western Cape Province of South Africa, with the other three in the Gauteng and Eastern Cape provinces. Given the small number of schools in the context of a population of 52 million people, clearly many children with ASD are unable to attend these facilities. Other sources of education include special programming within regular schools and attendance of schools that target children with special educational needs in a much broader sense. Unfortunately, there are no systematic data on the quality and suitability of the education obtained by ASD individuals at these organizations, and it is assumed to be highly variable across different regions. While the existing educational programs listed above often refer to ABA, TEACCH, Makaton, and PECS, it is unclear whether more contemporary, relationship-based intervention approaches
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such as the Early Start Denver Model, JASPER, Pivotal Response Training, etc., are utilized in schools. It is also worth bearing in mind that the statutory school-going age is 7 years and that no governmental preschools exist. No child with autism under the age of 6 or 7 will therefore have access to public sector early intervention or educational programs. Assessment and Diagnosis It is assumed that university hospitals in South Africa provide diagnostic services for individuals with symptoms suggestive of ASD. However, with only eight medical schools in South Africa, the capacity is insufficient to ensure that a majority of individuals receive an early diagnosis. There are only nine qualified neurodevelopmental pediatricians in South Africa, with a national training capacity of two candidates per year, and only around 46 specialist child and adolescent psychiatrists in South Africa (de Vries et al. 2013; Skosana 2014). In the Western Cape, which is a home to three relatively large centers (Tygerberg Hospital, Lentegeur Hospital, Red Cross Children’s Hospital), around ten individuals are evaluated for ASD per week. In addition, the organization Autism South Africa runs an assessment clinic once a month in Johannesburg, targeting individuals who do not have access to academic medical centers. The availability of accurate diagnostic services within the private sector is unclear. There are a very small number of appropriately experienced pediatricians and child and adolescent psychiatrists in South Africa, and anecdotal evidence suggests that general practitioners (who form a significant majority of overall providers) are often uncomfortable with diagnosing neurodevelopmental disabilities (Bateman 2013). Residential Treatment Available residential treatment is provided by NGOs and includes the Horizon Farm in the province of KwaZulu-Natal (Horizon Farm Trust 2014) and Lethabo Le Khutso Adult Care center in Pretoria, Gauteng province (Association For
South Africa and Autism
Autism 2014). The Horizon Farm is operated by the Horizon Farm trust, has been in operation since 1994, and is situated on a 30-acre farm, facilitating an outdoor lifestyle for 30 individuals with intellectual disabilities, including but not limited to ASD. This facility reports an everincreasing waiting list. The Lethabo Le Khutso Adult Care center is a facility that provides residential care for adults with ASD and is an initiative of the Association For Autism, an NGO. In most parts of South Africa, there appear to be no residential treatment facilities available specifically set up for adults with autism with/without intellectual disability. Other Services The bulk of additional supports and services are provided by NGOs. These include day programs, like the CARE Day School in Johannesburg, Gauteng province (Centre For Autism Research and Education 2014), and the Star Academy with locations in Gauteng and KwaZulu-Natal (The Star Academy 2014). These organizations provide a range of services with a prominent focus on applied behavioral analysis. The organization Autism South Africa, highlighted previously, acts broadly in providing screening clinics and helping parents get connected with available treatment while serving as advocates for the ASD community at the governmental level (Autism South Africa 2014). The Els for Autism Foundation was founded in 2009 by the well-known South African golfer Ernie Els and his wife Liezl, who have a son affected with ASD (Ernie Els Foundation 2014). The goal of this NGO is to support an e-learning platform that provides access to ASD education and therapy, as well as online training at no cost. These and other similar organizations are a significant part of the ASD treatment landscape in South Africa.
Overview of Research Directions Prior to 2012, there was no systematic research programs on autism anywhere in Africa,
South Africa and Autism
including in South Africa. In 2012 Prof Petrus de Vries founded the Center for Autism Research in Africa at the University of Cape Town. The goals of this initiative are to develop an interdisciplinary center for ASD and related disabilities across themes of awareness, training, clinical work, and research (Malcolm-Smith et al. 2013). Current programs within the center include: 1. The translation and validation of screening tools for ASD such as the social communication questionnaire (SCQ) and modified checklist for autism in toddlers (M-CHAT) into Afrikaans, isiXhosa, and Kiswahili. In addition to validation of psychometric properties, efforts are being made to ensure that these measures are culturally appropriate using mixed-method research approaches. 2. The translation and validation of diagnostic tools like the Autism Diagnostic Observation Schedule (ADOS) and Autism Diagnostic Interview-Revised (ADI-R) into Afrikaans, isiXhosa, and Kiswahili. 3. Development of post-diagnostic programs, including programs for psychoeducation and parent training. Tied to the development of these programs is the evaluation of existing programs developed in the UK, with assessment of their suitability for the South African context. 4. Identification of evidence-based interventions and, in particular, parent-/caregiver-led interventions, which are particularly salient given resource limitations and the limited number of expert therapists available. 5. The training of staff in primary, secondary, and tertiary level care centers in early detection, diagnosis, and treatment of ASD. 6. Evaluation and development of service models. Given the limited existing services, even in better-resourced parts of South Africa, evaluation of existing educational services (including needs and gap analysis) is crucial for the development of sustainable, integrated services for individuals with ASD and their families.
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7. Investigating the potential for use of technology in low-resource environments to facilitate the abovementioned aims and improve early detection, diagnosis, and treatment. This group therefore has an ambitious research agenda, which is likely to be highly impactful in improving understanding of ASD in South Africa. However broader involvement from multidisciplinary clinical and academic colleagues across all academic medical centers remains an important need.
Cultural Factors and Controversies There is a significant lack of awareness surrounding ASD, particularly in rural areas of South Africa (Autism South Africa 2014). This, in combination with the dearth of available evidencebased treatments for autism, has maintained widespread misconceptions about the nature of the disorder, as well as propagation of pseudoscientific diagnostic and treatment practices. In certain communities, ASD continues to be viewed as evidence of demonic possession, or a sign of punishment from the individuals’ ancestors (Autism South Africa 2014). As reported by Grinker et al. (2012), parents of children with ASD in rural communities reported taking their children to traditional healers for consultations about treatment. However, it is of interest that these parents also reported the utility of having the “western” diagnosis of ASD provided to their children, as it reduced their concerns about a potential spiritual cause for the child’s difficulties. This study also noted an interesting fact about child-care centers or crèches operating in these communities. Staff working at crèches reported the expectation that after the age of 2, direct eye contact with elders would be discouraged and potentially seen as disrespectful – an interesting finding suggesting the possibility of differential cultural expectations regarding certain social communication behaviors that are used as red flags for autism.
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In wealthier, urban areas, a different set of challenges exist, with a number of expensive, private practices offering nonevidenced-based explanations and treatments for individuals with ASD. These practices are run by individuals with a broad range of qualifications and most often offer treatments centered on viewing autism as a consequence of gastrointestinal dysfunction. These interventions may include restrictive diets, herbal medications, homeopathic remedies, and supplementation. Many practitioners operating in this paradigm report their alignment with the now-defunct Defeat Autism Now! Program. The surprising prominence of such practitioners is likely a consequence of the limited availability of adequately trained child and adolescent psychiatrists and neurodevelopmental pediatricians in South Africa.
References and Reading Ametepee, L., & Chitiyo, M. (2009). What we know about autism in Africa: A brief research synthesis. The Journal of the International Association of Special Education, 1(1), 11–14. Association For Autism. (2014). Retrieved October 31, 2014, from http://www.afa.org.za/ Autism South Africa. (2014). Retrieved October 31, 2014, from http://www.aut2know.co.za Bateman, C. (2013). Autism – Mitigating a global epidemic. South African Medical Journal, 103(5), 276–277. https://doi.org/10.7196/samj.6915. Centre For Autism Research and Education. (2014). Retrieved October 31, 2014, from http://www. thecarecentre.co.za Charity SA. (2014). Vera school. Retrieved October 31, 2014, from http://www.charitysa.co.za/veraschool-for-autistic-learners.html de Vries, P. J., Venter, A., Jacklin, L., & Oliver, C. (2013). Behavioural phenotypes and neurodevelopmental disorders in Africa. Journal of Intellectual Disability Research, 57(9), 793–795. Durkin, M. S., Maenner, M. J., Meaney, F. J., Levy, S. E., DiGuiseppi, C., Nicholas, J. S., . . . Schieve, L. a. (2010). Socioeconomic inequality in the prevalence of autism spectrum disorder: Evidence from a U.S. crosssectional study. PloS One, 5(7), e11551. Ernie Els Foundation. (2014). Els for Autism. Retrieved October 31, 2014, from http://www.elsforautism.com Grinker, R. R., Chambers, N., Njongwe, N., Lagman, A. E., Guthrie, W., Stronach, S., . . . Wetherby, A. M. (2012). “Communities” in community engagement:
SPA Lessons learned from autism research in South Korea and South Africa. Autism Research : Official Journal of the International Society for Autism Research, 5(3), 201–210. Horizon Farm Trust. (2014). Retrieved October 31, 2014, from http://www.horizonfarmtrust.org.za Kanner, L. (1943). Autistic disturbances of affective contact. Nervous Child, 2, 217–250. Lotter, V. (1978). Childhood autism in africa. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 19(3), 231–244. Malcolm-Smith, S., Hoogenhout, M., Ing, N., Thomas, K. G. F., & de Vries, P. J. (2013). Autism spectrum disorders – Global challenges and local opportunities. Journal of Child & Adolescent Mental Health, 25(1), 1–5. Mankoski, R. E., Collins, M., Ndosi, N. K., Mgalla, E. H., Sarwatt, V. V., & Folstein, S. E. (2006). Etiologies of autism in a case-series from Tanzania. Journal of Autism and Developmental Disorders, 36(8), 1039–1051. Scanza. (2014). Alpha school for autism. Retrieved October 31, 2014, from www.scanza.dk/en/projects/alphaschool-for-autism/ Skosana, I. (2014, April 4). Autism and its uncommon angels. Mail and Gaurdian. Retrieved October 31, 2014, from http://mg.co.za/article/2014-04-03autism-and-its-uncommon-angels The Dream Tree School. (2014). The Dream Tree School – The school for children with Autism. Retrieved October 31, 2014, from http://www.thedreamtreeschool.co. za The Star Academy. (2014). Retrieved October 31, 2014, from www.thestaracademy.co.za The Unica School for Autism. (2014). Retrieved October 31, 2014, from www.unicaschool.co.za Tomlinson, M., & Swartz, L. (2003). Imbalances in the knowledge about infancy: The divide between rich and poor countries. Infant Mental Health Journal, 24(6), 547–556. https://doi.org/10.1002/imhj. 10078. UCT Department of Psychology Autism Research. (2014). UCT autism research. Retrieved October 31, 2014, from http://www.uctautism.com World Health Organization. (2013). WHA resolution on “Comprehensive and Coordinated Efforts for the Management of Autism Spectrum Disorders.” Retrieved October 31, 2014, from http://www.who.int/mental_ health/action_plan_2013/eb_resolution_childhood/en/
SPA ▶ Sensory Processing Assessment
Spain and Autism
Spain and Autism José Luis Cuesta Gómez Faculty of Education, Universidad de Burgos, Burgos, Spain
Historical Background Much of the data that allows us to reconstruct the history and the current situation of autism in Spain has been taken from the compilation prepared by Adam Feinstein, principally through Mercedes Belinchón, Doctor in Psychology and professor of the Autonomous University of Madrid, who has played an important role in that institution and promoted different initiatives in relation with education and research of special importance (Feinstein 2016). The support services specifically for people with ASD (autism spectrum disorder) that began to emerge in Spain were promoted by professionals, principally with links to the health services, many holding a clear psychodynamic orientation that characterized their early training. The most important references in those first phases are the Centro TAURE (Madrid) that opened in 1973 and the Centro Carrilet (Barcelona), founded in 1974. In these first years of life, the professional life of Doctor Ángel Díez Cuervo (Neuro-pediatrics) may be highlighted. He has been a leading light in Spain, supporting the launch and the growth of institutions. His important contributions have added to our knowledge of scientific evidence-based practices and their development. This first phase in the development of specific support services extended up until the mid-1970s, a time at which a family-centered model begin to gain in popularity, through associations that characterize the reality in which we now live. They have managed to generate the majority of resources specific to our country. In 1976, two of the associations were launched that supported the successive formation of other organizations of parents of people living with autism; APNA
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(Madrid) and La Garriga (Barcelona), managed by Isabel Bayonas and Joan Roca, respectively, which have for decades contributed to the development of policies of support for the collective and the launch of associations in other geographical areas of the country. Before the end of the 1970s, other entities were set up that continue to be nationwide references to this day, such as the Asociación Guipuzcoana de Autismo (Gautena). It enjoys the technical support of the child psychiatrist Joaquín Fuentes Biggi, a reference for autism both within and outside Spain. In 1979, the Association New Horizons (Madrid) was born, one of the first entities that has developed services for adults with ASD in Spain. The establishment of these organizations went on to generate cooperative alliances between families and professionals, which were shaping a model of services where education came to be seen as the best development tool for people with ASD. The psychodynamic perspective has gradually lost ground at centers and support services, except in Cataluña, a regional community where it is still very popular. The mothers and fathers who pioneered it, together with professionals such as Ángel Díez Cuervo and Ángel Rivière Gómez, contributed the philosophical and conceptual groundwork that still serves as a reference today for many associations and has assisted them in their development throughout Spain. Their contributions reinforced the biological factor as the origin of autism, burying the myths that blamed the families, and the value of integral education as a better tool with which to promote the development for people with ASD. It had special importance at the I International Symposium on Autism, organized in Madrid in 1978, by APNA and SEREM (Servicio de Recuperación y Rehabilitación de Minusválidos Físicos y Psíquicos) [Recovery and Rehabilitation Service for the Physically and Mentally Disabled] under the catchphrase “Help me to know who I am.” This meeting, directed at professionals, families, public authorities, and society in general, had many repercussions throughout Spanish territory. As from that point, people began to
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hear of a different approach to autism, unlike the erroneous theories and interpretations that had up until the present been prevalent. This initiative was crucial, as it placed the different models that coexisted at that time in our country on the table: psychodynamic, behavioralist, and the perspective that defined the biological origin of autism and interventions from the cognitive behavioral point of view through educational processes of intervention. In that symposium, the families reaffirmed the need and the responsibility to play an active role in setting up services and demands for support. Another of the important consequences of the Symposium was the launch on 4 May 1978 of a Committee composed of families of nine different nationalities (Australia, France Germany, Holland, Ireland, Norway, Portugal, Spain, and Sweden), for the organization of the International Coordinating Office for Parents of Autistic Children and Adults, with the objective of lobbying for people with autism and recognition that each autistic person needs education, treatment, and specialized and individualized care; has the right to develop his or her potential to a maximum as human beings; and to have a decent place in society (Martínez et al. 2010). Moreover, in the field of the associations, one initiative that has profoundly affected the development of autism in Spain has been the birth of the AETAPI, now the Asociación Española de Profesionales del Autismo [Spanish Association of Autism Professionals], driven by the professionals themselves as a space of exchange, development, and education. This organization has contributed to generating a consensual model of intervention, based on good practice with scientific evidence, and at present supports professionals, people living with ASD, families, and organizations in the defense of rights and a strategy to confront unscientific practices that are still sold today as “miracles.” Every 2 years, AETAPI organizes a congress on autism and promotes the development of good practice and research, through the Angel Rivière Prizes. This organization has over the years counted on the support of professionals such as Javier Tamarit, who began his links with autism through his work at CEPRI, at the end of the 1970s,
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and contributed in a very significant way to the development of knowledge on ASD in Spain. Over time, associations have been extending across national territory and have been grouping themselves together in federations, in different autonomous communities and, at a national level, in confederations. The FESPAU Confederation, born in 1994, at present represents 25 organizations, and the Spanish Autism Confederation, born in the same year, groups together 77 entities comprising families, people with ASD, and professional liked to the collective. In 2004, the Asperger Spain Federation was launched. At present, the three organizations jointly collaborate on some initiatives, especially those oriented towards the promotion of state policies for assistance to people with ASD. In 2001, the Grupo de Estudios de los Trastornos del Espectro del Autismo (GETEA) [Study Group on Autism Spectrum disorders] was formed, as a part of CISAT (Centro de Investigación sobre el Síndrome del Aceite Tóxico) [Research Center on the Toxic Oil Syndrome], the forerunner to the present-day Instituto de Salud Carlos III [Institute of Health Carlos III] with the objective of conducting a project to gain greater knowledge of the reality of autism in Spain and to recommend ways of improving assistance, training, and research into ASD. This group knew how to harness the necessary interest, knowledge, and experience to confront the present-day challenges of research and practice related with autism, consisting in the study of the causes of the increased prevalence and etiology of these disorders, as well as the aspects related with the detection, diagnosis, and intervention directed at people who present it. As a result of the work of this group, four guides of good practice were published for the early detection, diagnosis, treatment, and investigation of autism spectrum disorders in the Revista Española de Neurología [Spanish Journal of Neurology]. At present, the IIER continues to be involved in the development of ASD-related projects in both the state sector and the European context and collaborates at an international level in epidemiological research into these types of disorders.
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Legal Issues, Mandates for Service This historic moment coincided with the transition to democracy in Spain, which was accompanied by greater involvement and initiative in civil society to transform reality, generating a significant movement of associations in the 1970s and 1980s. Moreover, legislation and initiatives were advanced that favored the recognition of the rights of people with disability and their inclusion in education, society, and the workplace, as well as the consolidation of entities responsible for the development of actions directed at these collectives, among the most important of which are the following: Ley General de Educación [General Law on Education] of 1970 that recognizes the right to education of people with autism, adopting the term Special education and recognizing it as a specific modality. Decreto de 23 de mayo de 1975 [Decree of 23 May, 1975], establishing the Instituto Nacional de Educación Especial (INEE) [National Institute of Special Education] as an autonomous organism, dependent on the Ministry of Education and Science, which will be a key piece in the progress and the development of attention to students with special educational needs through specialized centers. Approval of the Royal Board of Special Education in Real Decreto de abril de 1976 [Royal Decree of April 1976], chaired by H.M. Queen Sophia, the forerunner of the present-day Real Patronato sobre Discapacidad [Royal Board on Disability]. In addition to the Ministry of Education and Science, the Ministries of the Treasury, Territorial Government (now defunct), Justice, Employment, and Social Security all formed part of it, as well as other personalities of recognized prestige, appointed by H.M. Queen Sophia. The Spanish Constitution (1978) dedicated its article 49 to recognizing the rights of the collective of people with disability, mentioning that the public authorities will uphold a policy
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of prevention, treatment, rehabilitation, and integration, and will ensure the delivery of the specialized attention that they require and will give them special help for full enjoyment of the rights that this provision grants to all of its citizens. The Ley de Integración Social de los Minusválidos, de 7 de abril de 1982 (LISMI) [Law of Social Integration of the Disabled, of 7 April 1982] is a norm of special importance in Spain, because it explicitly regulates the right of disabled people to inclusion in social, educational, and employment activities. In the area of education, under the third article of Title VI, their inclusion in the ordinary system of general education is contemplated, receiving in each case the support and the resources that the law envisages. The Real Decreto del 6 de marzo de 1985 [Royal Decree of 6 March 1985] regulates Special Education in a specific way as an option that forms part of the national educational system, contemplating the inclusion in ordinary centers with the support that each person requires, and where serious difficulties exist, schooling will take place in specific units or centers. The Real Decreto 2377/1985, por el que se aprueba el Reglamento de Normas Básicas sobre conciertos educativos [Royal Decree 2377/1985, in approval of the Rule of Basic Norms on educational agreements], supported the creation of many of the specific educational centers, the majority promoted and managed by families, and subsidized with public funds. The consolidation of educational integration came in 1990, with the Ley de Ordenación General del Sistema Educativo [Law on the General Organization of the Educational System], which incorporates the terms of pupils with special educational needs and determines that they will have access to the necessary supporting resources and the curricular adaptations that they may require. Ley 39/2006, de 14 de diciembre, de Promoción de la Autonomía Personal y Atención a las personas en situación de dependencia [Law 39/2006, of 14 December, on the Promotion of Personal Independence and Attention to
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people in a situation of dependency], which regulated the universal right to different modalities of services and their delivery to people living with disabilities. On that basis, the professional figure of the Personal Assistant was defined as personalized support in the fields and times at which the disabled person may most require so. On 13 December 2006, the Convention on the Rights of Persons with Disability was adopted in New York and its Optional Protocols, international legal instruments that were ratified by Spain and entered into force on 3 May 2008, having since then formed part of our legal order. Real Decreto Legislativo 1/2013, de 29 de noviembre, por el que se aprueba el Texto Refundido de la Ley General de Derechos de las Personas con Discapacidad y de su Inclusión Social [Royal Legislative Decree 1/2018, of 29 November, in approval of the Consolidated Text of the General Law of the Rights of People with Disabilities]. This regulation was based on the proposal in the Convention of the Rights of People with Disability (ONU 2006). In 2013, the rights of people with disability advanced in Spain, with the recognition, among others, of the right to full participation and inclusion, initiated at the earliest possible stage through the enactment of the Ley General de Derechos de las Personas con Discapacidad y de su Inclusión Social [General Law of the Rights of Persons with Disability and of their Social Inclusion] published in the BOE of 3 December 2013. Its drafting was based on the proposal advanced at the International Convention of the Rights of Persons with Disability, approved at the United Nations General Assembly in 2006 (UNO 2006). Article 26 of that Convention specifically refers to the right to education in the context of “full inclusion and participation in all aspects of life.” Both initiatives are based on the principle of nondiscrimination towards persons with disability, and they understand education as part of comprehensive habilitation and rehabilitation services. It is stressed that education should start at the
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earliest possible stage and should be based on a “multidisciplinary assessment” of the “individual needs and strengths” of the person with disability, as well as opportunities in their own community. Appropriate habilitation and rehabilitation services and support will be oriented towards decision-making and measures to attain full independence. It also considers that the public administrations should promote the maintenance of effective and appropriate attention, through the coordination of resources and habilitation and rehabilitation services in the areas of health, employment, education, and social services, with the purpose of guaranteeing that people with disability are offered appropriate and varied services and programs in their own communities, in both rural and urban zones. The most recent initiative in matters of autism promoted by the State has been the preparation of the Spanish Strategy on Autism Spectrum Disorders, approved by the Council of Ministers and sponsored by the Ministry of Health (2015). It is at present in a phase of development. This initiative seeks, among other questions, to encourage measures that promote the detection and diagnosis of ADS, appropriate educational attention, including the stages prior to the obligatory schooling of pupils with ADS, and evidence-based practices, especially in the case of professionals linked to early attention and infant education. The strategy also promotes the strengthening of a varied, appropriate, and specialized network of educational centers in all the territories that have the necessary means to provide individualized and quality education to pupils with ADS (professional profiles, ratios, resources, etc.). At the same time, it supports specialized, inclusive, and quality education for pupils with ADS, which is flexible and innovative in relation to the existing educational offer (procedures and choice of schools: combined education, ordinary education with support, specific classrooms in ordinary education and special education), in such a way that it responds to the individual needs and maximizes educational success,
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personal development, and social inclusion. It moreover highlights the need to promote resources and support systems for people with ADS in adult life, guaranteeing their inclusion in working life, the possibility of developing an independent life outside the family home, legal protection of their rights, and full social participation in their own communities.
Overview of Current Treatments and Centers At present, the map of ADS organizations in Spain is still incomplete, with provinces that have no specialized services, although associations have been created in some of them. In these situations, the majority of people with ADS with greater needs for support attend generalist centers for people with disabilities. The proposal of family organizations in our country is to generate networks of support services that cover the needs of people with ADS throughout the life cycle and in all areas (diagnosis, early attention, schooling stage, training services, and support for the socioeconomic inclusion of adult persons in the workplace, at home, in leisure activities, etc.). In a parallel although not in a generalized manner, support services related to access to higher education and university especially have been established for people with ADS without the associated intellectual disability. In relation to the models of intervention, there is quite widespread consensus over the need to incentivize actions based on educational objectives and the promotion of the strengths of people with ADS throughout their life. The perspective of intervention is flexible, integrating the contributions of different psychoeducational models and, especially, those that offer greater scientific evidence: the TEACCH method, positive behavioral support, and different sociocommunicative methods. Guaranteeing the quality of life for people with ADS and their families constitutes a clear objective for the majority of organizations and professionals, in such a way that the services are
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oriented towards inclusion and the development of people in their own communities, as well as considering fundamental dimensions such as emotional and physical well-being, selfdetermination, social relations, etc. If we center on the educational stage, the modalities of schooling that exist in Spain at present are as follows (Alonso and Cuesta 2017): Ordinary Educational Centers Ordinary classroom: In general, nowadays, in the field of Infant and Primary Education, a majority of children with ADS attend ordinary schools. ADS classrooms located in ordinary educational centers: some centers, in their majority dependent on family associations, have classrooms located at ordinary centers (the number of specialized professionals at the center remaining unchanged). Stable classrooms, an inexistent option in some autonomous regions. There is no official norm that regulates this approach to educational inclusion. The initiatives that have developed emerged from collaborative agreements between the provincial educational administrations, associations of autism, and ordinary centers where those classrooms are located. Under this modality, the pathway to inclusion in the ordinary classroom may be adapted in accordance with the strengths of each pupil. We can find stable classrooms in the three communities that are the object of our study. Stable classrooms may be found in the three communities that are studied in this paper. Preferential centers of rehabilitation, in which the inclusion of the pupils with ASD is prioritized, because these centers have specialized support resources. Combined or Mixed Schooling: permits the pupil to attend two different educational centers, generally one for special education and another for ordinary education, which makes it possible to combine the contribution of both centers. The times of permanence at each one will be established in an individual plan for each pupil, agreed between both centers.
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Specific Special Education Centers: those in which all the classrooms are specifically for pupils with ASD. General Centers of Special Education: welcome children with different types of disability. With respect to adults, there are many organizations that have promoted support services, such as: Day centers for adults, at which training programs are developed and the social and labor inclusion of people with ASD is promoted. Employment support services, in which professional training is facilitated, intermediation with firms, labor practices, and incorporation in the workplace. Inclusion in the work place through supported employment has allowed the real incorporation of people with ASD in the world of work. Different modalities of housing services: protected housing for people who require intermittent support, housing with permanent support, residences. Leisure programs. Programs that promote the practice of sports. Support for access to higher education. Many of the autism organizations have also specialized diagnostic services, which generally cooperate with the public services that are involved and that are complemented with programs of early attention. The Spanish Autism Confederation (2009, 2016) has proposed a catalogue of support services for persons with ADS throughout their life, on the basis of the needs identified by organizations, professionals, and people with ASD. This document is called “Calidad de vida, hoy [Quality of life, today]” (Vidriales et al. 2015).
Overview of Research Directions After the first large-scale investigation on autism in our country, conducted by GETEA Group
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(Instituto de Salud Carlos III), another relevant initiative took place to dynamize this sector in Spain. This was the celebration of the I Encuentro de Investigadores sobre Autismo [First Meeting of Researchers on Autism], organized by the Centre of Applied Psychology of the Autonomous University of Madrid (Belinchón 2010). As follow-up to this meeting, a further initiative led to an up-to-date review of the different investigations that have contributed to greater understanding of autism from the perspective of neurobiology and genetics. The work also offers relevant information with practical implications for diagnosis and intervention (Alonso 2014). Güemes et al. (2009) completed an investigation that evaluated the efficiency of psychoeducational interventions in the ASD. Moreover, various investigations have been completed in relation to the needs of people with ASD and their families, with the purpose of promoting knowledge and raising awareness of the relevance of specialized support (Belinchón 2001; Belinchón et al. 2008; Saldaña et al. 2006). One of the most relevant collaborative investigations into the quality of life of people with ASD has been promoted by Autism Spain in the project Red para la Calidad de Vida [Network for the Quality of Life]. This initiative in which 73 families, 70 people with ASD, and professionals have participated generated various results, among which is the design of an evaluation methodology of the specific quality of life for people with ASD, which contemplates the design of an information tool, ICalidad, that facilitates the evaluation process and includes a specific questionnaire (Vidriales et al. 2015). Other investigations have referred to the needs emerging in relation to the support services: aging (Vidriales et al. 2016; Ruggieri et al. 2019), women with ASD, through the European project Autism in Pink, and the situation on the access of people with ASD to higher education and the university, in the framework of the European project Autism and Uni. Participation in the European project ASDEU may also be highlighted, the objective of which is to analyze the social and economic costs of
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autism; to review the existing programs on early detection and to develop proposals for their introduction and improvement; to prepare a plan for the improvement of professional training; to validate biomarkers associated with ASD; and to improve knowledge on diagnosis, comorbidity, and the effectiveness of care and support in adult life and in the elderly with a diagnosis of autism. Finally, one of the more recent and relevant initiatives is the Bebe-Miradas Project, promoted by the Burgos Autism Association and the Miradas Foundation, consisting in the identification of signs in babies that warn of the presence of an Autism Spectrum Disorder (ASD), by means of “eye tracking,” referring them to specialized and specific intervention that prevents and minimizes the impact of the disorder in the development of the child.
Overview of Training In parallel to the creation of groups of families and people with ASD, training and dissemination initiatives have started to emerge that to a great extent raise the awareness of public administrations and the development of professional practices that may have distanced themselves from psychoanalytic perspectives for the development of models based on a cognitive behavioral approach. A group of young professionals, linked to the first associations as technical advisors, were deeply involved in this process, such as Ángel Díez Cuervo and Ángel Rivière Gómez. At present, the association of AETAPI professionals has been turned into a reference for training in autism care, through the biannual congresses that it organizes as well as thanks to other online training initiatives and one-day conferences on specialization that it has offered. The refresher course that this body promotes has seen over 250 participants in the three editions that it has celebrated up until today. Likewise, AETAPI subscribes to and supports the training and specialization of professionals through collaborative agreements with different Universities (Autónoma de Madrid, Burgos, Sevilla, Zaragoza, and the Universidad Nacional a Distancia), and it collaborates in initiatives such
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as the European IPA+ project Inclusion of People with Autism in Europe. Towards a specialized training model for professionals, in collaboration with other countries, in order to design a training curriculum that responds to the needs of professionals and the specialized organizations.
Social Policy and Current Controversies The edition of the DSMV generated broad debate on the diagnosis of ASD among specialized professionals and the creation of a working group within the framework of AETAPI that conducts an exhaustive analysis of the implications that this new model had in the field of autism. One of the principal controversies was generated around the suppression of the diagnostic categories established in earlier editions, and especially one related with Asperger syndrome, because of its implications for the identification and the social recognition of the people who form part of this collective. At this time, agreement exists to continue gradually to include the terminology proposed in this new edition, without sidelining the concept of Asperger syndrome. There are many challenges in the area of autism: increased prevalence generates new needs and the planning of different support services, from a flexible perspective, which contemplates diversity of demand in a collective characterized by great variability of degrees and manifestations of symptoms. It is necessary to continue working to define, on the basis of the evidence contributed by science and experience, the most appropriate model to understand and to intervene in autism, burying interpretative proposals or “miracle cures” which are a cause of great concern to people with ASD and their families (AETAPI 2011; Ruggieri and Cuesta 2017). The initiative of the government to promote the Spanish Strategy on Autism Spectrum Disorders has awakened great hope. It implies a step forward in the recognition of the rights of people who form part of this group and in the visibility of ASD as a condition in its own right, which generates specific needs and that makes the development
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of specific support systems essential for the people who present them, throughout all stages of the life cycle.
References and Reading Alonso, J. R. (2014). Investigaciones recientes sobre el autismo. Valencia: Psylicom. Alonso, J. & Cuesta J. L. (2014). Necesidad de planes de formación interdisciplinar. Atención sanitaria en personas con Trastorno de Espectro Autista. International Journal of Developmental and Educational Psychology, Na 1, 2, 547–553. Asociación Española de Profesionales del Autismo (AETAPI). (2011). Propuesta para la planificación de servicios y programas para personas con trastornos del espectro del autismo y sus familias. Retrieved from: http://aetapi.org/documentos_aetapi.htm Belinchón, M. (Coord.). (2001). Las personas con Trastornos del Espectro Autista en la Comunidad de Madrid. Madrid: Ediciones Martín & Macías Belinchón, M. (Coord.). (2010). Investigaciones sobre el autismo en español. Problemas y perspectivas. Madrid: Universidad Autónoma de Madrid. Centro de Psicología Aplicada. Belinchón, M., Hernández, J. M., & Sotillo, M. (2008). Personas con Síndrome de Asperger. Funcionamiento, detección y necesidades. Madrid: Fundación ONCE. Confederación Autismo España. (2009). Autismo y Calidad de Vida, Hoy. Madrid: Confederación Autismo España. Retrieved from http://www.sobretodo personas.org/phocadownload/Bibliografia_ Discapacidad/Trastorno_del_espectro_autista__TEA/ Autismo,%20calidad%20de%20vida:%20hoy.pdf. Confederación Autismo España. (2016). Envejecimiento y Trastorno del Espectro del Autismo. Madrid: Confederación Autismo España. Retrieved from http://www.autismo.org.es/sites/default/files/ envejecimiento_informe_ejecutivo_version_web.pdf. Feinstein, A. (2016). Historia del autismo. Conversaciones con los pioneros. Ávila: Autismo Avila. Guemes, I., Martín, M. C., Canal, R., & Posada, M. (2009). Evaluación de la eficacia de las intervenciones psicoeducativas en los trastornos del espectro autista. Madrid: Ministerio de Ciencia e Innovación. Instituto de Salud Carlos III. Martínez, M. A., Cuesta, J. L., Esteban, N., Lezcano, F., Casado, R., Arnaiz, J., & Pérez, L. (2010). Historia y situación actual del movimiento asociativo para las personas con trastornos del espectro autista en Castilla y León. Siglo Cero, 41(4)(236), 78–96. Ministerio de Sanidad. (2015). Estrategia Española en Trastornos del Espectro del Autismo. Retrieved from: http://www.autismo.org.es/sites/default/files/blog/ adjuntos/estrategia_espanola_tea_ministerio_sanidad. pdf
SPANK-2 ONU. (2006) Convención Internacional de los Derechos de las Personas con Discapacidad. Disponible en https:// www.un.org/esa/socdev/enable/documents/tccconvs.pdf Ruggieri, V., & Cuesta, J. (2017). Autismo. Cómo Intervenir, desde la Infancia a la Vida Adulta. Buenos Aires: Paidós. Ruggieri, V., Cuesta, J., Merino, M., & Arberas, C. (2019). Aging and autism: Understanding, intervention, and proposals to improve quality of life. Current Pharmaceutical Design, 25. https://doi.org/10.2174/ 1381612825666191204165117. Saldaña, D., Álvarez, R., Moreno, M., López, A. M., Lobatón, S., & Rojano, M. A. (2006). Vida Adulta y trastornos del espectro autista Calidad de vida y empleo en Andalucía. Sevilla: Federación Autismo Andalucía. Retrieved from http://sid.usal.es/idocs/F8/ FDO22145/estudio_vida_adulta.pdf. Vidriales, R., Cuesta, J. L., Plaza, C., & Hernández, M. (2015). Personas con Trastorno del Espectro del Autismo con necesidades intensas y generalizadas de apoyo: Estrategias para mejorar su calidad de vida. Revista Española de Discapacidad, 3(2), 101–115. Vidriales, R., Hernández, C., & Plaza, M. (2016). Envejecimiento y Trastorno del Espectro del Autismo. Una etapa vital invisible. Edita: Autismo España. Retrieved from: http://www.autismo.org.es/sites/ default/files/envejecimiento_informe_completo_ver sion_web.pdf.
SPANK-2 ▶ SHANK 3
Sparrow, Sara Elena L. Grigorenko1,2 and Laurie Cardona3 1 University of Houston, Houston, TX, USA 2 Yale Child Study Center, Psychology, and Epidemiology and Public Health, Yale University, New Haven, CT, USA 3 Yale Child Study Center, Yale University, New Haven, CT, USA
Name and Degrees Sparrow, Sara S. M.A. Ph.D. (May 9, 1933–June 10, 2010)
Sparrow, Sara
Major Appointments (Institution, Location, Dates) 2003 Professor Emerita, Yale University, Child Study Center and Department of Psychology; Senior Research Scientist, Yale Child Study Center 1989–2003 Professor of Psychology, Chief Psychologist, The Child Study Center, School of Medicine; Professor, Department of Psychology, Yale University 1986–1989 Professor of Psychology, Chief Psychologist, The Child Study Center, School of Medicine; Lecturer, Department of Psychology, Yale University 1977–1986 Associate Professor of Psychology, Chief Psychologist, The Child Study Center, School of Medicine; Lecturer, Department of Psychology, Yale University 1977–2002 Director, Psychology Internship Program, The Child Study Center, Yale University 1974–1977 Assistant Professor, The Child Study Center and the Department of Psychology; Chief Psychologist, The Child Study Center, Yale University 1974–1975 Visiting Professor, Child Development Department, Connecticut College, New London, Connecticut 1972–1974 Research Associate and Lecturer, Department of Psychology and The Child Study Center, Yale University 1972–1973 Acting Chief Psychologist, The Yale Child Study Center, Yale University 1970–1972 Research Associate, Department of Psychology, Yale University 1969–1970 Research Staff, Department of Psychology, Yale University 1968–1969 Postdoctoral Fellow, Department of Psychology, Yale University 1965 Director, Summer Speech Clinic, Speech Department, University of Florida 1962–1964 Coordinator, Speech and Hearing Program, Orange County Schools, Orlando, Florida 1958–1961 Speech Therapist, Orange County Schools, Orlando, Florida
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Major Honors and Awards 2010 Lifetime Child Study Center Service Award 2009 Edgar Doll Award for “her outstanding research and sustained contributions to the understanding of intellectual and developmental disabilities,” APA Toronto 2009 Distinguished Career Award, International Neuropsychology Society, Atlanta, GA 2008 Outstanding Alumnus of the Year, University of Florida, College of Public Health and Health Professions, Gainesville, FL 2006 Distinguished Alumnus Award, University of Florida, College of Public Health and Health Professions, Gainesville, FL 2004–2005 President, Division 33, Mental Retardation and Developmental Disabilities, American Psychological Association 2003–2004 President-Elect, Division 33, Mental Retardation and Developmental Disabilities, American Psychological Association 2002–2003 President-Elect Designate, Division 33, Mental Retardation and Developmental Disabilities, American Psychological Association 2002 Career Scientist Award, The American Academy on Mental Retardation 2000–2002 National Research Council, Committee on Disability Determination for Mental Retardation, Washington, DC 1998 Invited Participant, NICHD Workshop on Measurements of Clinical Outcome, Surrogate Endpoints, and Diagnostic Markers in Pediatric Drug Trials. September 24–25, 1998 Rockville, MD 1998 Professional Achievement Award, 2nd Annual Learning Disorders Symposium, H.E.L.P. Group. September 18–19, 1998. Sherman Oaks, CA 1994–1997 Elected to the Board of Governors, International Neuropsychological Society 1993–1999 Coeditor, Journal of Child Neuropsychology 1993 Cochairperson, Human Resources Development Task Force, Center for Health and Human Services 1992 National Advisory Board: “Development and Implementation of a Plan to Assess the
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Neurodevelopment of Infants and Children Exposed to Drugs in Utero.” NIMH 1990 Facilitator: National Conference on Implementing Public Academic Linkages for Clinical Training in Psychology. NIMH and APA, Dec. 10–12, 1990 1990 Project Review Committee, National Institute of Mental Health Training Grants 1989 “National Conference on Clinical Training for Multidisciplinary Services for Seriously Emotionally Disturbed Children and Adolescents.” Georgetown University Leavey Conference Center, Washington, DC 1989 Member Consensus Development Panel, National Institutes of Health, Treatment of Destructive Behaviors in Persons with Developmental Disabilities 1988 Fellow, American Psychological Association (Div. 33) 1987 Review Panel Member, Research Project “The Higher Cerebral Function Disorders in Children,” NICHD, Washington, DC, 1987, 1988, Chairman 1989–1994 1985–1986 Chairman, Quality Review Committee, Connecticut State Planning Council on Developmental Disabilities 1984 Fellow, American Psychological Association (Div. 12) 1984 First Recipient, Connecticut Psychological Association “Award for Distinguished Effort by a Connecticut Psychologist, Utilizing Psychological Knowledge and Skills, Which Has Made Contribution to the Welfare of the Public,” for development and publication of revised Vineland Adaptive Behavior Scales (1984,1985), at Fairfield University, December 7, 1984 1984 Project Review Committee, National Institute of Mental Health Training Grants 1982 Elected to American Academy of Mental Retardation 1980–1986 Member, Connecticut State Planning Council on Developmental Disabilities (appointed by Governor). 1971–1972 Committee on Book Reading, International Reading Association 1970 Who’s Who in American Women 1968 American Men of Science
Sparrow, Sara
Landmark Clinical, Scientific, and Professional Contributions Vineland Adaptive Behavior Scales, Second Edition (Vineland-II)
Short Biography Dr. Sara S. Sparrow was a North American psychologist, whose work focused on children and their developmental trajectories, ranging from intellectual giftedness to severe mental retardation. Dr. Sparrow made contributions to the fields of child neuropsychology and developmental disabilities across a wide range of diagnostic groups of children and also across cultures. She was particularly engaged with children with autism spectrum disorders, intellectual disabilities, and learning disabilities. Dr. Sparrow trained many mental health professionals at the doctoral and postdoctoral levels; she was the founder of the child psychology internship program at the Yale Child Study Center. She was also an influential and highly valued teacher and clinical supervisor within various Yale University programs. Several generations of her former trainees work all over the world, leading clinical science programs for children with special needs. Dr. Sparrow’s landmark contribution was the Vineland Adaptive Behavior Scales, Second Edition (Vineland-II), which is one of the most broadly used psychological assessment instruments used throughout the world. She was the senior author and a devoted promoter of this instrument. The Vineland is used extensively in clinical, educational, and research settings as a means of evaluating individuals’ personal and social skills in everyday living. It was the first life-span, norm-referenced instrument measuring adaptive behavior in multiple domains. The Vineland is a particularly critical diagnostic tool in the assessment of individuals with intellectual and developmental disabilities, including autism spectrum disorders. A native of Minneapolis, Minnesota, Dr. Sparrow graduated summa cum laude from
Speaker Versus Listener-Oriented Disfluency
Montclair State College in New Jersey in 1958 and received her professional degrees from the University of Florida, M.A., in speech pathology in 1962 and Ph.D. in clinical psychology/ clinical neuropsychology in 1968. She went to Yale in 1968 as a postdoctoral fellow in the Department of Psychology. She joined the department’s research staff the following year, then served as acting chief psychologist at the Child Study Center in 1972–1973. She was appointed to the rank of assistant professor in 1974, associate professor in 1977, and full professor in 1986. Dr. Sparrow’s contributions to psychology included more than 120 articles and book chapters in the fields of child neuropsychology, learning disabilities, test development, and adaptive behavior in a broad range of child psychiatric conditions. Additionally, Dr. Sparrow maintained active clinical practice within the Yale Child Study Center, where she was Chief Psychologist from 1997 to 2002, and then continued working with patients and conducting research as Professor Emerita of Psychology and Advisor to the Yale Academic Skills Clinic until her untimely death. Beyond Yale, Dr. Sparrow assumed leadership roles in psychology at the national level. She served as President of Division 33 of the American Psychological Association (Intellectual and Developmental Disabilities), was a member of the National Research Council (National Academy of Sciences) Committee on Disability Determination for Mental Retardation, and was cofounder of the Journal of Child Neuropsychology. Dr. Sparrow’s scientific contributions have been recognized through numerous awards including Alumnus of the Year by the College of Public Health and Health Professions at the University of Florida; she and her husband, Dr. Domenic Cicchetti, shared the first Scientific Achievement Award given by the Connecticut Psychological Association; she was a recipient of the Edgar Doll Award from Division 33 of the APA which is the division’s highest recognition of outstanding scientific contributions to the field of intellectual and developmental disabilities; and in
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2002 Dr. Sparrow received the Career Scientist Award from the American Association of Mental Retardation for her lifetime contributions to the field.
See Also ▶ Vineland Adaptive Behavior Scales (VABS)
References and Reading Sparrow, S. S., & Cicchetti, D. V. (1987). Adaptive behavior and the psychologically disturbed child. The Journal of Special Education, 21, 89–100. Sparrow, S. S., & Davis, S. M. (2000). Recent advances in the assessment of intelligence and cognition. Journal of Child Psychology and Psychiatry, 41, 117–131.
Spastic Diplegia ▶ Cerebral Palsy
Spastic Paralysis ▶ Cerebral Palsy
Speaker Versus ListenerOriented Disfluency Paul Edward Engelhardt School of Psychology, University of East Anglia, Norwich, Norfolk, UK
Definition Deficits in pragmatic language and social communication are characteristic of verbal individuals with autism spectrum disorders (ASD). Disfluencies are pauses and interruptions, which
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break the continuity of speech outputs. Disfluencies are often classified into different categories, primarily including pauses, repetitions, and repairs. These disfluencies are produced by all people in the normal course of speaking, and estimates place their occurrence at 6–10 per 100 words produced. Crucially, these disfluencies are distinguished from “dysfluency,” which refers to pathological fluency issues (i.e., stuttering or stammering). However, it is important to note that several reports have indicated stuttering-like disfluency in some individuals with ASD (see Scott 2015). Disfluencies may serve pragmatic purposes in that they cue problems within the speaker for the benefit of the listener. Speaker-oriented disfluencies are thought to be due to speakerinternal factors (i.e., difficulties associated with the process of planning and producing language). In contrast, listener-oriented disfluencies are believed to be produced in interactive dialogue, specifically for the listener and for the benefit of communicative exchanges/efficiency. This entry provides a review of existing evidence relating to speakerand listener-oriented disfluency in ASD. Evidence for speaker-oriented (or speaker-internal causes of) disfluency could come from different sources. However, the pattern one would expect is that there should be negative correlations between rates of disfluency and speakerinternal variables. In short, higher rates of disfluency would be associated with lower ability individuals, including individual differences variables, such as executive functions, intelligence, and also higher ASD symptom severity. In contrast, listener-oriented disfluency should show positive correlations between higher social-communication abilities, and also higher abilities in individuals differences variables. The debate between speaker- and listeneroriented disfluency has been applied to ASD, given that individuals with ASD tend to take an egocentric approach to dialogue and as such should be less likely to produce disfluencies that are specifically helpful for listeners in conversation.
Speaker Versus Listener-Oriented Disfluency
Historical Background It has long been known that language impairments are fundamental to ASD (e.g., Rutter 1970). There has been a great deal of recent interest in the production of disfluency by individuals with high-functioning forms of ASD. However, this line of research has its beginnings in the 1970s, and the first report of excessive disfluency production in ASD was provided by Simmons and Baltaxe (1975). Those authors found that approximately half of their sample showed hesitations, repetitions, prolongations, and nonfluencies. (Definitions and examples of these categories were not provided.) Abnormalities in the fluency of speech outputs in the 1970s were classified as faults in rhythm, and tightly linked with abnormalities in prosody. To provide some historical and current context, a histogram is provided showing the number of studies examining disfluent speech in ASD over time (see Fig. 1). This includes Phd dissertations, conference presentations, and published research studies. As can be seen in the figure, a clear uptick occurred approximately 15 years ago.
Current Knowledge This review follows the standard disfluency classification scheme from psycholinguistics (i.e., filled pauses, unfilled pauses, repetitions, repairs, and “other”) and presents results of each type of disfluency separately. The main focus is the debate about which forms of disfluency are speaker-oriented and which are listeneroriented. The latter (i.e., communicative purposes) are the type hypothesized to be produced less often in individuals with ASD, due to their impairments in socially oriented pragmatic language use (De Marchena and Eigsti 2016; TagerFlusberg et al. 2005). These impairments, specified in the DSM-5, include failure of normal back-and-forth conversation and failure to initiate or respond to social interactions. However, the criteria also specify that individuals with deficits in social communication but not
Speaker Versus Listener-Oriented Disfluency Speaker Versus ListenerOriented Disfluency, Fig. 1 Histogram showing frequency of studies related to disfluent speech in ASD
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ASD Disfluency Publications by Year 16 14 12 10 8 6 4 2 0
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otherwise meeting the criteria for ASD should be screened for social (pragmatic) communication disorder, a recent addition to the DSM-5, which overlaps substantially with ASD diagnostic criteria. The conversational profile for socialcommunication disorder includes difficulties following social rules of communication in conversation, which leads to problems (1) communicating effectively and engaging in a social manner, (2) matching speech outputs to context, (3) rephrasing when misunderstood, (4) making appropriate inferences, and (5) understanding non-literal language. These kinds of issues are all related to socially oriented pragmatic language use. With these general pragmatic language issues in mind, it is important to situate disfluency production within a theoretical context. The first theoretical context is the process of language production. Many prominent models of production assume a stage-based architecture. For example, Levelt (1989) proposed a three-stage model consisting of conceptualization, formulation, and articulation. The basis of any model of production is the conversion of nonlinguistic thoughts into the articulated words and syntax structures of speech output. Most models also assume some type of monitoring mechanism, which is responsible for detecting faults or problems in prearticulated speech plans. As mentioned previously, speaker-oriented disfluencies are likely related to difficulties arising in one or more stages of production. Difficulties could arise from a number of causes (e.g., planning
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problems, memory retrieval difficulty, lapses of attention, or individual difference variables). For example, if lemma selection (choosing which words to convey the intended message) involves inhibitory control, then we should observe individual differences in inhibition relating to rates of disfluency. The second theoretical context involves theories of ASD, which are surprisingly not often considered in disfluency studies. If interactive dialogue relies on taking the listeners’ needs into account or being overtly helpful to the listener via disfluency, then Theory of Mind has implications for listener-oriented issues (BaronCohen et al. 1985). In contrast, if disfluency production is more related to individual differences in executive functions, then theories of Executive Control in ASD become relevant for speakeroriented (or speaker-internal) issues (e.g., Hill 2004). With this theoretical context in mind, a couple of caveats should be noted. The first is that despite existing disfluency studies examining children, adolescents, and adults, there is currently not sufficient evidence to determine how disfluency production changes over the course of development. Therefore, in the following review, there is no discussion of developmental effects. The second is that studies have used both controlled and more naturalistic speaking situations, and again, there are limited studies of each type, which limits firm conclusions about how task differences affect disfluency production. Thus, task differences are only highlighted in the event of conflicting and/or mixed findings.
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Filled Pauses Many studies have focused on the production of “fillers,” which in English, primarily constitute “UMs” and “UHs.” The production of fillertype disfluencies is believed to arise from difficulties experienced by the speaker, for example, in thinking or planning what one wants to say (Clark 1994). In short, the speaker experiences a problem, they know it, and in response, produce UM or UH to signal the listener. Therefore, fillers serve communicative purposes in interactive dialogue. Past studies (e.g., Clark and Fox Tree 2002) have suggested that speakers produce fillers as a collateral signal to the listener that they are not finished speaking, want convey some uncertainty, or signal an upcoming delay (i.e., they need more planning time). Research has shown that individuals with highfunctioning autism (HFA) produce fewer fillers compared to controls, and given the socialpragmatic deficits in ASD, fillers are thought to provide evidence of listener-oriented disfluency. UMs tend to occur at the beginning of prosodic phrases and are typically followed by longer pauses than are UHs. Some studies (e.g., Lake et al. (2011) and MacFarlane et al. (2017)) did not differentiate UM and UH but did report fewer filled pauses in ASD. In contrast, Suh et al. (2014) did not find significant group differences (ASD vs. control) in a monologue story telling task. However, in a key study, Irvine et al. (2016) found that UM production differentiated groups, but UH did not. Moreover, UM rate was correlated with ASD symptom severity, but did not correlate with other key individual difference variables (age, verbal IQ, nonverbal IQ, language ability, or executive function). Further studies have confirmed this dissociation between UM and UH in ASD (e.g., Gorman et al. 2016). A key advancement in filler production was the calculation of UM to UH ratio (i.e., UM/(UM + UH)). On the basis of the evidence, it has been concluded that UM production reflects social-communication skills and is therefore a clear listener-oriented type of disfluency (see Table 1, for mean UM-UH ratios across studies). Moreover, low UM production has even been postulated as clinical-diagnostic marker of ASD, specifically
Speaker Versus Listener-Oriented Disfluency Speaker Versus Listener-Oriented Disfluency, Table 1 Results for UM/UH ratio across studies Study Gorman et al. (2016) (estimated from Fig.) Heeman et al. (2010) (turn initial) Heeman et al. (2010) (utterance initial) Heeman et al. (2010) (utterance medial) McGregor and Hadden (2018) Parish-Morris et al. (2017) (boys) Means
Control .77
ASD .40
.69 .63
.47 .30
.80
.40
.92 .78 .77
.70 .56 .47
in boys with the disorder (McGregor and Hadden 2018). Females with ASD, in contrast, were not different than controls in UM-UH ratio. However, this gender finding should be replicated before firm conclusions are drawn. Two additional findings are worth highlighting. The first finding relates to the location of filled pauses. Heeman et al. (2010) analyzed the location of filled pauses under the assumption that location may reveal information about their function. Filled pauses were coded as to whether they occurred turn initially, utterance initially, or mid-utterance. UH rates were similar across all three positions. However, participants with ASD used UM less frequently across all three positions. UMs were half as frequent at turn-initial positions, one-fifth as frequent at utterancemedial positions, and utterance-initial position fell in between. The second finding relates to speaking task. The highest rates of filled pauses occurred in conversation, as compared to a describing task and a play task. Crucially, UMs seemed to vary by position and task type, but the differences between ASD and controls remained significant, whereas UHs showed no difference between groups despite some variance in position and task type. It is currently unknown how much of the task differences are associated with the face-to-face nature of the speaking situation, but filled pauses are typically not produced in monologue speaking situations. Interestingly, Heeman et al. also reported no significant differences in the length of pauses following fillers
Speaker Versus Listener-Oriented Disfluency
between the two groups. In the psycholinguistic literature, it has been argued that UM is used as a signal of an upcoming long delay and UH an upcoming short delay (Clark and Fox Tree 2002). In summary, individuals with ASD do not produce filled pauses, in particular, UMs as frequently as do controls. Estimates suggest that UMs are produced approximately 2-to-1 over UHs in controls, but approximately 1-to-1 in ASD. Thus, it seems that individuals with ASD tend to produce unfilled pauses instead of filling pauses with UM. Moreover, differences in filled pause production do not seem to be related to age, more general language abilities, IQ, or executive functions. Several studies have examined whether UM usage is correlated with ASD symptom severity. Two studies reported significant negative correlations (Irvine et al. 2016; Parish-Morris et al. 2017), and two have not found a significant association between UM production and ASD symptom severity (Gorman et al. 2016; McGregor and Hadden 2018).
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Unfilled (or Silent) Pauses Several studies have also examined the production of unfilled or silent pauses. Both Lake et al. (2011) and Shriberg et al. (2001) reported higher rates of unfilled pauses in ASD. One potential issue with unfilled pauses is the criterion of what counts as an unfilled pause. For example, Lake et al. defined unfilled pauses as 3 sec or longer, whereas Engelhardt et al. (2017) used 250 ms. This may or may not be a problem, as research has only begun to examine the distributions of the length of pauses between ASD and controls to determine whether one has a tendency to produce particularly long or short pauses. In general, the distributions of pauses (in controls) are positively skewed (see Fig. 2 for an example). In monologue production tasks, the group effect (ASD vs. control) on unfilled pauses appears to turn on how groups are matched. Age and gender matched groups tend not to show differences. The Engelhardt et al. study utilized a memorizeand-repeat sentence production paradigm, which relied heavily on memory. Their results showed a
Speaker Versus ListenerOriented Disfluency, Fig. 2 Histogram showing an example of the length and frequency of unfilled pauses in typically developing adults
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group effect (ASD vs. unmatched controls) over and above variance accounted for by working memory (i.e., working memory ability and ASD status were both significant predictors of unfilled pauses). Thurber and Tager-Flusberg (1993) also investigated unfilled pauses, but their conclusion was that individuals with ASD expended less effort in constructing narrative discourses (i.e., in a story re-telling task). As a result, they found the opposite pattern from previous studies. That is, ASD participants produced fewer nongrammatical pauses, because their stories were shorter, less complex, and had less lexical diversity, which resulted in between-group differences in cognitive load. In contrast, grammatical pauses (i.e., pauses produced a major syntactic boundaries or points of emphasis in speech) were not significantly different between groups. Heeman et al. (2010) examined the length of pauses between conversational turns under the hypothesis that individuals with ASD would have less awareness or concern about social cues to minimize silence, or alternatively, whether longer pauses would be due to slower planning and overall slower response times. Thus, they believed that pauses could be due to either speaker-internal factors or poor social-pragmatic communication skills. Their results showed mean pause lengths of 876 ms (control) vs. 1115 ms (ASD). Similar between-group mean differences were observed, specifically following questions. Wiklund and Laakso (2019) also reported longer pauses within a conversational turn. Perhaps more interestingly, Heeman et al. also examined the length of pauses depending on activity type. In conversation, the difference between groups following a question was not significant, but in a play activity, there were significant differences and the mean difference between groups was much higher (mean difference of 569 ms). These findings were independent of threshold, as the authors were able to determine the length of silence between all turns in the conversation. However, this study did not control for individual differences measures, and so, to the extent that these data shed light on the speaker- vs listener-oriented issue remains to be determined. That is, studies
Speaker Versus Listener-Oriented Disfluency
examining the length of pauses in dialogue did not consider individual difference variables or ASD symptom severity. At present, most evidence suggests that unfilled pauses are produced more frequently in individuals with ASD, but at the same time, this effect also seems to be affected by (group) matching variables, which suggests speakeroriented production issues. Clearly, more work is needed to examine pause frequency differences and length of pause differences based on speaking task. Also, given the findings of Thurber and Tager-Flusberg, more attention concerning the type of pause is warranted. Repetitions The first report of abnormal repetitions was provided by Simmons and Baltaxe (1975). They observed high rates of repetitions in four of their seven children with ASD. Other studies have reported similar findings (Lake et al. 2011; Shriberg et al. 2001; Suh et al. 2014), but one study found no significant differences in repetitions (Thurber and Tager-Flusberg 1993). One key issue with repetitions concerns the relationship between repetitions and verbal IQ. Engelhardt and colleagues across a range of (non-ASD) studies have shown that differences in repetitions are highly related to verbal IQ (e.g., Engelhardt et al. 2019; Engelhardt et al. 2013). In an ASD study, when verbal IQ was covaried, the group effect (ASD vs. control) was no longer significant (Engelhardt et al. 2017). This study highlighted a key issue with respect to study protocols and matching of groups on individual difference variables. Verbal IQ is a particularly interesting individual difference measure with respect to ASD because verbal IQ in ASD often shows a large range. That is, a fair number of individuals with ASD have higher verbal IQ than controls, and others, depending on the overall severity of ASD, are generally lower functioning and have lower verbal IQ. At this point, it seems that the tendency to repeat words and phrases is likely due to a specific speaker-internal factor and that if differences in verbal IQ are taken into account, then individuals with ASD do not show higher rates of repetitions than do controls.
Speaker Versus Listener-Oriented Disfluency
It is important to bear in mind, however, that the proportion of variance explained by verbal IQ is typically between 25% and 50%, and so, other (unknown) factors likely contribute to variance in repetitions. At this point, it is also unclear why verbal IQ is related to repetitions. It is also important to note that no study to date has systematically examined repetitions and other language ability measures (e.g., Clinical Evaluation of Language Fundamentals, which assess expressive and receptive language abilities). Repairs Repairs are also referred to as revisions and false starts in the literature. With repairs, the speakers stop speaking and then restart with a new word or phrase. As such, these disfluencies reveal instances in which speakers correct, clarify, or elaborate on the content of their output. This type of disfluency presents the most complicated picture in terms of the speaker- vs. listeneroriented debate, because repairs have been shown to be associated with many different individual difference variables. There are also mixed findings in the literature concerning whether individuals with ASD produce more repairs than do controls. However, the majority of studies show that individuals with ASD do produce more repairs (De Marchena and Eigsti 2016; Engelhardt et al. 2017; Shriberg et al. 2001; Sisskin 2006; Suh et al. 2014). The one study which concluded that repairs are a listener-oriented form of disfluency did so simply on the basis that individuals with ASD produced fewer repairs. In a later study, Engelhardt et al. found the opposite, individuals with ASD produced more repairs than controls. Engelhardt et al. reported that the increased tendency to produce repairs was associated with several Autism Quotient subscales (i.e., autism symptom severity). In addition, the group on repair findings held even when covarying individual differences and demographic variables, and so, with the exception of Lake et al., it appears that more repairs are produced by individuals with ASD. Two additional points are worth mentioning here. The first is the notion of self-corrections. Obviously, certain types of errors in the content
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of speech will lead to conversational breakdowns via the risk of mis-communication. Therefore, the only way to “rescue” a listener-oriented conclusion about repairs is to assume that higher rates of repairs are due exclusively (or largely) to situations in which the speaker needs to correct an outright mistake. Similar points about selfcorrections were made by both De Marchena and Eigsti (2016) and Wiklund and Laasko (2019). The former found higher rates of repairs in a shared (common ground) condition in controls compared to ASD, suggesting that individuals with ASD did not adjust, correct, or clarify when it was appropriate to do so. Wiklund and Laasko argued that individuals with ASD showed marked deficits in linguistic and morpho-syntactic abilities, but were able to monitor sufficiently well these deficits and to correct in the event of possible misunderstanding for listeners. This again suggests some listener-oriented motivation for the production of repairs, but obviously, more research is needed to definitively understand whether some repairs may have a listener-oriented function. The second point is that rates of repairs differ based on the speaking task. For example, MacFarlane et al. (2017) found that ADOS dialogue was a much stronger predictor of disfluency rates compared to telling a story from a book, despite not observing differences between ASD and controls. However, MacFarlane et al. included some additional detail about the nature of the repairs. They distinguished whether the repair was a true error (e.g., turn left. . ..right), what MacFarlane et al. referred to as a “revision,” or whether it involved a deletion (e.g., my old dog. . ..my dog..) or insertion (e.g., my dog. . ..my old dog..) of linguistic material. They also classified false starts (e.g., Dogs. . ..My friend likes dogs.) as a separate category. This more detailed classification scheme may prove informative in future studies to elucidate the specific form of repairs produced by individuals with ASD, as well as shedding light on the speaker- vs. listeneroriented issue. In summary, evidence clearly points to individuals with ASD producing more repairs than controls. Also, there is no concrete evidence at this
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point to suggest that repairs are a listener-oriented form of disfluency. Instead, the tendency to produce repairs is related to many different types of individual difference variables and also to ASD symptom severity. Other (Interjections, Prolongations, and Atypical Disfluency) “Other” forms of disfluency have received much less empirical investigation compared to the types reviewed above in both the clinical and psycholinguistic literatures. This is partially due to the fact that they are produced much less frequently overall and also they do not fit well within the typical classification schemes and as such, are classified as “other.” Obviously, interjections (I mean, you know, etc.) could be communicatively beneficial in terms of signaling something important to a listener. No study to date has examined the production of interjections in ASD, but Heeman et al. (2010) did analyze “discourse markers” and “acknowledgments.” Prolongations (e.g., theeeeeee vs. the) have been noted in the psycholinguistic literature, as a mechanism in which the speaker “buys” themselves more time, presumably due to difficulty with an upcoming element in the speech stream (Arnold et al. 2004). However, again, no study has examined the production of prolongations in ASD. Atypical disfluencies are quite interesting: They were noted in the original studies in the 1970s but have rarely been classified and/or observed in later studies (cf. Plexico et al. 2010; Scaler Scott et al. 2014). Some common examples are final syllable repetitions (e.g., speech. . .eech, animal. . .mal) and stuttering-like disfluency, including part-word perseverations (e.g., d.d.d.d. d.dog), single syllable repetitions (e.g., I.I.I.I), and dysrhythmic blocks. Readers interested in atypical disfluency and cluttering (i.e., excessively fast speech that overloads the production system) and the incidence rates of these things in ASD should consult Scott (2015), Scaler Scott et al. (2014), and Sisskin (2006). Summary This review has focused on how individuals with ASD show differences in disfluency in their
Speaker Versus Listener-Oriented Disfluency
speech outputs compared to controls. Some of these can be clearly linked with speaker-internal factors and some with more listener-oriented functions. It is also important to note that there are several reports that suggest that some types of disfluency are associated with comprehension problems for listeners, in particular, repairs (see Wiklund and Laasko 2019), which suggests that the production of disfluency can be communicatively problematic. Filled pauses provide the clearest example of listener-oriented disfluency, and here, the research shows that individuals with ASD produce fewer UMs than do controls. The rate of UHs is not affected. Unfilled pauses are produced more frequently in individuals with ASD, and particularly, in unmatched samples. The evidence tends to indicate that unfilled pauses are related to speaker-internal factors and as such, are likely a speaker-oriented form of disfluency. Likewise, repetitions are highly associated with individual differences in verbal IQ, and at this point, not linked to ASD (cf. Suh et al. 2014). Finally, repairs have been linked with executive functions and individual differences in IQ. However, they are also produced more frequently in individuals with ASD. This may be due to more basic language-related impairments. The strength of the relationships between repairs and individual difference variables and ASD symptom severity are much more variable (perhaps due to differences in task demands) as compared to the other types of disfluency.
Future Directions Below are two “general” future directions (points 1 and 2) which are based on re-occurring themes arising throughout this review. I have also highlighted two “specific” future directions (points 3 and 4) which take the study of disfluency in ASD in novel and exciting new directions. 1. A recurring theme throughout the review was the differences in task demands associated with the speaking situation in which disfluencies were analyzed. For example, does the task involve monologue or dialogue and is the
Speaker Versus Listener-Oriented Disfluency
speech scripted or naturalistic. Many studies have examined disfluencies produced in ADOS assessments, which is an excellent source of data given its widespread use and semi-structured nature. Relatedly, what are the cognitive load differences associated with the speaking task? It is likely that overall rates of disfluency (and type of disfluency produced) will differ depending on the speaking situation and task demands. Further studies are needed to assess disfluency production when speaking with and without eye contact and about emotional topics. 2. With respect to control variables and the matching protocols between groups (ASD vs. control), further research is need to determine which variables are key so that confounds can be avoided. Past research suggests that demographics (age, gender), individual difference variables (specifically verbal IQ, nonverbal IQ, executive functions), language abilities, and comorbid disorders are all important in some situations. These issues are especially important for speaker-oriented disfluencies. There are also a number of issues that need to be investigated concerning the relationship between verbal IQ and repetitions. 3. A recent study has linked disfluency production to particular genetic signatures in mothers who had a child with a developmental disorder (Klusek et al. 2018). To establish a relationship between an etiologically more basic (causal) factor and disfluent speech is an exciting avenue of research. It remains to future research to determine how these genetic differences are linked to structural-functional brain abnormalities, resulting in a behavioral profile characterized by fluency issues. 4. The time and cost associated with disfluency analysis is immense. It requires hand coding of the data, typically by a research team, in order to gain the requisite inter-rater reliability, especially for more subjective forms of disfluency (e.g., unfilled pauses). If we are to take McGregor and Hadden’s suggestion of using disfluency as a clinical-diagnostic marker of ASD seriously, the field needs to simplify, speed, and reduce cost. Therefore, automated
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annotation of speech samples is critical. Currently, automated disfluency analyses only detect disfluency generally, not specific categories or subcategories (i.e., filled pauses, repetitions, repairs, etc.) (see Morley et al. 2014).
See Also ▶ Pragmatic Language Impairment ▶ Pragmatics ▶ Reciprocal Communication/Interaction ▶ Social Communication ▶ Speech/Communication Disabilities ▶ Verbal Communication
References and Reading Arnold, J. E., Tanenhaus, M. K., Altmann, R. J., & Fagnano, M. (2004). The old and thee, uh, new: Disfluency and reference resolution. Psychological Science, 15, 578–582. Baron-Cohen, S., Leslie, A. M., & Firth, U. (1985). Does the autistic child have a “theory of mind”. Cognition, 21, 37–46. Clark, H. H. (1994). Managing problems in speaking. Speech Communication, 15, 243–250. Clark, H. H., & Fox Tree, J. E. (2002). Using uh and um in spontaneous speaking. Cognition, 84, 73–111. De Marchena, A., & Eigsti, I.-M. (2016). The art of common ground: Emergence of a complex pragmatic language skill in adolescents with autism spectrum disorders. Journal of Child Language, 43, 43–80. Engelhardt, P. E., Nigg, J. T., & Ferreira, F. (2013). Is the fluency of language outputs related to individual differences in intelligence and executive function? Acta Psychologica, 144, 424–432. Engelhardt, P. E., Alfridijanta, O., McMullon, M. E. G., & Corley, M. (2017). Speaker- vs. listener-oriented disfluency: A re-examination of arguments and assumptions for autism spectrum disorders. Journal of Autism and Developmental Disorders, 44, 2885–2898. Engelhardt, P. E., McMullon, M. E. G., & Corley, M. (2019). Individual differences in the production of disfluency: A latent variable analysis of memory ability and verbal intelligence. Quarterly Journal of Experimental Psychology, 72, 1084–1101. Gorman, K., Olson, L., Presmanes Hill, A., Lunsford, R., Heeman, P. A., & van Santen, J. P. H. (2016). Uh and um in children with autism spectrum disorders or language impairment. Autism Research, 9, 854–865. Heeman, P. A., Lunsford, R., Selfridge, E., Black, L., & van Santen, J. (2010). Autism and interactional aspects of dialogue. In the proceedings of the 11th annual
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4540 meeting of the special interest group on discourse and dialogue. (pp. 249–252). Hill, E. L. (2004). Executive dysfunction in autism. Trends in Cognitive Sciences, 8, 26–32. Irvine, C. A., Eigsti, I.-M., & Fein, D. A. (2016). Uh, Um, and autism: Filler disfluencies as pragmatic markers in adolescents with optimal outcomes from autism spectrum disorder. Journal of Autism and Developmental Disorders, 46, 1061–1070. Klusek, et al. (2018). Curvilinear association between language disfluency and FMR1 CGG repeat size across the normal, intermediate, and permutation range. Frontiers in Genetics. https://doi.org/10.3389/fgene.2018.00344. Lake, J. K., Humphreys, K. R., & Cardy, S. (2011). Listener vs. speaker-oriented aspects of speech: Studying the disfluencies of individuals with autism spectrum disorders. Psychonomic Bulletin and Review, 18, 135–140. Levelt, W. J. M. (1989). Speaking: From intention to articulation. Cambridge, MA: MIT. MacFarlane, H., Gorman, K., Ingham, R., Presmanes-Hill, A., Papadakis, K., Kiss, G., & van Santen, J. (2017). Quantitative analysis of disfluency in children with autism spectrum disorder or language impairment. PLoS One, 12(3), eo173936. McGregor, K. K., & Hadden, R. R. (2018). “Um” fillers distinguish children with and without ASD. Journal of Autism and Developmental Disorders. https://doi.org/ 10.1007/s10803-018-3736-1. Morley, E., Hallin, A., Roark, B. (2014). Challenges in automating maze detection. In Proceedings of the ACL Workshop on Computational Linguistics and Clinical Psychology. Parish-Morris, J., et al. (2017). Linguistic camouflage in girls with autism spectrum disorder. Molecular Autism, 8, 48–60. Plexico, L. W., Cleary, J. E., McAlpine, A., & Plumb, A. M. (2010). Disfluency characteristics observed in young children with autism Spectrum disorders: A preliminary report. Perspectives on Fluency and Fluency Disorders, 20, 42–50. Rutter, M. (1970). Autistic children: Infancy to adulthood. Seminars in Psychiatry, 2, 435–450. Scaler Scott, K., Tetnowski, J. A., Flaitz, J. R., & Yaruss, J. S. (2014). Preliminary study of disfluency in schoolaged children with autism. International Journal of Language and Communication Disorders, 49, 75–89. Scott, K. S. (2015). Dysfluency in autism spectrum disorders. Procedia – Social and Behavioral Sciences, 193, 239–245. Shriberg, L. D., Paul, R., McSweeny, J. L., Klin, A., Cohen, D. J., & Volkmar, F. R. (2001). Speech and prosody characteristics of adolescents and adults with high-functioning autism and Asperger syndrome. Journal of Speech, Language, and Hearing Research, 44, 1097–1115. Simmons, J. Q., & Baltaxe, C. (1975). Language patterns of adolescent autistics. Journal of Autism and Childhood Schizophrenia, 5, 333–351.
Special Education Sisskin, V. (2006). Speech disfluency in Asperger’s syndrome: Two cases of interest. Perspectives on Fluency and Fluency Disorders, 16, 12–14. Suh, J., Eigsti, I., Naigles, L., Barton, M., Kelly, E., & Fein, D. (2014). Narrative performance of optimal outcome children and adolescents with a history of an autism spectrum disorder (ASD). Journal of Autism and Developmental Disorders, 44, 1681–1694. Tager-Flusberg, H., Paul, R., & Lord, C. (2005). Language and communication in autism. In F. R. Volkmar, R. Paul, A. Klin, & D. Cohen (Eds.), Handbook of autism and pervasive developmental disorders, Vol. 1: Diagnosis, development, neurobiology, and behavior (3rd ed., pp. 335–364). Hoboken: Wiley. Thurber, C., & Tager-Flusberg, H. (1993). Pauses in the narratives produced by autistic, mentally retarded, and normal children as an index of cognitive demand. Journal of Autism and Developmental Disorders, 23, 309–322. Wiklund, M., & Laasko, M. (2019). Ungrammatical utterances and disfluent speech as causes of comprehension problems in interactions of preadolesecents with high functioning autism. Clinical Linguistics and Phonetics, 33, 654–676.
Special Education Pamela Brucker Special Education and Reading, Southern Connecticut State University, New Haven, CT, USA
Definition Special education is a broad term that refers to a variety of direct instructional and related services that are available for students ages birth–21 who meet specified criteria for eligibility under the Individuals With Disabilities Education Act (IDEA). These services are designed to give the eligible student a “free and appropriate public education.”
See Also ▶ Individuals with Disabilities Education Act (IDEA)
Special Education Service Utilization in ASD
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References and Reading
Historical Background
IDEA Partnerships. Retrieved 11 July 2011 from www. fape.org Nation Dissemination Center for Children with Disabilities. Retrieved 11 July 2011 from www. nichcy.org Turnbull, A., Wehmeyer, M. L., & Turnbull, R. (2009). Exceptional lives: Special education in today's schools (pp. 8–21). Upper Saddle River: Prentice-Hall. U.S. Department of Education. Retrieved 11 July 2011 from www.ed.gov Vaughn, S., Bos, C., & Schumm, J. S. (2010). Teaching exceptional, diverse, and at-risk students (pp. 2–7). Upper Saddle River: Prentice-Hall.
Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by symptoms of social-interaction and social-communication impairments and restricted and repetitive interests and behaviors (American Psychiatric Association [APA] 2013). These prominent features of ASD often limit students’ functioning in schools (Wei et al. 2014). In addition to these core diagnostic symptoms, many children with ASD present with problem behavior (e.g., noncompliance, aggression, and disruptive behaviors; Ashburnere et al. 2010), and other associated difficulties. As a result, many children with ASD receive longterm intervention, often in the school setting (Kang-Yi et al. 2016). Generally these services in the schools address symptoms and behavior that interfere with overall learning and functioning in the school setting (Kang-Yi et al. 2016), which may include academic, behavioral, and therapeutic supports (Kang-Yi et al. 2016; McDonald et al. 2019; Wei et al. 2014; White et al. 2007). Given the amount of time spent in the school setting, access to services in schools is critically important for children with ASD (McDonald et al. 2019; Narendorf et al. 2011) and may be the most accessible service option (White et al. 2007). In addition, services in the schools often lead to service provision outside of the school setting (White et al. 2007). Prevalence estimates of children with ASD have documented an increase of 29% between 2008 and 2010, with the majority of this increase attributed to the growing number of children with ASD without intellectual disability (Center for Disease Control and Prevention [CDC] 2014, 2016). For example, the CDC (2016) documented that in 2012, the number of students identified with ASD with average or above average cognitive ability had risen to 43.9%. Due to the growing number of children with ASD in the schools, special education services are challenged (Yeargin-Allsop et al. 2003). According to the USDOE (2017) Annual Report, 9.1% of the children ages 6–21 accessing special education services were served under the special education classification of Autism.
Special Education Classroom ▶ Self-Contained Classroom
Special Education Needs ▶ Special Needs
Special Education Service Utilization in ASD Christin A. McDonald Nationwide Children’s Hospital’s Center for Autism Spectrum Disorders, Westerville, OH, USA
Definition The investigation of what special education services (e.g., speech, occupational, physical therapies; 1:1 aide support; behavior support plans; classroom placements; and academic supports) children with autism spectrum disorder (ASD) currently access.
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However, the number served under the classification of Autism may be misleading and represent an underestimate of the number with a clinical diagnosis of ASD served in special education (CDC 2014: McDonald et al. 2019; Rubenstein et al. 2018). For example, Rubenstein et al. (2018) examined special education trends of 8-year-olds (n ¼ 6010) who had been diagnosed with ASD from 2002 to 2010, and discovered that more than 36% of these children did not receive special education eligibility under the classification of Autism during each surveillance year. These findings are a low estimate compared to those reported by the CDC (2014), who found that between 30% and 69% of students diagnosed with ASD received special education services under the classification of Autism. However, Rubenstein et al. (2018) also noted that the number of children accessing special education services via a classification of Autism increased by 3.6 percentage points from 2002 to 2010, with the greatest proportional increase documented amongst Hispanic students diagnosed with ASD. Given these findings, many students clinically diagnosed with ASD receive special education services under a different educational classification than Autism (CDC 2014; McDonald et al. 2019; Rubenstein et al. 2018; Sansosti 2010; Toomey et al. 2009). These other special education classifications encompass such options as Other Health Impaired (OHI), Speech and Language Impairment (SLI), Emotional Disturbance (ED), Specific Learning Disability (SLD), and Multiply Disabled (MD; CDC 2014; Toomey et al. 2009). McDonald et al. (2019) conducted a survey of special education classifications of children with ASD without ID and found that the majority of children with ASD without ID were classified under Autism (56%), followed by OHI (27%) as the second largest category of classification. This is in contrast to the findings of Toomey et al. (2009), who found that children with ASD without ID were most frequently classified under OHI, while only 17.6% were classified under the Autism category. While the percentage of children classified under Autism for special education services may vary, the distribution of children diagnosed with ASD among variable special
Special Education Service Utilization in ASD
education classifications continues to indicate that the unique features and needs of students should be examined when designing specific academic, social, and behavioral interventions. This is in contrast to school personnel relying on the specific special education category to guide intervention decision-making (Toomey et al. 2009).
Current Knowledge Given the number of children with ASD accessing special educational services, more information is needed regarding how they are accessing these services. Zuckerman et al. (2017) assessed service use as well as documenting diagnostic age and delay among 722 elementary-school children with ASD, using the CDC’s Survey of Pathways to Diagnosis and Services data. Of this sample, 63% of the children with ASD demonstrated a functional delay. Zuckerman et al. (2017) found that children who were diagnosed later (after 4 years of age) were less likely (72%) to access school-based services than peers who had been diagnosed before the age of 4 (90%). Children were diagnosed later were also more likely to take psychotropic medications (Zuckerman et al. 2017). Further, as time-to-diagnosis increased for those children with ASD with functional delays, Zuckerman et al. (2017) found that these children were less likely to receive school-based therapies. Finally, Zuckerman et al. (2017) expressed concern that nearly a quarter of elementary schoolaged children with ASD did not receive schoolbased services. Of those who have been accessing special education services in the schools, there has been a recent trend of placing students with ASD in inclusive classrooms in the general education setting (Lauderdale-Littin et al. 2013; Martins et al. 2014). For example, the United States Department of Education (USDOE 2012) has reported that between 2004 and 2010, the number of students with ASD attending public schools has doubled. In addition, there was a 244% increase of students with ASD in full-inclusion classrooms from 1992 to 2006 (USDOE 2010). Researchers have found that students with higher cognitive and social
Special Education Service Utilization in ASD
abilities were more apt to be placed in inclusive settings (Lyons et al. 2011; White et al. 2007). Conversely, students with ASD with lower IQs, lower adaptive behavior skills, and higher ASD symptoms were likely to be placed in more restrictive special education settings (Eaves and Ho 1997; White et al. 2007). Supporting the trend toward inclusion, recent studies have reported a positive impact on the level of social engagement of students with ASD in the inclusion setting (Sansosti and Sansosti 2012). For example, studies examining subgroups of children with ASD without ID have demonstrated that they engage in increased social interaction in the mainstream classroom (Dahle 2003), have larger networks of friends, or are included in peer activities at the same rate as their same-age peers without disabilities (Chamberlain et al. 2007). Others note that an inclusive placement alone may be insufficient to fulfill the social and academic needs of children with ASD (Leyser and Kirk 2004; Sansosti 2010). In addition, special education placement only describes a minute portion of special education service use. Therefore, further examination of services used within the parameters of special education is warranted. In a study of 101 children with ASD without ID, White et al. (2007) examined relationships between child demographic factors and special education, and found that children with ASD without ID with lower cognitive and language abilities were more likely to receive special education services. In general, service use was highest in the third and fourth grades, and lowest in eighth grade. Further, White et al. (2007) found that the children with ASD’s level of social ability was not related to special education service use. This is concerning, given the deleterious effect of children with ASD’s social deficits on their performance in the classroom setting (Thomeer et al. 2017), especially with the reported overarching suppression of social ability among children with ASD (White et al. 2007). Finally, the majority of students with ASD in special education received speech therapy, followed by physical and/or occupational therapies (White et al. 2007). Social skills instruction was much less frequently reported for children in the younger
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elementary grades, with none of the parents of older children (grades 7 and 8) reporting that their children received this service. Given the potential benefits of social skills instruction in the schools (Thomeer et al. 2017), services that would directly ameliorate social deficits continue to be an area of unmet need (White et al. 2007). Wei et al. (2014) examined three national datasets of functionally heterogeneous students with ASD for patterns of special education services use. Wei et al. (2014) found that students with ASD and higher levels of ASD symptoms were more likely to receive more special education services, and that service use generally declined with increasing age. In addition, they noted how educational priorities may shift as a student ages, with age moderating how the students experience special education services and supports. For elementary school students, the two most common therapies were speech and language therapy (84.6%), followed by occupational therapy (50.0%). In contrast, a relatively lower percentage of students received a behavior management program (43.8%), service coordination (39.5%), adaptive physical education (39.1%), learning strategies/study skills support (29.5%), or social work services (6.4%). Secondary school children with ASD also followed a similar pattern, with speech services being the most frequently accessed service for children with ASD in special education. While these service usage results reported by Wei et al. (2014) are consistent with prior research (White et al. 2007), the authors also noted the discrepancy between the widespread social and behavioral impairments experienced by students with ASD and lack of social/behavioral services delivered in schools. Therefore, this represents an area of unmet need in the special education system (Wei et al. 2014; White et al. 2007). In addition, the low percentage of elementary school children with ASD accessing learning supports reported by Wei et al. (2014) further confirmed the assertion that these services are underemphasized in the IEPs developed for students with ASD (White et al. 2007). Finally, Wei et al. (2014) found that 3.4% of elementary/middle school and 6.7% of high school children with ASD did not receive
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any of the three services (i.e., speech and language therapy, occupational therapy, behavior management program) thought to directly address ASD symptoms. McDonald et al. (2019) examined the service use of a sample of 89 children (ages 6–11 years) diagnosed with ASD without ID. The children’s IEPs were evaluated for special education classification, type of service received (e.g., speech therapy, occupational therapy, physical therapy, individualized behavior support plan, social skills instruction, counseling, and 1:1 aide), duration of service, and type of placement (i.e., full inclusion, partial inclusion, and self-contained classroom). For the purpose of their study, McDonald et al. (2019) defined full inclusion as the placement for a child who remained in the mainstream, general education setting for the entirety of the school day. Partial inclusion was defined as the placement for children who attended general education for the majority of their school day, but less than 100% of all subjects were taught in the general education setting. Students in this placement typically were removed from the general education setting briefly for more intensive instruction in a specific subject area, such as math and/or reading. Finally, a self-contained classroom was defined as the placement for children who primarily remained in this more restrictive special education classroom (with a smaller teacher-to-student ratio) and only were included into the general education setting for a maximum of two academic classes. McDonald et al. (2019) reported that partial inclusion was the most frequent placement (40%), followed by full inclusion (34%) and selfcontained classroom settings (26%). Over half of the participants (n ¼ 50, 56%) were classified under Autism, followed by OHI (27%). In addition, the researchers found that children classified under Autism were about twice as likely to receive 1:1 aide services as children with ASD who met other special education classifications. In regards to the whole sample, the most frequently provided services included speech therapy (71%) and occupational therapy (56%) in line with prior research (Wei et al. 2014; White et al. 2007). However, unlike past research, counseling services were the
Special Education Service Utilization in ASD
third reported most frequent service (44%). This is noteworthy, as evidence for the use of cognitivebehavioral strategies with children with ASD without ID is increasing (Ho et al. 2018), although counseling has not been widely examined in the special education service use literature. In contrast with these top three services accessed, social skills instruction and behavior support plans were the least common (17% and 16%, respectively) services rendered. Although not specifically examining counseling, in a rare study looking at mental health services in the schools, Narendorf et al. (2011) examined a sample of adolescents with ASD (n ¼ 920), drawn from the National Longitudinal Transition Study-2 (NLTS2) who qualified for special education services via an educational classification of Autism. Of these youth, 49% of the sample reported using a mental health service in past, with 47% of these youth accessing mental health services through the special education system in schools. Factors that increased the odds of accessing these services via the school system included being African American, or having a household income below $50,000 (in comparison to families having a household income greater than $70,000; Narendorf et al. 2011). Parent involvement in the IEP process, as well as child characteristics of displaying poor social skills, experiencing bullying, or perpetrating bullying were associated with accessing mental health services, but not associated with accessing these services in schools.
Future Directions Research in the area of special education service use by children with ASD and its relationships with child and placement variables continues to be limited (McDonald et al. 2019; Wei et al. 2014; White et al. 2007). Further research is needed that examines what special education services children with ASD receive, as well as the amounts and types of special education services accessed and their relationship to the children’s demographic variables (e.g., age, IQ, level of ASD symptoms,
Special Education Service Utilization in ASD
communication ability, and other adaptive behaviors; Lauderdale-Littin et al. 2013; McDonald et al. 2019; White et al. 2007). Documenting special education service use by children with ASD is important as it may inform the training needed for school personnel who work with children with ASD (McDonald et al. 2019; Wei et al. 2014). In addition, the documentation of special education service use may be a resource used to inform families as to what special education services a child with ASD typically receives (or may need), and what they might be inclined to advocate for (McDonald et al. 2019; Wei et al. 2014). For example, Thomas et al. (2007) found that parents of children with ASD report that not only is speech therapy one of the most widelyused services in special education, but it is considered by many parents as one of the best services (followed by occupational therapy) their child receives in the school. While speech services are often necessary (especially given the documentation of lower communication skills in this population), it is essential to understand the child demographic and behavioral characteristics associated with special education placement and service use decisions (Lauderdale-Littin et al. 2013) and inform families, school personnel, and other stakeholders of these relationships. Given the disparities between level of social/behavior services provided in schools and the social/behavioral needs of children with ASD highlighted by multiple researchers (McDonald et al. 2019; White et al. 2007; Wei et al. 2014), families and school staff of students with ASD should advocate for appropriate social and behavioral interventions in schools. In addition, further examination of how children with ASD are or are not succeeding socially in the various special education placements is warranted (White et al. 2007). This research should also focus its investigation on the potential existence of a social benefit purported to be received from placement in the general education setting (White et al. 2007). Future research also needs to focus on understanding the barriers to accessing special education services throughout students with ASD’s school tenure (Wei et al. 2014). Most importantly,
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more examination of the reasons behind the decline of speech and occupational therapies and behavioral services needs to be conducted, as this also indicates that accessing special education services may become more difficult as children with ASD age (Wei et al. 2014). In addition, barriers to minorities accessing special education services needs to be examined, as research has mixed results on the success these subgroups have had in receipt of services, which is typically below their Caucasian peers (Wei et al. 2014), aside from the propensity to access mental health care in the school setting at higher rates (Narendorf et al. 2011). Finally, further examination of the implementation of counseling and other mental health services in the schools should be studied, in order to gain more information as to the implementation of and access to these services across the various age ranges.
See Also ▶ School-Clinic Care Coordination for Youth with ASD ▶ Service Use
References and Reading American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Arlington: American Psychiatric Association. Ashburnere, J., Ziviani, J., & Rodger, S. (2010). Surviving in the mainstream: Capacity of children with autism spectrum disorders to perform academically and regulate their emotions and behavior at school. Research in Autism Spectrum Disorders 4(1), 18–27 Center for Disease Control and Prevention (CDC). (2014). Prevalence of autism spectrum disorder among children aged 8 years – Autism and developmental disabilities monitoring network, 11 states, United States, 2010. MMWR, 63(SS-2), 1–21. Center for Disease Control and Prevention (CDC). (2016). Prevalence and characteristics of autism spectrum disorder among children aged 8 years – Autism and developmental disabilities monitoring network, 11 states, United States, 2012. MMWR, 65(SS-3), 1–23. Chamberlain, B., Kasari, C., & Rotherman-Fuller, E. (2007). Involvement or isolation? The social networks of children with autism in regular classrooms.
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4546 Journal of Autism and Developmental Disorders, 37, 230–242. https://doi.org/10.1007/s10803-006-0164-4. Dahle, K. B. (2003). Services to include young children with autism in the general classroom. Early Childhood Education Journal, 31, 65–70. https://doi.org/10.1023/ A:1025193020415. Eaves, L. C., & Ho, H. H. (1997). School placement and academic achievement in children with autism spectrum disorder. Journal of Developmental and Physical Disabilities, 9, 277–291. https://doi.org/10.1023/ A:1024944226971. Ho, B. P. V., Stephenson, J., & Carter, M. (2018). Cognitive-behavioral approaches for children with autism spectrum disorder: A trend analysis. Research in Autism Spectrum Disorders, 45, 27–41. https://doi. org/10.1016/j.rasd.2017.10.003. Kang-Yi, C. D., Locke, J., Marcus, S. C., Hadley, T. R., & Mandell, D. S. (2016). School-based behavioral health service use and expenditures for children with autism and children with other disorders. Psychiatric Services, 67(1), 101–106. https://doi.org/10.1176/appi.ps.201400505. Lauderdale-Littin, S., Howell, E., & Blacher, J. (2013). Educational placement for children with autism spectrum disorders in public and nonpublic school settings; The impact of social skills and behavior problems. Education and Training in Autism and Developmental Disabilities, 48(4), 469–478. Retrieved from: http://daddcec.org/ Portals/0/CEC/Autism_Disabilities/Research/Publica tions/Education_Training_Development_Disabilities/ Full_Journals/ETADD_48(4)_469-478.pdf Leyser, Y., & Kirk, R. (2004). Evaluating inclusion: An examination of parental views and factors influencing their perspectives. International Journal of Disability, Development, and Education, 51, 271–285. https://doi. org/10.1080/1034912042000259233. Lyons, J., Cappadocia, M. C., & Weiss, J. A. (2011). Social characteristics of students with autism spectrum disorders across classroom settings. Journal on Developmental Disorders, 17, 77–82. Retrieved from: http:// hdl.handle.net/10315/33012 Martins, M., Harris, S. L., & Handelman, J. S. (2014). Support inclusive education. In F. R. Volkmar, S. J. Rogers, R. Paul, & K. A. Pelfrey (Eds.), Handbook of autism and pervasive developmental disorders. Vol. 2: Assessment, interventions, and policy (4th ed., pp. 858–870). New York: Wiley. McDonald, C. A., Donnelly, J. P., Feldman-Alguire, A. L., Rodgers, J. D., Lopata, C., & Thomeer, M. L. (2019). Special education service use by children with autism spectrum disorder. Journal of Autism and Developmental Disabilities, 49, 2437–2446. https://doi.org/10. 1007/s10803-019-03997-z. Narendorf, S. C., Shattuck, P. T., & Sterzing, P. R. (2011). Mental health service use among adolescents with an autism spectrum disorder. Psychiatric Services, 62(8), 975–978. https://doi.org/10.1176/appi.ps.62.8.975. Rubenstein, E., Daniels, J., Schieve, L. A., Christensen, D. L., Van Naarden Braun, K., Rice, C. E., Bakian,
Special Education Service Utilization in ASD A. V., Durkin, M. S., Rosenburg, S. A., Kirby, R. S., & Li-Ching, L. (2018). Trends in special education eligibility among children with autism spectrum disorder, 2002-2010. Public Health Reports, 133(1), 85–92. https://doi.org/10.1177/0033354917739582. Sansosti, J. M., & Sansosti, F. J. (2012). Inclusion for students with high-functioning autism spectrum disorders: Definitions and decision making. Psychology in the Schools, 49(10), 917–931. https://doi.org/10.1002/ pits. Sansosti, F. J. (2010). Teaching social skills to children with autism spectrum disorders using tiers of support: A guide for school-based practitioners. Psychology in the Schools, 47,(3), 257–281. https://doi.org/10.1002/ pits.20469. Thomas, K. C., Morrissey, J. P., & McLaurin, C. (2007). Use of autism-related services by families and children. Journal of Autism and Developmental Disorders, 37, 818–829. https://doi.org/10.1007/s10803006-0208-9. Thomeer, M. L., McDonald, C. A., Rodgers, J. D., & Lopata, C. (2017). High-functioning autism spectrum disorder: A framework for evidence-based practice. School Mental Health. https://doi.org/10.1007/ s12310-017-9236-1. Toomey, J. A., Lopata, C., Volker, M. A., & Thomeer, M. L. (2009). Comprehensive intervention for highfunctioning autism spectrum disorders: An in-depth case study. In M. T. Burton (Ed.), Special education in the 21st century (pp. 95–118). Hauppauge: Nova Science. United States Department of Education. (2010). 32nd Annual report to Congress on the implementation of the individuals with Disabilities Education Act. Retrieved from https://www2.ed.gov/about/reports/ annual/osep/2010/parts-b-c/32nd-idea-arc.pdf United States Department of Education. (2017). 39th Annual report to Congress on the implementation of the individuals with Disabilities Education Act. Retrieved from https://www2.ed.gov/about/reports/ annual/osep/2017/parts-b-c/39th-arc-for-idea.pdf United States Department of Education, National Center for Education Statistics. (2012). Digest of Education Statistics, 2011 (NCES 2012-001). Chapter 2. Wei, W., Wagner, M., Christiano, E. R. A., Shattuck, P., & Yu, J. W. (2014). Special education services received by students with autism spectrum disorders from preschool through high school. The Journal of Special Education, 48(3), 167–179. https://doi.org/10.1177/ 0022466913483576. White, S. W., Scahill, L., Klin, A., Koenig, K., & Volkmar, F. R. (2007). Educational placements and service use patterns of individuals with autism spectrum disorders. Journal of Autism and Developmental Disabilities, 37, 1403–1412. https://doi.org/10.1007/s10803-006-0281-0. Yeargin-Allsop, M., Rice, C., Karapurkar, T., Doemberg, N., Boyle, C., & Murphy, C. (2003). Prevalence of autism in a U.S. metropolitan area. Journal of the
Special Needs Planning (SNP) American Medical Association, 289, 49–55. https://doi. org/10.1001/jama.289.1.49. Zuckerman, K., Lindly, O. J., & Chavez, A. E. (2017). Timeliness of autism spectrum disorder diagnosis and use of services among U. S. Elementary school-aged children. Psychiatric Services, 68(1), 33–40. https:// doi.org/10.1176/appi.ps.201500549.
Special Needs Jacqueline Kelleher Education, Sacred Heart University Isabelle Farrington School of Education, Southern Connecticut State University, Fairfield, CT, USA
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References and Reading Klin, A. (2006). Autism and Asperger’s syndrome: An overview. Retrieved 15 Jan 2011 from http://www. scielo.br/scielo.php?script¼sci_arttext&pid¼S151644462006000500002&lng¼en&nrm¼iso&tlng¼en National Academy of Sciences and Committee on Educational Interventions for Children with Autism. (2001). Educating children with autism. Washington, DC: National Academy Press. Volkmar, F. R., & Wiesner, L. A. (2009). A practical guide to autism: What every parent, family member, and teacher needs to know. Hoboken: Wiley. Volkmar, F. R., Paul, R., Klin, A., & Cohen, D. (2005). The handbook of autism and pervasive developmental disorders (3rd ed.). Hoboken: Wiley.
Synonyms
Special Needs Planning (SNP)
Accommodations; Modifications; Special education needs
Annemarie M. Kelly and Christina N. MarsackTopolewski College of Health and Human Services, Eastern Michigan University, Ypsilanti, MI, USA
Definition Special needs is a term often used to describe the needs of individuals with disabilities or who are at risk of developing disabilities and their unique needs as a result of deficits related to a disability or disorder, who require or may require in the future special services or treatment. Needs typically refer to special care, services, or accommodations necessary to support the individual or those supporting the individual with a disability or disorder. Special needs most often refer to intervention, curricula, behavior management, and transition planning; however, individual accommodations and modifications related to deficit of the disability are addressed under this term.
Definition Special needs planning (SNP) is a process of collecting, coordinating, and consolidating legal, financial, health, and personal wellness information concerning an individual with disabilities to ensure that the individual receives support in the future. SNP activities can help to improve an individual’s quality of life and provide resources for goods and services that are not covered under government benefits programs or private health insurance plans. The overarching goal of SNP activities is to create a well-informed plan for an individual’s future care and resources.
Principles of Special Needs Planning See Also ▶ Accommodations ▶ Modifications ▶ Transition Planning
SNP activities can assist individuals who have autism spectrum disorder (ASD) in making financial resources available to pay for current and future needs that are not covered by government
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Special Needs Planning (SNP)
Special Needs Planning (SNP), Table 1 Examples of SNP benefits and instruments Potential benefit Ensuring funds are available to a beneficiary without jeopardizing eligibility for government benefits Ensuring assigned individuals available to assist the beneficiary as needed
Ensuring that funds are well managed to meet the beneficiary’s needs Document preferences for future transition-of-care planning to prepare for a time when a beneficiary’s current caregivers can no longer provide care
benefits or private health insurance (MarsackTopolewski and Church 2019; Thomas et al. 2016; Parish et al. 2015; Horlin et al. 2014; and Montes and Halterman 2008; see also Krakovich et al. (2016) and Cidav et al. 2012). The person who receives the benefit of SNP activities is often referred to as the “beneficiary.” SNP has the potential to offer multiple forms of assistance and security to a beneficiary. See Table 1, Examples of SNP benefits and instruments. The individual beneficiary should be involved in the planning process to the greatest extent possible (National Council on Disability 2018). Other individuals involved in the SNP process often include family members, friends, and legal guardians or conservators (Trentacosta et al. 2018). There are three fundamental phases of SNP activities: (A) Identifying costs for essential needs; (B) Identifying costs for preferred goods and services; and (C) strategizing with guidance from legal and finance professionals. See Table 2, Twelve key steps in the SNP process. The initial steps involve confirming information about the beneficiary. Information collection for the SNP process is often conducted by a close caregiver of the beneficiary. First, the caregivers must document which short and long-term expenses are absolutely essential, and which are merely desirable. The time spent collecting detailed information about the beneficiary’s desires and needs will allow legal and financial planners to offer personalized guidance for SNP strategies (Russell and Grant 2010). With clearly organized information about an individual with disabilities’ unique
Examples of instruments that provide assistance Special needs trust (SNT) and Achieving a Better Life Experience account (ABLE or 529A account) Decision-making support agreement, guardianship, conservatorship, letter of intent (LOI), Power of Attorney for money and property, and Power of Attorney for healthcare Legal or financial agent agreement, decision-making support agreement, guardianship, and conservatorship Letter of intent (LOI), Power of Attorney for healthcare, and advance directive
circumstances, professional advisors are better able to provide well-informed SNP guidance. To gather the necessary information for SNP, individuals with disabilities and/or their caregivers should outline short- and long-term goals for the beneficiary’s living, healthcare, wellness, educational, vocational, and emergency needs (Tencza and Forsythe 2019; Lauderdale and Huston 2012). As a next step, the beneficiary or caregiver must create a list of all of the goods and services that are necessary to meet the beneficiary’s goals. Each item on the special needs planning list should correlate to an “out of pocket” cost – a good/service that is not covered under the beneficiary’s government benefits programs and/or private health insurance. Individuals who are working to collect details to inform a special needs plan must consider that SNP information comes from a variety of sources that include many professional disciplines. One of the preferred means to create a thorough, wellinformed summary of the beneficiary’s needs is to seek information about the beneficiary’s needs from multi-disciplinary groups of experts, particularly those in health and human services fields. See Table 3, Health and human services professionals who may assist during the SNP information collection phase. Many technical SNP services are performed by an experienced attorney, financial planner, and/or tax advisor (see generally, American Bar Association Diversity and Inclusion Center Commission on Disability Rights Resources 2019). Though many for-profit and non-profit businesses offer
Special Needs Planning (SNP)
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Special Needs Planning (SNP), Table 2 Twelve key steps in the SNP process A. Identify costs for essential needs not covered by government programs and private health insurance Step 1 What are the beneficiary’s short- and long-term goals in the following areas: healthcare, personal wellness, education, and vocation? Step 2 Which essential goods or services are required for the beneficiary to satisfy the goals identified in Step 1? Step 3 Which of the goods or services identified in Step 2 are not covered by government benefits programs and/or private health insurance? Step 4 What are the out-of-pocket costs for goods or services identified in Step 3 (per item/session, monthly, and annually)? Step 5 Of the goods and services identified in Step 4, which are needed for a limited amount of time and which are permanently needed? Can any of the beneficiary’s healthcare providers provide additional information to help answer this question? B. Identify costs for preferred goods and services not covered by government programs and private health insurance Step 6 Considering the goals identified in Step 1, which goods or services are desirable/preferred for the beneficiary? Step 7 Which of the goods or services identified in Step 6 are not covered by government benefits programs and/or private health insurance? Step 8 What is the out-of-pocket cost for goods or services identified in Step 7 (per item/session, monthly, and annually)? Step 9 Of the goods and services identified in Step 7, which are needed for a limited amount of time and which are permanently needed? Can any of the beneficiary’s healthcare providers provide additional information to help answer this question? C. Strategize with guidance from legal and finance professionals Step Which attorney can best assist in writing and implementing the legal instruments for the special needs plan 10 that is based on the information collected in in Steps 1–9? What is the attorney’s guidance on how to budget for emergency expenses in addition to the goods and services identified in Steps 1–9? Step Which financial planner can best assist in analyzing wealth management and tax reporting strategies to 11 maximize the funds for the special needs plan? Step Review the special needs plan annually and update it to reflect any changes in the beneficiary’s needs and 12 circumstances.
generalized SNP forms online, it is a strongly recommended best practice to retain an attorney to write and implement legal instruments that are tailored to the beneficiary’s unique needs. Legal instruments for SNP include, but are not limited to: special needs trusts (SNTs), Achieving a Better Life Experience accounts (ABLE or 529A accounts), Last Will and Testament documents, guardianships, conservatorships, letters of intent (LOIs), Power of Attorney for money and property; Power of Attorney for healthcare; money management services; and supported decisionmaker agreements (Andersen and Gary 2018). Because a beneficiary’s preferences, health, and financial circumstances can change over time, it is a recommended best practice for caregivers to review a beneficiary’s special needs plan annually and to work with legal and financial advisors to make updates as needed.
In the United States, the SNP process should not be confused with the term “Medicare Special Needs Plans (“Medicare Advantage”), which are tailored health coverage plans that assist people with specific health conditions in accessing provider specialists, particularly drug formularies, and other unique benefits.
Common Barriers to Special Needs Planning Over the past several years, multiple qualitative studies have found that a significant proportion of individuals with disabilities and their families are unready or unwilling to make future plans (Walker and Hutchinson 2018). Burke et al. (2018) summarize this concern as follows:
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4550 Special Needs Planning (SNP), Table 3 Health and human services professionals who may assist during the SNP information collection phase Aging studies expert Art therapist Music therapist Nurse practitioner, registered nurse, and/or home health nurse Occupational therapy (OT) practitioner Physiatrist Physical therapy (PT) practitioner Physician’s assistant Primary physician Psychologist Recreational therapist Registered dietitian Social worker Special education expert Speech pathologist
Special Needs Planning (SNP), Table 4 Commonly cited reasons why individuals do not engage in effective SNP Lack of available services Financial challenges Reluctance of family members Lack of time The emotional nature of future planning Inertia and/or disinterest in SNP issues Lack of family members to be caregivers Perceived lack of need due to the existence of current caregivers Lack of awareness and understanding about SNP processes Lack of confidence in available housing options Lack of confidence in available options for retaining SNP professionals Existence of mutually supportive relationships Opinion that the beneficiary does not require any goods or services that are not covered by public benefits programs
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Researchers report numerous barriers to future planning using SNP tools (Burke et al. 2018; and Bowey and McGlaughlin 2007). See Table 4, commonly cited reasons why individuals do not engage in effective SNP. Walker and Hutchinson (2019) have identified three overarching themes that arise in SNP scenarios: (1) external barriers to planning involving reservations about available services; (2) internal barriers within family and caregiver groups that prevent planning (e.g., mutual dependency and sense of helplessness); and (3) existence of diverse ideas and misconceptions about planning and ways of managing the future. Though a range of external and internal factors pose as barriers to SNP processes, a growing body of evidences suggests positive outcomes for individual beneficiaries and families once they engage in a future planning process (Brennan et al. 2020; Weiss et al. 2014; Heller and Kramer 2009).
See Also ▶ Achieving a Better Life Experience Savings Account (ABLE Savings Account, ABLE Account, 529A Savings Plan, and 529A Account) ▶ Conservatorship (Full Conservatorship and Limited Conservatorship) ▶ Guardianship ▶ Letter of Intent ▶ Power of Attorney (Financial and Property Management) ▶ Power of Attorney for Healthcare (Durable Healthcare Power of Attorney and Medical Power of Attorney) ▶ Social Security Amendments of 1965 (or “Medicare Act of 1965” and/or the “Medicaid Act of 1965”) ▶ Special Needs Trust (SNT)
References and Reading Although many families want to engage in future planning, families often treat future planning as more aspirational than definitive. . .Parents often do not create actual plans; however, when parents create plans, their wishes for their offspring are more likely to be fulfilled.
American Bar Association Diversity and Inclusion Center Commission on Disability Rights Resources. (2019). State Bars: Disability Entities Directory. Retrieved from https://www.americanbar.org/groups/diversity/ disabilityrights/resources/state_bar_disability_entities/
Special Needs Trust (SNT) Andersen, R., & Gary, S. (2018). Understanding trusts and estates (6th ed.). Durham: Academic. Bowey, L., & McGlaughlin, A. (2007). Older carers of adults with a learning disability confront the future: Issues and preferences in planning. British Journal of Social Welfare, 37(1), 39–54. https://doi.org/10.1093/ bjsw/bcl052. Brennan, D., McCausland, D., O’Donovan, M. A., Eustace-Cook, J., McCallion, P., & McCarron, M. (2020). Approaches to and outcomes of future planning for family carers of adults with an intellectual disability: A systematic review. Journal of Applied Research in Intellectual Disabilities. https://doi.org/ 10.1111/jar.12742. Burke, M., Arnold, C., & Owen, A. (2018). Identifying the correlates and barriers of future planning among parents of individuals with intellectual and developmental disabilities. Intellectual and Developmental Disabilities, 56(2), 90–100. https://doi.org/10.1352/19349556-56.2.90. Cidav, Z., Marcus, S. C., & Mandell, D. S. (2012). Implications of childhood autism for parental employment and earnings. Pediatrics, 129(4), 617–623. https://doi. org/10.1542/peds.2011-2700. Heller, T., & Kramer, J. (2009). Involvement of adult siblings of persons with developmental disabilities in future planning. Intellectual and Developmental Disabilities, 47(3), 208–219. https://doi.org/10.1352/ 1934-9556-47.3.208. Horlin, C., Falkmer, M., Parsons, R., Albrecht, M. A., & Falkmer, T. (2014). The cost of autism spectrum disorders. PLoS One, 9(9), e106552. https://doi.org/10. 1371/journal.pone.0106552. Krakovich, T. M., McGrew, J. H., Yu, Y., & Ruble, L. A. (2016). Stress in parents of children with autism spectrum disorder: An exploration of demands and resources. Journal of Autism and Developmental Disorders, 46(6), 2042–2053. https://doi.org/10.1007/ s10803-016-2728-2. Lauderdale, M., & Huston, S. (2012). Financial therapy and planning for families with special needs children. Journal of Financial Therapy, 3(1), 62–81. https://doi. org/10.4148/jft.v3i1.1494. Marsack-Topolewski, C. N., & Church, H. L. (2019). Impact of caregiver burden on quality of life for parents of adult children with autism spectrum disorder. American Journal on Intellectual and Developmental Disabilities, 124(2), 145–156. https://doi.org/10.1352/ 1944-7558-124.2.145. Montes, G., & Halterman, J. S. (2008). Association of childhood autism spectrum disorders and loss of family income. Pediatrics, 121(4), e821–e826. https://doi.org/ 10.1542/peds.2007-1594. National Council on Disability. (2018). Beyond guardianship: Toward alternatives that promote greater selfdetermination. Retrieved from https://ncd.gov/publica tions/2018/beyond-guardianship-toward-alternatives Parish, S. L., Thomas, K. C., Williams, C. S., & Crossman, M. K. (2015). Autism and families’ financial burden: The association with health insurance coverage.
4551 American Journal on Intellectual and Developmental Disabilities, 120(2), 166–175. https://doi.org/10.1352/ 1944-7558-120.2.166. Russell, L. M., & Grant, A. (2010). Planning for the future: Give your child with a disability the gift of a safe and happy life (7th ed.). Palatine, Illinois: Planning For The Future. Tencza, M., & Forsythe, L. (2019). Transition-of-care planning: Preparing for the future care of the individual with intellectual and developmental disabilities. Journal of Intellectual Disabilities. https://doi.org/10.1177/ 1744629519883453. Thomas, K. C., Williams, C. S., deJong, N., & Morrissey, J. P. (2016). Examination of parent insurance ratings, child expenditures, and financial burden among children with autism: A mismatch suggests new hypotheses to test. Pediatrics, 137(Supplement 2), S186–S195. https://doi.org/10.1542/peds.2015-2851Q. Trentacosta, C. J., Irwin, J. L., Crespo, L. M., & Beeghly, M. (2018). Financial hardship and parenting stress in families with young children with autism: Opportunities for preventive intervention. In Handbook of parent-implemented interventions for very young children with autism (pp. 79–91). Cham: Springer. Walker, R., & Hutchinson, C. (2018). Planning for the future among older parents of adult offspring with intellectual disability living at home and in the community: A systematic review of qualitative studies. Journal of Intellectual & Developmental Disability, 43(4), 453–462. https://doi.org/10.3109/13668250.2017. 1310823. Walker, R., & Hutchinson, C. (2019). Care-giving dynamics and futures planning among ageing parents of adult offspring with intellectual disability. Ageing and Society, 39(7), 1512–1527. https://doi.org/10.1017/ S0144686X18000144. Weiss, J. A., Wingsiong, A., & Lunsky, Y. (2014). Defining crisis in families of individuals with autism spectrum disorders. Autism, 18(8), 985–995. https://doi.org/10. 1177/1362361313508024.
Special Needs Trust (SNT) Annemarie M. Kelly and Christina N. MarsackTopolewski College of Health and Human Services, Eastern Michigan University, Ypsilanti, MI, USA
Definition A special needs trust (SNT) is a legal instrument established to hold money or property for the benefit of a person with disabilities. The recipient
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of SNT assets is commonly referred to as a “beneficiary.” An SNT allows a benefactor, often called a “grantor,” to give money or property to a beneficiary without disrupting the beneficiary’s eligibility for government benefit programs (U.S. Social Security Administration 2020a). Assets that are held in an SNT account for a beneficiary are managed by a trustee. Generally, SNT holdings are not considered as a source of income to a beneficiary. Specific legal requirements for creating, administering, and funding an SNT are set forth in federal statutes, state statutes, and administrative regulations from federal and state agencies (e.g., the Social Security Administration (SSA), Centers for Medicare and Medicaid Services (CMS), and Internal Revenue Service (IRS)).
Principles of Special Needs Trusts An SNT can be a useful special needs planning tool for individuals with autism spectrum disorder (ASD) and their caregivers to manage financial resources. When properly designed and administered in accordance with legal requirements, SNTs help people with disabilities to receive financial resources for their benefit while also qualifying to receive assistance from government programs like Medicare, Medicaid, and Social Security Incomes (SSI). For this reason, SNTs are sometimes referred to as “supplemental needs trusts.” Individuals with disabilities cannot receive benefits through Medicaid and other government programs if they own significant financial resources that are not sheltered in an SNT or other special needs planning instrument. SNT legal doctrine considers state and federal government benefits as essential healthcare resources for individuals with disabilities. As a whole, SNT laws safeguard qualified individuals’ access to government benefits so that people can access comprehensive support networks and receive the care that they need. Many people with complex healthcare requirements rely on goods and services from government benefits to complete their activities of daily living (“ADLs” or basic selfcare tasks) and instrumental activities of daily
Special Needs Trust (SNT)
living (“IADLs” or tasks to organize, manage, and interact with one’s environment). An SNT is sometimes employed in instances where an individual has an influx of financial resources and wishes to maintain government benefits (e.g., Lewis v. Alexander, 685 F.3d 325, 332 (3d Cir. 2012)). Eligibility for state and federal government program benefits is based on Modified Adjusted Gross Income (MAGI). Assets held in SNTs for a beneficiary do not typically count toward the government’s MAGI calculations (U.S. Centers for Medicare & Medicaid Services 2020a). Before the creation of SNT laws, individuals with disabilities were forced to forego financial gains that placed them above the legal threshold for personal income so that they could retain their access to government benefits. Expenditures from an SNT are often used to pay for a range of goods and services for the beneficiary that are not covered by public benefits, including certain caregiving and personal wellness costs. Allowances from an SNT can help a beneficiary to pay for short- and long-term costs that enhance his or her quality of life (Jackins et al. 2019). As a general rule in most jurisdictions, SNT funds can only be used for the special and supplemental needs of a person with disabilities, and not their day-to-day living expenses such as food and shelter (e.g., New York Consolidated Laws Estates, Powers and Trusts Law § 7-1.12 (2020)). Because certain types of purchases cannot be made from SNT funds under state and federal laws, a trustee must ensure that all spending from an SNT satisfies all applicable legal requirements. Individuals who are interested in using an SNT should take care not to confuse an SNT document with a Last Will and Testament, sometimes called a “legal Will” (Andersen and Gary 2018). Both are legal documents that can convey money and property to a loved one; however, they differ in several respects (Wick 2019). In certain cases, if a grantor leaves his or her assets directly to a beneficiary through a Will and without an SNT or other special needs planning tool, the Will’s bequest could render the beneficiary ineligible for government sponsored-benefits. Assets in a Will are governed by probate law processes. All distributions from a Will must be reviewed by a probate
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court judge to confirm that the Will is valid and any bequests were not induced by fraud. Because they move through the court system, any asset distribution to a beneficiary in a Will becomes part of public record (Feder and Sitkoff 2016). As a general rule, a properly constructed SNT is not subject to the probate process and will not be deemed as income that could jeopardize the beneficiary’s eligibility for government benefits.
Creation of Special Needs Trusts Three key parties are required to create an SNT: (1) grantor (the provider of trust assets and often parents, family members, or friends of the trust beneficiary), (2) trustee (the manager of trust assets), and (3) beneficiary (recipient of trust assets) (Table 1). The terms of an SNT are confirmed in a trust document, which constitutes a binding legal contract between a grantor and trustee (Wilcenski and Pleat 2020, April). As a
recommended best practice, an SNT document should always be written by a qualified attorney. Ideally, grantors and beneficiaries (including beneficiary guardians and conservators) should analyze specific legal, investment, and tax strategies in detail with both a tax advisor/financial planner and their special needs planning attorney (Lauderdale and Huston 2012). Though several non-profit and for-profit organizations offer generalized forms for SNTs online, experts highly recommend obtaining personalized advice from experienced legal and financial professionals who can best tailor the SNT and other special needs planning tools to address particular needs and circumstances. As Hickman-Ladd (2019) explains, Clients with special needs or a client’s family member with special needs can range from a child to an older adult. The planning will differ from client to client and depend on where each individual with special needs is in his or her life. Therefore, an attorney will consider different planning tools for each client.
Special Needs Trust (SNT), Table 1 Three key parties involved in an SNT Party name Grantor
Description Provider of trust assets (money or property)
Trustee
Manager of assets
Beneficiary
Recipient of assets
Overview Grantors are often parents, family members, or friends of the trust beneficiary A grantor formally appoints a trustee for the SNT in a trust document By signing a trust document, grantors specify exactly how they would like the trustee to manage the SNT assets A signed trust document also specifies a grantor’s wishes regarding when the SNT assets should be distributed to the beneficiary – an SNT can be designed to take effect during the grantor’s lifetime or upon the grantor’s death Trustees have a legal duty to manage the SNT for the benefit of the beneficiary A trustee must avoid conflicts of interest between his or her personal finances and the beneficiary’s finances Because a host of distributions from an SNT are not permitted by state or federal law, trustees must ensure that all SNT expenditures are in accordance with legal requirements A trustee can be a specific individual or an organization such as a bank or other financial institution A beneficiary can receive funds from an SNT without losing his or her eligibility for government-sponsored benefits Depending on the wording of the trust document, a beneficiary can receive payments from an SNT immediately after the trust is created or be entitled to funds sometime in the future (e.g., upon the death of the grantor, the beneficiary’s 21st birthday, or other specified date)
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Special Needs Trust (SNT)
Because SNT laws are complex and situation specific, grantors and beneficiaries who engage in “Do-It-Yourself” SNT planning may render themselves liable for tax penalty fees for accidental noncompliance with federal and state requirements. By retaining professional assistance, SNT parties are more likely to avoid legal mistakes and see that their needs are comprehensively addressed. Moreover, professional guidance can help to ensure all short- and long-term goals for the beneficiary’s SNT finances account align with wills, guardianships/conservatorships, and other legal instruments that are specific to the beneficiary’s circumstances.
Types of Special Needs Trusts There are varying legal requirements for grantors, beneficiaries, and trustees under federal and state laws. Technical legal requirements concerning SNTs include how assets must be executed, funded, managed, allocated after a beneficiary’s
death, and reported to the U.S. Internal Revenue Service (IRS) for tax and income purposes (U.S. Internal Revenue Service 2020). The particular rules that apply in a given situation depend largely on the type of SNT that is created by the grantor and trustee for the beneficiary (Russell and Grant 2010). In the USA, there are three primary categories of SNT: self-funded trusts, third-party trusts, and pooled trusts (Table 2). Each type of SNT can be designed to hold assets without making the beneficiary ineligible for needs-based public benefits (U.S. Social Security Administration 2020b). SNT terminology sometimes varies among state jurisdictions and individual preferences. The terms “SNT” and “supplemental needs trusts” are often used interchangeably (Wilcenski and Pleat 2016). Nevertheless, individuals who are analyzing SNT issues should be aware that some state laws use the term “SNT” to refer only to selffunded and pooled trusts and define “supplemental needs trust” as solely third-party trusts (Table 3).
Special Needs Trust (SNT), Table 2 Three primary types of SNTs
Special needs trust type Self-funded trust
Third-party trust
Pooled trust
Summary SNT funded with the beneficiary’s own assets. Funds may include, but are not limited to: the beneficiary’s income from employment, inheritance, or a personal injury lawsuit settlement. This SNT can only be established for beneficiaries under age of 65 SNT established by a third-party and funded entirely with third-party assets. The beneficiary’s own assets cannot be used to fund this trust SNT created and administered by a nonprofit entity that creates individual trust for the beneficiary to use during his or her lifetime. A pooled trust contains the assets of several different individuals that are maintained in separate accounts for each beneficiary. Account funds are pooled together for investment purposes. This SNT can be established for an individual with disabilities any age
Occurrences after a beneficiary’s death Any remaining assets may be subject to Medicaid Estate Recovery (payment collection by the government)
Any remaining assets are freely transferable by the grantor (and not subject to Medicaid Estate Recovery)
Key federal statutes and social security administration regulations 42 U.S.C § 1396p(d) (4)(A); POMS SI 01120.200(B)(4)
42 U.S.C. § 1396p (d)(2)(A)(iii)–(iv); POMS SI 01120.200(B)(16) Depending on state law, some or all of 42 U.S.C. § 1396p the assets remaining in a pooled trust (d)(4)(C); POMS SI account may revert to the other 01120.200(B)(9) remaining beneficiaries or be kept by the charity trustee. In cases of firstparty pooled trusts, any remaining assets may be subject to Medicaid Estate Recovery. In cases of thirdparty pooled trusts, any remaining assets are freely transferable by the grantor
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Special Needs Trust (SNT), Table 3 Common terminology differences among SNT types Self-funded trust First-party trust OBRA-93 trust, OBRA’ 93 trust, or OBRA trust d4A trust Grantor trust
Pooled trust Pooled income trust OBRA-93 trust, OBRA’ 93 trust or OBRA trust d4C trust First-party pooled trust
Self-settled special needs trusts Self-funded payback trust Medicaid payback trust
Third-party pooled trust Pooled supplemental needs trust Pooled Medicaid payback trust
As its name suggests, a self-funded SNT (also known as a “first-party SNT,” “OBRA-93 trust,” or “d4A trust”) is financed with a beneficiary’s own assets (42 U.S.C. § 1396p(d)(4)(A) (2020)). Funds for a self-funded SNT may include, but are not limited to, funds from an inheritance, retirement plan, divorce settlement, life insurance policy, or personal injury lawsuit settlement. Under certain conditions, any assets that remain in a selffunded SNT after a Medicaid enrollee has passed away may be used to reimburse the government for Medicaid benefits provided to the beneficiary during his or her lifetime. This process is often called “Medicaid Estate Recovery” (U.S. Centers for Medicare and Medicaid Services 2020b). Under federal law, states may not recover from the estate of a deceased Medicaid enrollee who is survived by a spouse, child under age 21, or a child of any age who is blind or has a serious disability (42 U.S. Code § 1396p (2020)). In cases of pooled SNTs, a non-profit agency serves as the trustee and maintains a separate account for multiple individual beneficiaries (42 U.S.C. § 1396p(d)(4)(C) (2020)). This type of trust maintains one large account and individual beneficiaries have sub-trust accounts. With each individual trust account in one group, a single trustee or group of trustees can streamline investment and management tasks for the collective SNT pool. Weikel (2018) discusses the efficiencies of pooled trusts stating, The benefit of a pooled trust is that the sub-trust accounts are pooled together for investment and management purposes, making the cost of
Third-party trust Supplementary trust Pooled supplemental needs trust Third-party pooled trust Self-funded payback pooled trust
administration low (depending upon the choice of non-profit trustee). Furthermore, pooled trusts comprise professional trustees armed with more sophisticated knowledge of the complexities of SNT administration than the average family member of a disabled beneficiary serving as trustee.
Depending on which state law applies in a given situation, some or all of the assets remaining in the individual’s trust may revert to the other remaining beneficiaries or be kept by the charity trustee after one pooled trust beneficiary dies. Third-party SNTs require a third-party donor to contribute the assets for the benefit of someone else (i.e., not for themselves) (42 U.S.C. § 1396p (d)(2)(A)(iii)–(iv) (2020)). Typically, if a balance remains in this trust upon the beneficiary’s death, the remaining assets are freely transferable by the grantor and not subject to estate recovery. Thirdparty SNTs can be included in the grantor’s Will. If an SNT is created through a Will, the trust will not activate and allow the beneficiary to access trust funds until after the grantor’s death. In cases where multiple donors wish to fund an SNT, it is preferable to create an SNT that is independent of a Will.
Key Considerations for Grantors When Creating a Special Needs Trust During the process of creating a trust, grantors must consider seven primary issues: (1) Which assets (cash, stocks, bonds, real estate, and other property of value) will I put into the trust?;
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(2) Who will receive the assets as the trust’s beneficiary?; (3) What is the name of the individual or organization that will manage the assets as the trustee? (Wilcenski and Pleat 2019, March); (4) What are the stipulations for this trust – i.e., should the trust funds be available immediately for the beneficiary or become eligible for withdrawal at a later date? (e.g., Sullivan 2010); (5) In the event I can no longer oversee the trustee’s actions because of a serious disability or injury, should my grantor responsibilities for this SNT refer to my named Power of Attorney?; (6) Can I structure this SNT to coordinate with the beneficiary’s Achieving a Better Life Experience (“ABLE” or “529A”) account?; and (7) Which special needs planning attorney and financial professional can best assist me in creating and implementing this SNT? (Table 4).
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In many cases, an SNT is most advantageous for a beneficiary when combined with other special needs planning tools as part of a comprehensive long- and short-term budget plan (Rephan and Groshek 2016) (Table 5). For example, if an SNT beneficiary has an ABLE account, Hershey and Kelly (2019) recommend coordinating the use of relatively lower amounts in an ABLE account in tandem with greater savings in a third-party SNT. In this scenario, the ABLE account should be spent down entirely before the beneficiary’s death to avoid a loss of funds in the Medicaid Estate Recovery process. Funds remaining in the SNT can be freely transferred to another person or charity organization after the beneficiary’s passing (Hershey et al. 2015). All in all, a special needs planning attorney can help to ensure that the terms of a trust document are written correctly to meet the grantor’s goals.
Special Needs Trust (SNT), Table 4 Seven key considerations for grantors when creating an SNT Issue Type of trust
Content of the trust Trust funds recipient (beneficiary)
Trust funds manager (trustee)
Contract terms within the trust document (your ideas should be written by a special needs planning attorney using precise legal terms in the trust document)
Future plans for the trust with grantor’s Power of Attorney
Hiring a special needs planning attorney and financial professional
Key questions to consider Which SNT type will best assist the beneficiary? What are the potential challenges and benefits associated with this type? Which assets will I put into the trust (e.g., cash, stocks, bonds, real estate, and other property of value)? Who will receive the assets as the trust’s beneficiary? How will the SNT assets assist the beneficiary? How can I structure this SNT to best satisfy the beneficiary’s shortand long-term financial needs? What is the name of the individual or organization that will manage the assets as the trustee? Can the trustee objectively administer the trust in the best interest of the beneficiary and adhere to the terms of the trust contract? What are the stipulations for this trust? Which type of SNT should I create? Should the trust funds be available immediately for the beneficiary or become eligible for withdrawal at a later date? How often should trust payments to the beneficiary occur – i.e., monthly installments, annual deposits to the beneficiary’s checking, or Achieving a Better Life Experience (“ABLE” or “529A”) account? In the event I can no longer oversee the trustee’s actions because of a serious disability or injury, should my grantor responsibilities for this SNT refer to my named Power of Attorney? Is my Power of Attorney document up to date? Which special needs planning attorney and financial professional can best assist me in creating and implementing this SNT?
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Special Needs Trust (SNT), Table 5 Comparing Achieving a Better Life Experience (ABLE) accounts and SNTs Key considerations What does the setup process include?
Self-funded trust Attorney writes the trust document
Third-party trust Attorney writes the trust document
Pooled trust Grantor typically uses the non-profit organization’s trust format, however, the language should also be reviewed by a special needs planning attorney
Is an attorney often used for the setup?
Yes, to ensure all trust terms are properly written and the grantor’s wishes are satisfied
Yes, to ensure all trust terms are properly written and the grantor’s wishes are satisfied
Yes, to ensure all trust terms are properly written and the grantor’s wishes are satisfied
If there is a balance remaining after beneficiary’s death, are the assets subject to government payback (Medicaid estate recovery)? Yearly fees
Yes
No
Yes or no, depending on the structure of the trust and state law
Yes, typically trustee and/or investment advisor fees
Yes, typically trustee and/or investment advisor fees
Yes, typically trustee and/or investment advisor fees
Yearly contribution limits Maximum contribution limits
No
No
No
No
No
No
Legislative History of Special Needs Trusts In 1993, the Omnibus Budget Reconciliation Act (OBRA) created many of the foundations
ABLE account Each state offers online enrollment for beneficiaries (or their legal guardians) to create an account. State ABLE account programs encourage users to set up their own accounts, but also warn of the need for legal advice from an attorney and financial planner/tax advisor. Though state ABLE account programs encourage do-ityourself setup, personalized guidance from an attorney and financial planner/tax advisor is strongly recommended Yes
Yes, account maintenance fees (depending on the beneficiary’s investment selections and the state ABLE program’s administrative rules) Yes, subject to the federal gift tax limit Yes (the total amount varies between state ABLE program jurisdictions)
of current SNT law (Pub. L. No. 103–66 1993). OBRA confirmed that people with a disability to have a right to use an SNT to supplement their government benefits without jeopardizing their eligibility for means-tested
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government benefits. Under the original wording of the OBRA law, a self-funded SNT could only be established by the SNT beneficiary’s parent, grandparent, or guardian, or by a court. In 2016, the 21st Century Cures Act amended federal law to expand regulations for creating self-funded SNTs (Pub. Law 114–255 2016). Under this law, individuals with a disability who have legal capacity can establish and make deposits into their own SNT savings accounts (excluding third-party SNTs). A beneficiary’s legal capacity to create an SNT must be distinguished from his or her mental ability (Dudley and Edmonds 2018). Legal capacity embodies three pillars of individual legal rights: to have standing to bring and defend lawsuits; to enter into/execute legally binding contracts; and to appreciate the basic challenges and risks involved with an SNT (e.g., legal compliance and income tax reporting requirements). A legal milestone for disability rights, the Cures Act empowers individuals with disabilities who wish to save their own money using an SNT.
See Also ▶ Achieving a Better Life Experience Savings Account (ABLE Savings Account, ABLE Account, 529A Savings Plan, and 529A Account) ▶ Conservatorship (Full Conservatorship and Limited Conservatorship) ▶ Guardianship ▶ Power of Attorney (Financial and Property Management) ▶ Power of Attorney for Healthcare (Durable Healthcare Power of Attorney and Medical Power of Attorney) ▶ Social Security Amendments of 1965 (or “Medicare Act of 1965” and/or the “Medicaid Act of 1965”) ▶ Special Needs Planning (SNP)
Special Needs Trust (SNT)
References and Reading 21st Century Cures Act. Pub. Law 114–255, 130 Stat. 1033. (2016). Codified at 42 U.S.C. § 201 et seq. Retrieved from https://www.govinfo.gov/content/pkg/ PLAW-114publ255/pdf/PLAW-114publ255.pdf 42 U.S. Code § 1396p. (2020). Liens, adjustments and recoveries, and transfers of assets: Treatment of trust amounts. Retrieved from https://www.govinfo.gov/ content/pkg/USCODE-2010-title42/html/USCODE2010-title42-chap7-subchapXIX-sec1396p.htm Andersen, R., & Gary, S. (2018). Understanding trusts and estates (6th ed.). Durham: Carolina Academic Press. Dudley, K., & Edmonds, N. (2018). Psychology’s role in guardianship and conservatorship evaluations. In S. S. Bush & A. L. Heck (Eds.), Forensic geropsychology: Practice essentials (pp. 257–279). Washington, DC: American Psychological Association. https://doi.org/ 10.1037/0000082-013. Feder, D. J., & Sitkoff, R. H. (2016). Revocable trusts and incapacity planning: More than just a will substitute. Elder Law Journal, 24, 1–48. http://www.law. harvard.edu/programs/olin_center/papers/pdf/ Sitkoff_857.pdf Hershey, L., & Kelly, A. (2019). Using Roth conversions of legacy retirement plans to fund special needs planning. Journal of Financial Service Professionals, 73(2). 80–88. Retrieved from https://www.emich.edu/ cob/documents/2019_hershey_kelly.pdf Hershey, L., Abbey, B., Stanaland, T. B., & Owens, T. L. (2015). When to stretch and when not to stretch an inherited IRA: The special case of the special needs trust. Journal of Financial Service Professionals, 69(2), 59–66. Retrieved from https://www.emich.edu/ cob/documents/2015_when_to_stretch_and_when_ not_to_stretch_an_inherited_ira.pdf Hickman-Ladd, C. (2019). Special needs planning: Some things to consider. American Bar Association Voice of Experience. Retrieved from https://www.americanbar. org/groups/senior_lawyers/publications/voice_of_ experience/2019/october-2019/some-things-toconsider-in-special-needs-trust/ Jackins, B., Blank, R. S., & Shulman, K. W. (2019). Managing a special needs trust: A guide for trustees. Exeter: disAbilities Books Press, LLC. Lauderdale, M., & Huston, S. (2012). Financial therapy and planning for families with special needs children. Journal of Financial Therapy, 3(1), 62–81. https://doi. org/10.4148/jft.v3i1.1494. Lewis v. Alexander, 685 F.3d 325, 332. (3d Cir. 2012). Retrieved from https://caselaw.findlaw.com/us-3rdcircuit/1604090.html New York Consolidated Laws Estates, Powers and Trusts Law § 7-1.12. (2020). Supplemental needs trusts established for persons with severe and chronic or persistent disabilities. Retrieved from https://www. nysenate.gov/legislation/laws/EPT/7-1.12
Special Olympics Omnibus Budget Reconciliation Act of 1993 (OBRA), Pub. L. No. 103–66, 107 Stat. 312–685 Stat. 1025. (1993). Codified at 42 U.S.C. § 1396p. Retrieved from https://www.govinfo.gov/content/pkg/BILLS103hr2264enr/pdf/BILLS-103hr2264enr.pdf Rephan, D. A., & Groshek, J. (2016). ABLE act accounts: Achieving a better life experience for individuals with disabilities with tax-preferred savings (and the old reliable special and supplemental needs trust). Mitchell Hamline Law Review, 42, 963. Retrieved from https://open.mitchellhamline. e d u / c g i / v i e w c o n t e n t . c g i ? a r t i c l e ¼1 0 3 4 & context¼mhlr Russell, L. M., & Grant, A. (2010). Planning for the future: Give your child with a disability the gift of a safe and happy life (7th ed.). Palatine: Planning For The Future, Inc. Sullivan, P. (2010). Wealth matters: Exploring trusts for the disabled. The New York Times. Retrieved from https:// www.nytimes.com/2010/11/06/your-money/06wealth. html U.S. Centers for Medicare & Medicaid Services. (2020a). How to count income & household members: What to include as income. Retrieved from https://www. healthcare.gov/income-and-household-information/ income/#magi U.S. Centers for Medicare & Medicaid Services. (2020b). Estate recovery. https://www.medicaid.gov/medicaid/ eligibility/estate-recovery/index.html U.S. Internal Revenue Service. (2020). U.S. Income Tax Return for Estates and Trusts: Instructions for form 1041 and schedules A, B, G, J, and K-1. Retrieved from https://www.irs.gov/instructions/i1041#idm14 0366338350960 U.S. Social Security Administration. (2020a). Spotlight on trusts: 2020 Edition. Retrieved at https://www.ssa.gov/ ssi/spotlights/spot-trusts.htm U.S. Social Security Administration. (2020b). SI 01120.203: Exceptions to counting trusts established on or after January 1, 2000. Program Operations Manual System (POMS). Retrieved from https://secure.ssa. gov/poms.nsf/lnx/0501120203 Weikel, E. (2018). Disabling the funds of the disabled: How the sole benefit rule frustrates the administration and purpose of supplemental needs trusts. Quinnipiac Probate Law Journal, 32, 60. Wilcenski, E., & Pleat, T. (2016). Supplemental needs trusts: The basics. New York State Bar Association Elder Law Section Program. Retrieved from https:// nysba.org/NYSBA/Meetings%20Department/Section %20Meetings/Trusts%20and%20Estates/TRUSFA 16%20Materials/Combined%20Self%20Settled% 20Materials.pdf Wilcenski, E., & Pleat, T. (2019, March). Administration of special needs trusts: Development of an improved approach (Part II). New York State Bar Association Journal, 91(2), 12–20. Retrieved from https://nysba.
4559 org/app/uploads/2020/04/March-Journal-2019-WEB. pdf Wilcenski, E., & Pleat, T. (2020, April). Administration of special needs trusts: Development of an improved approach (Part III). New York State Bar Association Journal, 92(3), 40–44. Retrieved from https://nysba. org/app/uploads/2020/04/NYSBA-April-2020-Jour nal_FinalWEBLinks.pdf Wick, D. W. (2019). The Expensive, Protective Estate Planning Strategy: A New Paradigm for Estate Planning Design and Administration. Elder LJ, 27, 115. Retrieved from: https://theelderlawjournal.com/wpcontent/uploads/2019/05/Wick.pdf
Special Olympics Timothy Shriver Special Olympics, Inc, Washington, DC, USA
Major Areas or Mission Statement With sports at the core, Special Olympics stands as a leader in advancing rights, opportunities, and social change for people with intellectual disabilities around the world. In 2011, Special Olympics served nearly four million athletes worldwide. While the majority of the athletes (64.8%) are school-aged (8–21 years old), 33.4% are adults (22+), and 1.8% are young children (2–7), with more males (62%) than females (38%) participating. Special Olympics has been growing rapidly; since 2000, the Special Olympics athlete count has grown from just under one million to the current total of just under million. Most staff in Special Olympics programs globally are volunteers with a wide variety of expertise, including credentialed sports coaches, health professionals, program administrators, and researchers. In 2011, more than 306,000 coaches supported Special Olympics athletes. In addition, 546,000 partners, or participants without disabilities, are now engaged in Unified Sports, a Special Olympics initiative that includes individuals with and without intellectual disabilities together. Nearly 29,000 of our athletes serve in leadership
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positions offered through another initiative, the Athlete Leadership Programs (ALPs). Because of its inclusive nature, Special Olympics does not collect disability-specific information on its athletes, and, as such, there is no nationwide data on the number of participants in Special Olympics who have autism spectrum disorder (ASD). However, studies suggest that between 6% (Weiss et al. 2003) and 19% (Glidden et al. 2011) of Special Olympics athletes have ASD.
Mission Statement “The mission of Special Olympics is to provide year-round sports training and athletic competition in a variety of Olympic-type sports for children and adults with intellectual disabilities, giving them continuing opportunities to develop physical fitness, demonstrate courage, experience joy and participate in a sharing of gifts, skills and friendship with their families, other Special Olympics athletes and the community.”
Landmark Contributions Brief History In 1962, Eunice Kennedy Shriver started a summer day camp for children and adults with intellectual disabilities in her backyard in Maryland. She hoped to not only provide this underserved population with access to recreational opportunities but also to explore their capabilities in a variety of sports and physical activities. Just 6 years later, this small summer camp grew into the First International Special Olympics Summer Games, held at Soldier Field in Chicago, Illinois, USA. Over 1,000 individuals with intellectual disabilities came from 26 U.S. states and Canada to compete in track and field and swimming. As the movement grew, so too did its national and international recognition as a serious sports movement. In 1971, the U.S. Olympic Committee gave Special Olympics official approval to use the name “Olympics” in the United States, making Special Olympics one of only two organizations authorized to do so. Fifteen years later, the United Nations proclaimed the International Year of
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Special Olympics (1986) under the banner “Special Olympics – Uniting the World,” and just 2 years later, the International Olympic Committee (IOC) officially endorsed and recognized Special Olympics (1988). The backyard camp had grown into an internationally recognized organization. The subsequent decades were dedicated to expanding existing Special Olympics activities, as well as implementing new initiatives, such as Unified Sports, which creates an inclusive sports experience for participants with and without disabilities; Healthy Athletes, which provides free health screenings for athletes; and the Global Messenger Program, which offers public speaking and presentation skills training to athletes who are interested in representing the movement internationally. The organization also expanded to include cutting-edge research and evaluation, including the “Multinational Study of Attitudes toward Individuals with Intellectual Disabilities” (Siperstein et al. 2003), the most comprehensive study ever conducted on the subject. When Special Olympics celebrated its 40th anniversary in 2008, it had grown to serve three million athletes in 173 countries around the world. Today, the organization stands as an international leader in research and advocacy for persons with intellectual disabilities. In fact, in 2010, the first Special Olympics Global Congress was held in Marrakech, Morocco, bringing together hundreds of movement leaders from countries around the world to chart the next 5 years of work. Special Olympics is currently led by: • Timothy P. Shriver, Ph.D., Chairman and Chief Executive Officer • J. Brady Lum, President and Chief Operating Officer Major contributions: 1. Special Olympics has shown that people with disabilities can participate in and benefit from sports and recreation. The movement has demonstrated that the same motivation, desire, and ability to play and compete are present in people, irrespective of the presence of a disability.
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2. Special Olympics has documented the developmental significance of sports and recreation across the lifespan for people with disabilities, from motor skill development in young children ages 2–7 to community engagement for adults. 3. Special Olympics has led a movement of community inclusion by engaging millions of volunteers in meaningful experiences with people with intellectual disabilities, while also creating inclusive sports activities. 4. Special Olympics has acted as a public education and attitude change vehicle, presenting people with intellectual and developmental disabilities in valued and respected social and community roles all around the world. 5. Special Olympics has globalized the notion of sports and recreation for people with ID, particularly in developing countries. 6. Special Olympics has demonstrated its ability to change attitudes – challenging and eliminating the stigma associated with intellectual or developmental disabilities – and instead highlighting the value of and skills possessed by people with ID, inspiring people to become advocates for inclusive education, employment, health care, and public policy. 7. Special Olympics has pushed the boundaries of scholarly inquiry into the methods and models of personal and social change for people with intellectual disabilities. 8. Special Olympics has launched a worldwide movement to combat health disparities and discrimination in health-care systems and the largest public health system has established Healthy Athletes, for people with ID in the world.
Major Activities Overview of Special Olympics programs: • Sports Training and Competition: Special Olympics offers a variety of sports training and competitions for people with intellectual disabilities. In 2011, over 53,000 competitions were organized around the world, resulting in an average of 146 competitions every day.
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• Unified Sports: Unified Sports brings together equal numbers of Special Olympics athletes (with intellectual disabilities) and unified partners (peers without disabilities) on sports teams for organized, structured training and competition against other unified teams. • Healthy Athletes: Health Athletes offers free health screenings and health information to athletes and families at local, regional, and world games. • Young Athletes: Young Athletes is a curriculum designed to promote motor and social development for children ages 2½ to 7 through motor play activities and games. • Motor Activity Training Program: The Motor Activity Training Program is designed to provide individualized training programs for athletes with severe or profound intellectual disability who are unable to participate in official Special Olympics sports competitions because of their skill and/or functional capabilities. • Get Into It: The Get Into It educational resources explore the subjects of inclusion, acceptance, and social justice through the lens of core curriculum requirements and a servicelearning framework. • Project UNIFY: Project UNIFY is a schoolbased initiative that uses sports and education programs to activate young people to develop school communities where all youth are agents of change – fostering respect, dignity, and advocacy for people with intellectual disabilities. • Athlete Leadership Programs (ALPs): The Athlete Leadership Program allows athletes to explore opportunities for greater participation in Special Olympics beyond sports training and competition as coaches, officials, team captains, spokespeople, and Board and committee members. • Research and Policy: Special Olympics conducts cutting-edge research and evaluation to better understand the many challenges faced by people with intellectual disabilities and the impact of Special Olympics on their lives. This research is also a driving force for realizing improved policies, laws, and rights for
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people with intellectual disabilities around the world. • Special Olympics Family Support Network: Family members of a person with intellectual disability often feel confused and alone. Special Olympics Family Support Network provides them acceptance, resources, hope, and a chance to become advocates – making them a valued voice in our movement. • Special Olympics Camp Shriver: A camp experience for people with intellectual disabilities to learn new sports skills, participate in individual and team sports, and build friendships. The camp creates an atmosphere of understanding, learning, and sharing so that new friendships between athletes and participants without intellectual disabilities (partners) are created and continue to thrive once camp is over. The Value of Special Olympics There is growing empirical evidence demonstrating the value of Special Olympics for its participants, both with and without disabilities. Studies have shown that Special Olympics athletes gain not only improved sports skills but also improved physical fitness (Balic et al. 2000), increased selfesteem (Castagno 2001), increased selfperceptions of physical competence and social acceptance (Weiss et al. 2003), and improved social competence (Dykens and Cohen 1996). Indeed, athletes and families typically choose to participate in Special Olympics not only because of its sports opportunities but also because it is fun and offers prospects for social interaction (Harada and Siperstein 2008; Harada et al. 2011). There is also evidence that participation in Special Olympics decreases problem behaviors (Rosegard et al. 2001) and increases adaptive behaviors (Favazza and Siperstein in press; Favazza et al. under review). Further, typically developing individuals who participate in Unified Sports alongside teammates with intellectual disabilities have also demonstrated higher self-esteem, as well as greater willingness to interact with and more positive perceptions of individuals with disabilities (Castagno 2001). In addition, parents of Special
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Olympics athletes frequently find that participation in Special Olympics raises their expectations of their son or daughter’s abilities (Glidden et al. 2011; Kersh and Siperstein under review). Although the benefits of participation in sports and recreation – and particularly in Special Olympics – have been well established for individuals with intellectual disabilities, only recently has the field begun to recognize the potential impact of sports and recreation on the social and motor development of individuals with autism spectrum disorder (ASD). Children with ASD frequently demonstrate motor impairments (Getchell and Lynch 2007), may be less physically active than their typically developing counterparts (Pan 2008), and are less involved in social and recreational activities with peers than those without disabilities (Solish et al. 2010). However, in their recent review of research on exercise and persons with ASD, Lang et al. (2010) found that persons with ASD participating in regular exercise experienced reductions in stereotypic, aggressive, and off-task behaviors. In addition, studies have suggested that participation in physical activity and recreational programs may have positive social benefits for individuals with autism, such as improved social behavior (Pan 2010) and increased social interaction (Garcia-Villamisar and Dattilo 2010), in addition to personal benefits, such as decreased stress and improved quality of life (Garcia-Villamisar & Dattilo). Of course, given the variability in specific deficits associated with ASD, some individuals may struggle with certain elements of team sports, including verbal and nonverbal communication with peers, loud noises, or personal contact (Crollick et al. 2006). Therefore, to maximize the benefits of sports and recreations, some adaptations – such as visual supports to present clear and predictable expectations (Fittipaldi-Wert and Mowling 2009), smooth transitions between activities (Crollick et al. 2006), and reinforcement of appropriate social and physical behaviors (Reid et al. 2003) – are often necessary. The inclusive nature of Special Olympics requires that athletes not be singled out on the basis of their specific disabilities, and as a result,
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Special Olympics does not have distribution numbers by specific intellectual/developmental disability subcategories. However, there is burgeoning preliminary evidence that individuals with ASD have participated in and enjoyed Unified Sports, inclusive summer camps (Special Olympics Camp Shriver), and traditional Special Olympics competitions. Additionally, families have spoken of a number of benefits of Special Olympics for their child with ASD, particularly in improved social skills and increased opportunities for social interaction (Kersh and Siperstein under review). Moving forward, Special Olympics will focus on how to best adapt its array of activities in order to accommodate its growing number of participants with ASD.
References and Reading Balic, M., Mateos, E., Blasco, C., & Fernhall, B. (2000). Physical fitness levels of physically active and sedentary adults with Down syndrome. Adapted Physical Activity Quarterly, 17(3), 310–321. Castagno, K. S. (2001). Special Olympics unified sports: Changes in male athletes during a basketball season. Adapted Physical Activity Quarterly, 18, 193–206. Crollick, J., Mancil, G., & Stopka, C. (2006). Physical activity for children with autism spectrum disorder. Teaching Elementary Physical Education, 17(2), 30–34. Dykens, E. M., & Cohen, D. J. (1996). Effects of special Olympics international on social competence in persons with mental retardation. Journal of the American Academy of Child and Adolescent Psychiatry, 35(2), 223–229. Favazza, P. C. & Siperstein, G. N. (in press). Young athletes: A Special Olympics motor skill development program. Journal for National Association of State Boards of Education. Favazza, P. C., Siperstein, G. N., Zeisel, S., Odom, S. L., & Moskowitz, A. L. (under review). Young Athletes motor curriculum: Impact on motor development. Adaptive Physical Activity Quarterly. Fittipaldi-Wert, J., & Mowling, C. (2009). Using visual supports for students with autism in physical education. JOPERD: The Journal of Physical Education, Recreation & Dance, 80(2), 39–43. Frey, G. (2007). Physical activity and youth with developmental disabilities. In Handbook of developmental disabilities (pp. 349–365). New York: Guilford Press. Frey, G. C., Stanish, H. I., & Temple, V. A. (2008). Physical activity of youth with intellectual disability:
4563 Review and research agenda. Adapted Physical Activity Quarterly, 25(2), 95–117. Garcia-Villamisar, D., & Dattilo, J. (2010). Effects of a leisure programme on quality of life and stress of individuals with ASD. Journal of Intellectual Disability Research, 54(7), 611–619. Getchell, N., & Lynch, A. (2007). Motor competencies in children with Autism spectrum disorders. Journal of Sport & Exercise Psychology, 29, S6–S7. Glidden, L. M., Bamberger, K. T., Draheim, A. R., & Kersh, J. (2011). Parent and athlete perceptions of Special Olympics participation: Utility and danger of proxy responding. Intellectual and Developmental Disabilities, 49(1), 37–45. Harada, C. M., & Siperstein, G. N. (2008). The sport experience of athletes with intellectual disabilities: A national survey of Special Olympics athletes and their families. Adapted Physical Activity Quarterly, 26, 1–20. Harada, C. M., Siperstein, G. N., Parker, R., & Lenox, D. (2011). Promoting social inclusion for people with intellectual disabilities through sport: Special Olympics International, global sport initiatives, and strategies. In Disability in the global sport arena: A sporting chance. Routledge/Taylor & Francis. Kersh, J., & Siperstein, G. N. (under review). Mutual benefits of Special Olympics participation for individuals with intellectual and developmental disabilities and their parents. Adaptive Physical Activity Quarterly. Lang, R., Koegel, L., Ashbaugh, K., Regester, A., Ence, W., & Smith, W. (2010). Physical exercise and individuals with autism spectrum disorders: A systematic review. Research in Autism Spectrum Disorders, 4(4), 565–576. McHale, J. P., Vinden, P. G., Bush, L., Richer, D., Shaw, D., & Smith, B. (2005). Patterns of personal and social adjustment among sport-involved and noninvolved urban middle-school children. Sociology of Sport Journal, 22(2), 119–136. Pan, C. (2008). Objectively measured physical activity between children with autism spectrum disorders and children without disabilities during inclusive recess settings in Taiwan. Journal of Autism & Developmental Disorders, 38(7), 1292–1301. https://doi.org/10.1007/ s10803-007-0518-6. Pan, C. (2010). Effects of water exercise swimming program on aquatic skills and social behaviors in children with autism spectrum disorders. Autism: The International Journal of Research and Practice, 14(1), 9–28. Reid, G., O’Connor, J., & Lloyd, M. (2003). The autism spectrum disorders physical activity instruction: Part III. Palaestra, 19(2), 20–26, 47–48. Rosegard, E., Pegg, S., & Compton, D. M. (2001). Effect of Unified Sport on maladaptive behaviors among Special Olympics athletes. World Leisure Journal, 43(2), 39–48. Siperstein, G. N., Norins, J., Corbin, S., & Shriver, T. (2003). Multinational study of attitudes toward
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4564 individuals with intellectual disabilities: General findings and call to action. Washington, DC: Special Olympics and University of MassachusettsBoston. Solish, A., Perry, A., & Minnes, P. (2010). Participation of children with and without disabilities in social, recreational and leisure activities. Journal of Applied Research in Intellectual Disabilities, 23(3), 226–236. https://doi.org/10.1111/j.1468-3148.2009. 00525.x. Weiss, J., Diamond, T., Demark, J., & Lovald, B. (2003). Involvement in special Olympics and its relations to self-concept and actual competency in participants with developmental disabilities. Research in Developmental Disabilities, 24(4), 281–305.
Specific Language Impairment ▶ Childhood Aphasia ▶ Expressive Dysphasia ▶ Expressive Language Disorder
Specific Learning Disability ▶ Developmental Disabilities ▶ Learning Disability
Specific Learning Disorder ▶ Learning Disability
Specific Reading Disability ▶ Dyslexia
Specific Language Impairment
Spectrum/Continuum of Autism Somer Bishop Department of Psychiatry, University of California, San Francisco, CA, USA Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
Definition Autism is one of a spectrum of disorders that includes Asperger syndrome, pervasive developmental disorder-not otherwise specified (PDD-NOS), childhood disintegrative disorder (CDD), and Rett’s disorder. These disorders are currently classified under the umbrella term of “pervasive developmental disorder (PDD)” in both the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV; American Psychiatric Association [APA] 1994) and the International Classification of Diseases (ICD-10; World Health Organization [WHO] 1992), but the term “autism spectrum disorder (ASD)” (also sometimes called autism spectrum condition (ASC)) is now more commonly used. Given the current state of knowledge, ASD appears to be a more useful description than PDD. It identifies autism as the reference point and suggests that at least some of the other disorders classified as PDD (i.e., atypical autism, PDD-NOS, Asperger syndrome) are differentiated from autism primarily by the age of onset, severity of symptoms, and presence of language delay and/or intellectual disability, whereas they are differentiated from non-spectrum disorders on the basis of several qualitative (and presumably biological) variables. Although CDD and Rett’s disorder are also currently included in the PDD/ASD category, both of these disorders are exceedingly rare and differ significantly from other ASDs in the pattern of onset. In addition, given that there is now an identified genetic cause for the majority of cases of Rett’s syndrome (Amir et al. 1999), it is unlikely that this disorder will be
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included in the ASD category in DSM-V. Thus, the following discussion focuses on autism, PDDNOS, and Asperger syndrome, referred to collectively as ASD.
Historical Background Individuals with ASD present with diverse sets of symptoms and behaviors that vary as a function of many different factors, including age, language, IQ, and severity of social/communication symptoms and restricted and repetitive behaviors. The concept of a spectrum is therefore useful because it suggests that two individuals “on the spectrum” may look very different from one another even though they are classified under the same diagnostic umbrella. A child who avoids eye contact, flaps his hands, says only one or two words, and shows little interest in social interactions might share a diagnosis of ASD with a child who uses complex sentences and is socially motivated under certain circumstances but lacks the social awareness or skills to know how to behave in social situations or maintain successful peer relationships. Aided in part by advances in standardized diagnostic instruments and by opportunities to study larger and more diverse samples of individuals with ASD, there is increasing awareness that the spectrum of autism encompasses a wide range of difficulties and strengths and that no two individuals with ASD can be expected to look exactly the same. Within the broader spectrum of autism, current diagnostic classification systems provide guidelines for assigning specific diagnoses (i.e., for determining whether a diagnosis of autism or Asperger syndrome or PDD-NOS is most appropriate). Unfortunately, these diagnoses tend to be highly unreliable across different sites and clinicians (Mahoney et al. 1998; Lord et al. 2012). Particularly with respect to Asperger syndrome, diagnostic conceptualizations vary widely within the field, making it difficult for professionals to agree on or be consistent in their use of terminology (Ozonoff et al. 2000). For example, whereas
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some research groups adhere to the strict DSM-IV definition when diagnosing Asperger syndrome, which requires that autism be ruled out first, other groups diagnose Asperger syndrome even if the criteria for autism is also met (Szatmari et al. 2003). Similarly, although PDD-NOS is technically intended as a category to describe atypical autism (ICD-10) or “not quite autism,” it often ends up meaning “mild autism” or is used to classify young children to whom professionals do not yet feel comfortable giving an autism diagnosis (Walker et al. 2004). Disagreement about diagnostic criteria for differentiating within ASDs may help explain why, unlike earlier comparative studies, more recent investigations have not found clear differences between individuals identified as “high functioning autism (HFA)” and those diagnosed with Asperger syndrome. In one study, Bennett et al. (2008) found that adolescent outcomes in autism symptoms and adaptive behavior were better predicted by structural language impairment (defined by the presence of deficits in grammar or syntax) in childhood than by a clinical diagnosis of HFA vs. Asperger syndrome. Repetitive behaviors have also not been found to differ significantly between these diagnostic groups (Cuccaro et al. 2007).
Current Knowledge From a clinical standpoint, the specific ASD diagnosis a child receives is arguably much less important than the fact that he/she falls somewhere on the autism spectrum. However, inconsistent use of labels often leaves parents confused about what to make of their child’s diagnosis. For example, Asperger syndrome has come to be portrayed in the media as a diagnosis characterized by special skills, high intelligence, and “quirky” personality traits, whereas autism is more commonly described as being associated with low IQ, limited language abilities, aloofness, and a poor prognosis. For parents of a child with ASD, their conceptualization of the disorder, including decisions about what interventions or
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resources to seek out and expectations about the future, is likely to differ depending on whether the child receives a diagnosis of autism or another spectrum diagnosis that is perceived as being less severe. Moreover, if a child is initially given a diagnosis of PDD-NOS by one professional but then given a diagnosis of autism by another professional, the parent may erroneously assume that something about the child changed when in fact each professional simply had his/her own idiosyncratic way of deciding on one diagnosis over the other. Disagreement about how diagnoses are assigned within the spectrum also has a number of important implications for ASD research. For example, if all “mild” cases of autism are referred to as Asperger syndrome or PDD-NOS, then a diagnosis of autism will be more likely to be related to intellectual disability. If Asperger syndrome is defined as any case of mild autism without intellectual disability, samples of children with PDD-NOS will be lower in IQ than if Asperger syndrome has a narrower definition (Klin et al. 2005). From a neurobiological research perspective, clinical diagnostic designations are most useful if they are associated with different etiologies. But accumulating evidence suggests that these disorders are indeed on the same continuum and are therefore unlikely to be useful as a primary variable by which to stratify samples for etiological investigations. Analyses of datasets that include children with autism, PDD-NOS, and Asperger syndrome, as well as typically developing children and those with non-spectrum disorders, indicate that symptoms in the areas of communication, reciprocal social interaction, and restricted and repetitive behaviors constitute an “autism factor” that differentiates children with ASD as a group from children not on the spectrum (Constantino et al. 2004). Therefore, although the severity of symptoms in these domains differs among individual children with ASD, particular impairments in communication and reciprocal social interaction and the presence of restricted and repetitive behaviors set apart
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individuals across the whole autism spectrum from those with other disorders (see also Lord and Bishop 2010). Evidence that diagnostic distinctions within the spectrum may simply represent different degrees of the same disorder has also come from longitudinal follow-ups of children with ASD. Whereas movement “off” the spectrum is rare, it is not uncommon for children to change diagnoses within the spectrum depending on their symptom presentation at different points in development. A sizable proportion of children who initially receive a diagnosis of PDD-NOS in early preschool may go on to be diagnosed with autism in school age when their symptoms become more apparent and/or when the clinician becomes more certain about the diagnosis. Alternatively, some children diagnosed with autism may later receive a diagnosis of PDD-NOS when their symptoms improve (Lord et al. 2006; Stone et al. 1999). Such findings about changes in diagnostic status would be surprising if autism and PDDNOS were indeed qualitatively distinct, but the concept of a spectrum allows for the idea that a child may move within the spectrum depending on their particular symptom profile while still continuing to meet the criteria for an ASD at each time point. There is no question that the behavioral phenotypes of individuals with ASD are tremendously heterogeneous in general, or that an individual with a specific ASD designation of Asperger syndrome, for example, may look quite different than someone with a diagnosis of autism. However, research continues to indicate that whereas professionals (and diagnostic instruments) are reliable in indicating whether a child is somewhere on the spectrum or not on the spectrum, there are substantial inconsistencies in how specific ASD diagnoses are assigned by different practitioners (Lord et al. 2012). Therefore, it is likely that all specific ASD designations will be collapsed into a single ASD diagnosis in the forthcoming fifth revision of the DSM (see www.dsm5.org). This proposed revision for DSM-V has caused significant controversy. Many professionals,
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parents, and individuals with ASD feel that it is important to retain specific designations within the spectrum. Despite the issue of generally low levels of inter-rater reliability, it is argued that in some cases, diagnostic distinctions of autism vs. Asperger syndrome vs. PDD-NOS are useful or necessary in helping professionals communicate about different sorts of children or adults with ASD, or in helping families find resources that are appropriate. For example, parenting handbooks or social groups might be designed for individuals with “high functioning autism or Asperger syndrome” because the activities or suggestions are only relevant for those with fluent language. Similarly, a support group for parents of children with autism might be specifically intended to cater to families with children whose ASD symptoms are quite severe. Furthermore, some adults with ASD are more comfortable selfidentifying as having Asperger syndrome (as opposed to autism or ASD) because they feel that this diagnosis more accurately reflects their profile of strengths and difficulties than the more classic definition of autism. Nevertheless, as they are currently defined, specific diagnoses within the spectrum have proven