The Autism Spectrum : Scientific Foundations and Treatment 9781139781633, 9780521116879

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The Autism Spectrum Scientific Foundations and Treatment

The Autism Spectrum Scientific Foundations and Treatment Mark E. Reber Woods Services, Langhorne, PA, USA

cambridge university press Cambridge, New York, Melbourne, Madrid, Cape Town, Singapore, São Paulo, Delhi, Mexico City Cambridge University Press The Edinburgh Building, Cambridge CB2 8RU, UK Published in the United States of America by Cambridge University Press, New York www.cambridge.org Information on this title: www.cambridge.org/9780521116879 © Cambridge University Press 2012 This publication is in copyright. Subject to statutory exception and to the provisions of relevant collective licensing agreements, no reproduction of any part may take place without the written permission of Cambridge University Press. First published 2012 Printed and bound in the United Kingdom by the MPG Books Group A catalog record for this publication is available from the British Library Library of Congress Cataloging in Publication data The autism spectrum : scientific foundations and treatment / [edited by] Mark Reber. p. ; cm. Includes bibliographical references and index. ISBN 978-0-521-11687-9 (hardback) I. Reber, Mark. [DNLM: 1. Child Development Disorders, Pervasive. WS 350.8.P4] 618.920 85882–dc23 2012013411 ISBN 978-0-521-11687-9 Hardback Cambridge University Press has no responsibility for the persistence or accuracy of URLs for external or third-party internet websites referred to in this publication, and does not guarantee that any content on such websites is, or will remain, accurate or appropriate. Every effort has been made in preparing this book to provide accurate and up-to-date information which is in accord with accepted standards and practice at the time of publication. Although case histories are drawn from actual cases, every effort has been made to disguise the identities of the individuals involved. Nevertheless, the authors, editors and publishers can make no warranties that the information contained herein is totally free from error, not least because clinical standards are constantly changing through research and regulation. The authors, editors and publishers therefore disclaim all liability for direct or consequential damages resulting from the use of material contained in this book. Readers are strongly advised to pay careful attention to information provided by the manufacturer of any drugs or equipment that they plan to use.

Contents List of abbreviations List of contributors Preface xi

page vi ix

Section 1: What We Know About Autism and How We Know It 1.

Autism nosology: historical perspectives 1 Mark E. Reber

9. Habilitative treatments for children with ASDs: speech and occupational therapy, assistive technology 217 Joseph E. Campbell and Kathleen M. Mears 10. Behavioral treatments for children with ASDs 239 Scott Spreat

2.

Epidemiology of autism Mark E. Reber

34

3.

Developmental neuropsychology of autism 59 Gerry A. Stefanatos

4.

Neuropathology of autism Gerry A. Stefanatos

5.

Etiology: syndromic autism Mark E. Reber

6.

Etiology: essential autism Mark E. Reber

11. Medication and nutritional treatments for children with ASDs 258 Shivani Upendra Mehta, Raghavendra Rao Siragavarapu, and Mark E. Reber

83 112 145

Section 2: Assessing and Treating Children with Autism Spectrum Disorders 7.

Autism screening and diagnostic evaluation 179 Mark E. Reber

8.

Educational treatments for children with ASDs 201 Judy L. Horrocks

Section 3: Assessing and Treating Adults with Autism Spectrum Disorders 12. Diagnosis and assessment of adults with ASDs 285 Susan V. McLeer and Donna N. McNelis 13. Service and treatment planning for adults with ASDs 305 Donna N. McNelis and Susan V. McLeer

Index

327

The color plates are to be found between pages 178 and 179. v

Abbreviations AAC ABA ABAS ABC ABLLS-R AD ADDM ADI-R ADOS AML AOTA AS ASD ASHA AVB AVP BA BOT2 CAM CAM CARS CC Cg CGH CHAT CNV COPDD CSWS CT CTM DAMP DIR dTAP DTI DTT EAHCA EPS ESAT ESDM ESES fMRI vi

augmentative and alternative communication applied behavioral analysis Adaptive Behavior and Assessment System Autism Behavior Checklist Assessment of Basic Language and Learning Skills-Revised autistic disorder Autism and Developmental Disabilities Monitoring Network Autism Diagnostic Interview-Revised Autistic Diagnostic Observation Schedule angiomyolipoma American Occupational Therapy Association Asperger’s syndrome autism spectrum disorder American Speech-Language-Hearing Association Analysis of Verbal Behavior arginine-vasopressin Brodmann area Bruininks Oseretsky Test of Motor Proficiency cellular adhesion molecule complementary and alternative medicine Childhood Autism Rating Scale corpus callosum cingulum comparative genomic hybridization Checklist for Autism in Toddlers copy number variations childhood onset pervasive developmental disorder continuous spike-wave during slow-wave sleep cortical thickness comprehensive treatment model deficits in detention motor control and perception Developmental–Individual Differences–Relationship Model diphtheria–tetanus–pertussis (vaccine) diffusion tensor imaging discrete trial teaching Education for All Handicapped Children Act extrapyramidal side effects Early Screening for Autistic Traits Early Start Denver Model electrical status epilepticus in sleep functional MRI

List of abbreviations

FXS FXTAS GARS GERD GFCF GWAS HFA IDEA IDEIA IEP IFSP ITC IVIG LGS LKS LOD MCDD M-CHAT NCLB OCD OT OWLS PDD PDD-NOS PDDST-II PECS PFC PLSI PPV PSV PWS RRBs RS SCERTS SCQ SEGA SEN SETT SFA SI SGD SIPT SNAP SNP SRS SSRI

fragile X syndrome fragile X-associated tremor/ataxia syndrome Gilliam Autism Rating Scale gastroesophageal reflux disease gluten-free, casein-free genome-wide association study high-functioning autism Individuals with Disabilities Education Act Individuals with Disabilities Education Improvement Act Individualized Education Plan Individualized Family Service Plan Infant Toddler Checklist intravenous immunoglobulin treatment Lennox–Gastaut syndrome Landau–Kleffner syndrome logarithm of odds multiple complex developmental disorder Modified Checklist for Autism in Toddlers No Child Left Behind obsessive–compulsive disorder oxytocin Oral and Written Language Scales pervasive developmental disorder Pervasive Developmental Disorder, not otherwise specified Pervasive Developmental Disorders Screening Test-II Picture Exchange Communication System prefrontal cortex Pragmatic Language Skills Inventory positive predictive value preserved speech variant Prader–Willi syndrome restricted and repetitive behaviors Rett syndrome Social Communication, Emotional Regulation, and Transactional Support Social Communication Questionnaire subependymal giant cell astrocytoma subependymal nodule Students, Environments, Tasks and Tools School Function Assessment sensory integration speech-generating device Sensory Integration and Praxis Tests Swanson, Nolan, and Pelham Questionnaire single nucleotide polymorphism Social Responsiveness Scale specific serotonin reuptake inhibitor

vii

viii

List of abbreviations

STAT-II STS TCVs TEACCH TOM TS TSC VABS VAERS VBM VB-MAPP VCFS VPA WATI

Screening Tool for Autism in Two-year-olds superior temporal sulcus thimerosal-containing vaccines Treatment and Education of Autistic and related Communication handicapped CHildren theory of mind temporal stem tuberous sclerosis complex Vineland Adaptive Behavior Scales Vaccine Adverse Events Reporting System voxel-based morphometry Verbal Behavior Milestones Assessment and Placement Program velocardiofacial syndrome valproic acid Wisconsin Assistive Technology Initiative

Contributors Joseph E. Campbell M.Ed., OTR/L Senior Occupational Therapist Woods Services Langhorne, PA, USA. Judy L. Horrocks, Ed.D. Hatfield, PA, USA. Susan V. McLeer, M.D., M.S. Professor and Chair Department of Psychiatry Drexel University College of Medicine Philadelphia, PA, USA. Donna N. McNelis, Ph.D. Professor and Vice Chair for Administration and Program Development Department of Psychiatry Drexel University College of Medicine Philadelphia, PA, USA. Kathleen M. Mears, M.Ed., CCC-SLP Senior Speech-Language Pathologist Woods Services Langhorne, PA, USA. Shivani Upendra Mehta, M.D. Division of Child and Adolescent Psychiatry

Department of Psychiatry Drexel University College of Medicine Philadelphia, PA, USA. Mark E. Reber, M.D., M.Ed. Director of Psychiatry Woods Services Langhorne, PA, USA. Raghavendra Rao Siragavarapu, M.D., M.S. Division of Child and Adolescent Psychiatry Department of Psychiatry Drexel University College of Medicine Philadelphia, PA, USA. Scott Spreat, Ed.D. Vice President for Behavioral Health Woods Services Langhorne, PA, USA. Gerry A. Stefanatos, D.Phil. Associate Professor Director Cognitive Neurophysiology Laboratory Eleanor M. Saffran Center for Cognitive Neuroscience Temple University Philadelphia, PA, USA.

ix

Preface In the past decade, there has been a virtual explosion of information with regard to autism – in the media, on the Internet, and in professional journals. As a child and adolescent psychiatrist who has provided clinical care to children and adults with autism for over 25 years, I have been troubled by some of this information, particularly the frequent surmises regarding the causes of autism spectrum disorders (ASDs) and some presentations of available treatments. It is my opinion that physicians and other professionals who work with children and adults with autism and with their families need to have up-to-date, accurate scientific information, in order to dispel myths and correct misunderstandings. These professionals also need a clear explication of the existing literature on autism so that they can undertake critical reading of new research. I decided that I could assist this process by putting together a book on what we know now about ASDs. My primary goal was to make the book accessible and clear. I invited contributions from colleagues who, like me, are clinical practitioners and service providers and who could bring the perspective of their own professional experience to bear as they described the scientific underpinnings of their work and reviewed treatment approaches. Structurally, this book is divided into two sections: scientific foundations and treatment, with the second section separated into treatments for children and – an emerging field – programming for adults. Each chapter stands on its own as an essay on a particular topic in autism research and treatment, but taken together, these essays should provide a relatively comprehensive overview of our current knowledge of the heterogeneous disorders that we now locate on the “autism spectrum.” I want to express my gratitude to many people who contributed to this book – first and foremost: to the writers of the individual chapters and, more broadly, to the thousands of authors whose research we all read and endeavored to understand and present. I also wish to thank my employer, Woods Services Inc., and individuals at Cambridge University Press who contributed to this project: Marc Strauss, Richard Marley, Monica Finley, Katie Dunlavey, Katharine Hickling, Robert Sykes, Joanna Chamberlin, Jonathan Ratcliffe, Sara Brunton, and Mary Collier. Specific thanks go to Jennifer McGuire, Manely Ghaffari, and Harry Getz who introduced me to topics and supplied me with reading material; Seth Sheffler-Collins, Saba Ahmad, and Xilma Ortiz-Gonzales, who reviewed sections of the manuscript; and Clara Arizmendi, Sally Arnold, and Nancy Gancarz, who provided a range of supports. My gratitude toward family members (Rebecca Reber, Kate Reber, and Sean Sheffler-Collins), who contributed to this project by compiling tables and reference lists and creating figures, is enormous. My gratitude to my primary assistant, supporter, and exhorter – my wife Karen – is without bound. Mark E. Reber, MD

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Section 1

What We Know About Autism and How We Know It

Chapter

Autism nosology: historical perspectives

1

Mark E. Reber

In January 2009, the White House posted an action agenda on its website. In it, the new Obama administration made a commitment to “supporting Americans with Autism Spectrum Disorders,” by increasing funding for research, treatment, screening, public awareness, and support services. Particular mention was made of advancing research on the treatment and causes of autism; improving lifelong services; enhancing federal and state programs; and implementing universal screening. This statement was noteworthy, not only for its recognition of autism as a significant public health concern, deserving increased federal funding for research and treatment, but also for the use of the term autism spectrum disorders. By choosing this phrase to characterize the condition commonly known as autism, the White House was recognizing that autism is not a single disorder, but many. A central theme of this book is that autism is a clinically and etiologically heterogeneous condition. Although people diagnosed with autism share certain characteristics – a triad of unique and severe deficits in social interaction, difficulties in verbal and nonverbal communication, and a restricted repertoire of interests and behaviors – they vary remarkably in the nature of these deficits, in accompanying symptoms, in intellectual functioning, and in underlying cause. Given this heterogeneity, it is essential that there be some consensus on how to define and classify the disorders that we now refer to as autistic. Such a consensus is needed for research, so that investigators can agree upon the phenomena they are studying; for clinical care, so that patients can be diagnosed with consistency and treatments tailored for recognized diagnoses; and for the legal system, for provision of government and educational services, and for health insurance (Volkmar and Klin, 2005). Beginning with the initial description of autistic disorder in 1943 by Leo Kanner, there has been a lengthy process of refining and validating this particular diagnosis and those of the related conditions we now group as autistic spectrum disorders (Wing, 2005). The purpose of this chapter is to review the presently agreed upon diagnostic and classification system for autism, specifically the diagnoses in the last published editions of the Diagnostic and Statistical Manual of the American Psychiatric Association, the DSM-IV (APA, 2000), and the International Classification of Diseases, ICD-10 (World Health Organization, 1992). The history of the clinical description of these diagnostic entities will be presented; controversies surrounding their use will be discussed; and some alternative conceptualizations and classification schemes will be reviewed. The changes in the definition and criteria for diagnosing autistic conditions that are anticipated for DSM-5, to be published in 2013, will also be discussed. The Autism Spectrum, ed. Mark E. Reber. Published by Cambridge University Press. © Cambridge University Press 2012. 1

Section 1: What We Know About Autism

2

Terminology At present, there are five separate diagnoses in DSM-IV (and in DSM-IV-TR, the 2000 version with revised accompanying text), comprising the disorders we commonly call autism: Autistic Disorder; Pervasive Developmental Disorder, not otherwise specified; Asperger’s Disorder; Childhood Disintegrative Disorder; and Rett’s Disorder. With some minor variation, the diagnostic categories in ICD-10 are similarly named. However, differences between the two systems can be confusing. There are, moreover, a number of alternative terms used in autism. Table 1.1 attempts to provide some clarity by listing the diagnoses used in DSM-IV and ICD-10, together with less formal, largely equivalent terms. It should be noted that these diagnoses as a group are formally referred to as pervasive developmental disorders (PDDs), a term that was originally introduced in DSM-III (American Psychiatric Association, 1980) and is roughly equivalent to the term autism spectrum disorders (ASDs). The latter term is based on the concept of a spectrum, or continuum, of autistic characteristics that are shared by all these disorders (Wing, 2005). In DSM-5, these separate diagnoses are expected to be subsumed under a single diagnosis: Autism Spectrum Disorder. As already mentioned, the term autism itself can be applied collectively to all PDDs or ASDs and is generally so used in this book. Historically, however, autism referred only to autistic disorder (childhood autism in ICD-10, also known as Kanner syndrome, infantile autism and full-syndrome autism), the first-described, best-validated and generally most severe of the PDDs. One other source of confusion deserves mention, especially for parents. Although all ASDs are called pervasive developmental disorders, DSM-IV has a category of Pervasive Developmental Disorder, not otherwise specified (PDD-NOS). As discussed further below, this diagnosis is used in two ways: as a provisional diagnosis when there is insufficient information to specify what type of PDD a person has, and for cases of “atypical autism,” when criteria for autistic disorder or one of the other PDDs are not fully met. It is not uncommon, however, for the “NOS” to be dropped in referring to such cases, and parents are often confused by the fact that their children have “PDD,” not autistic disorder, when autistic disorder, properly speaking, is a PDD. Table 1.1 Diagnostic terms for conditions classified as pervasive developmental disorders.

DSM-IV

ICD-10

Equivalent Terms

Autistic disorder (AD)

Childhood autism

Kanner syndrome, Infantile autism, Full-syndrome autism, autism

Pervasive developmental disorder, not otherwise specified (PDD-NOS)

Atypical autism, Pervasive developmental disorder, unspecified

Asperger’s disorder

Asperger syndrome (AS)

Asperger’s syndrome

Childhood disintegrative disorder

Childhood disintegrative disorder

Heller’s syndrome

Rett’s disorder

Rett syndrome

For the group as a whole: Pervasive developmental disorders (PDDs)

Pervasive developmental disorders

Autistic spectrum disorders (ASDs), autism

Chapter 1: Autism nosology

3

Three other terms relevant to the diagnosis and classification of autism that are frequently used are primary, secondary, and syndromic autism. The latter two terms are roughly equivalent and refer to the occurrence of autism in a person who has a medical condition or syndrome that is assumed to be the cause of the autism. Primary autism occurs in persons who do not have an identified, purportedly causative medical condition. Another term for primary autism is essential autism. The latter term is used in this book. Still one more term in frequent use is broad autism phenotype. This construct is used in genetic research and refers not to autism, but rather to traits in relatives of autistic individuals that are qualitatively similar to symptoms of autism, but not of sufficient severity to confer a PDD diagnosis.

Autistic disorder: history of a diagnosis Most of the diagnoses of mental disorders in DSM-IV and ICD-10 are based on clinical features – observed behaviors and reported symptoms – that are characteristic of the disorder and meet defined qualitative criteria. In the case of autistic disorder, the characteristic features were originally recognized in detailed clinical descriptions; the criteria for diagnosis were arrived at through expert consensus, tested for reliability and validity and refined over time. Establishing diagnostic criteria requires selection of essential or core features of the disorder that are expected to occur in every case, and separating these out from other, associated features that may accompany the disorder but are not universally present. This fact must be taken into consideration when considering autistic disorder as it is presented in DSM-IV and ICD-10. The consensus criteria permit a reliable diagnosis to be made, but they do not provide a comprehensive description of the disorder. Individuals with autistic disorder can vary enormously, and their treatment needs and prognosis depend on their individual patterns of strengths and deficits, developmental course over time and social context. The recognition of autistic disorder began in 1943, when Leo Kanner, a psychiatrist at Johns Hopkins Hospital, published a report on 11 children he had examined who “differed markedly and uniquely” from children previously described in the psychiatric literature. He provided detailed clinical descriptions of these children, including his own observations, parents’ narratives, physical examinations, EEG reports, and cognitive testing. These children, Kanner asserted, represented a unique syndrome. Kanner went on to describe what he considered to be the defining features of this syndrome. First and foremost was the children’s “inability to relate themselves in the ordinary way to people and situations.” This inability was present from the beginning of life and manifested by a child’s “acting like people weren’t there,” being “perfectly oblivious to everything around him,” “failing to develop the usual amount of social awareness” – in the words of the patients’ parents. Kanner called this feature extreme autistic aloneness and titled his paper “Autistic Disturbances of Affective Contact.” (The term autism was borrowed from Eugene Bleuler’s seminal work on schizophrenia and meant a withdrawal into the self and disregard of the outside world.) Another prominent feature of the syndrome was disturbance in language. Three of the children described by Kanner failed to speak at all or spoke fewer than five words – by the ages of 4 years, 11 months; 5 years, 2 months; and 11 years. The others were delayed in language acquisition. Moreover, their speech did not “serve to convey meaning to others.” They showed excellent rote memory, could name lists of objects and recite nursery rhymes and

4

Section 1: What We Know About Autism

prayers, but, in Kanner’s words, their communication was “deflected in a self-sufficient, semantically and conversationally valueless . . . memory exercises.” Their speech had other peculiarities: parrot-like repetitions – uttered immediately (echolalia) or days later (“delayed echolalia”) – literalness with regard to prepositions, and repetition of personal pronouns as heard (saying “you” to refer to themselves). A third feature was what Kanner called “an anxiously obsessive desire for the maintenance of sameness.” This desire was manifest in the children’s distress in response to any intrusions, such as loud noises, repositioned objects and parents’ demands; by repetitive noises and motions; and by intense emotional reactions to changes in routine or rearrangements of furniture. Connected to this need for sameness was “limitation in the variety of spontaneous activity” and relating better to objects than to people – in part, Kanner opined, because objects do not change appearance and position and do not interfere with a child’s aloneness. These three features were the core of the syndrome described by Kanner and they remain, with some alteration and elaboration, the defining features of autistic disorder in the present. Kanner also made other observations that influenced ways in which autistic children were subsequently regarded, but are not part of contemporary criteria for the syndrome. He surmised that the 11 children he described were “unquestionably endowed with good cognitive potentialities.” This description led to the perception, held for several decades, that intellectual disability (mental retardation) and autism are mutually exclusive developmental disorders. This is not the case: the social cognitive deficits and developmental language problems that are associated with autism can be accompanied by global cognitive deficits. Epidemiologic studies have reported that as many as 70% of children with autistic disorder also have intellectual disability (Coleman and Betancur, 2005). More unfortunate for a generation of autistic children and their families was Kanner’s brief description of the parents of the 11 children he presented. He mentioned that they were all “highly intelligent,” but also made note of “a great deal of obsessiveness in the family background” and “few really warmhearted fathers and mothers.” Although Kanner explicitly stated that he could not attribute the children’s “aloneness from the beginning of the life” to the way in which these parents related to their offspring and said that “these children have come into the world with innate inability to form . . . affective contact with people, just as other children come into the world with innate physical or intellectual handicaps,” other mental health professionals in the 1950s and 1960s attributed autism to aloof parenting (Bettelheim, 1967). This view was in keeping with theories, dominant at the time, that regarded nearly all childhood psychiatric disorders as caused by family environment. The result was that many parents were made to feel guilt over having caused their children’s severe developmental disabilities by their own behavior, when no evidence of such an etiology ever existed. For nearly 40 years after Kanner’s initial description, and despite his assertion that he was describing a unique developmental syndrome, autism was classified as a psychotic disorder. The only diagnosis in the first two editions of the DSM (1952 and 1968) that was available for the children Kanner described was childhood schizophrenia. The terms childhood psychosis, childhood schizophrenia, and autism were often used interchangeably (Rutter, 1978). During this same period, there was disagreement about what constituted the essential features of the disorder. Kanner himself amended his original description to permit onset after a period of normal development of one to two years, and also reduced the essential symptoms to two – extreme aloneness and preoccupation with preservation of sameness – downplaying the

Chapter 1: Autism nosology

5

abnormalities of communication noted in his initial formulation (Eisenberg and Kanner, 1956). In contrast, other authors suggested that a specific cognitive deficit involving language function was the core feature of the disorder (Rutter et al., 1971). Still others considered disturbance of perception and motility – not even mentioned by Kanner – to be the essential symptoms of the disorder (Ornitz and Ritvo, 1976). By the late 1970s, sufficient research evidence had accumulated to confirm Kanner’s conception of autistic disorder as a unique and valid diagnosis and to clarify its essential features. In 1978, the British psychiatrist Michael Rutter published a summary of this research. He stated that there were three broad groups of symptoms found in nearly all children with infantile autism, but much less frequently in children with other psychiatric disorders. These were “a profound and general failure to develop relationships; language retardation with impaired comprehension, echolalia and pronominal reversal; and ritualistic or compulsive phenomena.” Rutter went on to cite studies that distinguished autism from intellectual disability by the pattern of associated cognitive deficits (but emphasized, at the same time, that autism and intellectual disability can coexist and that developmental level must be taken into consideration when assessing an autistic child’s behavior). Rutter also cited research that distinguished autistic disorder from childhood psychosis, neurosis, and developmental language disorders. He made the observation that autism can develop in children “with heterogeneous disease states” and suggested it could very well be a behavioral syndrome without a single cause, but with a common biological mechanism of causation, like cerebral palsy. According to Rutter, the principal symptoms of autism could be grouped as three diagnostic features: (1) Impaired social relationships. Symptoms include lack of attachment behavior and relative failure of bonding that is most marked in the first 5 years, with characteristics including not seeking parents for comfort and not developing “kiss-and-cuddle” bedtime rituals. Withdrawal and lack of eye-to-eye gaze are not, however, invariable symptoms. Symptoms of social impairment in older autistic children are lack of cooperative play, failure to form friendships, and lack of empathy. (2) Language and pre-language skills. Early symptoms include lack of imitation and use of gestures, such as “bye-bye;” failure to make meaningful use of toys; lack of imaginative play; failure to use communicative gestures, such as pointing at desired objects. Development of receptive language is delayed. Many children – Rutter cited a figure of 50% – fail to develop useful speech. Acquired speech is uniquely characterized by immediate and delayed echolalia and “I–you” pronominal reversal. Speech is often used without intent to communicate and without the reciprocal, back-and-forth nature of conversation. (3) Insistence on sameness. Like Kanner, Rutter used this phrase to cover a wide range of stereotyped behaviors and routines. Symptoms of this feature of autism include limited play patterns that lack variety and imagination; attachment to objects; unusual preoccupations (in older children: with bus routes, train timetables, patterns, and numbers); repetitive, stereotyped asking of questions; and resistance to environmental change. Rutter proposed these three features, plus onset by 30 months of age, as diagnostic criteria for autism. His criteria received broad acceptance and were used around the world in clinical work and epidemiologic research (see Chapter 2). The condition originally described by

6

Section 1: What We Know About Autism

Kanner, clarified over time and validated by the studies cited by Rutter, was now widely accepted as a unique childhood psychiatric diagnosis – clearly recognizable and distinguished from other pediatric mental and developmental disorders. At around the same time as Rutter’s 1978 article, the first standardized diagnostic and classification systems designating autism as a distinct condition were published. These were ICD-9 and DSM-III. In ICD-9, infantile autism was classified under “psychoses with origin specific to childhood.” DSM-III defined infantile autism largely according to Rutter’s criteria. It classified this condition, together with similar conditions, as pervasive developmental disorders (a new term). The other conditions in this category were residual autism (for cases in which children no longer met the full criteria for autism, but had done so when younger), childhood onset pervasive developmental disorder (COPDD, for cases with onset after 3 years of age), residual COPDD, and atypical PDD (for autistic-like conditions that could not otherwise be classified). The introduction of the term pervasive developmental disorders was considered an important advance by most authors (Rutter and Schopler, 1988). It highlighted the fact that autism was the result of distortion in several developmental processes and was not a type of functional psychosis. The term also provided a diagnostic home for a number of conditions occurring in early childhood with features resembling autism, but apparently distinct from it. These autistic-like syndromes included children with severe intellectual disability and the triad of impaired social behavior, communication deficits, and repetitive behaviors; children who had been described by Asperger; children whose symptoms began beyond infancy; and children who showed profound regression after 3–4 years of normal development and then experienced loss of social skills and language and the onset of repetitive behavior – a condition called “dementia infantilis” by Heller (1930). The existence of conditions that resembled infantile autism in some ways, yet differed from it – not actually meeting the diagnostic criteria proposed by Rutter – was emphasized in a 1979 epidemiologic study by Wing and Gould. The authors investigated the prevalence of severe impairments in social interaction, communication and social play in children with developmental disabilities in the Camberwell borough of London. They found a subgroup of this population with “Kanner’s early childhood autism,” but these children shared many abnormalities with other socially impaired children. Based on this work, Wing (1988, 2005) went on to describe a “continuum,” and later a “spectrum” of autistic disorders, conceptualizing what we now call pervasive developmental disorders as “a range of clinical pictures that differ from each other but have an underlying unity,” including the “most severe to the subtlest manifestations of the autistic triad” (Wing, 2005). In 1987, the APA published a major revision of the DSM-III, the DSM-III-R. In this edition, the triad of autistic features continued to be the basis of the diagnosis, but infantile autism was renamed autistic disorder, and the diagnostic process required selection of 8 of a total of 16 separate, descriptive criteria in the three broad areas of impairment. The requirement for early age of onset was dropped; the categories of residual autism, COPDD, and atypical autism were eliminated. The diagnostic category pervasive developmental disorder, not otherwise specified (PDD-NOS) was introduced, to be used for late-onset and atypical cases, and for conditions on Wing’s autistic spectrum, such as Asperger’s syndrome. These changes had the effect of broadening the category of autistic disorder beyond the boundaries suggested by previous diagnostic systems (Volkmar and Klin, 2005). Also departing from past approaches was the elimination of the age-of-onset requirement, a clear departure from Kanner’s and Rutter’s formulations. The new criteria did, however, provide a better foundation for diagnosing autistic disorder in older children (Volkmar and Klin, 2005).

Chapter 1: Autism nosology

7

DSM-III-R was replaced by DSM-IV in 1994. The DSM-IV criteria for PDDs were designed to be consistent with ICD-10, although there are some differences between the two systems. Specifically, ICD-10 has separate diagnostic guidelines for clinical and research purposes, while DSM-IV uses one diagnostic system for both. There are also minor differences between the two systems in terminology, and ICD-10 retains the category of atypical autism. Both systems are in the process of revision, with DSM-5 publication planned for 2013. Table 1.2 presents the DSM-IV criteria for autistic disorder (DSM-IV, APA, 1994; DSMIV-TR, APA, 2000). Of note is that the early-onset criterion (with evidence before age 3 of delays or abnormal function in at least one of the three areas of social interaction, language, and imaginative play) has been restored. There are 12 separate symptom criteria, reduced from 16 in DSM-III-R. To be diagnosed with autistic disorder, a child must meet six of these 12 criteria, with at least two symptoms indicating impairment in social interaction and one symptom each of impaired communication and restrictive, repetitive, and stereotyped behavior and interests. This weighting of deficits in social interaction reflects the primacy that both Kanner and Wing attributed to social impairments in autism. In the descriptive material that accompanies the table of criteria in DSM-IV-TR, there are a number of elaborations. With regard to peer relations (criterion A1b), it is stated that Table 1.2 Diagnostic criteria for 299.00 Autistic Disorder. A.

A total of six (or more) items from (1), (2), and (3), with at least two from (1), and one each from (2) and (3): (1) qualitative impairment in social interaction, as manifested by at least two of the following: (a) marked impairment in the use of multiple nonverbal behaviors such as eye-to-eye gaze, facial expression, body postures, and gestures to regulate social interaction (b) failure to develop peer relationships appropriate to developmental level (c) a lack of spontaneous seeking to share enjoyment, interests, or achievements with other people (e.g. by a lack of showing, bringing, or pointing out objects of interest) (d) lack of social or emotional reciprocity (2) qualitative impairments in communication as manifested by at least one of the following: (a) delay in, or total lack of, the development of spoken language (not accompanied by an attempt to compensate through alternative modes of communication such as gesture or mime) (b) in individuals with adequate speech, marked impairment in the ability to initiate or sustain a conversation with others (c) stereotyped and repetitive use of language or idiosyncratic language (d) lack of varied, spontaneous make-believe play or social imitative play appropriate to developmental level (3) restricted repetitive and stereotyped patterns of behavior, interests, and activities, as manifested by at least one of the following: (a) encompassing preoccupation with one or more stereotyped and restricted patterns of interest that is abnormal either in intensity or focus (b) apparently inflexible adherence to specific, nonfunctional routines or rituals (c) stereotyped and repetitive motor mannerisms (e.g. hand or finger flapping or twisting, or complex whole-body movements) (d) persistent preoccupation with parts of objects

B.

Delays or abnormal functioning in at least one of the following areas, with onset prior to age 3 years: (1) social interaction, (2) language as used in social communication, or (3) symbolic or imaginative play.

C.

The disturbance is not better accounted for by Rett’s disorder or Childhood Disintegrative Disorder.

Reprinted with permission from the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (Copyright © 2000). American Psychiatric Association.

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younger individuals may have little or no interest in establishing friendships, and older individuals may have an interest in friendship but lack understanding of the conventions of social interaction. Lack of social reciprocity (criterion A1d) is described as not actively participating in simple social play or games, preferring solitary activities, or involving others in activities only as mechanical aids. Autistic individuals may be oblivious to other children, have no concept of the needs of others or fail to notice another’s distress. Communication impairment is further described as follows. When speech does develop, the pitch, intonation rate, rhythm, or stress may be abnormal . . . Grammatical structures are often immature and include stereotyped and repetitive use of language (e.g., repetition of words or phrases regardless of meaning; repeating jingles or commercials) or idiosyncratic language (i.e., language that has meaning only to those familiar with the individual’s communication style). Language comprehension is often very delayed and the individual may be unable to understand simple questions or directions. A disturbance in the pragmatic (social use) of language is often evidenced by an inability to integrate words with gestures and understand humor or nonliteral aspects of language such as irony or implied meaning. Imaginative play is often absent or markedly impaired. (APA, 2000, pp. 70–71)

A detailed description of restricted, repetitive and stereotyped patterns of behavior and interests is also provided: Individuals with autistic disorder display a markedly restricted range of interests and are often preoccupied with one narrow interest (e.g., dates, phone numbers, radio station call letters). They may line up an exact number of playthings in the same manner over and over again or repetitively mimic the actions of a television actor. They may insist on sameness and show resistance to or distress over trivial changes (e.g., a younger child may have a catastrophic reaction to a minor change in the environment such as rearrangement of the furniture or use of a new set of utensils at the dinner table). There is often an interest in nonfunctional routines or rituals or an unreasonable insistence on following routines (e.g., taking exactly the same route to school every day). Stereotyped body movements include the hands (flapping, finger flicking) or whole body (rocking, dipping, and swaying) . . . These individuals show a persistent preoccupation with parts of objects (buttons, parts of the body). There may also be a fascination with movements (e.g., the spinning wheels of toys, and the opening and closing of doors, an electric fan or other rapidly revolving object). The person may be highly attached to some inanimate object (e.g., a piece of string or a rubber band). (APA, 2000, p. 71)

DSM-IV-TR also makes note of symptoms and behaviors that may accompany autistic disorder but are not criteria for the diagnosis: hyperactivity; short attention span; impulsive, self-injurious, and aggressive behaviors; hypersensitivity to sounds, light, touch, smells; abnormalities in eating; sleep disturbance; and abnormalities of mood and affect. The criteria for childhood autism in ICD-10 are broadly compatible with those for autistic disorder in DSM-IV. Prior to development of the DSM-IV, a multinational field trial was undertaken in which 125 clinicians rated 977 cases of autism and related disorders, using DSM-III, DSM-III R, and proposed ICD-10 criteria (Volkmar et al., 1994). ICD-10 criteria were found to be more specific and to yield fewer false positive diagnoses than DSMIII R criteria. This field trial and subsequent discussion led to the decision to utilize criteria similar to those in ICD-10 in DSM-IV. The two official diagnostic systems thus define autistic disorder (childhood autism) according to criteria that grew out of Kanner’s seminal clinical work and Rutter’s

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reconfigured criteria – clarified, sharpened, and validated over time. These criteria show good reliability and validity, particularly with regard distinguishing autistic disorder from non-autistic psychiatric conditions (Volkmar et al., 1994; Rutter and Schopler, 1988; Volkmar and Klin, 2005). What is less clear is the validity of the distinction between autistic disorder and other PDDs, as they are defined in DSM-IV and ICD-10 (Mahoney et al., 1998; Myhr, 1998; Klin et al., 2000; Witwer and Lecavalier, 2008). The absence of valid distinctions among the PDDs is one of the primary reasons that the plan for DSM-5 is to abandon the present distinctions among the PDDs and establish criteria for a single diagnosis, Autism Spectrum Disorder, that will unite the historic diagnoses of Autistic Disorder, Asperger’s syndrome and PDD-NOS, which will then cease to be used.

Asperger’s syndrome In 1944, one year after Kanner described infantile autism, Hans Asperger, an Austrian pediatrician, published his postgraduate thesis. In this paper he described four boys, aged 6–11, as examples of a group of youngsters with what he called “autistischen psychopathen im Kindesalter,” best translated as “autistic personality disorders in childhood.” Asperger’s report appeared in Austria during World War II. He was unaware of Kanner’s work, and his borrowing the same word – autistic – from Bleuler was a coincidence. Asperger’s paper remained largely unknown outside Austria, Germany, the Netherlands, and the Soviet Union until 1981, when Lorna Wing published “a clinical account” of what she called Asperger’s syndrome. Wing emphasized the following features of the syndrome, as described by Asperger. (1) Absence of any delay in speech, a full command of grammar, but some problems initially with using pronouns correctly. Speech is described as pedantic and consisting of lengthy disquisitions on favorite subjects, with some repeated and stereotyped word usage. (2) Impaired nonverbal communication, with limited gestures and facial expression; monotonous, droning or exaggerated vocal intonation; and poor comprehension of other people’s expressions and gestures. (3) Inability to understand the unwritten rules governing social behavior in such areas as speech, gesture, posture, eye contact, and choice of clothing. Accompanying this deficit are inappropriate social approaches (not adapted to the needs and personalities of others), sensitivity to criticism, and ineptitude with the opposite sex. (4) Repetitive activities and resistance to change, including intense attachment to certain objects. (5) Clumsy and ill-coordinated gross motor movements; odd posture and gait. (6) Particular skills: excellent rote memory; intense interest in one or two subjects such as “astronomy, geology, history of the steam train . . . bus timetables . . . prehistoric monsters,” at the exclusion of others. Talk about these areas of special interest consists of long repetition of memorized facts – whether or not the listener is interested. (7) Appearing markedly eccentric in the school environment, with possible consequences of being bullied and becoming secondarily fearful, or being accepted as “eccentric professors.” At school, these children may follow their own interests regardless of their teachers’ instruction. Based on her own experience with cases similar to Asperger’s, Wing proposed several modifications to Asperger’s account. Unlike Asperger, who indicated that symptoms are

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not evident before age 3, Wing noted a lack of sharing pleasure with others; absent or restricted imaginative play; and a lack of the expected urge to communicate with babbling, gestures, and speech in infants and toddlers. Although Asperger specified that his patients develop speech early, Wing found that half of her cases were slow to talk. She also noted that when these children acquired speech, the language was impoverished, in the sense that it was copied from others and “gave the impression of being learned by heart” – despite the presence of well-developed vocabulary and good grammar. Wing also disagreed that these individuals were creative within their restricted areas of interest. She characterized their thought processes as narrow, pedantic, and literal. Wing’s discussion of Asperger’s syndrome reflected the perspective derived from Wing and Gould’s (1979) Camberwell study, namely that certain problems in child development clustered together: absence or impairment of social interaction, absence or impairment of comprehension or use of verbal and nonverbal language, and absence or impairment of flexible imaginative activities. Like children with Kanner’s autism, people with Asperger’s syndrome displayed common, co-occurring developmental deficits. The two conditions were, therefore, related. Wing left open the issue of whether the conditions were separate entities – something she believed could best be determined when their etiologies were finally known. In the meantime, she suggested that there was clinical value in using the concept when working with children and adults with features of Asperger’s syndrome. Wing’s paper had enormous influence. The term Asperger’s syndrome was adopted by clinicians worldwide and many research studies were undertaken to better define and validate the disorder. Diagnostic criteria were proposed by a number of authors, each with a somewhat different perspective on what constituted the defining features of the condition (Szatmari et al., 1989; Gillberg, 1991; WHO, 1992; APA, 1994). The diagnoses Asperger syndrome and Asperger’s Disorder were included in ICD-10 and DSM-IV. In both ICD-10 and DSM-IV, the specific criteria for impairment in social interaction and for repetitive, restricted, and stereotyped behaviors in Asperger’s syndrome are the same as those for autistic disorder. In addition, DSM-IV requires that there be no significant general delay in language, in cognitive development or in the development of self-help skills and adaptive behavior (other than in social interaction) and curiosity about the environment. If full criteria are also met for another PDD, then the diagnosis of Asperger’s Disorder is excluded. This has been called the “precedence rule” (Klin et al., 2005). ICD-10 is somewhat more specific with regard to absence of language and developmental delays, specifying that single words should have developed by the age of 2 years, communicative phrases by the age of 3 years. It states that self-help skills, adaptive behavior, and curiosity about the environment during the first 3 years should be “consistent with normal intellectual development.” ICD-10 also adds mention of motor skills, stating that motor milestones may be somewhat delayed and that motor clumsiness is “usual.” It also states that isolated special skills, associated with preoccupations, are common, although not required for diagnosis. Earlier diagnostic systems proposed by Szatmari and Gillberg included social dimensions that are not diagnostic criteria in DSM-IV and ICD-10. Both had a criterion of lack of desire for, or interest in, making friends. These authors also mentioned inability to “appreciate social cues” (Gillberg), “difficulty sensing feelings of others”, and “clumsy social approach” (Szatmari) – features of the syndrome emphasized by Asperger and Wing. With regard to restricted interests and behavior, Gillberg required the presence of an “all-absorbing narrow interest” and imposition of routines on oneself or others.

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Despite Asperger’s original formulation, ICD-10 and DSM-IV do not include abnormalities of speech and language in their criteria for Asperger’s syndrome. They do require that the children lack any significant delay in language development, something emphasized by Asperger in the cases he described (but not invariably present in Wing’s cases). In contrast, Gillberg and Szatmari included both odd speech and abnormalities in nonverbal communication as separate diagnostic criteria. The role of intellectual functioning is not addressed in any of these diagnostic systems. However, the requirements in DSM-IV and ICD-10 that there be no clinically significant delays in language, cognitive development, self-help skills, adaptive behavior, and curiosity about the environment in the first 3 years of life makes it unlikely that most children with intellectual disability will meet criteria for Asperger’s syndrome. These approaches to diagnosing Asperger’s syndrome were all developed and published between 1989 and 1994. Their purpose was to select from available clinical descriptions the essential defining features of the disorder. Unlike the criteria for infantile autism offered by Rutter in 1978, these diagnostic approaches were not the product of years of research that had validated Asperger’s syndrome as a unique disorder. Rather, they were offered as a means for undertaking such studies – to specify a clinical population that could be the target of research. Indeed, as Klin et al. (2005) pointed out, Asperger’s syndrome was included in the official diagnostic systems, ICD-10 and DSM-IV, only after intense debate, and the selected diagnostic criteria were tentative. Their inclusion was to provide some consensus on how to define the disorder at a time when researchers were “using the label idiosyncratically,” defining the disorder according to their own clinical experiences and theoretical perspectives (Klin et al., 2005). Since the development of ICD-10 and DSM-IV, there have been many studies that have addressed the validity of Asperger’s syndrome as a unique diagnosis. To be valid, a psychiatric diagnosis must be clearly differentiated from other psychiatric disorders in ways that are independent of its diagnostic criteria. Generally, a psychiatric disorder must have face validity: it must be clinically meaningful. It should also have external validity, i.e. it should be distinguishable from other psychiatric disorders by: (1) differential etiology, (2) differential response to treatment, (3) different clinical course and outcome, and (4) differential relation to measures other than those used to establish the diagnosis (South et al., 2005). With regard to the clinical meaningfulness of Asperger’s syndrome as a diagnosis, there is little doubt as to its utility. The descriptions provided by Asperger, Wing, and the narrative texts in ICD-10 and DSM-IV-TR clearly identify a group of youngsters who would be readily recognized by many educators, pediatricians, mental health professionals, and parents. These are self-involved, verbally adept, often intelligent, eccentric children and adolescents – mostly boys – who have difficulty getting along with people because they fail to read social cues, have trouble comprehending other people’s emotions and reading their expression in voice and gesture; make clumsy social approaches; talk at people rather than with them; and spend most of their time in the intense pursuit of narrow specialized interests, about which they speak at length. They also tend to be socially isolated (sometimes teased and rejected) and to react to peers with incomprehension, indifference or resentment. The act of recognizing these individuals and providing them with a diagnostic label that they can accept and a description that “strikes a chord” with them and their families lays the foundation for their treatment. The diagnostic label of Asperger’s syndrome thus has enormous clinical value (Wing, 2000; Frith, 2004).

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One question with regard to face validity, however, is whether the specified diagnostic criteria of ICD-10 and DSM-IV permit clinicians to adequately capture the group that they would want to designate with Asperger’s syndrome. The criteria have been criticized for being too restrictive – in large part because of the precedence rule – and for failing to address the unique language problems of individuals historically identified as having Asperger’s syndrome (Mayes et al., 2001; Miller and Ozonoff, 2000; Szatmari, 2000). For example, Mayes et al. (2001) applied DSM-IV criteria to 157 children with pre-existing clinical diagnoses of autistic disorder (AD) and Asperger’s syndrome (AS) and found that all had communication disorders of sufficient severity to shift them into the DSM-IV category of AD. Other authors have focused on the fact that the presence or absence of speech and cognitive delays in the first 3 years of life serves as the primary differentiator between AD and AS (Leekam et al., 2000; Wing, 2005). By this criterion, many of Wing’s (1981) cases – children who acquired speech late – would not have been diagnosed with AD. Hippler and Klicpera (2003) revisited 44 of Asperger’s own case records to see how children he diagnosed as having “autistic personality disorder” would be classified under ICD-10. They found that 95% of cases displayed some kind of language and communication abnormalities – which Asperger considered to be “one of the most dominant characteristics of the disorder.” Of these cases, 68% could still be diagnosed with AS under ICD-10, but only if the precedence rule were ignored. Forty-five per cent of Asperger’s cases would have met symptom criteria for autism (including language impairment), but had normal speech and cognitive development in the first 3 years of life; 25% of Asperger’s cases would not have met the requirement of normal development before age 3 and therefore would be diagnosed with childhood autism. Taking a similar approach, Woodbury-Smith et al. (2005) re-examined the DSM-IV field trial data to determine if cases diagnosed with AS by experienced clinicians would be so classified under ICD-10 or DSM-IV. The investigators found that 69% of cases would continue to be designated as having AS. Twenty-three per cent would have to be reassigned to the AD category under DSM-IV and to childhood autism under ICD-10; 9% would have been designated with PDD-NOS or atypical autism. Of their sample, 38% had impairments in all developmental domains (including language), but did not have delays in early linguistic and cognitive development. The authors noted, however, that much of the early developmental information on these cases was suboptimal, and they suggested that the onset criteria did not constitute a reliable differentiator between Asperger’s syndrome and autistic disorder. They also suggested that the diagnostic criteria for AS need to be reworked, to address this problem and to provide for better accounting of the communication abnormalities in AS. There are, thus, a number of recognized problems with the face validity of AS as defined in DSM-IV and ICD-10. With regard to external validity, dozens of research studies have been completed, most of which have looked at the differentiation of AS from AD, especially for autistic disorder without intellectual disability, so-called high-functioning autism (HFA). This research has been the subject of a number of reviews (Volkmar and Klin, 2000; Howlin, 2003; Macintosh and Dissanayke, 2004; Frith, 2004; Klin et al., 2005; Witwer and Lecavalier, 2008). One problem noted by these reviewers is that the definition and criteria used for Asperger’s syndrome differed among studies. Wing’s original description was used, in addition to the criteria of Gilberg, Szatmari, ICD-10, and DSM-IV. More often than not, DSM-IV and ICD-10 criteria were modified, e.g. by ignoring the precedence rule in order to increase the number of subjects who could be diagnosed with AS. Many studies also failed to

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control for IQ, which would be expected to be higher in AS. Some studies were circular: they confirmed differences between HFA and AS based on differences in diagnostic criteria. Despite these limitations, there is substantial useful research on specific dimensions needed for external validation of a diagnosis. Neurobiological variables that might reflect differential etiology have been looked at, including history of neonatal adversity, presence of neurological impairment, results of brain imaging, and family/genetic history. In their review, Macintosh and Dissanayke (2004) found no differences between AS and HFA in history of obstetric insults or other natal events. Klin et al. (2005) noted that there was scant neuroimaging data to differentiate AS from AD, but cited one study (Lotspeich et al., 2004) that suggested narrow anatomical differences among individuals with low-functioning autism, high-functioning autism, AS and controls, with the autistic subjects having increased cerebral gray matter, and the AS subjects having gray matter volume in between the autistic subjects and controls. Family/genetic studies have suggested continuity among the PDDs. There are many examples of AS and AD and PDD-NOS and AD co-occurring in siblings, even in monozygotic twins, strongly suggesting a common genetic predisposition (Frith, 2004; Klin et al., 2005; Witwer and Lecavalier, 2008). Comparisons of clinical course and outcome were judged to be preliminary at best by Klin et al. (2005). There is some evidence that children with AS are mainstreamed in education earlier than those with HFA and have higher academic achievement, but there is also evidence that they experience more social difficulties (Macintosh and Dissanayke, 2004). Saulnier and Klin (2007) found that AS and HFA groups were equally impaired in adaptive functioning, as measured by the Vineland Adaptive Behavior Scale, despite the AS groups having higher verbal IQ scores and less symptomatology. Szatmari et al. (2003) explored the relationship between cognitive abilities and outcome in children with AS and HFA who were followed from age 4–6 years to age 10–13 years. Significant predictors of outcome (as measured by symptom severity and adaptive functioning) were early language skills and nonverbal IQ, but the association between language skills and outcome was stronger for the HFA group than for the AS group. In a separate paper, Szatmari’s research group (Bennett et al., 2008) reported that specific language impairment at age 6–8 years was a better predictor of outcome than an initial diagnosis of HFA or AS, based on the history of language delay. Howlin (2003) found no differences between adults with AD and adults with HFA on several measures of functioning. Another variable of interest in the research on validating AS is neuropsychological functioning, including pattern of performance on intelligence testing, executive function, social cognition, and motor skills. Again, studies have been equivocal – some supporting the notion that AS is a valid, distinct diagnosis from AD, others challenging it. As would be expected from the diagnostic criteria, individuals with AS as a group tend to have higher verbal IQs than individuals with HFA (Koyama et al., 2007). In addition, Klin et al. (1995) reported that their subjects with AS had a significantly higher verbal IQ than nonverbal IQ, whereas those with HFA tended to have comparable verbal and nonverbal performance. This finding was partially confirmed by Miller and Ozonoff (2000) and Ghaziuddin and Mountain-Kimchi (2004). In their review of these studies and others, Witwer and Lecavalier (2008) noted that differences in IQ profiles provide some support for subtype distinction, but wonder if this finding is an artifact of diagnostic criteria and subject selection. Studies of executive and motor functioning have failed to differentiate AS from HFA (Howlin, 2003; Macintosh and Dissanayke, 2004; Witwer and Lecavalier, 2008). Measures of social cognition (including theory of mind, the ability to understand other people’s beliefs and motives) have shown differences between the two groups, but performance on these

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measures correlates with verbal abilities and therefore may not be independent of diagnostic criteria (Macintosh and Dissanayke, 2004). Another dimension for validation is symptomatology – the type and severity of symptoms displayed by individuals with AS. One approach has been to focus on repetitive behaviors. South et al. (2005) hypothesized that circumscribed interests would be more characteristic of AS, while repetitive behaviors would be more prominent in HFA, but found no differences. Other authors have suggested that there are more differences in repetitive behaviors between HFA and AS during the preschool years, but that these differences diminish in time. Measures of social interaction show a similar trend, with the young AS children demonstrating fewer deficits in reciprocal social interaction, but with differences lessening over time (Macintosh and Dissanayke, 2004). On diagnostic measures of core features of autism, results have been mixed. Some studies report that children with AD exhibit a higher number of core symptoms at age 4–5 than those with AS, but the effect of IQ was not taken into consideration. Other studies were equivocal (Witwer and Lecavalier, 2008). Of significance in these studies is the finding that children with AS also had significant symptoms in the realm of verbal communication. The official diagnostic systems in use today are categorical: an individual either has or doesn’t have Asperger’s syndrome, autistic disorder or PDD-NOS. Yet, as the above discussion makes clear, Asperger’s syndrome as presently defined is a category of equivocal validity, difficult to separate from high-functioning autism. Indeed, there are clear continuities with autistic disorder, based on family/genetic history. Also, the differences between HFA and AS in early speech and cognitive development – differences that determine their diagnostic distinction – tend to diminish as children grow older, making the two conditions harder to distinguish. There is some evidence that individuals with Asperger’s syndrome have a different pattern of performance on IQ testing from those with autistic disorder, but other neuropsychological, biological, and core symptomatic features are not significantly different in people with AS and HFA. Even clinical outcome – which one would expect to be better in a group of developmentally disabled individuals with higher IQ and better verbal abilities – appears to depend more on an individual’s intelligence and communication skills after school entry than it does on whether that individual meets diagnostic criteria for Asperger’s syndrome or autistic disorder. There are, moreover, inherent problems with the current diagnostic criteria used for Asperger’s syndrome in DSM-IV and ICD-10, because they fail to include verbal communication deficits that have been well described in the disorder. Yet the diagnosis of Asperger’s syndrome has proved to be of significant clinical utility. As Uta Frith (2004) has stated: Abandoning the label Asperger syndrome would lose the historical context and the wealth of information that has now been accumulated around it. The term has contributed significantly to an increase in the awareness of autistic disorder in the general public. One reason that the label was so keenly taken up was that it helped ordinary people to understand what might be the matter with the strange person with narrow obsessive interests and social ineptness whom they might have come across in their everyday environment. It makes sense to provide a special label to the least severe and least handicapping form of autistic disorder. It is less stigmatising and allows for the fact that at least some individuals are able to cope with minimal supervision or specialist help. (p. 675)

It is not clear what will happen to “Asperger’s syndrome” after the publication of DSM-5. The diagnosis will likely be subsumed under Autism Spectrum Disorder – in acknowledgment of the research just reviewed, which shows that presently used diagnostic distinctions between

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Asperger’s syndrome and high-functioning autism lack validity. However, there remains a compelling argument – eloquently expressed by Frith – that it is helpful for higher-functioning ASD individuals to describe themselves as having Asperger’s syndrome, and that clinical utility alone may be sufficient reason to retain the category (Ghaziuddin, 2011). An argument has also been made that elimination of Asperger’s syndrome as a separate diagnosis is premature, that what is needed is refinement of the criteria for the disorder (Ghaziuddin, 2010). Szatmari et al. (2009) presented longitudinal data suggesting that distinguishing “AS” from “autistic” patients according to presence or absence of structural language impairment yielded two groups whose developmental trajectories diverged in later childhood and adolescence – as reflected in measures of adaptive function and behavior. (This difference over time was not attributable to verbal IQ.) The authors, however, conceptualized the distinction between these two groups of higher-functioning ASD children as the presence or absence of a comorbid diagnosis of structural language impairment. As discussed later in this chapter, this approach is consistent with proposed DSM-5 procedures, which support making distinctions among ASD individuals on the basis of underlying and comorbid developmental disorders. Whatever the ultimate fate of “Asperger’s syndrome” as a diagnostic entity, it has, like “autistic disorder,” a rich clinical history and has been the subject of fruitful investigation. It is a term that will likely continue to be used by patients, families, and caregiving providers for a long time.

Pervasive developmental disorder, NOS Wing and Gould’s 1979 Camberwell study and Wing’s 1981 discussion of Asperger’s syndrome raised awareness of conditions that shared with autistic disorder a central feature of severe deviations in the development of social reciprocity, but differed from it in the quality of social impairment or in the severity of other features of the autistic triad. In 1980, DSM-III introduced the diagnostic category of atypical pervasive developmental disorder for children with autistic-like features who did not meet the criteria for infantile autism. DSM-III-R (1987) conferred upon these children the diagnosis pervasive developmental disorder, not otherwise specified (PDD-NOS). This term was retained in DSM-IV (1994), but three specifically named PDDs – Asperger’s disorder, Rett’s disorder, and childhood disintegrated disorder – were removed from the category. Another important change in DSMIV (1994) was a departure from the requirement that deficits in social interaction are necessary for the diagnosis. Significant impairment in any two of the three dimensions of the autistic triad were deemed sufficient. Thus, a child could be diagnosed with PDD-NOS if he had impaired verbal or nonverbal communication skills and repetitive, restricted or stereotyped behaviors, interests, and activities, without significant deficits in social interaction. This approach had the effect of broadening the category of PDD-NOS, and was changed in DSM-IV-TR (APA, 2000). Now, to be diagnosed with PDD-NOS under DSM-IV-TR, a child must have “severe and pervasive impairment in the development of reciprocal social interaction” plus some symptoms of either impaired communication or stereotyped behavior, interests, and activities. The child’s symptoms must also not be attributable to other PDDs, schizophrenia or a personality disorder. ICD-10 differs somewhat from DSM-IV-TR in its approach to PDDs that do not meet the criteria for childhood autism. Like DSM-IV-TR, ICD-10 provides separate criteria for the diagnoses Asperger’s syndrome, Rett’s syndrome, and childhood disintegrative disorder, but

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it also utilizes the category atypical autism and permits this diagnosis to be made if criteria are met in one or two, but not all three, of the areas of pathology required for a diagnosis of childhood autism. (In this regard, ICD-10 is consistent with the earlier, 1994 version of DSM-IV.) The diagnosis atypical autism is also to be used if criteria for childhood autism are met, but the age of onset is greater than 3 years. Additionally, ICD-10 includes the category pervasive developmental disorder, unspecified, which is to be used for cases that fit the general description of a PDD, but for which there is inadequate information or contradictory findings, thus precluding diagnosis with one of the specified PDDs. As these definitions suggest, PDD-NOS constitutes something of a residual or catchall diagnosis – for cases that meet some, but not all, of the criteria for autistic disorder. PDDNOS is a broad designation. Unlike Asperger’s syndrome, it can include individuals who had significant developmental delays in the first 3 years of life, but can also include individuals with high average or superior intellectual functioning who fail to meet criteria for either AD or AS. Epidemiologic studies suggest that PDD-NOS is far from rare: the prevalence of PDDNOS and Asperger’s syndrome, taken together, appears to be 10-fold that of autistic disorder (Fombonne, 2007). Towbin (2005) has summarized the clinical uses of PDD-NOS: (1) as a provisional diagnosis when information is lacking, (2) for milder impairments within the autistic spectrum, (3) for cases with a later age of onset, and (4) for conditions with significant deficits in social reciprocity that are not yet completely characterized. In Towbin’s view, Asperger’s disorder had once been one of these incompletely characterized conditions, formerly included within PDD-NOS (in DSM-III-R), but then identified as a distinct diagnosis in DSM-IV. Clinically described conditions cited by Towbin that could presently be diagnosed as PDD-NOS are multiple complex developmental disorder (MCDD, Buitelaar et al., 1999); deficits in detention motor control and perception (DAMP, Gillberg, 1995) and semantic–pragmatic language disorder, a condition described by speech-language therapists. Although PDD-NOS is acknowledged to be a heterogeneous category, it has nevertheless been criticized as unreliably differentiated from autistic disorder. Mahoney et al. (1998) showed that experienced clinical evaluators had a high rate of agreement during case reviews when distinguishing PDD cases from non-PDD and autistic disorder from Asperger’s disorder, but not when identifying children with atypical autism. Allen et al. (2001) found that preschool children with autistic disorder and those with PDD-NOS (using DSM-III-R criteria) did not differ in verbal and adaptive skills or in maladaptive behaviors when they were grouped by IQ. This problem has been addressed by Buitelaar et al. (1999) and Walker et al. (2004), who have recommended specific revisions of present diagnostic criteria to more clearly define a group of children who can be reliably diagnosed with PDD-NOS. Even if a firmer boundary could be defined to separate children with PDD-NOS from those with autistic disorder, it is likely that there would still be strong continuities between the groups. As presently defined, both conditions have been diagnosed in siblings, including monozygotic twins (Bailey et al., 1995; LeCouteur et al., 1996). According to Towbin (2005), neuroimaging and measures of neuropsychological functioning (including visual evoked potentials, gaze fixation, and theory of mind tasks) have failed to differentiate PDD-NOS from autistic disorder. Indeed, the concept of an autistic spectrum is based on the notion of assumed continuity between autistic disorder and the non-autistic PDDs. It is expected that PDD-NOS will disappear as a separate diagnostic category in DSM-5 and will be subsumed under Autistic Spectrum Disorder.

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Rett’s disorder (Rett syndrome) The syndrome that bears his name was described by Augustus Rett in 1966. It gained recognition following a report published by Hagberg et al. (1983). According to Van Acker et al. (2005), Rett syndrome is the second most common cause of intellectual disability in females, following Down syndrome. It is a progressive neurological disorder with a characteristic course of development that primarily affects females and – in more than 90% of cases – has been linked to mutations in MECP2, a gene on the X-chromosome that codes for a protein that silences various genes during brain development. The clinical course of Rett syndrome has been divided into four stages. Stage one, onset, is from 6 to 18 months of age. During this stage, there is developmental stagnation, deceleration of head growth and loss of interest in play activity. Stage two is from 1 to 4 years. During this stage, there is developmental regression, loss of hand use, with the appearance of “hand wringing” and other manual stereotypies; loss of expressive language; and, frequently, seizures. Self-injurious behavior may occur. Stage three is from 2 to 10 years. During this stage, autistic features diminish; there is increased communication and social interest; intellectual disability is apparent; and hand stereotypies persist. Other characteristics are ataxia, spasticity, apnea, bruxism, and seizures. Stage four, after 10 years of age, is marked by decreased mobility, motor neuron symptoms, growth retardation, and progressive scoliosis (Rett Syndrome Diagnostic Work Group, 1988). The rationale for including Rett syndrome among the PDDs in ICD-10 and DSM-IV was apparently based on symptoms of impaired social interaction and language and the presence of stereotypies, particularly during the preschool years. It is important to make the diagnosis of Rett syndrome for proper treatment of associated features such as seizures, apnea, spasticity, and scoliosis. The course of Rett syndrome is distinctive, however, and it is unlike most other PDDs. Many discussions of autism spectrum disorders do not include Rett syndrome because of its unique treatment needs and prognosis. Rett syndrome will be removed as a diagnostic category from DSM-5, but can be designated as an underlying etiology in comorbid cases of Autistic Spectrum Disorder and Rett syndrome. It is best conceptualized as a type of syndromic autism. (See Chapter 5.)

Childhood disintegrative disorder Childhood disintegrative disorder (CDD) is the oldest and rarest of the PDDs. The first cases in the literature were six children described by Theodor Heller, a Viennese educator, in 1908. These children experienced severe regression in their social and communication skills after developing normally for the first 3–4 years of life. Heller called the condition he described dementia infantilis. It has subsequently borne the names Heller’s syndrome, progressive disintegrative psychosis, and childhood disintegrative disorder – its name in DSM-IV. In ICD-10, the condition is labeled “other childhood disintegrative disorder,” in order to distinguish it from Rett syndrome. This condition was not specifically named in DSM-III or DSM-III-R, where it was presumed to be grouped with the dementias. The problem with this assignment, according to Mouridsen (2003), was the frequency with which no organic cause could be found for the regression experienced by these children. Also, most of these children come in time to resemble children with autistic disorder – and to meet criteria for the symptomatic presentation of AD – although their course of initial normal development, followed by late and dramatic regression, is quite different.

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Prior to inclusion of CDD as a pervasive developmental disorder in DSM-IV, Volkmar (1992) published a review of all the known cases of the condition in the world literature; he identified 77. Although under-recognition of this disorder was likely, CDD appeared to be quite rare. In a review of the epidemiologic evidence, Fombonne (2002) found the prevalence of CDD to range from 0.11 to 0.64 per 10 000, suggesting that only 1 in every 175 children with an ASD would be diagnosed with CDD. The DSM-IV criteria for CDD are presented in Table 1.3. The most striking – and for families, the most tragic – feature of CDD is the severe loss of previously acquired skills and the development of symptoms similar to autistic disorder after least 2 years of normal development (usually between age 3 and 4 years). Additionally, most children with CDD become severely cognitively impaired (Volkmar and Cohen, 1989; WHO, 1992; Mouridsen et al., 1998; APA, 2000). Children who were speaking in full sentences develop total mutism or deteriorate markedly in verbal ability; stereotyped behaviors emerge; and there may be general loss of interest in the environment (a diagnostic criterion in ICD-10). There is often marked deterioration in self-help skills, including toileting. The onset of these changes can be either acute (within days to weeks) or gradual (over weeks and months) (Volkmar et al., 2005). In the text that accompanies the diagnostic criteria for CDD in DSM-IV-TR, there is mention that the disorder is occasionally associated with a medical condition that accounts for the developmental regression. The accompanying text for ICD-10 states that there is usually no evidence of any identifiable organic disease or damage. As Mouridsen (2003) and Volkmar et al. (2005) have pointed out, most children with this kind of dramatic regression are investigated thoroughly with little result in terms of identifying an etiology. Nevertheless, allowing for the possibility of a specific neurodegenerative disorder as an etiology for CDD appears to be problematic, in that it broadens the category and considerably complicates its Table 1.3 Diagnostic criteria for 299.10 Childhood Disintegrative Disorder. A.

Apparently normal development for at least the first 2 years after birth as manifested by the presence of ageappropriate verbal and nonverbal communication, social relationships, play, and adaptive behavior.

B.

Clinically significant loss of previously acquired skills (before age 10 years) in at least two of the following areas: (1) (2) (3) (4) (5)

C.

expressive or receptive language social skills or adaptive behavior bowel or bladder control play motor skills

Abnormalities of functioning in at least two of the following areas: (1) qualitative impairment in social interaction (e.g. impairment in nonverbal behaviors, failure to develop peer relationships, lack of social or emotional reciprocity) (2) qualitative impairments in communication (e.g. delay or lack of spoken language, inability to initiate or sustain a conversation, stereotyped and repetitive use of language, lack of varied make-believe play) (3) restricted, repetitive, and stereotyped patterns of behavior, interests, and activities, including motor stereotypies and mannerisms

D.

The disturbance is not better accounted for by another specific Pervasive Developmental Disorder or by Schizophrenia.

Reprinted with permission from the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (Copyright © 2000). American Psychiatric Association.

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being distinguished from syndromic autism: cases of autistic disorder (with onset before age 3 years) and PDD-NOS (with onset after 3 years) that can be attributed to a specific neurologic disorder, such as infantile spasms, tuberous sclerosis, or subacute sclerosing panencephalitis. If one accepts that classical and atypical autism can result from known dementing disorders, then CDD, as presently defined, would best be understood as idiopathic. Differentiation of CDD from autistic disorder is still a subject of some debate. There is overlap between the diagnostic criteria for the two disorders if regression occurs between 2 and 3 years of age, although the presence of marked regression in adaptive behavior, motor skills, and bowel and bladder control would require a diagnosis of CDD. Also, once regression has occurred, CDD resembles autism symptomatically and in clinical course. Kurita et al. (2004) compared 10 children with CDD (mean age 8.2 years) and 30 matched children with AD who had a history of speech loss. They found only a few differences that supported the validity of CDD as a separate disorder, such as increased frequency of epilepsy and prominence of stereotypies in the CDD population. No differences were found in level of intellectual disability. Tuchman (2006) also reported a higher incidence of clinical epilepsy in CDD than in other PDDs (70% vs. 30%). Hendry (2000), however, asserted that CDD, once established, so closely resembles AD that developmental history becomes the only distinguishing feature. According to Hendry, methodological limitations of the few available studies and a paucity of neurobiological research make it difficult to establish CDD as a separate diagnostic category. The phenomenology of CDD raises another question: whether children who demonstrate a course of largely normal development followed by regression in social and linguistic skills constitute a unique subgroup within the ASDs, differing from children with “congenital” autism, where symptoms are evident in the early months of life. Childhood disintegrative disorder is rare and dramatic, and occurs late in the preschool years. But milder forms of developmental regression occur in autistic children in the second year of life in approximately one third of cases (Hansen, 2008; Baird et al., 2008). In many of these cases, loss of speech or deterioration in social relatedness is the first discernible sign of autism. The next section looks in more detail at regression in ASDs.

Autistic regression In a review of research on regression in ASDs, Stefanatos (2008) described three patterns of development in early autism: (1) early onset, in which behavioral manifestations of autism are observed in the first year of life, such as impairments in joint attention, eye contact, social interest and responsiveness, and communicative intent; (2) stasis or developmental arrest, usually in speech acquisition, following a period of normal development; (3) developmental regression or setback, in which a child appears to follow a course of near-normal acquisition of linguistic and social skills, then – around 15–30 months of age – loses previously acquired language abilities and/or social behaviors (such as making eye contact, initiating communication overtures and imitating). In this third developmental trajectory, stereotypic behaviors may emerge coincident with or following the loss of linguistic and social abilities. Recently, prospective studies have been undertaken of “at-risk” infants – younger siblings of children with ASDs. The developmental course of at-risk infants who were subsequently diagnosed with autism generally confirms, but also refines, Stefanatos’s description. The early-onset pattern also includes slowly emerging symptoms between 6 and 18 months of age. Other developmental patterns may include mixed features of early onset and regression,

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subtle diminution in skills, failures to progress (plateaus), and frank regression (Ozonoff et al., 2008, 2010; Rogers, 2009). Although it is but one of several developmental trajectories, the phenomenon of regression received particular attention in the late 1990s, when Wakefield offered his controversial hypothesis that there was a subset of children with autism who showed regression in their development and later onset of symptoms, and that the cause of their particular condition was the measles–mumps–rubella vaccine (Wakefield, 1998). Indeed, most parents and advocates who claim a connection between autism and vaccines or other specific, postnatal environmental exposures do so because they have observed regression and symptom onset following a period of presumed normal development. The issue of a proposed connection between vaccines and ASDs is addressed in Chapter 2. The focus of the present discussion is on the question of whether autistic regression represents a coherent subtype – a possibly unique phenotype – within the autistic spectrum. There are several intrinsic problems that make it difficult to define a subgroup of children with autistic disorder who have experienced regression: the fact that regression is often gradual or insidious, and documentation of the phenomenon is therefore based largely on retrospective parent reports; difficulty separating regression from developmental pauses or plateaus; and lack of agreement on what type of skill loss constitutes autistic regression (speech? receptive language? social skills?). These difficulties have been addressed by investigators in a number of ways. Lord et al. (2004) provided an operational definition of regression as loss of spontaneous, meaningful words. These researchers found that autistic regression was a reliably identifiable phenomenon, unique to ASDs. Similarly, Baird et al. (2008) defined regression as a loss of five or more words used communicatively for at least 3 months, or loss of some words together with other functional declines. In a populationderived group, Baird found regression to occur in 30% of children with autistic disorder, in 8% of those with broader autism spectrum disorder, but in less than 3% of children with nonautistic developmental disorders. Other researchers have utilized family videotapes to document regression. Werner and Dawson (2005) studied videos of 56 children’s first and second birthday parties: 15 ASD children with a history of regression, 21 ASD children with early-onset symptoms and 20 typically developing children. Using measures of joint attention, word use, declarative pointing, social gaze, orienting to name, repetitive behaviors, affect, and toy play, they were able to validate the existence of autistic regression following normal development – as reported by parents on a standard interview. Maestro et al. (2006) studied home movies taken from birth to 18 months in children with autistic disorder who were divided into an early-onset group and a regressive group, based on a measure devised for this purpose. Also studied were home movies of a group of typically developing children. Patterns of social interaction observed in the movies validated the authors’ definition of regressive autism: abnormalities were present from birth in the early-onset group but emerged later in the regressive group. (There were, however, some abnormalities observed in the regressive group from the early months of life, particularly in their relationship to objects – something the authors identified as an early sign of atypical development.) Thus, it appears that regressive autism can be reliably defined and that there is a pattern of regression unique to ASDs. Does regressive autism differ from early onset autism in ways that are independent of its definition? One phenomenon that has been investigated to see if it correlates with regressive autism is the occurrence of epilepsy – or, in the absence of clinical seizures, the presence of

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epileptiform abnormalities on EEG. There is a historically recognized connection between autism and epilepsy, with approximately one-third of individuals with autism developing clinical seizures by adulthood and a higher risk for epilepsy in children with autism and intellectual disability (Tuchman, 2006). In addition, there are a number of epilepsy syndromes that are associated with regression in language and other abilities and can produce symptoms overlapping with those of autism. These syndromes include West syndrome, Lennox-Gastaut syndrome, Landau–Kleffner syndrome, or LKS (also known as acquired epileptiform aphasia), and a syndrome that has been called continuous spike-wave during slow-wave sleep, or CSWS. (Both LKS and CSWS are associated with an EEG finding of electrical status-epilepticus during slow-wave sleep, or ESES.) Although usually occurring in older children (age 3–8 years), LKS is characterized by rapid language regression and epileptiform changes on EEG. Clinical seizures do not have to be present, and children with earlier onset LKS may also show autistic features (Lewine et al., 1999; Deonna and Roulet, 2006). Stefanatos et al. (2002) have conceptualized LKS as a spectrum disorder that may include regressive autism. Similarly, there is some symptom overlap between children with CSWS and those with autism (Deonna and Roulet, 2006). Children with CSWS can present with aphasia, signs of dementia, echolalia, and repetitive behavior (Stefanatos, 2008). As in LKS, seizures need not be frequent or severe. Several authors have reported a link between epileptiform activity (primarily interictal spikes and waves) and regressive autism. Lewine et al. (1999) compared children with ASDs and a history of regression between 20 and 36 months of age and no history of seizures with a group of children with LKS, using magnetoencephalography. They found that 82% of the autistic children had epileptiform activity similar to that found in the LKS group, although traditional EEG recording revealed epileptiform activity in only 68%. Tuchman and Rapin (1997), looking at ASD children without epilepsy, found that those with a history of regression were more likely to have epileptiform abnormalities on sleep EEGs than those without such history (19% vs. 10%). In contrast, studies by Canitano et al. (2005), Chez et al. (2006), and Baird et al. (2006) failed to show any significant differences between nonepileptic autistic children with and without a history of regression, in terms of frequency of epileptiform abnormalities. These studies led Fong et al. (2008) to conclude that there is no clear relationship between epilepsy and epileptiform EEG abnormalities and language and behavioral regression in autism. When looking at the comparison between children with autism who experienced language regression and non-autistic children with language regression, McVicar et al. (2005) found a much higher rate of EEG epileptiform abnormalities in regressed children without autism (60% vs. 31%). Tuchman (2009) elaborated on this distinction, stating that epileptiform abnormalities are found in only 20% of children who experience autistic regression and that their pattern of EEG abnormality – in particular, the rarity of ESES – distinguishes them from children with LKS, as do age of regression onset and differing clinical features. With regard to clinical epilepsy, very few children with autistic regression show a temporal connection between the onset of regression and seizures (Stefanatos, 2008). Tuchman (2006) summarized the research on regressive autism and epilepsy as follows: “Autism is not an epileptic encephalopathy and epilepsy and epileptiform activity is more likely to be associated with language regression than with autistic regression” (p. 109). Roulet-Perez and Deonna (2006) agreed that autism is not of epileptic origin. They suggested, however, that epilepsy is still a diagnostic consideration in the individual child with autistic regression and always needs to be investigated.

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Section 1: What We Know About Autism

Apart from epileptiform abnormalities, the majority of studies looking at biological differentiators of autistic regression have failed to find significant differences between autistic children with and without a history of regression. These include a number of studies of gastrointestinal disorders (Fombonne and Chakrabarti, 2001; Richler et al., 2006; Baird et al., 2008). Genetic factors, other than MECP2, have not been linked to regression in autism (Stefanatos, 2008), although Gregg et al. (2008) did note differences between autistic children with and without a history of regression in patterns of gene transcription – an epigenetic phenomenon that may be important for pattern of symptom onset and requires further investigation. Rate of head growth (a significant biological correlate of autism) was not found to be different in children whose parents reported regression (Webb et al., 2007). Hansen et al. (2008), in a large sample, found few medical differences between regressed autistic children and a group without loss of skills. Clinical course, by definition, differs in regressive autism, at least in age of onset and presumed normal development prior to regression, but differences here between regressed and non-regressed populations may be less than supposed. A number of studies have identified early developmental abnormalities in autistic children who subsequently lost spoken words and other skills. Ozonoff et al. (2005) reported that over 50% of children who experienced regression had a history of early social deficits in the first year of life, long before regression and onset of autistic symptoms. In their home movie study, Maestro et al. (2006) found an excessive interest in nonsocial stimuli in the regressed group during the first year of life (prior to skill loss). Richler et al. (2006), in a multi-center study of 351 children with ASD (163 with regression, 188 without), found that children with regression resembled children without regression on retrospective parent-reported measures of early communication. Only 30% of the regressed group demonstrated skills in the range of typically developing children. In a sample of 135 children, Meilleur and Fombonne (2009) noted that parents consistently reported developmental abnormalities in regressed children prior to loss of verbal or nonverbal skills. Research on functional outcome provides mixed evidence for considering regressive autism a meaningful subcategory or “phenotype” within the autistic spectrum. Richler et al. (2006) found lower IQ and decreased social reciprocity in the regressed group, although the groups did not differ on other measures of intelligence, autistic symptomatology, and adaptive functioning. Baird et al. (2008) reported higher rates of autistic symptoms in 9–14-year-old autistic children with a history of regression, when compared to a non-regressed group. Kalb et al. (2010) reported that a history of symptom onset in association with regression correlated with lower social functioning than did “plateau” or “no loss–no plateau” patterns of symptom onset in 2720 ASD children between 3 and 17 years of age. With a younger group of children (2–5 years), Hansen et al. (2008) found regressed autistic children to perform less well on two measures of communication (but noted that effect sizes were small). Meilleur and Fombonne (2009) found slightly more autistic symptomatology, based on parent interview, in regressed autistic children (mean mean age for sample: 6.3 years). In contrast, Lord et al. (2004), in a longitudinal study of 3–5-year-olds, found few differences between autistic children with and without a history of word loss; and Shumway et al. (2011) detected no relationship between pattern of symptom onset and later functioning. In summary, it can be said that approximately one-third of autistic children experience some developmental regression around the second year of life, best characterized as loss of meaningful, spontaneous, spoken words. Many of these children also had some developmental abnormalities – closely resembling those of non-regressed autistic children – that

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preceded the onset of their verbal loss and the recognition of their autistic syndrome. Regressed autistic children may have more severe autistic symptoms (as measured by parent report and standardized interview assessment) than non-regressed autistic children. They may also have lower verbal IQs. They do not clearly differ, however, on most medical or biological measures that have been systematically explored to date: seizure history, epileptiform abnormalities on EEG, gastrointestinal disturbance, or head growth. Like ASD children in general, those who experience regression in the second year of life probably represent a heterogeneous group, and identifying them as a unique group or a separate phenotype within the autistic spectrum is not justified by the presently available evidence.

Dimensional approaches to ASDs From the clinical perspective, the assignment of a specific PDD diagnosis (with the possible exceptions of Rett syndrome and CDD) has somewhat limited utility. Generally speaking, parents seek a doctor’s diagnosis for their child not merely to find out “What does he have?” but to answer the questions “How bad is it?” and “What can be done about it?” Providing a diagnosis of autistic disorder, Asperger’s syndrome or PDD-NOS is, at best, a first step toward answering these questions. As has been discussed, the PDD diagnostic categories represent heterogeneous groups of children, with a broad range of symptoms and skill levels; these diagnoses are not as clearly differentiated from one another as the established criteria would suggest; and prognosis depends more on an individual child’s cognitive and communicative abilities than on his particular diagnosis. Furthermore, if current plans for DSM-5 are implemented, the present diagnostic categories will no longer be used, and individuals who now receive separate PDD diagnoses will be grouped together under Autism Spectrum Disorder. The diagnostic process, therefore, must go beyond identifying what type of PDD a child has. It should be highly individualized – based on a thorough assessment of a child’s particular symptoms, clinical course, skills, family background, and any associated medical and comorbid psychiatric conditions. Treatment will be determined more by an individual’s unique profile than by particular PDD diagnosis. Nonetheless, the question of where a child stands relative to other autistic children (“How bad is it?”) needs to be answered somehow, and researchers have long sought ways to characterize autism along dimensions that can be roughly quantified. Using a dimensional approach can provide both an index of symptom severity for individuals with ASDs and an alternative way to conceptualize and subdivide the autistic spectrum. In a number of publications, Lorna Wing and colleagues have advocated a dimensional approach to diagnosing and subtyping autism (Wing and Gould, 1979; Wing, 1988, 2005; Leekam et al., 2002). As mentioned previously, Wing introduced the concept of a spectrum of autistic disorders (originally called the autistic continuum) following her study, with Judith Gould, of developmentally disabled children in the Camberwell section of London (1979). In Wing’s conception, once it has been established that a child has the triad of autistic impairments – and adequate information is obtained on accompanying symptoms, patterns of skills and disabilities, etiology (if known), comorbid conditions, and social situation – then social interaction patterns can be used as a basis for a multi-dimensional classification that places the child along a continuum. She described three patterns of social interaction that can be reliably distinguished: aloof and indifferent to other people; passive (not making spontaneous contact, but accepting others’ approaches); and active but odd (making social

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Section 1: What We Know About Autism

approaches in an odd, one-sided fashion) (Wing, 1988). By grouping ASD children according to these patterns, Wing found – in the Camberwell and subsequent studies – that other clinical features cluster with each group. Thus, the aloof group tend to be severely impaired in verbal and nonverbal communication; to display echolalia, pronoun reversal, atypical speech intonation and literalness; to show little or no evidence of imagination; to insist on complex routines for daily activities; and to have motor stereotypies. The passive group generally show initiative, but not imaginative play, have better-developed speech (even large vocabularies and good grammar) but their speech content is confined to a narrow range of subjects. They have repetitive routines, but less intense resistance to interruptions of these routines than aloof children. The active-but-odd group talk at others in a self-serving way, but not for the pleasure of social interaction; they have variable delay in speech development. Their eye contact and use of communicative gestures are inappropriate, and they lack understanding of social conventions. In Wing’s conception these groups are overlapping and thus constitute a continuum – not rigidly defined, discrete categories. Beglinger and Smith (2001), in a review of various approaches to subtyping autism, found four studies that supported the validity of Wing’s social interaction dimension. It has been suggested, however, that IQ may be the key differentiator among the groups, with lower ranges of IQ in the aloof group (Volkmar and Klin, 2005). Other researchers have expanded the notion of a continuum of social interactive abilities to apply not just to individuals with autism, but across the general population. In this conception, core competencies such as cognition and social communication, when suitably measured, are presumed to be continuously distributed in the general population. What distinguishes people with autism is where they fall on the continuum of social interaction skills, just as people with intellectual disability are distinguished by where they fall on the continuum of cognitive skills. Constantino and Todd (2003) used the Social Responsiveness Scale (SRS, Constantino et al., 2003) – a parent-report instrument addressing reciprocal social behavior, social use of language, and behaviors characteristic of PDDs – to evaluate 788 twin pairs from the general population. Because the SRS has a unitary factor structure, autistic traits could be quantitatively expressed by total SRS score. These scores were found to be both highly hereditable and normally distributed across the population; 1.4% of boys and 0.3% of girls were found to have scores above the (previously determined) means for children with PDDs. In a separate study of children with PDDs and their siblings and children with other psychiatric disorders and their siblings, SRS scores were found to be continuously distributed, elevated in children with PDDs and elevated to a lesser degree in their siblings (Constantino et al., 2006). Thus, a diagnostic instrument used for quantifying severity of a core dimension of the disorder also seemed to be able to place ASD individuals along a continuum contiguous with a similar continuum in the general population. It might therefore be possible to conceptualize ASDs as constituting the severe end of a continuous distribution of socialcommunication skills and deficits and to use quantitative measures of social competence to subtype the ASD population. IQ itself has been proposed as a way of subtyping ASDs. As in intellectual disability, IQ and adaptive functioning have prognostic value in autism (Beglinger and Smith, 2001). The Vineland Adaptive Behavior Scales (VABS, Sparrow et al., 1984) have also been useful in subtyping lower-functioning autistic children, and may be applicable to HFA as well (Beglinger and Smith, 2001; Volkmar et al., 2009). It is important to note that quantitative

Chapter 1: Autism nosology

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measures of cognitive function and quantitative measures of social abilities are not completely independent (Skuse et al., 2009; Constantino, 2011). Both must be taken into consideration if either is to be used for subdividing the ASD population, just as one would consider height in assessing a child’s weight. Another method of defining dimensions along which the autistic spectrum can be subdivided and individuals compared is factor analysis. The usual factor-analytic approach involves selecting items from a standardized diagnostic instrument that has been administered to a large group of autistic subjects and applying a statistical analysis to identify homogeneous dimensions within these items. The identified factors are then externally validated. Using the Autism Diagnostic Interview-Revised (ADI-R; Lord et al., 1994), a categorical diagnostic instrument, Tanguay et al. (1998) analyzed 28 items and found 3 factors within the domain of social communication: affective reciprocity, joint attention, and theory of mind. Tanguay (2004) stated that their data defined a continuum of social communication on which an autistic individual could be “roughly placed.” He noted that even though this placement might not suit the purposes of a research diagnosis, it has been helpful in clinical formulation leading to treatment. Tadevosyan-Layfer et al. (2003) also used the ADI-R and developed six separate quantifiable factors: spoken language (verbal output), social intent, compulsions, developmental milestones, savant skills, and sensory aversions. The authors noted that items in each factor were highly inter-correlated, and, thus, the individual factors could be used as subscales or continua, along which the severity of autism could be measured. Factor 2 – social intent – captured dimensions of both social interaction and communication. Georgiades et al. (2007) used factor analysis of the ADI-R to identify three dimensions, along each of which it would be possible to measure symptoms quantitatively. These factors were named: social-communication, inflexible language and behavior, and repetitive sensory and motor behaviors. Only a small amount of the variance among these factors was attributable to cognitive functioning or developmental level. The author suggested that each child with PDD could be characterized by these dimensions, which would give both an informative picture of their clinical presentation and an estimate of the severity of their disability. Snow et al. (2009), in a study of 1861 children and adolescents with PDD using the ADI-R, found that a two-factor solution – comprising social/communication behaviors and restricted and repetitive behaviors – best fit their data. The authors offered the opinion that combining social and communication symptoms into one domain may better capture the core impairment of PDDs and result in a more accurate classification system. They also suggested that genetic studies could benefit by adopting a dimensional approach in which core autistic symptoms are measured quantitatively – defining a phenotype, for example, by using ADI-R items that are most strongly associated with the two core domains. Other investigators have explored different domains to try to find a reliable way to conceptualize and subdivide the autistic spectrum. Some have focused on restricted and repetitive behaviors (Cuccaro et al., 2003; Szatmari et al., 2006; Lam et al., 2008). Using selected items from the ADI-R, these authors identified separate factors of repetitive sensory and motor behaviors and insistence on sameness. Lam et al. (2008) found an additional factor of circumscribed interests. In their study, autistic subjects who had multiple subtypes of repetitive behavior tended also to have more severe impairments in social interaction and verbal communication. At present, there is no consensus on using dimensional approaches to describe, diagnose, and gauge the severity of impairment of the broad population of individuals with ASDs.

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Section 1: What We Know About Autism

Symptom severity can be quantitated using instruments such as the ADI-R and the ADOS (Autistic Diagnostic Observation Schedule; Lord, 1994). These instruments, however, were designed to make categorical diagnoses under DSM-IV. Factor-analytic approaches with these and other instruments have identified dimensions along which individuals can be placed, but different researchers have determined different factors. Scores on a social interaction measure (SRS) and IQ may be used to rank children with ASDs on continuous dimensions that are contiguous with those of the general population, but the process of so classifying children is complex and still evolving. Nonetheless, there are many reasons for investigators to continue to identify ways of quantifying autistic phenomena along dimensions – to ascertain severity, determine prognosis and, possibly, define subgroups or distinct phenotypes within the population. In the absence of clear biological determinants for subtypes within the autism spectrum, people will continue to look for other ways to measure impairment and define how autistic children can most meaningfully be grouped together.

Summary and future trends: DSM-5 This chapter has reviewed the present diagnostic system for autistic spectrum disorders, with particular emphasis on the history of the separate diagnoses and on recent research regarding their validity. The nature of developmental regression in autism and the question of whether children with regressive autism constitute a meaningful subgroup within the ASDs were also addressed. Dimensional and factor-analytic approaches to designating subtypes within the autism spectrum and quantifying symptom severity were briefly reviewed. When DSM-5 is published in May 2013, however, the major diagnostic categories that have been reviewed here will likely be combined into a single diagnosis, Autism Spectrum Disorder. Plans for DSM-5 have been regularly announced and updated on the website maintained by the American Psychiatric Association (dsm-5.org). As of this writing, field testing of proposed DSM-5 diagnoses is in progress. There are four diagnostic criteria proposed for Autism Spectrum Disorder: (1) persistent deficits in social communication and social interaction across contexts and not accounted for by general developmental delays; (2) restricted, repetitive patterns of behavior, interests or activities; (3) the requirement that symptoms must be present in early childhood (although they may not become fully manifest until later, when social demands exceed capacities); (4) the requirement that symptoms limit and impair everyday functioning. To meet the first criterion, a child must have all three of the following: deficits in social– emotional reciprocity (e.g. reduced sharing of interests and emotions, lack of initiation in social interaction); deficits in nonverbal communicative behaviors used for social interaction; and deficits in developing and maintaining relationships appropriate to developmental level (beyond those with caregivers). To meet the second criterion, a child must manifest at least two of the following four symptom domains: (a) stereotyped or repetitive speech, motor movements or use of objects; (b) excessive adherence to routines, ritualized patterns of verbal or nonverbal behavior or excessive resistance to change; (c) highly restricted, fixated interests that are abnormal in intensity or focus; (d) hyper- or hypo-reactivity to sensory input or unusual interest in sensory aspects of the environment. The rationale for combining autistic disorder, Asperger’s syndrome, childhood disintegrative disorder and PDD-NOS into a single diagnosis derives from studies that have shown

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that division of the autistic spectrum into these separate diagnoses lacks reliability and validity. In addition, there are common behavioral features to all the disorders that fall within the autistic spectrum: well-recognized social and communication deficits and restrictive interests and repetitive behaviors. The developmental disorder workgroup for DSM-5 determined that the autism spectrum would be best represented as a single diagnostic category that could be adapted to an individual’s clinical presentation by further inclusion of (1) “clinical specifiers,” such as symptom severity and verbal ability, and (2) associated features, such as known genetic disorders, epilepsy, and intellectual disability. Recognition and description of clinical specifiers and associated features will be an important part of the diagnostic process. Certain features of the DSM-5 proposal depart from previous diagnostic approaches that have been reviewed in this chapter. The first is that the historic autistic triad has been reduced to two domains: social/communication deficits, and fixated interests and repetitive behaviors. Social and communication deficits are combined into a single domain because they are highly inter-correlated and one is rarely found without the other – as has been demonstrated in factor analyses of autism diagnostic instruments. Communication, moreover, in this context refers only to nonverbal communication. Language deficits are not used as a defining feature of ASD because they do not occur across the spectrum. A child’s verbal abilities should be noted as a clinical specifier when a diagnosis of autism spectrum disorder is made. (From the historical perspective, it is of some interest that Kanner himself revised his criteria for autistic disorder in 1956, reducing symptom domains from three to two, downplaying problems with verbal communication, and keeping the domains of extreme isolation and preoccupation with maintenance of sameness.) A second new feature is that abnormalities in sensory reactivity can be used to help establish the diagnosis under the domain of restricted, repetitive patterns of behavior, interests or activities. The DSM-5 proposal will ultimately provide symptom examples for subdomains that reflect a range of chronological ages and language levels. The proposal also includes a measure of severity, expressed as requirements for level of support across the two domains of social communication and restricted interests and repetitive behaviors. Three levels are designated: “requiring very substantial support,” “requiring substantial support,” and “requiring support.” Some preliminary studies of the proposed DSM-5 criteria for autism spectrum disorder suggest that the two-domain approach has validity and that the diagnostic criteria have increased specificity, when compared to those in DSM-IV. Sensitivity, however, appears to be lower – implying that there may be individuals who would have been diagnosed with a pervasive developmental disorder under DSM-IV criteria who would not be diagnosed with ASD under DSM-5 (Mattila et al., 2011; Frazier et al., 2012; Mandy et al., 2012). If implemented as currently planned, the DSM-5 definition of autism spectrum disorder and diagnostic approach should have considerable clinical utility. The DSM-5 conception embraces Wing’s notion of an autistic spectrum and acknowledges the phenomenologic and etiologic heterogeneity of autistic conditions. The diagnostic process will involve specifying features of each child’s autistic symptoms (presence or absence of language disorder, epilepsy, known genetic mutation, etc.) that will help clarify the nature of his/her particular disorder and level of functioning. The usefulness of the approach for research, including genetic, epidemiologic, treatment, and outcome studies, remains to be seen. The autism spectrum is broad, and methods still have to be determined to identify homogeneous subgroups within this population, both for research and to help determine prognosis.

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Section 1: What We Know About Autism

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What We Know About Autism

Epidemiology of autism Mark E. Reber

Epidemiology is the science of public health. Information from epidemiologic studies can help address questions such as the frequency of occurrence of a disease, change in disease patterns over time, and the distribution of disease over different places and groups of people. Epidemiologic research can also provide information on risk factors and protective factors for disease. In a chronic condition such as autism, important public policy decisions about the allocation of resources and provision of supports are often based on the results of epidemiologic research. Although public surveys of intellectual disability (mental retardation) began in the early nineteenth century, autistic disorder and Asperger’s disorder were first described in the 1940s, and the first population survey of autism was not undertaken until the 1960s. In many ways, the epidemiology of autism is still a new and emerging field of study (HertzPicciotto et al., 2006; Newschaffer et al., 2007). The present chapter will present a review and discussion of recent epidemiologic research on autism. The focus will be on studies that address the rate of occurrence of autism spectrum disorders, changes in this rate over time, and specific risk factors. Epidemiologic research on vaccines and autism will also be discussed.

Definitions and methods A brief review of some epidemiologic terms and methodology can facilitate understanding of the studies to be discussed. Generally speaking, the studies reviewed in this chapter are of two types: descriptive and analytic. Descriptive studies yield information on the prevalence or incidence of a disease or disorder – of critical importance for planning services and estimating their public cost. In epidemiology, prevalence is the proportion of a population with a disease or disorder. It is defined as the number of existing cases of a disorder at a point in time, divided by the total population of concern. Incidence is the rate at which new cases of a disease or disorder arise. It is defined as the number of new cases of the disorder appearing over a defined period of time divided by the population at risk (Mausner and Kramer, 1985). Incidence data are most applicable in conditions where there is a clear onset of the disease, e.g. influenza. An epidemiologic survey might, for example, determine the incidence of influenza in Montana during a 3-month epidemic. With autism, which may not be diagnosed for several years after onset of recognizable symptoms, prevalence may be a more useful reflection of occurrence in a population. An epidemiologic study might, for example, determine the

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The Autism Spectrum, ed. Mark E. Reber. Published by Cambridge University Press. © Cambridge University Press 2012.

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prevalence of autism among 3–10-year-old children in Montana during the year 2010. This would include all autistic children in the specific age group, regardless of when they were identified. Descriptive epidemiologic studies can also provide other demographic information that contributes to our understanding of a disease: sex distribution, geographic location, and occurrence among groups who might differ along variables such as socioeconomic status, ethnic background, and IQ. Analytic studies differ from descriptive studies in that they examine associations and investigate possible causal relationships. A descriptive study that shows change in prevalence of a disorder over time or nonrandom distribution of a disorder over certain populations can raise questions about possible causes of the observed phenomena that can then be explored in analytic studies. Frequently, analytic studies look at the connection between exposure to a particular agent and occurrence of a disease – e.g. ambient tobacco smoke and asthma – or between an attribute and the disease – e.g. race and hypertension. Analytic studies used to generate hypotheses are called ecologic studies. An ecologic study design employs the total population as the unit of comparison, not the individual. An example of an ecologic study would be a time-trend analysis, in which occurrence of a disease over time would be compared to another change over time, e.g. rates of bladder cancer in a total population and consumption of saccharine in that same total population (Mausner and Kramer, 1985). Hypothesis-testing analytic studies are more likely to be cohort studies. A cohort is a component of a population, a designated group of persons – often anyone born during a particular period – followed over time (Last, 1988). A cohort study can be retrospective or prospective. In a cohort study, individuals who were exposed to an agent are compared with unexposed individuals. Both groups are followed over time to determine if the exposed group develops a disorder or disease at a different rate from the unexposed group. Results of cohort studies are reported as relative risk (also called a risk ratio) or as an odds ratio. Both of these numbers provide information on whether there is an increased risk of disease among the exposed group. When a risk ratio = 1.0 or an odds ratio = 1.0, the interpretation is that there is no difference in risk between the exposed group and the reference (unexposed) group. A risk ratio or odds ratio of 2 means that the risk of getting the disease for those in the exposed group is twice that of those in the unexposed group. A third approach to the association of risk factors and disease is the case-control study. Unlike a cohort study, which begins with exposure to a risk factor, the case-control study starts by identifying a group of individuals who already have a disease, then compares them with a matched control group for the presence or absence of a putative risk factor or attribute. For example, a group of patients with lung cancer could be compared to a matched group without lung cancer in total lifetime cigarette smoking. Case-control studies are usually retrospective and report results as relative risk.

Autism prevalence studies The first study examining the prevalence of autism in a specified pediatric population was Lotter’s 1966 survey of young children in Middlesex, England. He reported a prevalence of 2.0 per 10 000 for children with the core syndrome of autism – as defined by Kanner’s criteria – and 2.5 per 10 000 for those with a somewhat less consistent symptom pattern, a total of 4.5 per 10 000. For nearly 30 years, another 20 epidemiologic studies reported similar numbers.

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The prevalence of autism in England, France, Sweden, Ireland, and the US appeared to be, at most, 5 per 10 000. Only Japanese studies reported higher numbers (Fombonne, 1999, 2003a; Wing and Potter, 2009). Then, beginning in the 1990s, a number of large population surveys began to yield significantly higher prevalence rates: 15–40 per 10 000 cases for autistic disorder, 18–77 per 10 000 for ASDs other than autistic disorder (Wing and Potter, 2009). For the broad autistic spectrum, the rate that is most frequently cited for prevalence is now from 70 to 113 per 10 000 (Fombonne, 2009; Autism and Developmental Disorders Monitoring Network, 2009, 2012; Lazoff et al., 2010). The latter rate is close to 1% of the population and means that ASDs as a whole are now the second most common developmental disability after intellectual disability. How does one explain this dramatic rise in the prevalence of autism? How did a rare psychiatric disorder become a common developmental disability? Are we experiencing an “epidemic of autism,” as some have claimed, or was the condition underdiagnosed in the past? If the rise in autism is real, can we determine the cause? To begin to approach the answers to these questions, one must understand the methods used to determine prevalence in epidemiologic studies. Generally speaking, investigators will choose a base population of sufficient size to yield a useful number of cases, designate an epidemiologic population that covers all children in the larger population at risk for a disorder, have a systematic method to screen these children to ascertain all possible cases, and employ a reliable system for confirming the diagnosis in cases identified by screening (Rutter, 2005). Case ascertainment can be accomplished by reviewing administrative records of facilities such as schools, medical clinics and developmental centers where children with disabilities are referred, utilizing questionnaires that are filled out by parents and health care providers, or systematically screening the epidemiologic population with a standard instrument (e.g. at scheduled pediatric office visits). Case confirmation is ideally determined by clinical examination, but may also come from expert review of available records. Not all epidemiologic surveys have used the described approach, but examples of some recent US and British studies that have are studies from the Center for Disease Control and Prevention (Bertrand et al., 2001, Yeargin-Allsopp et al., 2003; Autism and Developmental Disabilities Monitoring Network, 2007, 2009 and 2012), the British Nationwide Survey of Child Mental Health (Fombonne et al., 2001), the Special Needs and Autism Project in South Thames, UK (Baird et al., 2006), and two studies of preschool children in Stafford, UK (Chakrabarti and Fombonne, 2001, 2005). A good illustration of a comprehensive, multi-stage investigation is the work of Chakrabarti and Fombonne in Staffordshire, England. In two separate studies, the authors screened two cohorts of children. The first cohort, totaling 15 500 children aged 2.5–6.5 years, had a standard health professional screening for developmental problems in 1998–99. On the basis of that screening, 576 children were assessed at a second stage by a developmental pediatrician. Of this group, 426 were intensively evaluated by a multi-disciplinary team, which conducted standardized diagnostic interviews and administered psychometric tests. A total of 97 children were confirmed to have a PDD. Calculated prevalences were 62.6 per 10 000 for all PDDs: 16.8 per 10 000 for autistic disorder and 45.8 per 10 000 for other PDDs. Of the 97 children, 26% had some degree of intellectual disability and 9% had an associated medical condition. In their second study, using similar methods in the same Midlands town, Chakrabarti and Fombonne screened a cohort of 10 900 children who were 4.0–6.0 years of age in 2002.

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Sixty-four children were diagnosed with a PDD. The estimated prevalence for all PDDs was 58.7 per 10 000, with 22.0 per 10 000 for autistic disorder and 36.7 per 10 000 for other PDDs. Of the children with PDDs, 29.8% had intellectual disability. The CDC study in Brick Township, New Jersey (Bertrand et al., 2001) provides another example of a comprehensive, multi-stage investigation. The target population consisted of children who were between 3 and 10 years of age in 1998, who lived in Brick Township that year, and who had autistic spectrum disorder (autistic disorder, PDD-NOS or Asperger’s disorder). Potential ASD cases were ascertained from four sources: school records, records from local clinicians providing diagnosis or treatment for developmental or behavioral disabilities, lists of children from community parent groups, and families who volunteered to participate following publicity. Diagnoses were verified by thorough examination and administration of a structured interview (ADI-R) and an observational assessment (ADOS-R). Out of a calculated population of 8896, 75 cases of ASD were ascertained; 53 participated in the multi-disciplinary assessment, and 22 others had their school and clinical records evaluated by an expert. Diagnoses were based on DSM-IV criteria. The prevalence for all ASDs combined was found to be 67 per 10 000: 40 per 10 000 for autistic disorder and 27 per 10 000 for PDD NOS and Asperger’s disorder. For those for whom testing was available, 50% had an IQ of less than 70. The Brick Township, New Jersey study was one of a series of epidemiologic studies undertaken in various regions of the US under the auspices of the National Center on Birth Defects and Developmental Disabilities of the Centers for Disease Control and Prevention (CDC). Similar community studies have been published for metropolitan Atlanta, Georgia (YearginAllsop et al., 2003), reporting a prevalence of ASDs of 34 per 10 000, and for 23 counties of South Carolina (Nicholas et al., 2008), reporting a prevalence of 62 per 10 000. The most comprehensive reports from the CDC are those of the Autism and Developmental Disabilities Monitoring Network (ADDM), combining epidemiologic data from up to 14 network sites around the US (Autism and Developmental Disorders Monitoring Network, 2007, 2009, 2012). The ADDM report of 2009 is an exemplary, large epidemiologic study that looked at the prevalence of ASDs (autistic disorder, Asperger’s syndrome, and PDD-NOS) in a total population of 307 790 children who were 8 years old in 2006. Results were compared with findings from an earlier ADDM study from the surveillance year 2002 (Autism and Developmental Disorders Monitoring Network, 2007). Cases at each site were ascertained from multiple health and education sources and confirmed by a record review by reliable clinician reviewers, using DSM-IV-TR criteria. Cases included both previously diagnosed children and those who were identified through the ascertainment process. Overall prevalence for ASDs across 11 network sites was 90 per 10 000, compared to 66 cases per 10 000 in the 2002 surveillance year. In ADDM sites with results for both surveillance years, there was a 57% average increase in ASD prevalence. The male to female ratio across all sites was 4.5 : 1, indicating that ASDs were prevalent in one out of 70 males and one out of 315 females. Among those cases for whom IQ testing was available, 41% had cognitive impairment (IQ ≤ 70). In 2012, the ADDM published a report on all 14 sites, with prevalence data on children who were 8 years old in 2008. This survey suggested a further increase in the prevalence of ASDs, with an overall rate of 113 cases per 10 000. Altogether, approximately one in 54 boys and one in 252 girls were identified as having ASDs. Fombonne (2009) and Wing and Potter (2009) have conducted reviews of dozens of comprehensive, community-based epidemiologic surveys of autism. There is agreement on the prevalence of ASDs in most studies performed in the past decade from communities all

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over the world, with most reporting the above-cited figure of 70 per 10 000 (roughly 1/150) and a few recent studies indicating a prevalence closer to 1/100 (Baird et al., 2006; Autism and Developmental Disorders Monitoring Network, 2009, 2012). These prevalence figures represent a significant increase over those found in earlier research. To these surveys can be added a number of retrospective studies using administrative records (without case confirmation) that have tracked an increase in prevalence in a single location over time. Among these are the work of Croen et al. (2002), who used 8 years of records from the California agency responsible for coordinating services for people with developmental disabilities, and Gurney et al. (2003), who tracked 20 years of special education data from the Minnesota Department of Children, Families, and Learning. The first study showed a near tripling of identified cases of autism; the second, a 17-fold increase. More recently, Lazoff et al. (2010) reported on a similar trend using special education records from 71 schools in the English Montréal school board, with increases in PDD cases from birth years 1991 to 2002, and an overall prevalence of 80 per 10 000.

Rise in prevalence: artifactual or real? Given the data just reviewed on the rising prevalence of autism, an important question to ask is: can the increase over time be attributed – in part or in whole – to factors that have nothing to do with an actual increase in autism itself? Several authors have addressed this question in comprehensive reviews (Charman, 2002; Fombonne, 2003b, 2009; Rutter, 2005; Williams, Higgins and Brayne, 2006; Wazana et al., 2007; Wing and Potter, 2009). All are in agreement that better case ascertainment, changes in diagnostic criteria and practice, growing recognition that autism occurs in children with other neurodevelopmental disorders, age of recognition, increased professional and public awareness of ASDs, administrative decisions, and development of services have almost certainly played a major role in the rising prevalence found in epidemiologic research. Case ascertainment, as in the studies described in the previous section, has improved in recent population surveys. Rutter (2005) noted that earlier studies “relied to a considerable extent on some form of screening based on children attending some clinical facility or special school or residential program. There was much less coverage of children attending ordinary schools, and both hospital and educational services for children with ASD were much less well-developed and much less freely available than is the case today” (p. 4). Pediatricians, family practitioners and parents were also less aware of autism in the 1970s and 1980s; pediatric practices did not routinely screen for autism; and children were less likely to be referred to hospitals and special education services. It is noteworthy that epidemiologic studies in the 1980s that detected a higher occurrence of autistic disorder than most US and European studies were from Japan, where repeated developmental checks of preschool children were routine (Wing and Potter, 2009). Changes in diagnostic definitions and practice have also led to an increase in identification of autistic children. Early studies – before the introduction of the various DSM and ICD criteria – relied on the diagnostic criteria of Kanner (Eisenberg and Kanner, 1956) and Rutter (1978). When several subsequent studies utilized both Kanner and DSM-IV/ICD-10 criteria, in order to see if these diagnostic systems captured the same children, it was found that all children diagnosed by Kanner criteria were picked up by the later diagnostic approaches, but 55–67% of children diagnosed by DSM-IV/ICD-10 criteria were not considered autistic using Kanner’s criteria (Wing and Potter, 2009). Thus, it can be assumed that half to two-thirds of children now considered to have autistic disorder would not have been so designated in early studies.

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Diagnostic boundaries have broadened in other ways. The most obvious is the inclusion of PDDs other than autistic disorder, beginning with the recognition of “atypical autism” in DSM-III; and PDD-NOS, Asperger’s disorder, Rett disorder, and childhood disintegrative disorder in DSM-IV. It is absolutely essential to understand that no study prior to 1997 included these PDDs (Wing and Potter, 2009; Fombonne, 2003a). Earlier epidemiologic studies counted children with autistic disorder only, not children with the full range of ASDs. But even with “full syndrome” autistic disorder – as pointed out by Wing and Potter (2009) – both DSM III-R and DSM-IV/ICD-10 criteria allow for a wider range of social and communication deficits and of repetitive activities than did earlier diagnostic systems, such as DSM-III. It has also been suggested that diagnostic substitution is responsible for some of the increase in identified cases of autism. Croen et al. (2002a) raised this possibility when they observed that cases of mental retardation reported to the California Department of Developmental Services declined during the same period that cases of autism dramatically increased. Gurney et al. (2003) did not find a decline in prevalence of mental retardation in their Minnesota population; and Croen and Grether (2003), in a separate analysis of their California data, looked at children who were diagnosed by age 4 years and found no change over time in the number of reported cases of mental retardation. Shattuck (2006), however, reported that his analysis of US special education data was consistent with substitution of autism for mental retardation as a diagnostic category over the period from 1994 to 2003. King and Bearman (2009) re-evaluated data from the California Department of Developmental Services for the period 1992–2005 and determined that 26% of the increased autism caseload over that time could be attributed to a diagnostic shift from mental retardation to autism. Certainly, there has been a change in diagnostic practice both with regard to children with severe and profound intellectual disability and with associated medical conditions. In the past, it had been common practice to exclude these children from the diagnosis of autism. Currently, however, children with mental retardation syndromes are also diagnosed as autistic if they meet certain criteria (Rutter, 2005). Recent epidemiologic studies that have looked at the question find that around 10% of children with ASDs have associated medical conditions, including mental retardation syndromes (Chakrabarti and Fombonne, 2001; Fombonne, 2003a). Perhaps the most significant change in the diagnosis of autism that has likely contributed to the upward trend in prevalence is age of diagnosis. In recent years – because of pediatric screening and increased awareness of the ASDs – children are being diagnosed at younger ages than in the past. This would be expected to contribute to an increase in identified cases. For example, if there were two studies targeting children aged 3–10 – an earlier one in which most children were not identified until school entry, and a later one in which children began to be picked up and referred at age 2 or 3 – the prevalence of autism would clearly be lower in the earlier study, because most of the preschool autistic children in the selected population would have been missed. Parner et al. (2008) specifically examined the effect of changing age of diagnosis on prevalence of autism in different birth cohorts in Denmark. They concluded that downward shifts in age at diagnosis inflated the observed prevalence of autism in young children in more recent cohorts, when compared to the oldest cohort. More difficult to quantify, but almost certainly affecting the increase in reported prevalence of ASDs, are public and professional awareness of the disorder and the increased

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availability of services, particularly for younger children. In the past 15 years, there has rarely been a month in which a newspaper or broadcast news service has not run a story about autism. Popular TV movies and novels in the US and Britain have featured characters with ASDs. Family doctors and pediatricians have become more familiar with the disorder and parents, often stirred by media stories linking autism and vaccines, have become more watchful for symptoms of autism and more direct in asking their doctors about possible symptoms in their children. With increased availability of services has come a greater willingness on the part of clinicians to make a tentative diagnosis of PDD and to refer children for further evaluation and intervention. Liu et al. (2010) published a study that looked at the effect of one of these “social” factors on the increasing prevalence of autism: residential proximity to a child with an ASD diagnosis. After controlling for competing explanations (e.g. environmental toxicants, viral transmission), the authors concluded that living near a child with autism led to more children being referred and diagnosed at younger ages. They estimated that 16% of the increase in ASD prevalence in California could be attributed to this social influence. Given these methodological factors and changes in practice that have contributed to the rising prevalence of autism in epidemiologic studies, is it possible to say that the observed increase is artifactual rather than real – the result of relative underdiagnosis in the past? Or has there been a real increase in ASDs over time, secondary to some genetic or environmental effect on early brain development? Ideally, the answer to this question would be sought in studies of incidence, which would measure the number of new cases emerging per year in a series of birth cohorts. Incidence studies are problematic, however, with a disorder like autism because – in contrast to a disease like chickenpox – autism is not diagnosed when it emerges. Thus, incidence studies depend on subsequent case finding and are affected by the same confounding effects of changing diagnostic criteria, decreasing age of recognition, diagnostic substitution, and expanding awareness of the disorder as prevalence studies. Four recent studies of incidence of pervasive developmental disorders – utilizing the UK General Practice Research Database (Smeeth et al., 2004b), the Danish Psychiatric Central Register (Lauritsen et al., 2004), the Rochester Epidemiologic Project in Olmsted County, Minnesota (Barbarisi et al., 2009), and three Australian databases (Nassar et al., 2009) – all showed an increase in incidence over a course of one to two decades, but the authors of each attributed much of this change to altered diagnostic approaches, age of diagnosis, availability of services and improved awareness (although all stated that a real increase in incidence could not be ruled out). Hertz-Picciotto and Delwiche (2009) used data from the California Department of developmental services from 1990 through 2006 to calculate change in incidence of autism diagnoses over time. They noted an increase in incidence and attributed 56% of this increase to inclusion of milder cases, 12% to younger age at diagnosis. They concluded that these and other artifacts could not account for all the increase in incidence. However, as Grinker and Leventhal (2009) pointed out, there are problems with equating onset of a disorder with when a case is entered into a database, calling to question whether true incidence could be inferred from this study. In addition to inquiring about incidence, one could ask if the increase in ASD prevalence is continuing or whether it is beginning to abate. If changes such as broadening the diagnostic boundaries, younger age of diagnosis, diagnostic substitution, and increased awareness of autism have led to the rise in prevalence, their influence would be expected to wane in time, and the number of identified cases should level off (or the rate of increase slow).

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Here, too, the available evidence is equivocal. Hertz-Picciotto and Delwiche (2009) reported that there was no sign in their data that the observed increase in incidence of ASDs was plateauing. In contrast, Hagberg et al. (2010) presented data on successive birth cohorts from the UK General Practice Research Database that showed an increase in incidence of ASD diagnosis for earlier cohorts (birth years 1988–1996), followed by stability in incidence for birth years 1997–2001. Maenner and Durkin (2010), using special education data from all Wisconsin elementary school districts between 2002 and 2008, found that an earlier rise in prevalence was leveling off – at about 120 cases per 10 000 students. Another approach to the question of whether the increase in the occurrence of autism over time is real has been to apply analytic models to the available epidemiologic data. Williams et al. (2006) used a multivariate meta-regression model to analyze 37 studies reporting prevalence of typical autism and 23 studies reporting prevalence of ASDs. The authors concluded that 61% of the variation in prevalence estimates among studies could be explained by methodological factors: diagnostic criteria, age of children screened per year, and study location. Wazana et al. (2007) used a prediction analysis to model the effects of three methodological factors – distribution of age at diagnosis, efficacy of ascertainment and year of birth – on a hypothetical population of children with an assumed true prevalence of 15 cases of autistic disorder per 10 000, and a mean age of entry to services of 6.8 years, and 50% efficiency of ascertainment. They then analyzed what the prevalence would look like under scenarios of specified quantitative variation in these three factors, comparing their results to actual prevalence studies that showed changes over time. For this analysis, the authors selected only those studies that were undertaken on repeated occasions in the same geographic location. They concluded that conservative changes in the three methodological factors in their model were sufficient to explain the upward trend in the prevalence of autism over time. There is obviously a difference between showing that methodological factors alone may explain the rising prevalence of autism and proving that these factors are the sole cause. Nonetheless, one point can be made with confidence: there is no epidemic of autism. A large part of the rise in the prevalence of ASDs is artifactual, because the conditions under which studies were done and the methods used have changed over time. Only with careful, longitudinal incidence studies can the actual rise in autism be tested. At present, the magnitude of this real rise is unknown, but it is not nearly as large as the increase in reported prevalence would suggest.

Autism and the MMR vaccine Real or not, the increase in the occurrence of autism has given rise to many hypotheses about its cause, and a number of epidemiologic studies have been done to test suggested hypotheses and explore possible etiologic mechanisms. Of these hypotheses, none has received more attention than the two that have linked the rise in autism to childhood vaccines – first, the measles–mumps–rubella vaccine (MMR), and second, vaccines that contain thimerosal, an ethylmercury compound used as an antiseptic preservative. A connection between autism and the MMR vaccine and a suggestion that MMR injections could be responsible for the increase in cases of autism were first raised in a 1998 article in the Lancet (Wakefield et al., 1998) and in a news conference held by the lead

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author at the time of its publication. The article, characterized in its title as an “early report,” presented a series of 12 children with PDD who had experienced regression in their development, diarrhea, and abdominal pain, and who had abnormalities on intestinal biopsy characterized as ileo-colonic lymphoid hyperplasia. The parents or physicians of eight of these children “linked” onset of behavioral problems with MMR vaccination. The study actually advanced three overlapping hypotheses. (1) There is a type of autism characterized by regression after normal development and accompanied by gastrointestinal symptoms. (2) The intestinal symptoms are the cause of the developmental regression and of the autistic syndrome. The proposed mechanism is increased intestinal permeability to certain peptides that directly or indirectly affect neuroregulation and brain development. (3) The MMR vaccine, specifically the combining of three viral antigens, may play a role in this syndrome. The article stated that no association was proven between the MMR vaccine and the described syndrome; but, in his news conference, Wakefield expressed a belief that the MMR caused autism and recommended that immunization for measles, mumps, and rubella be given in separate vaccines at intervals spread over a year (Offit, 2008). In subsequent publications, Wakefield elaborated these claims. He attributed the rise in cases of autism to the introduction of the MMR vaccine (Wakefield, 1999); raised the possibility that delayed or chronic onset (as opposed to immediate onset) of autistic symptoms could follow MMR administration (Wakefield, 2000); and implicated persistent measles virus as the cause of the bowel pathology. The persistent measles connection was supported by several studies that reported finding measles virus or measles virus genes in the intestines, white blood cells, blood, and spinal fluid of autistic children (O’Leary et al., 2000; Uhlmann et al., 2002; Martin et al., 2002; Singh and Jensen, 2003). Wakefield’s claims and the publicity – indeed, public furor – they engendered had the effect, in Britain, of causing many parents to refuse MMR vaccination for their children. Acceptance of MMR vaccination declined from 92% to 79%; several sizable outbreaks of measles occurred; and one child died (Ellman and Bedford, 2007; Smith et al., 2008). Many parents in the US also became wary of the MMR, especially after the theorized link to autism was given a public hearing by Representative Dan Burton’s Committee on Government Reform in April 2000 (Offit, 2008). Because the 1998 Lancet paper presented cases of relatively acute developmental regression following MMR injection (within 1–14 days), Taylor et al. (1999) undertook a population-based study in eight London health districts, in which they identified 498 cases of autism born after 1979. Information from clinical records was linked to immunization data. The authors found a steady increase in cases of autism over time, but no acceleration of the rate of increase or “step-up” following introduction of the MMR vaccine. Furthermore, there was no difference in the age at diagnosis among the cases vaccinated before 18 months of age, after 18 months of age or never vaccinated. Developmental regression was not clustered in the months after vaccination. Although one limitation of the study was that age of diagnosis is clearly not the same as age of symptom onset – which is often uneven or prolonged over time – the authors were still able to conclude the following: if there is a syndrome of a developmental regression following MMR vaccination, it is so rare that it could not be identified in this large regional sample. After Taylor’s study, several other investigations carried out ecologic, time-trend analyses to look for a possible link between MMR vaccination and change in autism prevalence. Kaye et al. (2001) surveyed general practices in the UK and found no correlation: cases of autism increased markedly for cohorts of 2–5-year-old boys born from 1988 to 1993, while there was

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no change in the prevalence of MMR vaccination, which was consistently greater than 95%. Dales et al. (2001) did a retrospective analysis of immunization records of California kindergarten children born from 1980 to 1994 and of autism cases reported to the California Department of Developmental Services who were born during the same years. For these cohorts, there was a marked, nearly fourfold rise in autism cases, but a relative increase of only 14% in immunization with the MMR. Fombonne et al. (2006) found a steady increase in recognized cases of pervasive developmental disorder in successive birth cohorts in a large school district in Montreal, despite a decreasing trend in MMR vaccination coverage. The rate of increase in PDD cases was also not affected by introduction of a second MMR injection, beginning in 1996. More definitive epidemiological studies have involved comparisons of populations of children who had received MMR vaccinations with those who had not. Because of concern about the mumps component of the MMR, use of the vaccine was discontinued in Japan in 1993. Honda et al. (2005) examined the cumulative incidence of ASDs up to age 7 in successive birth cohorts of children from a large section of Yokohama, who were born from 1988 to 1996. They found that the incidence of ASDs did not decline, but rose dramatically, after 1993. Madsen et al. (2002) conducted a retrospective cohort study of all children born from 1991 to 1999 in Denmark, a country where every child is assigned a unique identification number and records reflect who has and has not received the MMR. There were 537 303 children in the cohort (316 with autism, 426 with other ASDs); 82% of these children had received the MMR vaccine. Comparisons were made between the vaccinated and unvaccinated populations and adjustments made for confounding variables, such as socioeconomic status, parental education, birth weight, and gestational age at birth. After adjustment, there was no difference between the two groups in risk for autism. The relative risk for the vaccinated group was 0.92 (95% confidence interval (CI) = 0.68–1.24) for autistic disorder and 0.83 (95% CI = 0.65–1.07) for other ASDs. There was also no association found between age at the time of vaccination or the time between vaccination and the development of autistic disorder. Other epidemiological studies have utilized a somewhat different approach to address the MMR–autism link. Smeeth et al. (2004a) did a case-control study using the UK General Practice Research Database. The authors compared children who were diagnosed with PDD with age-, sex-, and practice-matched controls for a history of vaccination with MMR. No association was found between MMR and increased risk of PDD (odds ratio = 0.86, 95% CI = 0.68–1.09). Findings were similar when restricted to children with a diagnosis of autistic disorder. DeStefano et al. (2004) reported on a CDC case-control study using the population in greater metropolitan Atlanta. Similar proportions of case and control children were vaccinated before 18 or before 24 months. More cases than controls were vaccinated before 36 months. Lastly, a number of non-epidemiologic studies have been done looking at other aspects of Wakefield’s complex hypothesis. One is the report by Libbey et al. (2007), who found no differences among four groups of children – those with classic autism, those with a regressive onset form of autism, age- and gender-matched controls, and children with Tourette’s disorder – in antibody titers to measles, mumps, and rubella viruses. Similarly, Baird et al. (2008) found no differences on a serum measure of antibody response to measles among three community groups of children who had been vaccinated with the MMR – those with ASD, those with special education needs but without ASD, and typically developing controls.

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A third report relevant to Wakefield’s hypotheses is a major, multi-site study by Richler et al. (2006) that clarified the nature of regression in ASDs, providing evidence that when regression occurs, it is preceded by prior abnormal development – thereby precluding an effect of vaccination as a primary etiology. A fourth study is the multi-site report of Hornig et al. (2008) on intestinal biopsies of children with autism and case controls. This study showed no differences in the two groups in the presence of measles virus genes (RNA). Onset of gastrointestinal (GI) symptoms in the autistic group was also not found to be related to MMR timing. The conclusion that can be drawn from these MMR-related reports – in particular, the epidemiologic studies just reviewed – is that there is no evidence that the MMR vaccine is associated with the onset of autistic spectrum disorders or with the increased prevalence of autism in recent decades. Otherwise typically developing children do not acquire autism because they have received the MMR vaccine. Parents can be reassured that they are not placing their children at increased risk of autism by allowing them to be vaccinated. With regard to other aspects of Wakefield’s hypothesis – his designation of a specific syndrome of autistic regression that is associated with GI symptoms, ileo-colonic lymphoid hyperplasia, persistent measles infection, and increased gut permeability to peptides with opioid effects – it can be said that the evidence supporting the existence of this syndrome is generally weak. Studies using up-to-date methods do not show persistence of measles virus in the intestines of autistic children or in their white blood cells. Recent studies also failed to show any altered immune response on the part of autistic children who have been vaccinated, and technical problems with some earlier studies may have led to spurious results (Offit, 2008). The research evidence against Wakefield’s hypothesis stands on its own. It has to be mentioned, however, that in February 2010, the Lancet fully retracted the original 1998 article by Wakefield et al., noting that elements of the original manuscript had been falsified (Editors of the Lancet, 2010). This action followed a finding in January 2010 of the Fitness to Practice Panel of the UK General Medical Council that Wakefield had acted dishonestly and irresponsibly in his medical research (Dyer, 2010).

Autism and thimerosal-containing vaccines In the late 1990s, the Food and Drug Administration (FDA) undertook a review of the amount of mercury in childhood vaccines. Concern began to be raised about the number of vaccines given to infants in the US in the first 6 months of life – now up to three diphtheria, pertussis, tetanus (dTAP); two hepatitis B; three rotavirus; three Haemophilus influenza; and three pneumococcal vaccines. These vaccines were dispensed in multi-dose vials containing thimerosal, an ethylmercury preservative. With all the injections recommended in 1999, an infant would be exposed to a total of 187.5 μg of ethylmercury by age 6 months. Although thimerosal is a compound of ethylmercury and the only guidelines on potential mercury toxicity were based on methylmercury – which lasts much longer in the body – and although there was no evidence of harm from mercury-containing vaccines, the American Academy of Pediatrics and the Public Health Service issued a joint statement in 1999 recommending removal of thimerosal from infant vaccines as a precautionary measure (Centers for Disease Control and Prevention [CDC], 1999). A major motivation for this cautious approach had been a study done in the Faroe Islands, where whale meat contaminated with methylmercury was part of people’s diet. This study reported subtle effects on

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speech and measures of neuropsychological functioning in 7-year-old children who had been exposed to low levels of methylmercury in utero (Grandjean, 1999). Following the AAP-UPS statement, thimerosal was removed from all vaccines given to young children in the US. This was accomplished by dispensing vaccines in single-dose vials that did not require an antiseptic preservative. After 2001, only some influenza vaccines in multi-dose vials continued to contain thimerosal. The US joined other countries, including Sweden, Denmark, the UK, and Canada, that had previously stopped administering thimerosal-containing vaccines (TCVs). At the time of the AAP-UPS statement, there was no suggestion that TCVs had any connection whatever with autism. That link was proposed somewhat later by Bernard et al. (2001), who asserted that symptoms of mercury toxicity, as described in the medical literature, were similar to those of autism. These authors offered the hypothesis that many cases of autism are induced by early exposure to thimerosal and stated that this type of autism represented an “unrecognized mercurial syndrome.” Perhaps no aspect of autism has received more media attention in the US in the past decade than this hypothesized connection between TCVs and autism. As with the earlier concerns about the MMR vaccine and autism – an entirely separate issue, as the MMR vaccine never contained thimerosal – parents were being presented with a new, specific, and external explanation for their children’s autism. They were also offered a new hope: if mercury toxicity were the cause, then removing the mercury could be a possible treatment. Offit (2008) has described in detail the fierce contentiousness, actions of various advocacy groups, involvement of national politicians, lawsuits, and general anxiety over having children immunized that were engendered by this hypothesis and the ensuing publicity. While this turmoil was occurring, several epidemiologic studies were undertaken to address a possible link between TCVs and autism. These studies looked at the use of TCVs and the occurrence of autism over time (ecologic studies), or compared populations of children who received TCVs in infancy with populations of children who did not (cohort studies). As a number of reviews of this research have pointed out, no epidemiologic investigations have found any association between TCVs and the occurrence of autism, except for those done by one pair of researchers, Geier and Geier (Parker et al., 2004; DeStefano, 2007). In 2003, Geier and Geier published a cohort study based on data from the Vaccine Adverse Events Reporting System (VAERS), maintained by the CDC. They identified adverse events that followed administration of diphtheria–tetanus–pertussis (dTAP) vaccine and could be classified as neurodevelopmental disorders: autism, intellectual disability and speech disorders. They then compared the incidence of these adverse events among children who received dTAP with thimerosal and those who received dTAP without thimerosal. With regard to autism, a comparison of the two incidence rates yielded a significant relative risk of 6.0 for the group that received dTAP vaccine with thimerosal. (No confidence interval was provided for the risk ratio.) There were several methodological problems with this study (Parker et al., 2004; Rutter, 2005). In the first place, the VAERS is a passive database: someone has to take the initiative to report an adverse event. Thus, not all adverse events are reported and there is a potential bias in those events that are reported. Some reports are made by doctors, but others are submitted by parents and lawyers. Other problems with the study by Geier and Geier are lack of confirmation of diagnosis, failure to specify which dose or how many doses of dTAP preceded the adverse event (and whether earlier doses contained thimerosal), and lack of

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clarity in methodology. The primary outcome measure was based on incidence rates, but it is not clear how these rates were calculated, i.e. how the denominator for incidence – the number of dTAP doses given to children in the US in a particular year – was determined (Parker et al., 2004). In 2006, Geier and Geier published a report, also based on an analysis of VAERS data, that looked at the effect of removal of thimerosal from vaccines. They found a significant reduction in the number of reported neurodevelopmental disorders after 2002. In contrast to the work of Geier and Geier are several studies that utilized databases from Sweden, Denmark, Montréal, Britain, and California. Hviid et al. (2003) reported on a retrospective cohort study of all children born in Denmark in 1990–1996. The dTAP vaccine was the last one in use in Denmark that contained thimerosal and it stopped being produced in March 1992. Hviid et al. used vaccination data from the National Board of Health and ascertained cases of ASDs from the Danish Psychiatric Central Register. They utilized other records to control for confounding conditions. Their results showed a risk ratio for autism of 0.85 (95% CI 0.60–1.20) for children who received any thimerosal and no evidence for increased risk with higher thimerosal exposure (from 25 to 75 μg of ethylmercury). Stehr-Green et al. (2003) and Madsen et al. (2003) carried out time-trend ecologic studies that looked at whether elimination of thimerosal from vaccines led to a decrease in the incidence or prevalence of autism. Stehr-Green et al. used public health records from Sweden and Denmark and compared the incidence rate of ASD cases over 17 years (1980–1996) in Sweden and 18 years (1983–2000) in Denmark with the average cumulative ethylmercury dose received by children up to 2 years of age. There was no connection between the two time trends. Indeed, the incidence of ASDs rose in both countries despite declining amounts of ethylmercury in vaccines, and it increased dramatically in Denmark after thimerosal was eliminated. Madsen et al. expanded the Danish data to include the period 1961–2000. In earlier decades, cumulative ethylmercury exposure was higher, at 200 μg in the first 15 months of life in the 1960s and at 125 μg in the 1970s. The rate of autism was stable until 1990. In these Scandinavian studies, even the Madsen study, the amount of ethylmercury exposure each child received was relatively low compared to the exposure of children in the US in the 1990s (up to 237.5 μg by age 2 years). A similar observation can be made with regard to British cohort studies (Heron et al., 2004; Andrews et al., 2004) that showed no association between thimerosal exposure and developmental disorders. Vaccination practices in Canada, however, have more closely resembled those in the US. Fombonne et al. (2006) looked at the prevalence of PDDs in Montréal in cohorts born from 1987 to 1998, for whom the average ethylmercury exposure ranged from 100–125 μg (1987–1991) to 200–225 μg (1992–1995) to nil (after 1995). A statistically significant linear increase in prevalence of PDDs was found over the study period, and prevalence was significantly higher in thimerosal-free cohorts than in thimerosal-exposed cohorts. Regression analysis showed no significant effect of thimerosal exposure on the increasing prevalence of autism. A US study by Schechter and Grether (2008) utilized autism client data from the California Department of Developmental Services to determine if there has been any effect on the prevalence of autism in California since TCVs were eliminated in 2001. They found that the estimated prevalence in children aged 3–5 years increased consistently from January 1995 through March 2007 – a trend that was inconsistent with the hypothesis that thimerosal exposure was a cause of autism. A lack of association between thimerosal exposure and autism was also found in a large, US, retrospective, case-control study based on records from three managed care

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organizations (Price et al., 2010). This study addressed cumulative exposure to thimerosal from birth to 20 months of age and included prenatal exposure from maternally administered immunoglobulins. Children with ASDs who were born between 1994 and 1999 and identified between 6 and 13 years of age were matched with controls. Separate analyses were done for the entire ASD group and for a subgroup who were diagnosed with autistic disorder and a subgroup with a history of regression. No ASD cases were detected in the control population. No significant differences were found between any of the ASD groups and controls in cumulative exposure to ethylmercury, at the time of birth, from birth to 1 month, from birth to 7 months and from birth to 20 months. As a number of commentators have pointed out (Taylor, 2006; Offit, 2008), it is impossible to prove a negative in this type of empirical research. Epidemiologic studies cannot rule out the possibility that autistic symptoms in some individuals are somehow connected to a vaccine effect, perhaps through an interaction with undetermined genetic or autoimmune factors. The hypothesis linking thimerosal and autism has had some indirect support in studies of laboratory animals (Hornig et al., 2004). However, the relevance of these animal studies to a complex human developmental disorder is unclear. What can be said, based on epidemiologic research, is that there is no reliable epidemiologic support for the suggestion that TCVs were ever an etiologic risk factor for autism.

Autism risk factors Although use of the MMR vaccine and TCVs cannot be considered the cause of the recent rise in the prevalence of autism and much of this increase cannot be attributed to an actual rise in incidence, it is still essential to try to identify any factors that could be playing a real, causative role in the increase. The type of epidemiolgic research that is most useful in this endeavor consists of studies that have looked at risk factors for autism. Risk factors are individual attributes, life events or environmental exposures that increase the likelihood of a disorder. Detecting a statistical association between an attribute, event or exposure and the occurrence of a disorder can be thought of as the first step in identifying its cause. Further steps involve exploring the strength and consistency of the association, generating hypotheses to explain the association, and testing those hypotheses. The studies on vaccines and autism just reviewed failed, in general, to find the kind of association that would justify further exploratory studies. Other recent studies, however, have identified risk factors that could possibly be causal, requiring further investigation. An example of risk-factor research that generated further study was an association reported in the early 1990s between month of birth and autism. Gillberg (1990) and Mouridsen et al. (1994) both reported an excess of births in the month of March among children with autistic disorder in Scandinavian populations. This observation led to an investigation of a possible link between prenatal exposure to influenza and autism. Dassa et al. (1995), however, found that exposure to influenza epidemics during gestation was not a risk factor for autism in a British population. [The question of a month of birth as a risk factor for autism is, however, far from settled. Large cohort studies in Israel (Kolevzon et al., 2006) and Denmark (Atladottir et al., 2007) found no association between month or season of birth and the prevalence of ASDs. Other epidemiologic studies, however, have continued to show an association between month of birth (or month of conception) and risk of autism (Lee et al., 2008; Hebert et al., 2010; Zerbo et al., 2011). It is not clear what these findings may mean, other than that they reflect some yet-to-be determined, non-heritable

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risk factor, possibly infectious or environmental, which might be fruitfully pursued in further research.] This section of the chapter will review some of the risk factors for autism that have been identified in two types of epidemiolgic studies: (1) large population surveys that detect multiple risk factors, and (2) focused, hypothesis-driven investigations looking at specific events and exposures. As will be seen, most of this epidemiologic research deals with prenatal and perinatal experience.

Large population surveys Many of the large population surveys already mentioned in this chapter with regard to prevalence have also looked at sociodemographic risk factors for autism. Some significant risk factors noted in these studies have included: male sex, presence of associated medical conditions, maternal immigration, and race (Fombonne, 2003a; Yeargin-Alsopp et al., 2003; Autism and Developmental Disorders Monitoring Network, 2009; Williams et al., 2006). The ratio of males to females has ranged from 3 : 1 to 7 : 1, depending on the study, but the clear male dominance in population samples reflects that found in clinical samples and has given rise to various etiologic hypotheses, some of which are discussed in Chapter 6. A recent large, CDC case-control study in Atlanta (Bhasin and Schendel, 2007) focusing on sociodemographic risk factors found a twofold increase in risk of ASDs for black children, an increased risk in children born to women 35 years of age and older, and an association between higher social class and autism among those children who had intellectual functioning in the normal range. The authors speculated that this last finding might reflect a bias in case ascertainment in that many of these higher-functioning children were identified in preschool programs. More highly educated parents were assumed to be higher utilizers of health services, to have their children diagnosed earlier, and to secure their placement in early intervention programs. Although sociodemographic risk factors like parental age, immigration status, race, sex, and social class suggest the need for further exploration, they do not by themselves offer selective etiologic hypotheses. More useful in this regard are studies of prenatal and perinatal risk factors (some of which are also sociodemographic). Much of this research has been done in Scandinavia, where the availability of nationwide databases makes it possible to do largepopulation, case-control studies that identify children with autism and compare them with randomly selected controls for the presence of putative risk factors. Hultman et al. (2002), using a sample of all Swedish children born between 1974 and 1993, found an increased risk of autism in association with maternal smoking early in pregnancy, birth outside Europe or North America, caesarean delivery, being small for gestational age, 5-minute Apgar score less than 7, and presence of congenital malformations. Larsson et al. (2005), using a Danish cohort born after 1972 and at risk of being diagnosed with autism by 1999, found that autism was associated with breech presentation, 5-minute Apgar score less than 8, gestational age less than 35 weeks, and parental psychiatric history (especially schizophrenia-like psychosis and affective disorder). No association was found between autism and weight for gestational age or socioeconomic factors. Maimburg and Vaeth (2006), making use of somewhat different Danish data, found that risk of autism was increased with maternal age greater than 35 years, foreign citizenship, low birth weight, presence of congenital malformations, and maternal use of medication during pregnancy.

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In a later case-control study, Maimburg et al. (2008) investigated reasons for transfer to a neonatal ward as possible risk factors for autism. They found hyperbilirubinemia and abnormal neurological signs after birth to be significantly associated with risk of autism. A US study by Croen et al. (2002), identifying cases from the California Department of Developmental Services and utilizing state birth certificate electronic files for children born in 1989–1994, found an increased increased risk for autism associated with multiple births, maternal age (especially age greater than 35) and being born to a black mother. There was no association with immigration status. An Australian case-control study (Glasson et al., 2004) found older parental age, cesarean delivery, 1-minute Apgar score of less than 6, and several obstetric complications to be associated with increased risk of autism. A Canadian birth cohort study (Burstyn et al., 2010) identified a number of risk factors for ASDs, including advanced maternal age, low maternal pre-pregnancy weight, pre-eclampsia, breech presentation, and 1-minute Apgar score of less than 6. All of the above studies undertook statistical analyses to remove confounding effects of multiple variables. The identified risk factors are therefore assumed to be independent. In a review of these and other studies of prenatal and perinatal factors, Kolevzon et al. (2007) pointed to advanced maternal and paternal age, maternal immigration status, low birth weight, and evidence of intrapartum hypoxia as the most consistently identified risk factors for autism.

Focused investigations In addition to these large epidemiologic surveys identifying multiple risk factors, there have been a number of focused studies addressing specific prenatal and natal events and gestational environmental exposures. One prenatal – and preconception – effect that has been looked at in several studies is parental age. In addition to the studies already reviewed, there have been several largepopulation, birth-cohort studies that have confirmed the association between advanced maternal and paternal age and autism – making this a particularly robust finding (Reichenberg et al., 2006; Croen et al., 2007; Durkin et al., 2008; Grether et al., 2009; Hultman et al., 2010; Shelton et al., 2010). Shelton et al. (2010) found that every 5-year increase in a mother’s age raised her risk of having a child with autism by 18%. The effect of paternal age is, moreover, independent of that of maternal age. Offspring of men over 50 were 2.2 times more likely (CI: 1.26–3.85) to have autism than offspring of men aged 29, after controlling for maternal age and other documented risks of autism (Hultman et al., 2010); and the effect of paternal age was most pronounced when mothers were less than 30 years old (Shelton et al., 2010). Natal factors such as hyperbilirubinemia and prematurity have also been specifically looked at for autism risk. Maimburg et al. (2010) revisited their earlier finding of neonatal jaundice as a risk factor for autism. In a population-based, follow-up study of all children born in Denmark in 1994–2004, they found that there was an increased risk for both ASDs and infantile autism in infants who had had neonatal jaundice. This risk was increased in later-born children (possibly from immune-mediated hemolytic effects) and when the birth was between October and March (possibly related to decreased exposure to sunlight). Pre-eclampsia and low birth weight have also been studied. In a South Carolina case-control study, Mann et al. (2010) found a significant association between pre-eclampsia/eclampsia

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and later ASDs, with this association partially mediated by low birth weight. Buchmayer et al. (2009), in a Swedish population-based, case-control study, found an increased risk for ASDs in association with premature birth (odds ratio (OR) = 2.05, CI: 1.26–3.34 for very premature infants; OR = 1.55, CI: 1.22–1.96 for moderately premature infants), but these odds ratios were reduced when prenatal and neonatal complications were controlled for. The authors concluded that events such as pre-eclampsia, small-for-gestational-age birth, intracranial bleeding, congenital malformations, and neonatal seizures were the primary mediators of the association between prematurity and autism. Environmental exposures – beginning at conception and continuing into early postnatal life – have been implicated in the rise in prevalence of autism (Daniels, 2006). At present, however, findings identifying specific toxic or infectious risk factors are preliminary. Small case series have demonstrated links between autism and prenatal exposure to thalidomide (Strömland et al., 1994), and prenatal exposure to the anticonvulsant valproic acid (Christianson et al., 1994; Moore et al., 2000). A large case-control study found a twofold increased risk of ASD associated with maternal treatment with an SSRI antidepressant during the year before delivery, with the strongest effect associated with treatment during the first trimester (Croen et al., 2011). These findings implicate toxic prenatal exposures as a risk factor, but do not in themselves explain many cases of autism. Alcohol has been linked to autism in that children with congenital malformations secondary to alcohol have higher rates of ASDs (Arndt et al., 2005). However, a large Danish case-control study of children born between 1996 and 2002 found no association between maternal alcohol intake – including binge drinking – and autism (Eliasen et al., 2010). Some epidemiologic studies have looked at prenatal exposure to mercury. An ecologic study found a correlation between environmental mercury release from power plants and special-education enrollment of autistic children in Texas (Palmer et al., 2006). This finding was preliminary: this was an ecologic study and did not use any direct measure of actual mercury exposure. A repeat study (Lewandowski et al., 2009) using the same database for subsequent years found no association between mercury emissions and enrollment of autism cases in schools. Mothers’ exposure during pregnancy to exhaust fumes from motor vehicles was investigated, indirectly, by a measure of residential proximity to California freeways, based on hospital records of address at the time of delivery (Volk et al., 2011). After adjustment for sociodemographic factors such as maternal smoking, mothers of ASD cases were significantly more likely than mothers of controls to have resided near a freeway. A similar, casecontrol study used census tracts in the San Francisco Bay area for which US Environmental Protection Agency data were available on hazardous air pollutants (Windham et al., 2006). ASD cases born in 1994 were more likely than controls from the same cohort to have been exposed prenatally to high levels of heavy metals and chlorinated solvents, based on location of birth residence. Kalkbrenner et al. (2010) applied this same approach in counties of North Carolina and West Virginia. They, however, found few differences between ASD cases and controls in history of prenatal exposure to heavy metals and other toxicants identified in the California study. They did find a history of increased exposure to methylene chloride, quinolone, and styrene in ASD cases. Several studies have looked at prenatal exposure to thimerosal, which was also formerly used as a preservative in Rh immune globulin – given to pregnant women who lack Rh factor and are at risk of forming antibodies against the red cells of present or future fetuses (Miles and Takahashi, 2007; Croen et al., 2008; Price et al., 2010). No increased risk of autism was

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found for having an Rh-negative mother or for prenatal exposure to thimerosal-containing immune globulin. Recently, the CDC’s environmental health tracking program has begun to support research into the effects of agricultural pesticides. Roberts et al. (2007) reported on a study in a 19-county agricultural region in California. The researchers identified 465 children with ASDs who were born during 1996–1998 and matched them by maternal date of the last menstrual period with 6975 children who were in utero at the same time and who were born at term. The two groups were compared on proximity to pesticide applications. Type of pesticide was also included in the analysis. Results showed an association between organochlorine exposure and ASDs when exposure occurred during the first 8 weeks of pregnancy. ASD risk increased with the poundage of organochlorine applied and was highest for mothers who lived within 500 m of field sites. Given the size of this study and the number of analyses, these results are best considered preliminary (McGovern, 2007). In addition to toxic exposures, epidemiologic research has focused on maternal infections, maternal stress and maternal immune factors as possible risk factors for autism. A connection between congenital rubella infection and autism was recognized decades ago (Chess, 1971). Atladóttir et al. (2010) recently reported a link between subsequent ASD diagnosis and maternal admission to a hospital for viral infection during the first trimester, and for bacterial infection in the third trimester. In this case-control study of all children born in Denmark in 1980–2005, the adjusted hazard ratio for early viral infection was 2.98 (CI: 1.29–7.15). In contrast, children’s postnatal infection requiring hospitalization was not associated with autism (Atladóttir et al., 2010). Kinney et al. (2008b) reviewed the available evidence that stressful life events or hardship in an expectant mother could increase the risk of giving birth to a child with an ASD. They cited a wide range of studies linking maternal stress to brain changes that could be associated with the occurrence of autism in their children (neuroinflammation, seizures, immunologic abnormalities) and proposed some mechanisms by which maternal stress could produce these effects – e.g. by the direct effect of stress hormones on the fetal brain or by altering blood flow in the uterus and placenta. Kinney et al. (2008a) looked at maternal stress as a risk factor for autism by investigating a link between ASD prevalence among children born in Louisiana in 1980–1995 and the severity of hurricanes and tropical storms during that period. In this study, the occurrence of a natural disaster was assumed to be a marker for stress in pregnant mothers who experience that disaster. The prevalence of autism was found to increase with prenatal storm exposure, with the highest prevalence associated with being in utero in New Orleans at a time when a storm’s center passed directly through the city. Lower prevalence was associated with being in utero in a county outside New Orleans and having no prenatal exposure to a storm’s center. Although this study had no direct measure of maternal stress, it does use an indicator for stress that does not rely on a mother’s retrospective recall – a problem with some previous research. Li et al. (2009) used maternal bereavement as a measure of stress. In a cohort study of all children born in Denmark from 1978 to 2003, they looked at a history of maternal bereavement during pregnancy, defined as loss of a close relative during or up to one year before pregnancy. Thus defined, gestational maternal bereavement was not found to be associated with increased risk of autism in offspring. With regard to immune functioning, a theory that has led to epidemiologic research is that maternal autoimmune disease may play a role in the development of autism, specifically

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a mother’s auto-antibodies acting upon fetal brain protein and causing inflammatory changes in the developing brain. This research is reviewed in Chapter 6. What can these epidemiologic studies of multiple sociodemographic, prenatal, and perinatal risk factors tell us about the causes of autism? Taken together, they strongly suggest that autism arises early in life and that there is likely an interplay between genetic determinants and a range of prenatal and perinatal environmental influences in giving rise to the disorder. Effects of advanced paternal age, for example, may well be genetic, but older maternal age may constitute both a genetic and environmental risk factor, as may maternal race and immigration status. Identified risks of low birth weight, small size for gestational age, obstetric complications, and low Apgar scores may be the result of earlier genetic and prenatal insults that are the actual cause of the disorder – or they may be predisposing factors for neonatal adversity, which could be the event actually conferring risk. Preliminary studies on the risk of prenatal exposure to environmental toxicants lack consistency, but do point to a possible connection between such exposures and autism, possibly through some interaction between genetic predisposition and an environmental effect. These risk factor studies thus strongly suggest that studies to identify the causes of autism should focus on (1) prenatal brain development and how it is influenced by genes and the intrauterine environment, and (2) how these early factors interact with perinatal complications to affect postnatal development. That having been said, it should also be noted that many of the identified prenatal and perinatal risk factors are nonspecific; they also confer risk for other developmental disorders. Their role in the chain of events that leads to the neuropathology ultimately expressed in symptoms of autism must therefore be a facilitating one – again pointing to a likely interaction between genes and environment.

Summary and future directions This chapter has reviewed several topics in the epidemiologic study of autism: the prevalence of ASDs and possible reasons for the increase in prevalence observed over the past decade and a half; the question of a purported connection between autism and childhood vaccines; and specific, identified prenatal and perinatal risk factors for ASDs. The underlying concern in most of this research has been the public health challenge of the presently recognized prevalence of ASDs – approximately 1 in 100 – and the desire to identify causes of ASDs, particularly those that may be prevented. Thus far, the contribution of epidemiologic risk factor studies to determining the causes of autism has been somewhat limited. The large populations studied are invariably heterogeneous, and most of the research is retrospective and preliminary. Given the likelihood that many causal and predisposing factors for ASDs are genetic (see Chapters 5 and 6), what is needed are research models that address how prenatal, perinatal, and early life exposures and events might interact with genetic predispositions to give rise to the developmental patterns that characterize ASDs. Ideally, such research would be prospective and longitudinal. As of this writing, a number of promising epidemiologic studies are in progress under the auspices of several projects and consortia. Among these are the Childhood Autism Risks from Genetics and The Environment project (CHARGE, Hertz-Picciotto et al., 2006); the Autism Birth Cohort (ABC, Stoltenberg et al., 2010); and the Early Autism Risk Longitudinal Investigation network (EARLI study, 2011). The CHARGE project, which is based at the University of California, Davis and funded by the National Institutes of Health, is a

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prospective, population-based study of children with autistic disorder and two control groups: children with other developmental disabilities and a group from the regular population. In addition to parent interview and perinatal, labor and delivery, neonatal and pediatric records, data are derived from blood, urine, and hair samples from subjects, looking for specific immune markers and xenobiotic exposures. The ABC study is a collaboration between the Mailman School of Public Health at Columbia University and the Norwegian Institute of Public Health, gathering prospective information on 100 000 children. Its goal is to analyze gene × environment × timing interactions. Serial samples are being obtained from parental blood, maternal urine, cord blood, milk teeth, and rectal swabs – to be evaluated with genetic, immunologic, and microbiologic measures. ASD cases will be identified and compared with controls from the same birth cohort. The EARLI project is unique in that cases will be the “high-risk” newborn siblings of children previously diagnosed with ASDs. Prospective data can be gathered as soon as a new pregnancy is established and children followed after birth to see if they develop autism. This prospective case-control study will address genetics and environmental exposures during pregnancy, birth, and early life. It will also attempt to identify early biologic markers for autism and to provide a description of the symptomatic features and developmental trajectories of young children with ASDs. With information coming from these and other epidemiologic studies, there should soon be a rich database on etiological risk factors for autism, their interplay, and the timing of their effects.

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Hornig, M., Chian, D. and Lipkin, W. I. (2004). Neurotoxic effects of postnatal thimerosal are mouse strain dependent. Mol Psychiatry, 9(9), 833–45. Hultman, C. M., Sandin, S., Levine, S. Z. and et al. (2010, November 30). Advancing paternal age and risk of autism: new evidence from a population-based study and a meta-analysis of epidemiological studies. Mol Psychiatry, doi: 10.1038/mp.2010.121. Hultman, C. M., Sparén, P. and Cnattingius, S. (2002). Perinatal risk factors for infantile autism. Epidemiology, 13(4), 417–23. Hviid, A., Stellfeld, M., Wohlfahrt, J., et al. (2003). Association between thimerosalcontaining vaccine and autism. JAMA, 290 (13), 1763–6. Kalkbrenner, A. E., Daniels, J. L., Chen, J. C., et al. (2010). Perinatal exposure to hazardous air pollutants and autism spectrum disorders at age 8. Epidemiology, 21(5), 631–41. Kaye, J., del Mar Melero-Montes, M. and Jick, H. (2001). Mumps, measles, and rubella vaccine and the incidence of autism recorded by general practitioners: a time trend analysis. BMJ, 322, 460–3. King, M. and Bearman, P. (2009). Diagnostic change and the increased prevalence of autism. Int J Epidemiol, 38(5), 1224–34. Kinney, D. K., Miller, A. M., Crowley, D. J., et al. (2008a). Autism prevalence following prenatal exposure to hurricanes and tropical storms in Louisiana. J Autism Dev Disord, 38(3), 481–8. Kinney, D. K., Munir, K. M., Crowley, D. J., et al. (2008b). Prenatal stress and risk for autism. Neurosci Biobehav Rev, 32, 1519–32. Kolevzon, A., Gross, R. and Reichenberg, A. (2007). Prenatal and perinatal risk factors for autism: a review and integration of findings. Arch Pediatr Adolesc Med, 161, 326–33. Kolevzon, A., Weiser, M., Gross, R., et al. (2006). Effects of season of birth on autism spectrum disorders: fact or fiction? Am J Psychiatry, 163(7), 1288–90. Larsson, H. J., Eaton, W. W., Madsen, K. M., et al. (2005). Risk factors for autism: perinatal factors, parental psychiatric history, and socioeconomic status. Am J Epidemiol, 161(10), 916–25. Last, J. M. (Ed.). (1988). A Dictionary of Epidemiology (2nd ed.). Oxford: Oxford University Press, Inc.

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What We Know About Autism

Developmental neuropsychology of autism Gerry A. Stefanatos

The concept of “autism” originated with the Swiss psychiatrist Eugen Bleuler (1911), who coined the term to refer to the “detachment from the outside world” and the “preponderance of introversion” that he observed in adults with schizophrenia. Bleuler believed that the other pathological accentuation of autistic thinking that occurred in schizophrenia reflected a disintegration of mental function and was associated with other disturbances, including lack of initiative and persistence, aimlessness, impulsivity, bizarre behavior, disordered attention, obsessional and automatic acts, and loss of contact with reality. Around this time, several reports suggested that the onset of this form of deterioration, previously referred to as dementia praecox, could emerge in children following a period of seemingly normal development (DeSanctis, 1908; Heller, 1908). In due course, the concept of autism was adopted to describe congenital forms of psychopathology in which children failed to develop adequate or appropriate levels of affective contact with others, instead remaining withdrawn and self-absorbed. The Russian neurologist Ewa Ssucharewa, for example, embraced the concept in describing an “autistic attitude” that she had observed in children described as having a schizoid personality disorder (Ssucharewa, 1926). This “attitude” was reflected in social avoidance and a preference for fantasy stories and fairy tales, and was accompanied by characteristic oddities of thinking (perseveration, rumination, and rationalization), emotional dysregulation, and the presence of echolalia, impulsivity, and stereotypic behavior. The Austrian psychiatrist Hans Asperger subsequently utilized the term “autistic psychopathy” to characterize a very similar group of children who seemed self-absorbed in special interests or obsessions and were unaware or unconcerned with social norms and expectations to an extent that they had profound difficulties negotiating social interactions with others (Asperger, 1944). Despite these scattered reports, the significance of the concept of autism to understanding developmental psychopathology remained relatively unrecognized until the classic depiction of “infantile autism” by Leo Kanner (1943), an Austrian-American psychiatrist working at Johns Hopkins Hospital in Baltimore. Kanner described 11 children who exhibited a striking “inability to relate themselves in the ordinary way to people and situations.” The children demonstrated extreme aloneness and a fascination with objects and pictures that outweighed any interest in people. This profound lack of social awareness and disregard for affective contact with other people was intertwined with other behavioral disturbances, such as an “anxiously obsessive desire for the maintenance of sameness,” reflected in obsessional rituals and routines. In addition, their communications skills were severely compromised. Three of the 11 cases originally described by Kanner (1943) were seemingly mute, although The Autism Spectrum, ed. Mark E. Reber. Published by Cambridge University Press. © Cambridge University Press 2012. 59

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on rare occasions they could emit a full sentence. The remaining children produced speech characterized by semantic, syntactic, prosodic, phonologic, and, especially, pragmatic deficits. Verbal productions were often fluent but lacking in communicative content. Noteworthy features included: (1) pronoun reversals (you for I), (2) “metaphorical language,” characterized by verbal productions that had little or no association with conversational context, (3) immediate and delayed echolalia, and (4) unresponsiveness to verbal overtures or questions. The children’s comprehension of language was evidently limited. The symptom complex varied in specificity, severity, and developmental course, resulting in a cohesive but heterogeneous behavioral phenotype that in many respects resembled a childhood form of schizophrenia. However, Kanner contended that the syndrome could be distinguished from previously described childhood-onset forms of schizophrenia (DeSanctis, 1908; Heller, 1908) on the basis of its having been present “from the very beginning of life”. Given its early onset, Kanner speculated that the disorder was constitutional in origin, resulting from an “innate inability to form the usual, biologically provided affective contact with people.” Currently, there is a consensus that AD and related disorders reflect the final common pathway of perturbations of brain development that compromise the normal elaboration of neural processes mediating social behavior, communication, and flexible imaginative functions. Evidence in support of this conceptualization stems from numerous findings that the brains of autistic individuals are both functionally and structurally atypical. Abnormalities have been observed at multiple levels of analysis, ranging from cortical cytoarchitecture and neurochemistry to gross neuroanatomy, connectivity, and regional cerebral physiology and metabolism (for reviews, see Bauman and Kemper, 2005; Courchesne et al., 2007; Neuhaus et al., 2010). Concurrent with these advances, a sophisticated neurobehavioral description has emerged which, together with the neurobiological findings, is providing a more lucid framework for understanding the disorder and its diverse manifestations. The disorders seem to be based in fundamental limitations of the function of multiple neural systems to perform tasks requiring the processing of a variety of stimuli of social and communicative significance. Specific cortical networks that have been particularly implicated include frontal and temporal neocortex, as well as cingulate gyrus and subcortical structures such as the insula, limbic system, and the cerebellum. An overview of current conceptions of the neuropathological correlates of autism spectrum disorders is provided in the next chapter. Here, we will focus on recent advances in understanding the early phenomenology of these disorders from a developmental neuropsychological perspective.

Clinical characteristics The term autism spectrum disorder (ASD) has come into common usage to refer to autistic disorder (AD) and the other pervasive developmental disorders (PDDs) that most closely resemble it, specifically Asperger’s syndrome (AS) and pervasive developmental disorder not otherwise specified (PDD-NOS). These conditions comprise a diverse group of behaviorally defined syndromes characterized by slow, limited or otherwise faulty psychological development in three key behavioral domains: (1) social relatedness, (2) verbal and nonverbal communication, and (3) repertoire of interests and activities (often expressed in obsessional, repetitive, or stereotypic behavior) (Wing and Gould, 1979; Wing, 1988). The behavioral disturbances become evident in infancy and childhood, when social and communicative behaviors normally seen in the course of development fail to emerge in the usual timeframe

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or manner (negative symptoms) or when atypical or deviant behaviors arise (positive symptoms). Manifestations of the symptom complex can vary considerably in terms of the particular pattern of behaviors involved, the specificity and severity of impairment, and the developmental course. The core ASDs implicitly vary from each other on a continuum of severity and are distinguished from each other by such characteristics as age of onset, the number and severity of associated symptoms and developmental course. AD is the prototype for the category and is the most well-established, reliably diagnosed, and intensively studied of the disorders. AD will therefore be the primary focus of discussion in this chapter and the next. Disturbances of social relatedness and reciprocity are considered foundational and are reflected in diminished interest in and responsiveness to social interaction, poor or limited emotional reciprocity (empathy), impaired use of nonverbal behaviors (e.g. eye contact, facial expression, gestures) to negotiate social interactions, difficulties forming appropriate peer relationships, and problems intuitively understanding and seeking to share interests or enjoyment with others (Dawson and Bernier, 2007; Frith, 2001; Sigman et al., 2004). Associated communication impairments vary greatly. Communicative difficulties can extend from profound impairment of both expressive and receptive language to fluent speech characterized by anomalies of intonation, rate, rhythm, or stress. Variability of language skill is a hallmark of AD, with inconsistent ability both across the population and within the linguistic repertoire of individuals. As many as 50% of individuals with autistic disorder fail to develop functional language. Even when basic aspects of language production are unimpaired, children demonstrate pronounced difficulties in conversational skills (i.e. pragmatics) and understanding of implicit meaning (Rapin et al., 2009; Tager-Flusberg, 2006). Moreover, they have problems understanding the significance of nonverbal communication such as facial expressions, gestures, and body language (Mundy et al., 1990; Lord and Pickles, 1996). Problems with repetitive and restricted behavior and interests include stereotyped body movements (e.g. hand flapping), a preoccupation with parts of objects or their sensory qualities, and inflexible adherence to nonfunctional routines. In addition, limited imagination and flexibility of thinking are reflected in restricted patterns of play, repetitive use of objects, or unusual and encompassing interests (Bodfish et al., 2000; Szatmari et al., 2006; Wing et al., 2011). These difficulties remain the least well understood of behaviors in the autistic triad. However, some research suggests that they may be explicable in terms of problems with the neural systems involved in self-regulation, self-monitoring, and other aspects of executive control. Due to considerable individual differences in the pattern, severity, and developmental course of impairments in each of these psychological domains, the behavioral phenotype associated with AD is remarkably heterogeneous. AD is not regarded as a singular entity per se, but many different conditions that share a common constellation of symptoms in their behavioral expression (Wing, 1997). The core features of AD emerge in infancy or childhood and remain present in some form at all subsequent stages of development. For some children, disturbances emerge as delays in initial acquisition of social communication, or slow expansion of abilities, while for others (about 25–30%) disturbances may arise as a consequence of the loss of previously acquired abilities (Landa et al., 2007; Stefanatos, 2008). Some parents may report these regressive changes as emerging insidiously over the course of several months, while others report a sudden onset, indicating that the child one day started to reject people, act strangely, and ceased to use language as he or she had previously done. In a third category of onset described by parents, children show slow or normal developmental progress and then

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demonstrate a developmental plateau or a substantial reduction in their rate of progress. This pattern has been variously referred to as “developmental stagnation” (Siperstein and Volkmar, 2004) or a “developmental plateau” (Hansen et al., 2008). In these cases, the deviation of a child’s development from that occurring in peers becomes more noticeable with increasing age. Some have suggested that the slowing of developmental progress may be related to a failure to build upon reasonably intact early social skills in support of other maturational processes such as speech acquisition and social communication (Chawarska et al., 2007) or to scaffold the acquisition of developmentally more advanced abilities (Klin et al., 2004). In addition to these core features, children with AD will often demonstrate unusual or aberrant responses to sensory experiences (Klintwall et al., 2011). They may alternatively become inordinately disturbed by certain sounds or engrossed and fascinated by the way objects look. These symptoms can demonstrate tremendous variation ranging from mild to severe. Some features may become evident at particular points in development and then disappear, while the onset of others marks a developmental anomaly that becomes a permanent feature of the child’s symptom complex, even while changing in character over time.

Developmental precursors Kanner’s conceptualization of AD as a disorder of very early onset was based on his interpretation of retrospective accounts by parents, who recalled that, as infants, their children failed to demonstrate typical interpersonal responses such as raising their arms in anticipation of being picked up or conforming their posture to being held. It is now appreciated that most parents (~80%) recognize developmental anomalies or delays in their AD children by 2 years of age (De Giacomo and Fombonne, 1998) and 30–50% harbor concerns in the first year (Harrington et al., 2006; Young et al., 2003). Despite this, a definitive diagnosis of AD is often not made until children are 3–4 years of age (Howlin and Moore, 1997; Mandell et al., 2005; Yeargin-Allsopp et al., 2003). Much of the recent work on initial markers of AD has combined retrospective parental recall of the child’s history with systematic examination of the child’s behavior as captured on home videos recorded in the first year or two of life (Baranek, 1999; Clifford and Dissanayake, 2008; Goldberg et al., 2008). While this methodology has some inherent limitations (Saint Georges et al., 2010), notably the potential for bias in memory recall (Zwaigenbaum et al., 2007) and the sometimes idiosyncratic circumstances surrounding the video recording disclosing the child’s behavior (e.g., birthday parties, family holidays), this combination of methodologies has revealed important facets of early development that appear to be anomalous in AD as early as the first 12 months (Bryson et al., 2008; Reznick et al., 2007; Wetherby et al., 2008). These important findings have provided an impetus for a number of prospective studies aimed at characterizing the early development of children who are at risk for AD, such as later-born siblings of children identified with AD. By following their course and eventual diagnosis (e.g. Barbaro and Dissanayake, 2010; Bryson et al., 2007; Landa and Garrett-Mayer, 2006; Ozonoff et al., 2010), research is gaining a better understanding of the variable and diverse routes that eventuate in a diagnosis of AD.

The context: normal development AD can be considered as a developmental disorder that is quintessentially a disorder of social communication, fundamentally rooted in disordered communication and sharing

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information of social significance. From an evolutionary perspective, this aspect of human behavior provided the impetus for the emergence of the unique human capacity to utilize language. These capacities not only ensured the survival of the species; they also prompted our unparalleled cognitive capacities for planning, abstraction, creativity, and the capacity for dealing with increasingly higher levels of conceptual complexity (Deacon, 1997; Donald, 1998). Unlike most primates, human infants are extremely immature at birth, unable to feed or care for themselves. Infant survival is therefore contingent upon maternal responses for extended postnatal feeding and care of their offspring. Infant–mother social interactions are therefore extremely important for attachment and bonding. The development of competence in dyadic (infant–other) interactions and communication lays the groundwork for the development of critically important triadic interaction (child–other–object) which forms the basis for sharing experience with others (Trevarthen and Hubley, 1978). Given the developmental importance of dyadic communication, numerous studies aimed at identifying early social manifestations of AD have focused on behaviors involved in dyadic infant–mother social interaction and their precursors. In order to develop skills related to and dyadic communication, infants must first be able to differentiate people from objects and both recognize and show interest in other people. In addition, they must develop capacities that promote their ability to make valid connections with other people. This includes an understanding of others that recognizes common behaviors, interest, and goals (intentionality). It has been suggested that infants are born with a natural ability to understand and make connections with other individuals that is conferred by early perceptual biases and sensitivities (Legerstee, 2009; Trevarthen and Aitken, 2001). For example, through analysis of sensory input, neonates can discriminate between visual cues indicating goal-directed or non-goaldirected actions performed by others (Craighero et al., 2011; de Schonen et al., 2005). This is an early step in the incremental and protracted process of learning to identify the actions of others and their significance. Perceiving emotional states emerges in a primitive form midway through the first year of life (Flom and Bahrick, 2007), while analyzing intentions and motivations begins later, around 12–18 months (Johnson et al., 2008). In addition, infants must be able to develop mental representations of the behaviors of others that at some level recognizes common motivations, feelings, and beliefs sufficient to be able to infer those mental states in others. This knowledge provides a basis for the development of interpersonal relatedness, which involves the ability to coordinate affective perspectives with others (Hobson, 1986). In addition, it is critical prerequisites for the development of joint attention – the ability to coordinate one’s attention with another person to a common object or event (McArthur and Adamson, 1996). Mental representations that emerge sharing information and experiences provide the foundations for developing a theory of mind (TOM) – understanding that the thoughts and intentions of others are distinct and may differ from one’s own (Mundy et al., 1994). In the normal course of development, infants possess a number of biological adaptations that serve to promote early attachment and further the development of brain mechanisms that mediate social interaction and communication. Very early in the normal course of human development, infants demonstrate attentional and perceptual biases for processing socially relevant patterns. For example, despite their relatively poor visual acuity, newborns appear to have inherent predispositions to attend to faces (Leo and Simion, 2009) and biological motion (Bardi et al., 2011). The processing of these aspects of visual stimulation are critically important for establishing the identity and analyzing the actions of other people. Within days of birth, neonates show a preference for direct eye gaze (Farroni et al., 2007) and

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are sensitive to facial geometry and expressions (Leppänen and Nelson, 2009). As already mentioned, they also demonstrate evidence of being able to discriminate between visual cues indicating goal-directed or non-goal-directed actions in others. By 6 weeks, infants can recognize their mother’s face based solely on its internal configuration (de Schonen and Mathivet, 1990) and make direct eye contact (Csibra and Gergely, 2006; Itier and Batty, 2009). By 2 months, patterns of brain activation during face recognition are very similar to those observed in adults (Tzourio-Mazoyer et al., 2002). These inborn biological predispositions confer a natural ability for developing intersubjectivity, that is, the sharing of subjective states between individuals (e.g. attention, intentions, and emotions). In doing so, these capacities potentiate the ontogeny of complex neural networks that mediate the highly complex and specialized computational demands associated with social interaction and cooperation (Brothers, 1990; Dunbar and Shultz, 2007). The development of this network, sometimes collectively referred to as the “social brain” (Adolphs, 2009), is ultimately dependent on interactions between these genetically determined predispositions and experience. Precursors of language are also a key component of social communication. Early in child development, information is first conveyed through such gestures as gaze and facial expressions. As motor development proceeds, meaning-conveying gestures, such as pointing, emerge as critical additional elements in the communicative repertoire. Initial vocalizations are “vegetative” sounds, but later are produced concurrently with gestures (McCune et al., 1996; Volterra et al., 2005). Thereafter, gesture and speech co-evolve in a complex interrelationship that undergoes substantial change over time (Volterra et al., 2005). Findings that gestures serve as a parallel communication pathway during speech production (Melinger and Levelt, 2004), and that these movements are associated with lateral asymmetries consistent with language dominance (Kimura, 1973; Lausberg et al., 2007) suggest shared neural resources. Further support for common neurobiological influences includes the strong correlation between handedness and cerebral asymmetries for language (Knecht et al., 2000), a concordance of leftward asymmetries in language pathways (e.g. arcuate fasciculus) and the cortico-spinal tract in infancy (Dubois et al., 2009), and the association of left hemisphere damage to aphasia, apraxia, and deficits in pantomime production (Foundas et al., 1995; Goldenberg et al., 2003). Foundations for word learning are evident in the early months. Infants have uniquely adapted nervous systems with inherent attentional and perceptual biases that potentiate sensitivity to a variety of acoustic and temporal attributes of speech. At birth or soon thereafter, infants can discriminate a wide array of phonemes (Diehl et al., 2004; McMurray and Aslin, 2005), recognize their mother’s voice (Fifer and Moon, 2003; Kisilevsky et al., 2009), prefer voices to nonsocial sounds (Ecklund-Flores and Turkewitz, 1996), and favor speech sounds of their native language (Krentz and Corina, 2008; Kuhl et al., 2008). Within a few months, infants stop crying at the sound of their mother’s voice, indicating early influences on moderating their affective state. By about 5 months, they easily discriminate between happy, sad, and angry prosody (Flom and Bahrick, 2007). An infant normally demonstrates comprehension of words midway through their first year by responding to their name or shifting gaze to a person (e.g. Mommy) or object (Reznick, 1990; Thomas et al., 1981; Tincoff and Jusczyk, 1999). By 7–8 months, infants associate novel words and objects after only a few paired repetitions, although cross-modal synchrony is required during the pairing (e.g. saying the word as an object is moved) (Gogate, 2010). Concurrently, they begin to segment familiar words from a stream of

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continuous speech. Toward the end of their first year, more sophisticated object-related gestures become a part of the infant’s communicative repertoire, such as pointing to or picking up a designated object, and showing or offering it to another person (Baldwin, 1993; Woodward and Hoyne, 1999). During the latter half of the first year, infants vocalize more, moving from producing vowel sounds while babbling to shaping their vocalizations to approximate words they have heard and are attempting to imitate (Iverson and Fagan, 2004). Production of first words is a milestone achievement in an infant’s developmental progression that commonly occurs around or not long after their first birthday (Fenson et al., 1994). This is followed by a slow and then rapid expansion (~18 months) in their ability to comprehend new words heard in their day-to-day social environment (Hallé and de Boysson-Bardies, 1994; Reznick and Goldfield, 1992; Tincoff and Jusczyk, 1999). During the so-called “fast mapping” process associated with the prodigious rate of lexical expansion, children can acquire new words based on only a single exposure (Gershkoff-Stowe and Hahn, 2007). This description of the normal, expected course of social development provides a context for understanding the abnormalities in neuropsychological development of children who are at risk for, or later become diagnosed with, autistic disorder.

Early deficits in AD: eye gaze and eye contact Eye contact and eye gaze play a vital role in providing infants with visual experiences to promote early social interaction and communication. The configuration of the eyes (e.g. size, shape, spacing, color) provide salient visual cues for establishing the identity of another person, while the capacity to discern the direction of eye gaze is critical to establishing social contact and conveying emotional information and intentions (Schyns et al., 2007). For example, humans routinely probe each other’s eyes and scan each other’s faces in order to evaluate visual cues that signal the other person’s disposition, mood, level of attraction or degree of understanding. In addition, discerning and following another person’s eye gaze conveys information relevant to determining what is capturing that person’s interest and potentially provides an opportunity to share in those interests by directing gaze to the same object (joint attention). Eye gaze can also be used to convey, in a directive manner, to another person that they should allocate attention to a particular object or event. It has long been noted that school-aged children with AD are often avoidant of direct eye contact (Kanner, 1943), and deficient “eye-to-eye gaze” is a key symptom of their impaired use of nonverbal behaviors to regulate social interaction and communication. Recent studies have suggested that poor eye-to-eye gaze may, in fact, underlie some of the difficulties experienced by individuals with AD in recognizing faces and in deriving important social information from facial expressions. Children with AD who are unsuccessful in social perception often fail to spontaneously scan the socially informative eye region of faces, spending more time scanning less informative areas around the mouth (Falck-Ytter et al., 2010; Pelphrey et al., 2002). In social contexts, failure to make direct eye-to-eye contact may therefore limit access to the rich social information conveyed by the eye region of the face. Numerous studies have been directed to ascertaining whether anomalies of eye contact could potentially serve as early behavioral markers of AD. Diverse anomalies of gaze have been observed in the first 2 years, ranging from vacant empty staring (Dahlgren and Gillberg, 1989) to unusual intensity of eye contact (Wimpory et al., 2000). Children with AD often

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demonstrate inconsistencies in following another person’s eye gaze or making use of eye gaze to share experiences or accomplishments (Zwaigenbaum et al., 2005). A recent study by Clifford and Dissanayake (2008) investigated the onset and time course of the emergence of problems with eye gaze through examination of retrospective video material recorded during four 6-month time frames in the first 2 years of life (0–5, 6–11, 12–17, and 18–24 months) of children diagnosed with AD. In addition, these researchers conducted retrospective parental interviews referenced to the same time frames. Consistent with previous reports, subtle signs of anomalies of eye gaze emerged in the first year. However, on further analysis, it appeared that these differences were not reliably predictive of later social behavior. Problems with eye gaze seemed to progressively worsen between the first and second years, with active gaze aversion – the tendency to avoid eye contact – emerging in the latter part of this progression, just prior to the second birthday. The quality of eye contact, rather than its frequency, during the second year of life was an important precursor to later social responsiveness (Clifford and Dissanayake, 2009). Anomalies of eye contact at 12 months of age co-occur with signs of other abnormalities in the use of visual attention to mediate interactions with other individuals (Zwaigenbaum et al., 2005). Reduced social smiling and interest, a lack of imitation and difficulties coordinating eye gaze with actions during play accompanied poor visual tracking behavior. Additionally, infants considered at-risk for AD tended to show disproportionate visual fixation to nonsocial aspects of their visual environment (e.g. objects) and had difficulties disengaging their visual attention. The factors underlying these difficulties remain speculative but may involve impairment of neural networks involving the cerebellum, anterior cingulate, frontal lobes, and parietal cortex that mediate the development and control of visual attention (Bryson et al., 1990; Townsend et al., 2001).

Early deficits in AD: joint attention Impairments of visual orienting at 1 year are closely tied to problems with joint attention – the ability to share an attentional focus with that of another person towards some common object or event. Impaired joint attention is arguably one of the most striking characteristics of young children with AD (Charman, 2003; Colombi et al., 2009; Mundy et al., 1990). This capacity to coordinate self–other attention is critical to the development of social and communicative functioning, imitation skills, and symbolic thought (Dawson et al., 2004; Mundy et al., 2009; Yoder et al., 2009). In the normal course of development, the capacity to engage in joint attention begins to develop by about 4–6 months of age (Mundy et al., 2009). By around 10 months, typically developing children can use eye gaze to direct their parents attention to an object of interest (Striano and Rochat, 1999), and by the end of their first year, infants are able to engage in joint attention to negotiate fairly sophisticated object-related actions, such as pointing to or picking up a designated object and showing or offering it to another person (Baldwin, 1993; Woodward and Hoyne, 1999). Problems with joint attention seen in AD are reflected in a lack of responsiveness to others who attempt to engage and direct their attention to an object of potential interest (Charman, 2003; Clifford and Dissanayake, 2008; Mundy and Newell, 2007). In addition, children with AD initiate fewer attempts to engage others in joint attention. Diminished responsiveness may be evident in a failure to follow another person’s eye gaze or pointing to an object, whereas poor initiation of joint attention may be reflected in the relative absence of behaviors such as offering objects to others, pointing to objects, and

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protodeclarative showing. The relative paucity of these behaviors in the first 2 years implies fewer attempts of children with AD to engage other individuals in shared experience, and this appears to differentiate AD children from typically developing infants as well as infants with language delay (Clifford et al., 2007). Deficiencies in joint attention may result in a failure of the child to learn the reward value of social interaction, with consequent negative implications for the development of higherorder intersubjectivity and social understanding (Dawson et al., 2004; Mundy, 2003). These problems may therefore relate to a larger constellation of difficulties in orienting and attending to social stimuli (Dawson et al., 2004; Sasson et al., 2007), appreciating emotional cues and dispositions in other people (Baron-Cohen et al., 2009; Rieffe et al., 2007; Silani et al., 2008), understanding the mental states and intentions of others, and in developing a theory of mind (Charman, 2003; Frith, 2001; Hill and Frith, 2003). Lack of responsiveness to joint attention initiatives early in the second year (e.g. 14 months) is predictive of outcome at the end of the second year (Sullivan et al., 2007). The ability to respond to and initiate joint attention during this period, particularly between 18 and 24 months, is also predictive of social responsiveness later in development (Clifford and Dissanayake, 2009). Despite these associations, there has been inconsistency between studies as to whether joint attention-related behaviors in the first year of life, such as pointing and showing, can be successfully used to differentiate children with AD from typically developing or developmentally delayed children (Clifford et al., 2007; Werner and Dawson, 2005). A recent prospective study by Ozonoff and colleagues (2010) of children who were at either low or high risk for developing AD has shed some light on this issue. They examined the frequency of gaze at faces, shared smiles, and vocalizations directed to others at 6, 12, 18, 24, and 36 months. No differences were evident between the low- and high-risk groups at 6 months, but by 12 months, deficiencies were evident in the high-risk group. Moreover, subsequent evaluations revealed progressive declines in their performance in these areas, suggesting a regressive course. These findings parallel those of Clifford and Dissanayake (2008) and others in illustrating that a subtle but definite deterioration of function can occur in infants with AD between the first and second year of development. Behaviors that directly entail some aspect of social interaction are among the better predictors of a later diagnosis of AD (Dawson and Bernier, 2007; Wimpory et al., 2000). In the first year, children eventually diagnosed with AD often demonstrate diminished social initiative and responsiveness (Bryson et al., 2007) as well as anomalies of behavioral reactivity, social interest, and play. In addition, their behavior is characterized by variability of emotional expression with fewer expressions of positive affect (Zwaigenbaum et al., 2005). It should be noted, however, that anomalies observed in the first 6 months are not as consistent or as predictive of later social outcome as are differences seen at 12 months and later. Neurodevelopmental changes that are occurring between 12 and 24 months of age appear to have a significant impact on the behavioral repertoire of children at risk for AD, associated with apparent regression.

Early deficits in AD: prelinguistic communication impairment Eye contact and shared attention are critical components of early language development. Additionally, children at risk for autism may demonstrate deficits in other prelinguistic precursors of language acquisition, evident in their response to speech sounds, patterns of vocalization, oromotor coordination and their perception or production of gestures (Stefanatos and Baron, 2011).

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Retrospective studies of children diagnosed with AD have found atypical neurological and behavioral responses to linguistic auditory input early in development. Consistently observed early predictors of AD are failures to orient toward speech (Bebko et al., 2006; Osterling et al., 2002) and to respond to their name by 6–12 months (Baranek, 1999; Osterling and Dawson, 1994; Osterling et al., 2002; Saint Georges et al., 2010; Zwaigenbaum et al., 2005). These also are predictive of broader, receptive and expressive language problems. Unlike normally developing children whose word recognition skills begin midway through the first year, children with AD tend to acquire words and word combinations more slowly (Goldberg et al., 2008; Landa, 2007), and have fewer words or phrases by their first birthday (Mitchell et al., 2006). This disparity continues into their second year of life. Absence of functional language by age 2 is a diagnostic “red flag” (Ellis Weismer et al., 2010). In children categorized with an ASD at 24 months, delays in language were often evident by 14 months (Landa and Garrett-Mayer, 2006). Abnormalities of vocalization (Ozonoff et al., 2010) and babbling (Iverson and Wozniak, 2007) are also prodromal signs. Babbling is necessary for basic organization and building language-specific neuronal representations (Pulvermüller, 2002). The mimetic capacity to translate action-perception to action-production may be crucial to both speech and gestural communication, involving the neural systems that mediate imitation and praxis, including so-called “mirror neuron” networks (Gentilucci and Dalla Volta, 2008; Rizzolatti and Arbib, 1998). A comparison of vocal behavior in young, preverbal children found those with AD had no difficulty expressing well-formed syllables (i.e. canonical babbling), but had impaired vocal quality, so that they produced a greater proportion of syllables with atypical phonation, as well as deficits in joint attention. However, atypical features of their vocal behavior appeared to be independent of individual differences in joint attention, suggesting that a multiple-process model may best describe early social-communication impairments in these children (Sheinkopf et al., 2000). Motor and oromotor development are also predictive of expressive language proficiency. Gernsbacher and colleagues (2008) observed that blowing raspberries and grabbing dangling toys correlated with later verbal fluency. Oromotor difficulties may explain the limited phonological development of minimally fluent children with AD early in their development who, because of the relative ease with which they produce necessary articulatory movements, are more likely to produce vowels than consonants (Wetherby et al., 1989) and voiced rather than voiceless consonants (e.g. /d/ vs. /t/) (McCleery et al., 2006). Motor-related problems can affect phonological development which may subsequently interact with lexical and grammatical development at early stages of language acquisition. Failure to produce particular speech sounds will seemingly suspend the production of a word containing that sound (Vihman and Croft, 2007). Infants with AD also experience difficulty acquiring and understanding common communicative gestures (e.g. nodding, waving, clapping, extending arms to be picked up or hugged) (Mitchell et al., 2006), produce fewer gestures, and exhibit more anomalies of postural and rhythmic arm activity than typically developing children (Gernsbacher et al., 2008). Problems with gesture may remain evident at 18 months (Mitchell et al., 2006) and persist into later childhood (Luyster et al., 2008). Nonsocial communicative acts such as requesting items they want to obtain are not as impaired as social communication (e.g. showing, initiating joint attention), because the latter intrinsically requires integration of attention and social skills with communication (Sigman et al., 1986; Wetherby et al., 1998; Wetherby and Prutting, 1984). By the second year of life, there is substantial reduction in the diversity and frequency of gestural communication. Overall, the pattern of impairment

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implicates a widely distributed neural network, involving superior temporal sulcus, superior temporal gyrus, supramarginal gyrus, insula, inferior frontal gyrus, hippocampus, amygdala and cerebellum. Anomalies seemed to involve disturbances in the cellular microstructure in these areas as well as impaired development of interconnecting white matter pathways (see Stefanatos and Baron (2011) for a more extensive review). The identified anomalies appear to reflect the cumulative effects of genetic, epigenetic and environmental influences.

Early deficits in AD: repetitive and restrictive behaviors Repetitive, restricted, stereotyped and ritualized behaviors and a “need for sameness” have been considered a core feature of autism since the condition was first described by Kanner (1943). DSM-IV and ICD-10 designate four categories of restricted and repetitive behaviors: preoccupation with restricted interests, nonfunctional routines or rituals, repetitive motor mannerisms (stereotypies), and persistent preoccupation with parts of objects. It is anticipated that in DSM-5, these behaviors will constitute one of two major criteria for autism spectrum disorder. To meet this criterion, a child will have to manifest at least two of the following four symptom domains: (a) stereotyped or repetitive speech, motor movements or use of objects; (b) excessive adherence to routines, ritualized patterns of verbal or nonverbal behavior or excessive resistance to change; (c) highly restricted, fixated interests that are abnormal in intensity or focus; (d) hyper- or hyporeactivity to sensory input or unusual interest in sensory aspects of the environment. The neuropsychological underpinnings and early developmental manifestations for restricted and repetitive behaviors (RRBs) are less well understood than those for social communication deficits in autism. Research in this area has been complicated by inconsistent measures and differing theoretical approaches. Only in the past decade has there been the beginning of agreement on how best to define and measure RRBs, on possible etiologic factors, and on neurobiological correlates (Leekam et al., 2011). RRBs are commonly classified as “lower level” and “higher level” (Turner, 1999). The former are more characteristic of younger and lower-functioning children and occur in disorders other than ASDs, including intellectual disability from various causes, specific neurogenetic syndromes, and Tourette disorder. They include motor stereotypies, such as body-rocking, spinning, hand-waving, finger-flicking, tapping, and repetitive activity with objects, such as spinning the wheels on toys and pouring water. Some stereotypies have a sensory component, e.g. spinning one’s body (vestibular), covering one’s ears (auditory), and flicking a string in front of one’s eyes (visual). Higher-level RRBs include narrow interests, inflexible adherence to specific rules and rituals, insistence on particular foods or items of clothing, and resistance to environmental changes. They are sometimes compared to the compulsive behaviors and rituals seen in obsessive–compulsive disorder (Langen et al., 2011a). Both levels have been shown to occur across the autism spectrum, although lowerlevel RRBs are more apparent in preschool and developmentally delayed children. RRBs in autism differ from those found in other disorders in their prevalence and in their frequency and severity. Lord (1995) observed that young children with autism had a higher prevalence of hand and finger mannerisms (87% of a population of 2-year-olds) than children with mental retardation and no autism (38% of 2-year-olds). At age 3, these differences were more marked. Other comparisons of age-matched children with ASDs, those with mental retardation, and typically developing children have also reported significantly higher prevalence and severity/frequency of RRBs in the autistic population (Goldman et al., 2009; Kim and Lord, 2010; McDonald et al., 2007; Morgan et al., 2008; Richler et al., 2010). The topography – form

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and pattern – of RRBs, however, has not been found to be different in ASDs from that in other neurodevelopmental disorders (Bodfish et al., 2000; Leekam et al., 2011). A developmental feature of repetitive and restricted behaviors is that they may not emerge until the second year of life in some children with ASDs and may increase during the preschool years (Cox et al., 1999; Moore and Goodson, 2003). The pattern of repetitive restrictive behaviors may, however, change over time as children age, with decreases in some behaviors and increases in others. As previously noted, lower-level behaviors, such as repetitive use of objects, unusual sensory interests, and hand/finger mannerisms are more prominent at younger ages, while behaviors such as repetitive self-injury, circumscribed interests, and resistance to change tend to increase with age (Bishop et al., 2006; Lam et al., 2008; Richler et al., 2010). What causes RRBs in autism? In their review of the topic, Leekam et al. (2011) mentioned several hypotheses, but stressed that etiologic research is at an early stage. The suggestion that there is a strong genetic component derives from the occurrence of RRBs in a number of known neurogenetic disorders, such as Tourette disorder, fragile X syndrome, and Prader– Willi syndrome and the fact that their topography in these syndromes is indistinguishable from that in AD. There is also a presumed environmental component. Animals raised in experientially deprived environments show more stereotypic behaviors. It has been hypothesized that the early onset of the deficits in social, communicative, and adaptive behavior in infants with AD results in their creating their own restricted environment, with secondary development of repetitive behaviors. This hypothesis could explain the observed later onset of RRBs, when compared to deficits in social communication, in some ASD individuals. Both genetic and environmental effects are likely mediated by abnormalities in brain development, probably in cortico-basal ganglia circuitry (Lewis et al., 2007; Lewis and Kim, 2009). Langen and colleagues have found enlarged caudate nuclei (Langen et al., 2007) and a correlation between behavioral inhibitory control and anatomic variation in fronto-striatal white matter (Langen et al., 2011b) in adults with ASDs. Langen et al. (2011a) proposed that the mechanism underlying stereotypies in autism and several other neurologic disorders involves three parallel cortico-striatal circuits: a sensorimotor loop implicated in motor stereotypies, an association loop implicated in impulsivity and rigidity, and a limbic loop implicated in obsessions and compulsions. Another hypothesis with regard to etiology of RRBs in autism derives from observations of stereotypies in normally developing children. Thelen (1979, 1981) reported that rhythmic stereotypies occur frequently in normal infants during a wide range of situations. She suggested that these movements were manifestations of incomplete cortical control of endogenous patterning in maturing neuromuscular pathways. Developmentally, these stereotypies contribute to, and are incorporated into, adaptive motor behaviors, becoming more voluntary and purposeful. These sterotypies are more frequent early in infancy, when action is less under voluntary control. As an infant grows and develops, stereotypies occur less often generally, but can be triggered by environmental events and extreme arousal. The occurrence of stereotypies in infants and toddlers with AD, beyond the age when they would normally be present, could thus be attributable, at least in part, to other deficits in the development of adaptive behavior, or to anxiety. Related to this hypothesis is the suggestion that RRBs in autism can be explained by deficits in executive functioning – problems with the neural systems involved in self-regulation, self-monitoring, and impulse regulation, along with impaired generation of organized, goal-directed behavior. It is unclear, however, if executive function deficits have a direct causal role in the emergence of RRBs or are, rather, a consequence of their disruptive effect on neurocognitive development.

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RRBs have also been linked to aberrant sensory features: hyper-responsiveness, hyporesponsiveness, and sensory seeking – all of which have been described in ASDs and which are not mutually exclusive. As noted, abnormalities of hyper- or hypo-reactivity to sensory input or unusual interest in sensory aspects of the environment are among the proposed criteria for the symptom of repetitive restricted behaviors in DSM-5. Several researchers have reported a positive correlation between high levels of repetitive behaviors and sensory features in ASDs (Boyd et al., 2010; Gabriels et al., 2008). Boyd et al. (2010) found that high levels of hyper-responsive behaviors predicted high levels of repetitive behaviors in both a group of children with autistic disorder and in a group with developmental delays. This association was independent of mental age. There was no association between RRBs and hypo-responsiveness or sensory seeking. These authors suggested that hyper-responsive sensory features and RRBs in autism may share common neurobiological mechanisms. It has been recently recognized that repetitive behaviors and abnormal movement patterns may be expressed in some autistic children in early infancy and that RRBs may be predictive of more impaired cognitive functioning at a later age. Descriptions of these early RRBs come largely from studies of high-risk infants – inter-born siblings of children diagnosed with AD – who are followed from early infancy, along with companion groups. Loh et al. (2007) found few differences at 12 and 18 months in stereotyped motor behaviors between high-risk siblings later diagnosed with an ASD and comparison children, although arm-waving was noted in the ASD group at both ages. Ozonoff et al. (2008), focusing on use of objects, found that the ASD outcome group displayed significantly more spinning, rotating and unusual visual exploration of objects at age 12 months than had high-risk siblings who did not develop ASDs as well as a comparison group. These repetitive behaviors were also significantly related to cognitive status and autistic symptoms at 36 months. Studying a slightly older group of already diagnosed ASD children (18–24 months), Watt et al. (2008) also reported that a higher frequency and duration of repetitive and stereotyped behaviors with objects predicted severity of autistic symptoms at age 3 years. Morgan et al. (2008) found that repetitive behaviors with objects in the second year of life predicted a lower developmental quotient at age 3 in a group with autism, but not in typically developing children.

Later developmental deficits in autistic disorder Early preschool years: clinical features Abnormalities in socialization, mobility, attention, and communication become more salient in the second year (Adrien et al., 1993). In particular, slow or unusual language development becomes a prominent concern between 12 and 18 months of age (Siegel et al., 1988). Autistic children are less likely to utilize single words to communicate by 16 months and their expressive vocabulary may not expand in a developmentally appropriate manner. Rather than request items verbally, a child may lead his or her mother to a desired item, and place her hand on it. Socially, autistic children may have little interest in peek-a-boo games, fail to read facial expressions or understand gestures, demonstrate diminished eye contact, and show limitations in reciprocal emotional displays. They may initiate interaction mainly as a means to obtain objects of interest and exhibit inordinate reactions to frustrations and environmental events (e.g. excessive startle). They may become overexcited when stimulated (e.g. by tickling) or remain unusually calm or docile when left alone. During this period, they begin to demonstrate restricted or repetitive patterns of play, such as bizarre inspection of objects (e.g. focusing on

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minute details) or watching events out of the corner of their eyes or lining up toys rather than playing with them. Unusual attachments or fascination with particular objects (e.g. spinning objects) is often reported. They continue to demonstrate evidence of unusual sensory function such as overreactions to sound or dulled reactions to pain. Recognition of early behavioral anomalies associated with AD in the first 2 years of life has prompted development of early screening instruments to identify children, often between 18 and 24 months, who may be at risk for developing an ASD. These include the Checklist for Autism in Toddlers (CHAT; Baron-Cohen et al., 1992), the Modified Checklist for Autism in Toddlers (M-CHAT; Robins et al., 2001), the Pervasive Developmental Disorders Screening Test-II (PDDST-II; Siegel, 2004), the Screening Tool for Autism in Two-year-olds (STAT-II; Siegel, 2004), the Early Screening for Autistic Traits (ESAT; Swinkels et al., 2006), and the Infant Toddler Checklist (ITC; Wetherby and Prizant, 2002). Each screening tool seems to be associated with its own assets; among initial level screeners, the M-CHAT and the ITC appear very promising. (See discussion in Chapter 7.) The triad of autistic behaviors continues to evolve through toddlerhood into early childhood. As children move from 18 to 36 months of age, language delays become more obvious. In addition, children with AD are more likely to exhibit repetitive motor behaviors. They continue to show a relative lack of responsiveness to communicative overtures although they may be more responsive to their own name. They continue to demonstrate significant difficulties engaging others’ attention in ordinary, unstructured situations. While they may persist in demonstrating anomalies of social and emotional responsiveness, they may not be aloof. Most will respond to separation from parents, although some may be relatively unconcerned or unaware of the absence of their mothers. However, contrary to popular belief, failure of bonding or attachment is not characteristic of autism. Many children with AD demonstrate comparable levels of basic emotional attachment to caregivers as do age- and IQ-matched children (Sigman and Mundy, 1989), although the expression of these behaviors may be inconsistent or occur at unusual times or in idiosyncratic ways. In addition, delays in functional and symbolic play become increasingly apparent. While the expansion of motor skills in typically developing children provides opportunities to explore their environment, children with AD are more likely to be observed in repetitive or stereotypic motor behaviors such as unusual hand or finger mannerisms. By age 3, most autistic children will show deficits in all three diagnostic areas.

Language in the preschool years Abnormalities in language development and use become more obvious as autistic children grow older, and they are more likely to demonstrate echolalia. Between 2 and 3 years of age, they may be more responsive to their name or comprehend familiar, frequently used communications that occur in a particular social context. However, this may not extend to understanding the same words used outside of their usual context. Children with AD, to a greater extent than in other developmental disorders, demonstrate impairments in both receptive and expressive language development (Hudry et al., 2010). Luyster and colleagues’ (2008) study of children between 18 and 33 months found persisting receptive language problems, predicted by concurrent difficulties with gestures, nonverbal cognitive ability, and joint attention. Attainment of language milestones has a substantial influence on long-term prognosis (Szatmari et al., 2003). The advanced development of receptive over expressive ability that is normally evident in infancy appears to be substantially reduced in children with AD (Ellis Weismer et al., 2010; Tager-Flusberg and

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Caronna, 2007). In general, the receptive language deficit seen in AD is greater than that seen in other neurodevelopmental disorders, such as Specific Language Impairment. In those children with AD who develop expressive language, a number of characteristic disturbances may be present. These include phonologic abnormalities, certain semantic impairments, and a unique profile of anomalies in prosody and conversational pragmatics. In the early stages of language acquisition, toddlers with AD, like normally developing children, emit speech-like sounds linked to their language level. However, they also produce a number of atypical vocalizations, suggesting a failure to attend to their linguistic environment (Schoen et al., 2011). Several studies suggest that between a quarter (Rapin et al., 2009) and a third (Shriberg et al., 2001) of school-aged children with AD demonstrate phonological problems. A characteristic semantic impairment in AD is idiosyncratic word use and neologisms in place of generally accepted words for specific articles or events. A possible explanation lies in the behavioral impairments related to shared attention. When learning words, young children with AD may not fully engage joint attention and in its absence, strong associations between words and corresponding objects may not develop. A characteristic profile of AD is that prosody and pragmatics are impaired to a greater extent than other features of language, such as syntax and semantics (Sigman and Kim, 1999). Problems with prosody pertain to the unusual or incongruous intonation that characterizes autistic children’s verbal productions. Their speech may be inappropriately loud or soft, fast or slow, and have no emotional tone, a singsong quality, or may be generally high-pitched. The cadence, rhythm or tempo of verbal productions may also be unusual, marked by misplaced stress and increased repetitions and revisions. Pragmatic deficits in AD are characterized by impairment of social responsiveness and reciprocity. This is exemplified by deficiencies in the give and take of social communication, unusual or inadequate expression and understanding of ideas, and a variety of oddities in verbal interaction, indicative of impaired understanding of social norms and expectations (Rapin and Dunn, 2003; Surian et al., 1996). Despite overt fluency, autistic children’s verbalizations may be marked by disorganization of discourse, with difficulties in identifying topics of interest to their conversational partners and in changing topics in response to verbal and nonverbal conversational cues. Their conversation may be restricted in subject matter (personal preoccupations often predominate), and perseverative (e.g. engaging in repetitive questioning and using pedantic or stereotyped language; Bishop, 1989; de Villiers et al., 2007). These anomalies in part reflect an inability to interpret the social context of language. This can affect both language comprehension and expression. Children with AD commonly demonstrate difficulties understanding the communicative intent of others and are overly literal in their interpretation of language, having particular problems in interpreting idioms, metaphors, sarcasm, and irony (Norbury, 2004; Wang et al., 2006; Ziatas et al., 2003). A distinctive characteristic of their pragmatic impairment is the use of echolalia in conversational speech (Belger et al., 2011). It has been estimated that 75% of all children with AD have transitioned from a phase of echolalic production. Moreover, echolalia forms a larger percentage of verbal productions in those with AD compared to those with other language disorders.

Later childhood: clinical features As autistic children become school-aged, their behavior reflects the depth of their difficulties with social interaction. They have problems making friends and in carrying on conversations. When placed in social environments, their affective responses are also more obviously

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inappropriate and if language has progressed sufficiently, pragmatic difficulties in conversation are common, particularly in unfamiliar contexts or circumstances where scripts are inappropriate. In addition, the demands of the school environment highlight problems disengaging and shifting attention. During this stage of their development, they are most likely to be aggressive towards others or demonstrate self-injurious behavior (Gillberg and Coleman, 1992). Disruptive behaviors are common in the classroom environment, causing further separation from typically developing peers. In adolescence and adulthood they may demonstrate increased interest in developing social relationships and their social skills continue to increase. However, establishing and maintaining appropriate relations with peers typically remains highly problematic. Consequently, higherfunctioning autistic adolescents are more prone to depression (Perry et al., 2001). Lowerfunctioning adolescents may be more susceptible to disruptive aggressive and self-injurious behavior (Gillberg and Coleman, 1992). Sexuality becomes a significant issue in most cases, although it is often more readily manageable than might be expected (Hellemans et al., 2007). Language problems in most individuals with sufficiently well-developed language abilities are characterized by concrete and literal expression and poor pragmatics (Shriberg et al. 2001). Autism carries a variable long-term prognosis (Gillberg and Coleman, 1992). In general terms, the presence of communicative speech and overall intellectual level are the most potent factors determining prognosis (Korkmaz, 2000). Approximately two-thirds of individuals with autism remain dependent on others throughout their lives. About one-third can maintain some degree of partial independence, and a small but non-negligible minority lead productive, self-supporting lives as adults. There may be a slight increase in mortality in the first 30 years of life. It has been suggested that long-term outcome of regressive AD may be poorer than in children with AD who do not have a history of regression. Children with regressive AD are more likely to have long-term severe speech difficulties (Kurita, 1985; Hoshino et al., 1987) and demonstrate difficulties in initiating conversation, asking or answering questions, or conveying information verbally (Brown and Prelock, 1995). They are also more likely to have intelligence estimates in the mentally retarded range (Kurita, 1985; Hoshino et al., 1987), despite evidence to suggest a higher level of cognitive development prior to the regression. Future studies are required to confirm that this distinctive early history is, in fact, associated with a differential prognosis.

Summary and conclusion Characteristic deficits in social communication and certain repetitive and restrictive behaviors are the defining features of ASDs. There is considerable evidence – to be covered in the following chapter – that these features are associated with abnormalities in brain development and structure. From a functional viewpoint, there is evidence for fundamental limitations involving multiple neural systems needed to process a variety of stimuli of social and communicative significance. These impairments are correlated with physiological and structural abnormalities in specific cortical networks, including frontal and temporal neocortex, as well as cingulate gyrus and subcortical structures such as the insula, limbic system, and the cerebellum. The overall clinical picture reflects an array of primary, secondary, and tertiary residuals spanning multiple domains of function. Given the widespread distribution of the underlying pathophysiology and its developmental context, the nature of the association of the functional disturbance to physiological anomalies is complex and remains poorly understood. The present chapter has drawn on neuropsychological and clinical observations to describe early precursors and signs of

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autism, primarily developmental deviations in eye contact, joint attention, pre-linguistic communication and the emergence of repetitive, restricted behaviors. These early deviations from normal development have been delineated and, to the extent possible, correlated with neurobiology. Their developmental context and tentative connection to later features of autism has been emphasized. The following chapter will build on the material discussed in this one, looking at early brain development and the structural abnormalities in brains of autistic individuals that have been found in neuropathological and brain imaging studies.

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What We Know About Autism

Neuropathology of autism Gerry A. Stefanatos

The last frontier in this world – and perhaps the greatest one – lies within us. The human nervous system makes possible all that we can do, all that we can know, and all that we can experience. Its complexity is immense, and the task of studying it and understanding it dwarfs all previous explorations our species has undertaken. – Neil R. Carlson, Physiology of Behavior (2009).

Introduction In Kanner’s (1943) original description of infantile autism, he conjectured that the disorder was based in an “innate inability to form the usual, biologically provided affective contact with people” (1943, p. 250). He regarded the disorder as inborn, rooted in the constitution of the mind, rather than learned or acquired through experience. Kanner later abandoned this biological perspective in favor of one that emphasized the role of environmental factors, and in particular he implicated emotionally cold and unavailable parents as an important causal influence (Kanner, 1949). This “psychogenic” viewpoint was embraced by psychiatry and went largely unchallenged for many years. However, factions in psychiatry (e.g. Van Krevelen, 1958) and psychology (e.g. Rimland, 1964) began to voice strong opposition to this theory, arguing instead that the disorder resulted from profound central nervous system dysfunction of unknown origin. The biological perspective subsequently gained support from observations that children with what is now termed autistic disorder (AD) had a raised prevalence of certain minor physical anomalies (Walker, 1977; see Ozgen et al. (2010) for a recent review) and were more likely to demonstrate positive findings on neurologic examinations. Schain and Yannet (1960), for example, reported a high frequency of seizure disorder in their cohort of 50 children with autistic disorder attending a residential school for children with mental retardation. As pediatric neurologists, they highlighted the role of limbic dysfunction in epileptogenesis and speculated that AD may be based in limbic system damage or maldevelopment. In support of this assertion, they disclosed the results from a single autopsy case whose “only neuropathologic findings consisted of dropping out of cells in the hippocampal formation” (p. 565). While the number of reports implicating neurologic involvement in AD increased, subsequent neuropathological studies of AD were rare (Aarkrog, 1968; Creak, 1961; Darby, 1976; Williams et al., 1980) and often inadequately described. Periodic observations of positive findings failed to reveal consistent or unifying themes regarding the nature of or The Autism Spectrum, ed. Mark E. Reber. Published by Cambridge University Press. © Cambridge University Press 2012. 83

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potential causes for the disorder. Some of the identified anomalies appeared attributable to trauma that postdated the appearance of autistic symptoms (Williams et al., 1980), while other findings (e.g. minor cell loss and gliosis) were subtle and nonspecific. Overall, these observations illustrated that the gross structural features of the brain in individuals with AD were largely normal in appearance. There was typically no evidence of obvious lesions, abnormalities in gyral configuration, or disturbances of myelination. The significance of some of the subtle anomalies identified at the cellular level, such as cell loss, remained unclear. Since these early studies, advances in stereological analysis have made it possible to visualize the morphological structure of neural tissue in three dimensions and obtain reliable estimates of characteristics such as cell counts and density (number of cells per unit volume) (Gundersen, 1992). These technical innovations permitted more rigorous quantitative comparison of tissue obtained from various areas of the brain and in different individuals, resulting in a better understanding of normal variations in cortical development (Huttenlocher and Dabholkar, 1997; West and Gundersen, 1990). In addition, these enhanced and unbiased quantitative methods improved the ability to detect subtle anomalies of brain microstructure and organization in clinical populations, allowing identification of deviations that previously might have gone unnoticed (Golden and Hyman, 1994; Jones and Harris, 1995). In parallel with these advances, in-vivo and in-vitro animal neurophysiological studies began to disclose the important role of experience in shaping brain microstructure and neurochemistry (Blakemore and Van Sluyters, 1975; Greenough et al., 1987). Together, these developments had a fundamental impact on neuroscience and revitalized efforts to understand the neurobiological basis of autism. This chapter reviews the neuropathological findings in AD that have emerged in the last 30 years and discusses how these observations have influenced current conceptions of the neural basis of the disorder. This area of research has faced a number of formidable challenges, given the relatively small number of specimens available for study and the enormous variability in premorbid history, level of function, medical complications and use of medications. However, as more cases have been described, several themes have emerged that are broadly compatible with results obtained in recent studies using structural and functional neuroimaging techniques. This growing body of evidence suggests that AD is not the result of focal impairment, but rather is associated with widespread but subtle perturbations of neural structure and connectivity that impede the development and function of distributed neural networks mediating social and communicative function. Before turning to this discussion, we briefly review key elements of our current understanding of the complex events that shape the development and functional architecture of the normal brain. This will serve as an appropriate backdrop to contrast with the findings in AD.

Neurodevelopmental considerations The average newborn human brain weighs approximately 14 ounces, about 25% of its eventual adult weight. Its development can be traced to around the fifth week of gestation when cells begin to proliferate at the top end of the developing nervous system (neural tube), producing three swellings that will eventually form the forebrain, midbrain, and hindbrain. The remarkable structural metamorphosis that ensues involves a series of complex and highly orchestrated changes that do not end in fetal development but continue well into the second decade of postnatal life. The result is a 3–3.5 pound mass comprised about 86–100

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billion neurons (Pelvig et al., 2008; Lent et al., 2012) and trillions of synapses. With its sophisticated, dynamic, self-organizing architecture, it possesses unrivaled computational power capable of mediating the full spectrum of human abilities. While some would argue that it cannot be accurately quantified, the human brain’s memory capacity alone has been estimated to be in the order of 500–1000 terabytes or more (Frith, 2009; Reber, 2010). As a frame of reference, the entire printed collection of the US Library of Congress is in the order of 10 terabytes. The development of the cerebral cortex, which mediates virtually all higher cognitive functions, can be considered to occur in three overlapping stages. The first, cell proliferation, can be traced to around the 33rd embryonic day in humans (Bystron et al., 2006) and the rapid multiplication of progenitor cells (similar to stem cells) in a patch of tissue around the ventricles (ventricular zone) near the top of the so-called neural tube. Some of these cells differentiate into neurons (nerve cells) while others form the glia (supporting cells) of the central nervous system (Noctor et al., 2001; Takahashi et al., 1995). During this process, which is characterized by exponential growth until about the fourth month of gestation, neurons are born at rates as high as 250 000 per minute, and it has been estimated that at times during this exponential growth, over 50 000 neurons are formed every second (Cowan, 1979). Only a fraction of the billions of neurons produced during this period are projected to survive to maturity. While neurogenesis can continue in postnatal life, this is very limited in extent and distribution (Gage, 2002; Ming and Song, 2011) and does not include the neocortex (Bhardwaj et al., 2006). The second stage, migration, is characterized by the newly conceived neurons (called neuroblasts) migrating away from the ventricular areas to form the thin (~2–5 mm) multilayered plate of neurons that will eventually comprise the cerebral cortex (Caviness et al., 1989; Marin-Padilla, 1988; Rakic, 1995). The first migrating cells from the ventricular zone are glial cells that form a scaffold that subsequent migrating cells utilize to guide the formation of the cortical plate. Once a pre-plate structure is complete, at about the eighth week of gestation, early migrating cells form the deepest layers of the cortex (e.g. layer 6), while later migrating cells form successively more superficial layers of the cortex (Cooper, 2008). This process begins at approximately the tenth week of gestation, and continues until around the fifth month (~20 weeks) of fetal life. As new neurons position themselves, a process of selective aggregation ensues, resulting in a reasonably well-defined, six-layer laminar structure to the cortex, which is evident by around the seventh or eighth month. This thin bark of gray matter is composed mainly of glial cells and neurons, while the underlying white matter is comprised primarily of axons (myelinated and unmyelinated). Relatively early in the process of neural aggregation, before the laminar organization of the cortex is complete, cortical neurons begin to develop communicative links with neighboring cells in other layers of the cortex. This third stage of neurodevelopment, cortical organization, is characterized by the rapid development of axons and apical dendrites which extend from the neuron and represent the conduits for cell-to-cell communication. The treelike appendages that comprise the dendrites (dendritic arbors) of a neuron are essentially extensions of the cell body that are responsible for receiving inputs from other neurons. The distribution of dendrites varies according to the cell type, but is generally restricted to a small region around the main cell body. The extent and distribution of arbors reflects the cell’s role in processing information. In some, the dendritic pattern can be fairly extensive, spreading up to a millimeter in length. Dendrites may receive input from as many as 10 000 other cells and, in order to correctly receive this information, their morphology must be compatible

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with the structure of the presynaptic axons. As a consequence, the growth and maturity of dendritic branches and the dendritic spines that later develop at points of synaptic contact must be appropriately positioned and proportioned to attain the correct balance between excitatory and inhibitory influences. An excess of dendritic spines which support excitatory (glutamatergic) neurotransmission implies an excess of excitatory connectivity (Fiala et al., 2002). The proliferation of apical dendrites eventually forms the bulk of a dense, felt-like tangle of filaments in the outermost layer of the cortex (layer 1). This exuberant growth begins at about the twentieth gestational week and continues until about the second year of postnatal life. Known as the molecular layer, layer 1 also contains glial cells and an abundance of horizontally oriented axons which carry the output of cortical neurons (mainly those in layers 2 and 3) to other cortical neurons. Concurrently, connections also develop between cortical neurons (layers 4–6) and neurons in subcortical structures such as the thalamus, brainstem, cerebellum, and spinal cord. This growth results in major fiber pathways such as the cortical spinal tract and internal capsule beginning as early as 13 weeks (Huang, 2010; Jovanov-Milosevic et al., 2006). In addition, interhemispheric fiber tracts such as the anterior commissure become evident at this time while the corpus callosum starts to emerge at about 15 weeks. Similarly, intrahemispheric association tracts such as the uncinate fasciculus and inferior longitudinal fasciculus can also be identified at about 15 weeks. Both intra- and interhemispheric fiber tracts undergo significant growth in the course of fetal development, so that most tracts are present in the normal term infant. The formation of connections between aggregations of neighboring neurons dispersed across 2 through 6 cortical layers eventually gives rise to functional cylindrical units called minicolumns. Composed of 60–100 neurons, minicolumns are the basic processing units of the cerebral cortex (Buxhoeveden and Casanova, 2002; Mountcastle, 1997). Each minicolumn contains neurons having similar stimulus/response properties (Tanaka, 1997). Minicolumns therefore comprise elemental modular processing units that analyze specific types of information (e.g. edge detectors in visual cortex). Connections extending sideways from minicolumns (influenced by GABAergic interneurons) exert a lateral inhibitory influence on neighboring minicolumns, which serves to tune the minicolumn’s responses to sensory input by impeding the spread of excitation to neighboring minicolumns. Collections of up to 1000 minicolumns form larger units, macrocolumns, which represent a higher level of integration of neurons with related response properties (e.g., ocular dominance columns). Macrocolumns, in turn, form components of large-scale neural networks that mediate the highest levels of information processing. This hierarchical organization underlies the specialization of function that characterizes different areas of cerebral cortex (Buxhoeveden and Casanova, 2002). For example, Wernicke’s area, a complex area in posterior temporal cortex specialized for language comprehension, may be composed of roughly 3 million minicolumns. The increasing connectivity between neurons in the brain is reflected in axonal and dendritic growth and a remarkable expansion of the number of synapses. Dendritic spines first emerge within deep layers of cortex in the late prenatal period (Huttenlocher and Dabholkar, 1997), whereas spines in more superficial layers generally do not express until several months after birth (Koenderink and Uylings, 1995). Although synaptogenesis in some areas starts at about 25 weeks gestation, peak periods occur postnatally and are therefore susceptible to the influence of experience. In order to be effective, the number and structure of synapses must be carefully regulated to ensure an appropriate balance of

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excitatory and inhibitory influences. This process is governed, at least in part, by genetic influences on neuroligins and neurexins, which are proteins that mediate signaling across the synapse and allow for optimum communication between presynaptic and postsynaptic neurons (Varoqueaux et al., 2006). The most prolific periods of synaptogenesis in cortex take place postnatally according to regionally specific timetables (Ghassabian et al., 2011). Synaptogenesis occurs earlier for primary sensory and motor cortex than association areas of the brain such as prefrontal cortex (Huttenlocher and Dabholkar, 1997). During each of these stages, a surplus of neurons, axons, dendrites, and synapses is generated to ensure that sufficient numbers survive the process. Proliferation is followed by a protracted period during which subtractive processes lead to the elimination of inefficient or superfluous neurons, axons, dendrites, and synapses in a process akin to neural Darwinism (Edelman, 1987). For example, by the 32nd week of gestation, the fetal brain possesses more neurons than are present at the time of birth. Many are thought to be eliminated through programmed cell death (apoptosis) or are otherwise pruned to refine the circuitry of the central nervous system (Buss et al., 2006). Similarly, the child’s brain at 3 years of age contains about 50% more synapses than will survive subsequent pruning and remain present in the adult brain. Like the proliferative changes that preceded them, the timing of these subtractive modifications differs in various regions of the cortex. However, by comparison, subtractive changes follow a rather protracted time course. For example, the peak synaptic density in visual cortex is attained in the first year of an infant’s life, at which time, synaptic density is 50–60% higher than that seen in adults. Elimination of superfluous synapses in this area of cortex begins around this time and continues throughout childhood, eventually reaching adult levels at about 10 years of age. Like the regional timetables that characterized previous stages of neurodevelopment, subtractive changes follow an anterior to posterior gradient whereby frontal lobes mature later than more posterior areas of cortex (Huttenlocher and Dabholkar, 1997). Synaptic density in frontal cortex reaches adult levels at about 14 years. This trimming process enhances the function and adaptation of working networks and is necessary to make space for further growth and eliminate ineffective components that could obstruct healthy expansion. The human brain is nine times larger than expected given our body size, and this poses significant hurdles in passing the head through the pelvic canal during the birthing process. Excess proliferation or a serious failure of subtractive changes in utero could pose a danger to both mother and fetus as it may cause a corresponding enlargement of the head, which may not pass as easily down through the birth canal. Without the remodeling that takes place during the subtractive phase of neurodevelopment, the brain would be too large and have an excess of inefficient networks and patterns of connectivity (Ebbeson, 1984) that would be a drain on limited biological resources such as blood supply and nutrients. The brain consumes enormous metabolic resources during fetal development, utilizing approximately 70% of the total energy that the fetus receives via the umbilical cord. The process of neurodevelopment remains incomplete at birth. The infant brain at this point is comprised of 100 billion neurons and about a trillion glial cells, roughly the same number as are present in the adult brain (Pelvig et al., 2008). However, the neonatal brain has substantially fewer connections between neurons, reflected in fewer synapses per neuron (~2700) compared to the adult brain (up to ~10 000). This is a critical difference, as synaptic development is thought to be a major factor defining the limits of cognitive capacity and representational complexity of the human brain (Chiang et al., 2009; Goldman-Rakic, 1994; Roske et al., 2003). In postnatal development, synaptogenesis proceeds concurrently with

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axonal growth, dendritic expansion, and with myelination of the subcortical white matter tracts that connect different areas of cortex (Huttenlocher and Dabholkar, 1997). These changes coincide with cortical expansion, prompting significant changes to the complex gyral patterns that began to emerge earlier in neurodevelopment (Righini et al., 2010). In addition, there are substantial increases postnatally in glial cell number. The result of this extensive growth of the brain in the first 3 years of life is a quadrupling of brain weight. Because the neocortex and its thalamic interconnections remain immature at birth, lower-level structures such as the limbic system, cerebellum, and brainstem nuclei have a significant influence in mediating behavior. As the cortex matures in the first year, its role in mediating an infant’s behavior increases, and this results in changes in function (Greenough and Black, 1992). In addition, alterations in white matter pathways between and within each hemisphere also undergo substantial maturational changes during early childhood and in some areas (e.g. corpus callosum) these continue into adolescence. To some extent, myelination follows a somewhat similar regionally specific timetable for maturation, as do other aspects of cortical development. For example, myelination of thalamocortical pathways to Heschl’s gyrus (primary auditory cortex) appears to mature in the first 2 years of life, while prefrontal cortex remains immature until at least the twelfth year of life (Yakovlev, 1967). Callosal fibers, which form the major pathway of interhemispheric communication, may also take a decade or more to complete the process of myelination. Highly distributed neural systems that are dependent on long-range connectivity may not reach full capability until this process is complete. Perturbations in any number of factors during the crucial period following birth can potentially have significant functional implications that affect later development. Early lesions or anomalies may remain silent until relevant systems come “on-line” or reach a particular stage of maturity. Through this protracted process of developing increasing connectivity, integration, and refinement, neurons acquire specialized roles in the processing of particular kinds of information. Broca’s area, a region of the left inferior frontal lobe, for example, is particularly involved in mediating the production of articulate speech. Babbling and speech emerge in infancy at a time of increasing dendritic growth in this area (Scheibel, 1990), and the subsequent development of this area influences the emergence of expressive language (Amunts et al., 2003). Damage to this area in adulthood results in profound long-term language difficulties that include problems with fluent speech production (Lazar and Mohr, 2011). However, neuroplasticity of the developing brain is considerable, so that if Broca’s area is damaged early in life, the capacity to produce articulate speech can be subserved or “taken over” by other areas of the cortex (You et al., 2011). This reorganization often entails the vicariation of homologous areas in the contralateral cerebral hemisphere which can result in some compromise of cortical functions that would normally have been mediated by those areas. The displacement of abilities to areas of cortex already committed to other functions results in “crowding” and a consequential decrement in overall level of function. For example, reorganization of language in the left hemisphere often results in some compromise of spatial abilities (Lidzba et al., 2006; Strauss et al., 1990). Complex behavior is not the sole product of activity in a specific area of cortex per se, but is the result of dynamic interactions between multiple areas of the brain. Moreover, any given area may contribute to multiple aspects of function. Broca’s area, for example, not only mediates the production of complex motor programs to guide the sequencing and coordination of articulatory movements necessary to produce articulate speech, but it is also involved in the processing of grammar and in maintaining order in temporal sequences

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(Caplan, 2006; Muller and Basho, 2004). As a consequence, one-to-one mapping between a brain region and a mental state is neither possible nor biologically likely.

The dynamic influence of genetic and environmental factors It is estimated that 40% of all genes in the genome are expressed in the brain. As a consequence, thousands of genes may have direct or indirect functional implications on various aspects of cognitive development. While basic growth and differentiation of neurons and various structures in the brain appear to be genetically regulated, it has become increasingly apparent over the last 30 years that, at every stage of development, there are epigenetic and environmental factors that can modulate structural changes at the cellular level. Experience impacts both the proliferation of neuronal processes and their pruning (Cowan, 1986; Stoneham et al., 2010). Without appropriate experience, neural networks may not receive sufficient stimulation to strengthen connections between their various components. Experience not only plays a vital role in ensuring that neural components integrate into efficient functional networks, but it also determines the functional attributes of developing functional systems (Huttenlocher, 2002; Stiles, 2011). It serves to specify the range of information that a network can be expected to process (O’Shaughnessy and Fowler, 2011). This concept is neatly embodied by a simple principle expressed by the eminent neuropsychologist Donald Hebb: “neurons that fire together, wire together” (1949). The inherent plasticity of the brain and the capacity to optimize structures and functions can be viewed as driven by the stimulation that relevant systems receive during development (Ellison, 2010; James, 2010). In this way, experience is critical to the remodeling process, in that it enhances the tuning and efficiency of various neural systems. However, it is also possible that by the same or similar mechanisms, some experiences may result in maladaptive responses that could have negative implications for the subsequent development of other behaviors and the networks that mediate them. The dynamic relationship that exists between genes, the brain, behaviour, and the environment relates to an influential proposition much discussed in epidemiology known as the “fetal origins hypothesis”. This hypothesis posits that conditions early in life, including in utero, can have a long-term impact on the development of the organism, and can influence susceptibility to problems (diseases or disorders) that may emerge many years later (Gluckman and Hanson, 2006). Environmental influences affecting fetal development can include extrinsic factors such as nutrition and exposures to toxins or viruses. Another important consideration regarding the impact of environmental influences is recognition of the existence of “critical periods” in development. This concept emerged from neurophysiological studies showing that the timing of sensory deprivation is critical to the development of sensory systems, such that even brief deprivation of particular experiences during specific periods of infancy can produce long-term physiological changes in brain organization or function (Hubel and Wiesel, 1970; Rubenstein, 2011; Werker and Tees, 2005). This research indicates that the timing of experience can modulate the extent to which that experience can modify neural structure, organization, or processing. A corollary of this related to the fetal origins hypothesis is that there may be critical periods when the developing nervous system is particularly prone to the influence of adverse events. Some environmental events may have adverse effects only if they occur within particular critical points during development (Libbey et al., 2005).

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Gains in understanding the dynamic relationship that exists among genetic influences, experiences, and the environment have reframed our understanding of the processes potentially influencing brain development in AD. Alterations in brain development must be considered within a broader view that multi-directional interactions can influence neurodevelopment. Brain development is viewed as a cohesive, cumulative, and continuous process that is shaped by complex interactions between genetic influences, environmental factors, experiences, and reactions. Developmental time therefore emerges as an additional influence because there appear to be critical periods during development when children are particularly susceptible to adverse events that could increase risk for developing various problems.

Neural microstructure in AD: current conceptions Bauman and Kemper (1985) were among the first to utilize rigorous stereological analytic methods in an attempt to identify possible anomalies in the cellular structure in postmortem brain specimens from an individual with AD. In their initial study, they compared the brain of a 29-year-old autistic man with that of an age- and sex-matched control, systematically examining whole-brain serial sections in a pairwise manner. No abnormalities were observed in the cortex or several subcortical structures (basal forebrain, thalamus, hypothalamus, and basal ganglia). In addition, patterns of myelination were deemed no different from controls. However, anomalies were identified in limbic structures and in the cerebellum. Neurons in the hippocampus and amygdala were reduced in size and had increased cell-packing density (a higher number of neurons per unit volume). Similar findings were evident in tissue in the subiculum, entorhinal cortex, mammillary bodies, and medial septal nucleus. Hippocampal neurons in layers CA1 and CA4 also exhibited decreased complexity and extent of dendritic arbors, suggesting limitations of intercellular connectivity and communication. The overall pattern was thought to be characteristic of neural architecture at an early stage of brain maturation. These observations garnered considerable interest as both the amygdala and hippocampus had previously been implicated in AD based on similarities in patterns of behavioral deficits with patients with acquired damage to these structures (Boucher and Warrington, 1976; Damasio and Maurer, 1978). Although the results of this initial study were confounded by the presence of epilepsy (Amaral et al., 2003), similar findings were subsequently observed in five additional cases (reviewed in Kemper and Bauman, 1993) and in two subjects studied by Raymond et al. (1996), but in only one of five subjects studied by Bailey et al. (1998). Amaral et al. (2008) found no anomalies in neuronal size in amygdala, but did observe significantly reduced neuron counts overall. The cingulate gyrus, which is also part of the limbic system, was implicated in a later study by Kemper and Bauman (1993), who noted that the anterior cingulate cortex in five of their six cases was poorly laminated and unusually coarse. This finding has yet to be replicated in neuropathological studies by other investigators, although it is noteworthy that several functional neuroimaging studies have implicated cingulate involvement (Dichter et al., 2009; Marquardt et al., 2002). Bauman and Kemper (1985) also identified anomalies involving neocerebellar cortex and the inferior olivary nucleus of the brainstem. Several neuropathological studies have subsequently demonstrated similar anomalies (Bailey et al., 1998; Fatemi et al., 2002; Kemper and Bauman, 1998; Lee et al., 2002; Ritvo et al., 1986; Wegiel et al., 2010). The cerebellar abnormalities are evident primarily in decreased number and size of Purkinje cells and, to a

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lesser degree, granule cells in the cerebellar hemispheres, particularly in the posterior inferior neocerebellar cortex and neighboring archicerebellar cortex (Arin et al., 1991). Kemper and Bauman (1998) observed these anomalies in the absence of glial cell hyperplasia and suggested that they likely emerged early in the prenatal period (prior to 30 weeks gestation). However, other neuropathological studies suggest significant neurohistological heterogeneity in autism. Bailey et al. (1998), for example, observed that gliosis may sometimes accompany Purkinje cell loss, raising the possibility that postmortem findings in some cases reflect peri- or postnatal factors. The precise neurodevelopmental significance of this finding remains to be fully established. Regardless, the cerebellar anomalies appear to be one of the most consistent neuropathological findings in autism, present in almost three-quarters of known autopsy cases (Palmen et al., 2004). Along with the cerebellar abnormalities, neurons in inferior olivary nucleus often appear small, although not diminished in number (Bauman and Kemper, 2005b). The inferior olivary nucleus provides the sole source of climbing fibers that innervate the Purkinje cells of the cerebellar cortex and deep cerebellar nuclei. In addition, cerebellar nuclei send projections to the inferior olive. The role of this circuitry is not fully understood, but is thought to be involved in processing sensory information that may modulate motor coordination and have a regulatory influence on cerebellar learning. A very close relationship between the cerebellum and the inferior olivary nucleus emerges in fetal development and is fairly well established by 28–30 weeks gestation. As a result, loss of Purkinje cells after this time would likely result in retrograde degeneration of neurons in the inferior olive. Because they observed that the number of olivary neurons is preserved in the autistic brain, Bauman and Kemper (2005a) have argued that it is likely that the Purkinje cells loss in AD occurs early in neurodevelopment before the functional coupling emerges between the inferior olivary nucleus and cerebellum. The precise significance of these findings to the symptoms of autism remains somewhat speculative. Current theoretical accounts suggest that functional compromise of a frontocerebellar pathway extending from the right lateral cerebellar hemisphere to left frontal lobe may be related to impaired expressive language, working memory, attention, and aspects of gross motor function (Hodge et al., 2010; Rogers et al., 2011; Townsend et al., 2001). Reductions of vermis volume seem to be associated with more global impairments of development, including behavioral disturbances associated with AD (Bolduc et al., 2011). However, the findings are inconsistent (Scott et al., 2009). A number of studies (Bauman and Kemper, 2005a; Coleman et al., 1985; Guerin et al., 1996) have identified no anomalies of cerebral neocortex, although several recent reports have found subtle disturbances of neocortical development in a proportion of cases (Bailey et al., 1998; Casanova, 2007; Hof et al., 1991). Bailey et al. (1998), for example, noted thickened areas of cortex, increased neuronal density, irregular patterns of cortical lamination, and unusually oriented pyramidal cells in tissue specimens obtained from individuals with AD. There were also increased numbers of neurons in layer 1 which, as previously mentioned, is generally occupied primarily by extensive dendritic and axonal processes from neurons in lower laminar layers. In addition, disturbances of laminar structure and the presence of heterotopias (displaced neurons) was noted in four of six of the brains they examined. Similarly, Wegiel et al. (2010) recently reported the presence of subcortical, periventricular, hippocampal, and cerebellar heterotopias in 4 of 13 individuals (31%) who had AD. In addition, they described multiple areas of cortical dysplasia in the neocortex in four individuals (31%). Subcortical dysplasia involving entorhinal cortex and dentate gyrus

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(in hippocampus) was evident in two brains, while involvement of a section of the hippocampus was apparent in four brains. These findings implicate disturbances of neural migration and suggest that the disturbances were widespread, involving different areas of cortex as well as several subcortical areas. Consistent with this, Casanova et al. (2002) examined postmortem tissue from nine autistic individuals and found that cortical minicolumns were unusually configured in the brains of individuals with AD. Anomalies were evident in at least three areas of cortex: (1) Brodmann area (BA) 9 of prefrontal cortex, (2) BA 21 in the lateral temporal lobe, and (3) BA 22 in superior temporal cortex. Cell columns were smaller and showed more dispersion between cells. In addition, there was less of the space between cells that commonly contains dendrites, axons, and synapses (neuropil). Minicolumns were therefore abnormally narrow in frontal and temporal cortex, resulting in increased neural density. These organizational differences, it was hypothesized, may result in anomalous patterns of connectivity with adjacent columns as well as with thalamic afferent terminals. Specifically, it was suggested that the resulting circuitry may lack the inhibitory influences that minicolumns typically exert on adjacent columns. One potential result of the lack of lateral inhibition is diminished ability to discriminate sensory information due to an increase in “neural noise”. It has been suggested that this may account for the sensory hypersensitivity seen in many children with AD. In addition, islands of excessive excitatory activity could develop into seizure foci in these children (Casanova, 2006). The factors underlying the emergence of this atypical architecture remain unclear. These disturbances in modular organization may relate to excessive proliferation of neurons earlier in development, as the number of minicolumns in developing cortex is related to the number of cells produced in the ventricular proliferative zone at an earlier stage of development (Kornack and Rakic, 1998). Consistent with this, Courchesne and colleagues (2011) recently reported that areas of frontal cortex have excess numbers of neurons in AD compared to controls. The increase was most evident in dorsolateral prefrontal cortex (PFC), where individuals with AD had 79% more neurons, compared to a 29% increase in mesial PFC. This excess again implicates problems during embryogenesis, or alternatively, these observations may reflect a failure of pruning processes that normally follow exuberant growth. Hutsler and Zhang (2009) have provided evidence indicating that individuals with AD also demonstrate an excess of dendritic spines on apical dendrites of pyramidal cells in cortical layer 2 of frontal, temporal, and parietal cortex. A summary of the areas implicated in neuropathological studies of autism is presented in Figure 4.1.

Summary: neural microstructure in AD The interpretation of histopathological studies in AD is complicated by a large number of potential confounding variables. The very small number of cases comprising each of these studies is a serious problem that is exacerbated by inconsistencies in the quantitative histopathological techniques used and the tremendous heterogeneity of the disorder itself. Most of the studies have involved the examination of human pathological material obtained from deceased older children and adults, so the results are inevitably also confounded by an enormous number of potential epigenetic and environmental effects. Potentially important variables such as the presence of mental retardation, seizure disorders, medical complications, and use of medications have remained uncontrolled in many studies. It could therefore be argued that some of the findings described in these reports reflect the effects of having

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Neuropathology in Autism 30 (a) Williams et al. (1980)

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(c) Coleman et al. (1985)

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Figure 4.1 This figure represents a compilation of the number of brains that have been investigated in postmortem histopathological studies of individuals with autism. Along the x-axis are the different areas of the brain that have been implicated in autistic disorder. The y-axis represents the number of brains in which abnormalities were identified in the specified area. (For a color version of this figure, please see the color plate section.)

autism as much as the neurobiological circumstances that increased risk for developing the disorder. Overall, the observed histopathological abnormalities are diverse and appear to implicate different neuropathological mechanisms. One of the most consistent findings is the decrease in Purkinje cell counts in extensive portions of the cerebellar hemispheres. This seems to occur without significant gliosis in some cases and without significant cell loss in the inferior olivary complex. It has been suggested that this implicates a disturbance or damage affecting neurodevelopment with onset early in fetal life. However, as pointed out by Bailey et al. (1998), the disorder seems to be associated with considerable etiologic heterogeneity. Gliosis is apparent in some cases and implicates anomalies emerging late during fetal development or early in postnatal development. Purkinje cells have an exceptionally high metabolic demand and are susceptible to multiple pathogenic influences. Some of these influences may be associated with birth trauma (ischemia, hypoxia, excitotoxicity), while others may be more likely to exert effects on the brain during fetal development (viral infections, thiamine deficiency, and exposure to heavy metals and toxins) (Kern, 2003). Other findings such as increased neuronal density, variations in cortical thickness, disturbances of laminar structure, the presence of heterotopias, and the anomalies of minicolumn formation implicate excessive neural proliferation and migration or anomalies of program cell death (apoptosis) probably occurring in the first 6 months of gestation (Bailey et al., 1998; Gillberg, 1999; Piven et al., 1990). Evidence has been building in recent years implicating the possible role of a number of genes which have a direct impact on

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neurodevelopment. Several of these genes have influences on those aspects of neurodevelopment that occur during fetal growth. RELN, for example, is a gene that encodes a protein thought to regulate cell–cell interactions critical for cell positioning and neuronal migration during brain development (Fatemi et al., 2005). Other genes may affect fetal brain development, but are most likely to have greatest impact on changes that occur in the postnatal period. SHANK3, for example, is a gene on chromosome 22 that encodes proteins that play a role in synaptic formation and in the proliferation of dendritic spines (Peca et al., 2011). Similarly, mutations in the genes that encode neuroligins 3 and 4 (NLGN3 and NLGN4, respectively) are thought to be involved in AD. Neuroligins are molecules that are involved in postsynaptic cell-to-cell adhesion and are therefore critically involved in synaptic formation and remodeling (Gutierrez et al., 2009; Yamakawa et al., 2007). Finally, FMR1 is a gene located on the X chromosome that encodes a protein (FMRP) that plays a role in the regulation of synaptic plasticity (Bailey et al., 2001). Interestingly, recent evidence from structural and functional neuroimaging studies of living children with AD have pointed to some striking alterations of neurodevelopment that are manifested in fairly early postnatal development. These will be discussed in the following section.

Postnatal dysregulation of neurodevelopment in AD The histopathological evidence in AD suggests that the disorder has its origins in abnormal brain development in prenatal life. However, this does not seem to provide a full explanation of the neural basis of AD, given significant heterogeneity in patterns of onset and developmental course. For many children with AD, signs of the disorder emerge very slowly in the course of the first year of postnatal life, whereas for others, onset of the disorder is associated with a developmental regression after an extended period of seemingly normal development (reviewed in Stefanatos, 2008). Given evidence that the disorder is based in multiple genetic susceptibilities, questions arise: do variations in postnatal course reflect differences in the expression of specific genetic vulnerabilities, or do environmental events act in an additive or “second-hit” fashion to result in regressive forms of the disorder? Given these questions, there has been considerable interest in examining the natural history of the disorder in relation to anomalies of physical development and the dynamic changes in brain structure and function that occur during neurodevelopment.

Macrocephaly and brain volume In his original cohort of 11 children, Kanner (1943) did not observe significant dysmorphic features that would suggest anomalies of fetal development. However, numerous other investigators have since identified several anomalies of growth that are present with a higher frequency in children with the disorder (Cheung et al., 2011; Christianson et al., 1994; Ho and Eaves, 1997; Miles et al., 2008; Orstavik et al., 1997; Toriello, 2008). Kanner did note, however, that five of the children had “large heads” and numerous reports have since documented increased head circumference or “macrocephaly” in 10–20% of children with AD. By contrast, macrocephaly is seen in less than 1% of an unselected population of neonates (Petersson et al., 1999). The neurological significance of this finding has been examined in a series of retrospective studies. This research has revealed that children with AD, as a group, demonstrate normal or modestly below average head circumference at birth (Courchesne et al., 2003; Dementieva et al., 2005), but by 1–2 years of age a significant percentage of these infants

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demonstrate a head circumference that falls within the range of macrocephaly (> 97th percentile) (Courchesne et al., 2011). Given that head circumference is correlated with brain size in children (Bartholomeusz et al., 2002), the macrocephaly that emerges in this period may potentially reflect accelerated early postnatal brain growth. This working hypothesis has subsequently received support from studies employing volumetric analysis of high-resolution structural magnetic resonance imaging (MRI) scans. In these investigations, subjects undergo whole-brain T1-weighted MRI scans, which optimize gray/white brain matter differentiation. The slices comprising the scans are then combined into three-dimensional reconstructions of the brain; regions of interest are marked on these reconstructions; and the corresponding volume of the region is calculated. Several studies using this approach have shown that the volume of the brain of children with AD is larger than age-matched neurotypical controls (Hazlett et al., 2005; Sparks et al., 2002). The effect is most robustly seen in total brain volume. A question prompted by these findings concerns whether observed increases in brain volume reflect differences in white matter volume (Herbert et al., 2004), gray matter volume (Hazlett et al., 2006), or both. When these are analyzed separately, the enlargement can be observed both in cortical gray and cerebral white matter (Carper et al., 2002; Hazlett et al., 2005). However, there appear to be some systematic regional differences in this pattern of overgrowth. An anterior-to-posterior gradient is evident, with frontal lobes and anterior temporal lobes showing greatest enlargement (as much as 20%). By contrast, parietal and occipital lobe volumes were not significantly different from the normal control group. This pattern of accelerated growth eventually subsides after the second year and is followed by an extended period of slow growth. Some studies have reported normalization of total brain volume by mid-childhood (Courchesne et al., 2001; Stanfield et al., 2008), while others suggest that increased brain volume persists into adolescence and adulthood (Freitag et al., 2009; Hazlett et al., 2006). Interestingly, macrocephaly has also been found in about the same proportion of unaffected family members (Bolton et al., 2001). Consequently, cortical overgrowth is regarded as a potential risk factor rather than a specific marker of the neurodevelopmental anomalies associated with the full-blown symptom complex.

Regional variations in brain volume A number of studies have used advanced analytic methods to evaluate possible regional differences in brain volume in AD compared to neurotypical controls. In voxel-based morphometry (VBM), for example, each image is registered to a template which provides a standardized space to allow comparisons of each and every voxel. Such procedures increase sensitivity to volumetric differences involving smaller areas of cortex so volumetric differences involving particular structures may be more readily detected. Unfortunately, volumetric studies of AD have often been plagued by small group sizes and variability in diagnostic criteria. As a consequence, there is considerable inconsistency in the findings across studies. Reports of volumetric differences in amygdala (Munson et al., 2006; Schumann et al., 2009) and hippocampus (Aylward et al., 1999; Piven et al., 1998) have been inconsistent, with some reports describing increased volume while others report small decreases or no differences (Palmen et al., 2006; Zeegers et al., 2009). This disagreement may in part relate to age-related variation. Comparing results across studies, it appears that there may be some enlargement of the amygdala in early childhood but not in adolescence or adulthood (Stanfield et al., 2008).

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Several studies have reported diminished volume of particular structures of the cerebellum, although the results have also been inconsistent (Wilson et al., 2009). According to a meta-analysis done by Stanfield and colleagues (2008), this reduction is limited to lobules VI–VII of the vermis. In contrast to the volume reductions evident in the cerebellar vermis, enlarged volumes have been reported in basal ganglia, particularly in the caudate nucleus (Rojas et al., 2006; Voelbel et al., 2006). The caudate nucleus is a critical component of a network that exists between the frontal lobe and the striatum (caudate nucleus, putamen, and nucleus accumbens). The caudate plays a role in mediating inhibitory influences on behavior (Haznedar et al., 2006). Several recent studies have indicated that the volume of the caudate is increased in AD and this appears to be correlated with ritualistic repetitive behaviors (Brambilla et al., 2003; Hardan et al., 2003; Langen et al., 2007). More specifically, there is evidence suggesting that an enlarged putamen and right caudate may be particularly implicated in higher-order obsessive–compulsive repetitive behavior (Hollander et al., 2005). These structures also play an important role both in sensorimotor skill learning and in executive processes. A recent meta-analysis supported findings of increased volume of the caudate nucleus in AD (Stanfield et al., 2008). Several studies have shown other regional reductions in gray matter volume, and this appears to vary across subtypes of pervasive developmental disorder and with the severity of language impairment. McAlonan and colleagues (2008), for example, found that children with high-functioning AD demonstrated smaller gray matter volumes in fronto-striatal regions incorporating Broca’s area, while children with Asperger’s syndrome (AS), who have by definition lesser language involvement, had smaller gray matter volumes in caudate and thalamus. These observations suggest that difficulties with language acquisition may be correlated with of reduced gray matter volumes in the left prefrontal cortex in AD. The neurobiological implications of increases or reductions in gray matter as determined by volumetric studies are difficult to specify. These procedures are not intended to measure microstructure because they conflate information about morphology, position, and size. Moreover, automatized tissue classification can possibly increase the risk of artifacts influencing the results (Nickl-Jockschat et al., 2011). Cortical thickness (CT) is a more direct measure of anomalies of gray matter development – at least in the cerebral cortex – and can more closely reflect the kinds of cytoarchitectural anomalies that have been reported in a number of postmortem histological studies. Cortical thinning has been noted in adults with AD in peri-sylvian regions of frontal, temporal, and parietal cortex (Hadjikhani et al., 2006; Hardan et al., 2006) particularly in the left cerebral hemisphere (Wallace et al., 2010). These findings are age related, suggesting that they may reflect earlier or excessive synaptic pruning in adolescence in regionally delimited areas of cortex. The areas involved, are critical to both communication and social function. Similar findings have been reported in regions that may be more specifically involved in social perception and interaction (e.g., fusiform gyrus). By contrast, gray matter increases have been noted in primary and associative auditory and visual cortex, providing a possible structural correlate to certain observations of enhanced auditory and visual processing skills (Hyde et al., 2010).

AD and neuronal interconnectivity White matter comprises about 50% of brain volume and is critical to the development of higher cortical functions. Myelinated pathways enhance neural conduction speeds throughout the brain and allow a level of temporal synchrony of neural firing that is necessary for

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distinct areas of grey matter to integrate into functional neuronal ensembles. Short U-shaped association fibers provide connections between adjacent gyri, while long association fibers mediate the communication of information within gyri or between more widely distributed areas of cortex. Long association pathways, along with commissural pathways which mediate communication between the hemispheres, may be particularly important in mediating higher cortical functions such as social interaction and communication, which involve highly distributed neural networks. A number of the areas involved in mediating these functions are among the latest to reach maturity levels of myelination. The PFC is arguably one of the most well-connected regions of the brain, having connections with most, if not all, functional units in the rest of the brain. As a consequence, perturbations of development in frontal cortex functions can potentially arise, directly or indirectly, from damage or dysfunction in numerous other parts of the neocortex (Mesulam, 1998) or from compromise of the long association fibers that convey information to and from the frontal cortex (e.g. arcuate fasciculus, superior and inferior occipitofrontal fasciculus, superior and inferior longitudinal fasciculus, uncinate fasciculus, cingulum, extreme capsule). This possibility is given added weight by the observation that those areas of the brain most likely to demonstrate cortical pathological findings in AD are the areas with an abundance of long association fibers (Courchesne and Pierce, 2005; Herbert, 2005). A recent meta-analysis of the extant literature suggested that white matter volume is increased in the right arcuate fasciculus and left inferior fronto-occipital and uncinate fasciculi of individuals with ASD when compared to controls (Radua et al., 2011). The factors underlying the overgrowth of white matter are not entirely clear, but could include an excess of minicolumns, axons, and glial cells, or overgrowth of myelin (Casanova and Trippe, 2009; Courchesne and Pierce, 2005). Due to the sometimes conflicting information provided by volumetric analyses, several recent studies have also utilized a relatively new non-invasive MRI-based procedure called diffusion tensor imaging (DTI) to examine white matter pathways in AD. DTI provides measures of the condition of white matter related to the integrity of axons (fractional anisotropy, mean diffusivity), and the degree of myelination (inversely related to radial diffusion). A reduction of the size of the corpus callosum (CC) is one of the most consistent structural neuroimaging findings in individuals with AD (Hardan et al., 2009; Piven et al., 1997; Waiter et al., 2005). This major white fiber tract is comprised of approximately 200–350 million axons (Aboitiz et al., 1992) that carry signals from one cerebral hemisphere to the other. Projections passing through the corpus callosum account for approximately 80% of the fibers connecting the two cerebral hemispheres and mainly but not exclusively connect homologous regions of cortex. Anterior portions of this tract connect prefrontal cortices and are thought to play an important role in mediating interhemispheric inhibition. The middle third connects motor, somatosensory, and auditory cortices while the posterior fifth (isthmus and splenium) carries axons passing between temporal, parietal, and occipital lobes. These posterior portions may play a predominantly excitatory role that contributes to the integration of perception and action (Schulte and Muller-Oehring, 2010). Disruption of either anterior or posterior connections can impact neuropsychological functions that require integration of information between the hemispheres and may have a particular influence on social development and social communication (Rosema et al., 2012). Recent DTI studies have disclosed anomalies in the microintegrity in all of these subsections, although there has been considerable variability among studies (Noriuchi et al., 2010; Shukla et al., 2010; Thomas et al., 2011; Weinstein et al., 2011).

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Anomalies have also been identified in intrahemispheric long fiber tracts (Sundaram et al., 2008), including but not limited to the uncinate fasciculus (Kumar et al., 2010), the cingulum (Cg) (Weinstein et al., 2011), inferior and superior longitudinal fasciculus (Shukla et al., 2011), inferior fronto-occipital fasciculus, the temporal stem (TS) (Lange et al., 2010), and arcuate fasciculus (Fletcher et al., 2010). In addition, the microstructural integrity of white matter tracts connecting frontal and striatal structures may be compromised in AD (Langen et al., 2012). It has been suggested that white matter disturbances may constrain integrative processes because of limited bandwidth for transmitting necessary information between frontal and posterior cortical areas (Schipul et al., 2011). As a result of these reports, the concept of “disconnection syndrome” has re-emerged as a possible explanatory model (Frith, 2004; Geschwind and Levitt, 2007). Overall, these pathways include some of the most critical communication links for neural networks subserving language (Stefanatos and Baron, 2011; Weinstein et al., 2011), social interaction (Noriuchi et al., 2010; Poustka et al., 2011), regulatory behavior, and working memory (Thomas et al., 2011). A recent postmortem study by Zikopoulos and Barbas (2010) conveys a complex picture of the microstructural correlates of frontal lobe connectivity issues in AD. In regions below the anterior cingulate cortex, they found fewer large axons that communicate over long distances, whereas there were excessive connections between neighboring areas. In orbital frontal cortex, axons demonstrated decreased myelin thickness. Axons below the lateral PFC were relatively unaffected directly, but connectivity to this area could be indirectly influenced by the alterations seen in other prefrontal areas. These findings are broadly in keeping with growing evidence of local over-connectivity and long-distance under connectivity in AD (Wass, 2011).

Functional neuroimaging in AD Numerous studies have attempted to characterize the functional brain phenotype in autism by examining the patterns of neural activation that result when the brain is actively processing information and comparing this to brain activity during rest or a control task. Electromagnetic measures, such as electroencephalography (EEG), magnetoencephalography (MEG), and event-related potentials (ERP) provide fairly direct measures of neural activity, while hemodynamic measures such as positron emission tomography (PET) and functional MRI (fMRI) index neural activation indirectly through monitoring changes in cerebral blood flow. Interpretation of this research is complicated by the same kinds of methodological shortcomings, that confound structural imaging research in AD (e.g., small sample sizes, lack of standardized diagnostic criteria, heterogeneity in the sample, and a failure to account for moderating variables, such as age and gender). Since functional imaging studies require participants to comply with activation task demands and constrain movement for extended periods of time, studies are often limited to older and/or higher-functioning individuals, which limits their generalizability. Studies of younger children have typically involved only passive exposure to stimuli while participatients were sedated (Boddaert et al., 2004) or in natural sleep (Redcay and Courchesne, 2008). While overcoming some logistical issues, this methodology introduces other limitations in the interpretation of findings. Given the many complexities inherent to this avenue of investigation and the variety of procedures used, it is perhaps not unexpected that this research has provided a mixed picture of the neural correlates of AD. A full treatment of functional imaging studies of AD is beyond the scope of this chapter. Here, we provide only a very selective review of some of the key findings.

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Several studies have suggested that processing of speech stimuli is more difficult than processing nonverbal (e.g. musical) auditory stimuli. Using fMRI, Gervais and colleagues (2004) reported that individuals with AD failed to activate regions of superior temporal sulcus (STS) that selectively respond to vocal sounds, whereas they demonstrated normal activation in response to nonvocal sounds. However, others have noted abnormal responses even when processing simple auditory information (Gage et al., 2003). Indeed, the temporal lobes appear to exhibit hypoperfusion bilaterally even at rest (Gendry Meresse et al., 2005). The picture is also mixed when studies have used more complicated tasks requiring higher-level language processing. Muller et al. (1999), for example, reported a PET study that examined activation during auditory and language (both expressive and receptive) processing tasks in five high-functioning autistic adults. While passively listening to simple sentences, autistic adults demonstrated reduced interhemispheric asymmetries in temporal lobe activation, compared to the highly asymmetric left temporal activation seen in neurotypical controls. However, when generating sentences from word and sentence prompts, autistic subjects did show the expected left greater than right hemisphere activation, particularly in inferior frontal regions. Muller et al. suggested that the findings provided partial support for the notion of atypical or reduced hemisphere dominance for language in autism. Anderson et al., (2010) also noted reduced activation in the left posterior insula, which was thought to reflect impaired emotional processing of language. In addition, some studies have suggested that individuals with AD demonstrate a reduced capacity to integrate incoming linguistic information with their emotions and knowledge of the world (Tesink et al., 2011). Overall, these studies are suggestive of functional and organizational anomalies of the specialized computational network distributed in left frontal, temporal and insular cortex that is normally engaged during the processing of language. Poor connectivity and integration between the components of this network may contribute to these findings (Minshew and Keller, 2010; Stefanatos and Baron, 2011). Like language processing, the understanding and utilization of social information entails the efficient and rapid transmission of information between components of a highly distributed neural network commonly referred to as the “social brain” (Adolphs, 2009; Dawson and Bernier, 2007; Pelphrey et al., 2011). It is comprised of no less than 12 distinct areas, including the amygdala, hippocampus, insula, cingulate gyrus, fusiform gyrus, angular gyrus, superior temporal sulcus, Broca’s area, Wernicke’s area, angular gyrus, inferior frontal lobe, and orbital medial PFC (Brothers, 1997). This network enables us to sense another person’s thoughts, feelings, and intentions, and to utilize this knowledge to predict their behavior and to guide our own. In addition, it is involved in the development of empathy, and the ability to share socially relevant information in order to build cohesion and advance common goals. Many if not all of these areas have been implicated in a functional imaging studies examining some aspect of social information processing in AD (Ashwin et al., 2007; Pelphrey and Carter, 2008; Di Martino et al., 2009). Again, the long-range connectivity between components of this complex network appears to be compromised in AD. Kleinhans et al. (2008) found poor connectivity between the amygdala and fusiform gyrus, while Rudie et al. (2012) observed similar issues between amygdala and secondary visual areas. These anomalies may underlie some of the difficulties individuals with AD experience in reading the facial expressions and body language of others. Damage or dysfunction of frontal cortex and its connections to basal ganglia and cingulate cortex can result in reductions of mental flexibility, impulse control, and the ability to view problems or events from different perspectives. This circuitry has also been

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implicated in disturbances involving restricted and repetitive behaviors in AD. Shafritz et al. (2008), for example, examined cerebral activation in a group of high-functioning autistic individuals during performance of tasks that required shifts in behavioral response and cognitive set. They observed that high-functioning individuals with AD demonstrated reduced activation in frontal, striatal, and parietal cortex. The lesser the activation in anterior cingulate and posterior parietal regions, the greater the severity of problems with restricted and repetitive behaviors. Their findings supported a number of other previous studies suggesting a link between executive deficits and problems with repetitive and restricted behaviors (Lopez et al., 2005; Zandt et al., 2009). Despite the methodological and interpretive issues that surround functional neuroimaging studies in AD, when these studies are considered in the context of neuropsychological investigations, structural neuroimaging, and the results of neuropathological studies, several common themes appear to emerge. The substantial individual variation in the pattern, severity, and developmental course of impairments in each of the psychological domains that comprise the AD behavioral phenotype is, to some extent, conveyed in patterns of brain activation and connectivity. The disorder is clearly not a single entity, but many different conditions that share a constellation of behavioral symptoms (Wing, 1997) and some commonalities in the extent and distribution of the underlying pathophysiology. Impairment can vary greatly across the domains comprising the autistic triad (Ronald et al., 2005). These variations influence how individuals approach tasks and the extent to which compensatory strategies or skills are available to them. This, in turn, plays a part in determining the variations in patterns of activation seen during functional neuroimaging task performance. For example, while poor long range connectivity negatively impacts a variety of language, social and self-regulatory processes, excessive short range connectivity can result in strengths such as excellent figure ground perception. A close correlation appears to exist between social dysfunction and communication difficulties, and this correlation may relate in part to the considerable overlap in the cortical areas that mediate the development of social and communicative abilities. This is illustrated schematically in the color plates of Figures 4.2A and 4.2B, which depict different views of the human brain with superimposed color shading to indicate the regions mediating each domain of the autistic triad: social interaction, communication, and repetitive/restricted behavior and interests. Areas shaded in green reflect cortical regions that have been implicated in social impairment. These areas comprise a complex distributed network involving the orbito- and medial frontal cortex, frontal pole, cingulate cortex, superior temporal sulcus, temporal pole, fusiform gyrus, and amygdala. As can be appreciated, these areas are adjacent to or overlap considerably with areas shaded in red, which depict those areas commonly implicated in problems with communication. Clear overlap is particularly evident both in frontal and temporal cortex, areas that have been particularly implicated in AD. To a lesser extent, problems with repetitive/restricted behavior and interests (depicted in cyan) also share neural resources with cortical regions mediating social behavior. From an ontogenetic standpoint, there are also interdependencies in the cognitive capacities and underlying computational hardware that support the development of social and communicative skills. Early in development, there is a complex interplay between subcortical structures involved in attention (e.g. amygdala, cerebellum), brainstem/thalamic nuclei with cortical areas comprising the “social brain networks” such as dorsal medial and orbitofrontal frontal cortex, cingulate gyrus, fusiform gyrus, and superior temporal sulcus (Adolphs, 2009; Frith and Frith, 2010). This network mediates the development of

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Figure 4.2 (A). This figure depicts a lateral view of the human brain. Color-shaded areas indicate those brain regions thought to be functionally compromised in individuals with AD. Areas shaded in red are associated with problems with communication. These problems have been related to dysfunction of a complex distributed neural network involving the following areas and connecting pathways: inferior frontal cortex, superior temporal gyrus, superior temporal sulcus, supramarginal gyrus, insula, basal ganglia, thalamus, and cerebellum. Some structures are not visible given the plane of the image. Areas shaded in green reflect cortical regions that have been implicated in social impairment. These areas comprise a complex distributed network involving the orbito- and medial frontal cortex, frontal pole, cingulate cortex, superior temporal sulcus, temporal pole, fusiform gyrus, and amygdala. Problems with repetitive and stereotyped behaviors (cyan) have been related to dysfunction of a complex distributed neural network involving the following areas and connecting pathways: orbitofrontal cortex, posterior parietal cortex, supplementary motor cortex, cingulate gyrus, basal ganglia, thalamus, and cerebellum. Areas depicted in alternating green and red stripes represent regions of the brain that appear to be involved in mediating social behavior and communication. (B). Some of the medial cortical and subcortical structures involved in mediating behaviors in each of these three domains are shown. (For a color version of this figure, please see the color plate section.)

intersubjectivity, that is, the attunement and sharing of subjective states between individuals (e.g. attention, intentions, and emotions). This type of social connection is also important for the development of communicative abilities (Stefanatos and Baron, 2011; Tomasello, 2008). Medial prefrontal and temporal areas at or near the temporoparietal junction (including STS) play particularly critical roles in mediating advanced aspects of social cognition including theory of mind (Baron-Cohen et al., 1994; Saxe and Wexler, 2005), empathy (Chakrabarti and Baron-Cohen, 2006; Decety and Lamm, 2007; Harris, 2003), and reasoning about other people (Saxe et al., 2009). However, these areas are also engaged in lower-level computations, such as the processing of biological motion, that contribute to higher-level social cognitive abilities (Decety and Lamm, 2007; Pelphrey et al., 2007). As heteromodal association cortex, these areas are also involved in integrating information conveyed from diverse sensory (auditory, visual, somaesthetic) and motor areas with input from the limbic system and other areas of association cortex. The role of the limbic system also appears crucial in this regard. The hippocampus is a hub for linking together input from different areas of the cortex and orchestrating the synchronicity required between components of a network to encode and store new memories. It seems particularly important for consolidating episodic memories which are critical

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for associating emotional tone to events and to ascribing personal ownership to experiences. Children with AD commonly have difficulty remembering information from long-term memory that entails their personal involvement (Jones et al., 2011). They fail to benefit from personal involvement in recalling events (Hare et al., 2007; Millward et al., 2000) and indeed have relative difficulty recalling whether they themselves, or another person, engaged in a given activity (Russell and Jarrold, 1999). They demonstrate a tendency to adopt a thirdperson perspective (observer) in recalling past events rather than re-experiencing past events from their own viewpoint (Lind and Bowler, 2010; Williams et al., 2006). Memory performance may therefore falter when recall is dependent on aspects of self-awareness, awareness of others, or an understanding of social implications surrounding an event. Individuals with AD are better at tasks that require “knowing” (semantic memory), rather than “remembering” (episodic memory) (Ben Shalom, 2003).

Summary Overall, it appears that individuals with AD demonstrate atypical neurodevelopment associated with accelerated growth of the brain (both gray and white matter) in the first year or two of life, resulting in enlargement of brain volume (~10%) and potential macrocephaly in early childhood. Increases appear particularly evident in frontal, temporal, and parietal lobes as well as some subcortical structures such as the caudate. By contrast, other subcortical structures (e.g., thalamus, brainstem, amygdala, hippocampus) often show reduced volume. The phenotype is also sometimes associated with other gross morphological features such as polymicrogyria, macrogyria, and schizencephaly. Overall, the findings are suggestive of significant alterations of early corticogenesis resulting in anomalies that begin in the prenatal period and then persist in early postnatal development. Eventually, a slowdown in post-natal brain development results in normalization or near-normalization of brain volume, but many of the microstructural anomalies persist. Cortical thinning eventually becomes apparent later in development in brain areas critical to social, communicative and self-regulatory functions. This deviant growth trajectory can result in atypical patterns of functional connectivity (Lewis and Elman, 2008). Perturbations appear to include generalized reduction of white matter integrity (Groen et al., 2011) with an increase in short-range functional connectivity and decreased long-range functional connectivity. This failure to follow a normal developmental course eventuates in smaller and less synchronized neural networks (Koshino et al., 2008). This also seems to be associated with perturbations of hemispheric functional asymmetries that typically emerge in neurodevelopment. Individuals with AD have less lateralized language functions and also show unusual patterns of activation during the processing of faces and other socially relevant visual stimuli. Given the widespread distribution of the underlying pathophysiology and its developmental context, it results in a complex array of primary, secondary, and tertiary effects on behavior. Functions most impacted appear to be those dependent on long-range functional connectivity involving frontal and temporal cortex. These networks mediate functions critical to social interaction such as the ability to appreciate another person’s thoughts, emotions, and perspectives. Due to compromise of networks involved in mediating executive functions, individuals tend to be somewhat perseverative, concrete, and have difficulties viewing information from different perspectives. Deficiencies in both these areas have a significant impact on the development of communication skills.

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Section 1 Chapter

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What We Know About Autism

Etiology: syndromic autism Mark E. Reber

This chapter and the following will consider etiologies of autism. In the present chapter, the focus will be on autism that occurs in well-described neurologic and genetic disorders – referred to here as secondary or syndromic autism and distinguished from primary, essential or idiopathic autism that occurs in people who have no additional diagnosable neurologic disorder. This chapter will focus on several specific neurogenetic syndromes that are associated with a high rate of occurrence of autism. The next chapter will move on to address a range of biologic processes that independently or in combination may lead to the social and communication deficits and behaviors that define autism. Both syndromic and essential autism are assumed to be etiologically heterogeneous. In its heterogeneity, autism resembles intellectual disability (mental retardation), the other major neurodevelopmental disorder of childhood. Intellectual disability is also behaviorally defined (in terms of cognitive and adaptive delays) and is universally recognized to have many causes. According to Coleman and Betancur (2005), over 2000 disease entities have been described in persons with intellectual disability. In ASDs, the number of associated disease entities that have been reported in the literature is smaller – Coleman and Betancur indicated more than 60 – but this number is growing, particularly as new efforts are made to identify genetic variations that occur in autistic populations. Table 5.1 lists selected conditions that have been associated with autism. These include well-described neurogenetic syndromes, with and without stigmatizing features (congenital anomalies); congenital infections; toxic embryopathies; epilepsy syndromes; metabolic diseases; chromosomal abnormalities and single gene mutations. Most of these conditions involve prenatal (and some perinatal and early postnatal) insults to the developing nervous system. The occurrence of ASDs in known neurologic and genetic disorders raises a number of important questions – for patients, families, and researchers – about the nature of this type of autism. Among these questions are the following: – Is the medical condition the cause of the autism? Does the condition itself cause the social and communication deficits that lead to the autistic diagnosis? – Given that many of these conditions are also associated with intellectual disability, what is the role of impaired cognitive functioning in the occurrence or diagnosis of autism? – Is the phenomenology of autism in some way unique to the medical condition? Are the autistic symptoms of a child with the condition the same as those in children with primary or essential autism? The Autism Spectrum, ed. Mark E. Reber. Published by Cambridge University Press. © Cambridge 112 University Press 2012.

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Table 5.1 Examples of syndromes and genetic disorders associated with ASDs Neurogenetic syndromes with characteristic dysmorphology

Other neurogenetic syndromes

Fragile X syndrome

Rett syndrome

Angelman syndrome

Joubert syndromes

Prader–Willi syndrome

Teratogenic exposures/infections

22q11 deletion syndrome

Fetal alcohol syndrome

CHARGE association

Thalidomide

Down syndrome

Valproic acid

Sotos syndrome

Rubella

Smith–Lemli–Opitz syndrome

Herpes Simplex CMV CMV

Neurocutaneous syndromes

Metabolic disorders

Tuberous sclerosis complex

Phenylketonuria

Neurofibromatosis type 1

Mucopolysaccharidoses

Hypermelanosis of Ito

Mitochondrial disorders Disorders of creatine metabolism

– Perhaps most important: because some of the neurologic disorders associated with autism have been independently and extensively studied, what can our understanding of the pathophysiology of these conditions tell us about autism – as it occurs in these conditions and more broadly? The present chapter will review some of the research literature that addresses these questions. From the clinical point of view, it is important to remember that a child with a neurogenetic disorder and an ASD has two diagnostic homes. Both diagnoses have enormous implications for intervention and prognosis. Families need to be educated about the nature of both conditions and receive the appropriate genetic counseling. Their ability to make sense of their child’s neurodevelopmental problems and secure appropriate care is enhanced by their knowing how exactly their child came to suffer from these conditions. In neurogenetic conditions with a high prevalence of ASDs, it is clinically appropriate to identify the condition itself as the cause of a particular child’s autism. This is, of course, common practice in a condition like congenital heart disease when it occurs in Down syndrome: one has no hesitation in attributing the cardiac malformations to the syndrome. Similarly, in autism, there should be no equivocation in stating that a syndrome with a high rate of co-occurring autism is the cause of an ASD in a particular child.

Fragile X syndrome Fragile X syndrome (FXS) is the most common inherited cause of intellectual disability. It occurs in one per 3600 males and one per 8000 females (Cornish et al., 2008). FXS was first

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described in 1943 by Martin and Bell, who reported on a family that included 11 males with intellectual disability and several females with lesser impairments. They noted that the condition was sex-linked, inherited through minimally affected or unaffected females. Features of the syndrome in males included large ears, a long and narrow face and enlarged testicles. In the 1970s, sex linkage was explained when a fragile, or breakable, site was noted at the bottom end of the X chromosome on cytogenetic studies. Subsequent efforts to sequence the genes at this locus, coupled with a large number of molecular genetic, neuropathological, neuroimaging, and psychological studies, have led to a comprehensive, if still incomplete, understanding of FXS from the level of the gene to that of physical and behavioral symptoms. It is the best understood of all the neurogenetic conditions associated with autism.

Molecular genetics and neurobiology of FXS Fragile X syndrome was the first human disorder to be explained by triplet repeats of DNA bases as a type of genetic mutation. Once it had been sequenced, the fragile X gene, FMR1, was shown to code for the FMR1 protein (FMRP), a molecule that was later demonstrated to bind to messenger RNA and regulate the translation of many other genes, particularly in neurons. In FXS, production of FMRP is suppressed, because a portion of the FMR1 gene is non-functional. This is because it is dramatically expanded by the occurrence of a huge number of repeats of the three-nucleotide sequence: cytosine, guanine, guanine (CGG). These CGG repeats exist in the promoter region of the gene in normal individuals, but in relatively small number, in the range of 5–45 triplets. In FXS, there are more than 200 CGG repeats, and this elongated portion of DNA is subject to hairpin turns and increased methylation, with both processes effectively silencing the gene so that it is not transcribed and FMRP is not produced (Lombroso and Ogren, 2008). The inheritance pattern of FXS is also based on expansion of CGG repeats. Indeed, the mutation that leads to fragile X syndrome is expansion itself. There is a carrier state, or premutation, consisting of 55–200 CGG repeats (45–54 repeats are associated with “minor instability” according to Hagerman, 2006). Men with the premutation (often identified as the grandfather of an index FXS patient) pass on the premutation of 55–200 CGG repeats to their female offspring, without significant expansion. Females with the premutation (mothers of FXS patients), however, will pass on an expanded sequence of more than 200 CGG repeats – the mutation – to those who receive this X-chromosome, i.e. roughly half of their male and female offspring. Females with the mutation, however, also receive a normally functioning X-chromosome from their mothers and thus have less suppression of FMRP and, usually, a condition of lesser severity than their male siblings. The severity of FXS in an affected female will depend in part on which of their two X chromosomes is inactivated in the majority of their body cells. (See Figure 5.1 for a pedigree of an imagined FXS family, demonstrating the expansion of CGG repeats over three generations.) In recent years, there has been considerable research on the fragile X premutation and associated symptoms. It has become evident that the premutation is not benign. A disorder called fragile X-associated tremor/ataxia syndrome (FXTAS) has been described in older male carriers. It is characterized by intention tremor and gait ataxia, and may include Parkinsonian features, symptoms of peripheral neuropathy, autonomic dysfunction, anxiety, irritability, and deficits in memory and executive functioning (Hagerman, 2006). Women with the premutation have a higher-than-expected rate of mood, anxiety, and other psychiatric disorders (Bourgeois et al., 2009). They may also have premature ovarian failure (Hagerman, 2006).

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Figure 5.1 Three generations of a hypothetical family with two siblings with fragile X syndrome in the third generation. The number of CGG repeats in the FMR1 gene on each X chromosome is indicated. Shading reflects premutation (gray) and mutation (black) status.

Our understanding of the neurobiology of FXS derives from research on the function of FMRP. As mentioned above, this molecule binds to messenger RNA. Its usual function within cells is to form complexes with specific mRNAs, moving them into and out of the cell’s nucleus. In neurons, FMRP also helps transport these mRNAs to distant sites in dendrites and acts as a regulator of their translation into proteins at synapses. Some of these proteins are involved in both neuronal maturation and synaptic plasticity. More specifically, in excitatory synapses where glutamate functions as a neurotransmitter, FMRP has been shown to regulate the translation of the mRNAs to which it is bound, so that proteins are synthesized only in response to glutamate stimulation, not at other times. This regulatory action, occurring right at the location of glutamate receptors, plays a major role in synaptic plasticity – the strengthening of some synaptic connections and the weakening or elimination of others in response to synaptic activity – a process that underlies maturational changes in brain development as well as learning and memory. FMRP appears to regulate protein synthesis in tandem with the glutamate receptors MGluR1 and MGluR5. When FMRP is present, it counterbalances the effect of the MGluRs, which is to initiate mRNA translation and protein synthesis – while FMRP appears to inhibit these actions. When FMRP is absent, i.e. in FXS, the response to glutamate stimulation is abnormal; local protein synthesis is increased and synaptic plasticity is altered (Penegarikano et al., 2007; D’Hulst and Kooy, 2009). In a mouse model of fragile X syndrome (“knockout” mice who do not express the FMR1 gene), there is evidence of decreased plasticity in the developing visual cortex, impaired learning, and structural alteration in neurons, with an abnormally high density of dendritic spines (Ogren and Lombroso, 2008). Autopsies performed in people with FXS also show an

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increased density of dendritic spines, which are also immature (longer and thinner), suggesting dysregulation of their development and elimination (Penegarikano et al., 2007). Thus, some of the neuropsychiatric features of FXS can be attributed to insufficient FMRP and its effects on glutamatergic synapse formation, synaptic plasticity, and the developmental pruning of interneuronal connections. At the neuronal level, FXS can be conceptualized as a synaptic disease. The effects of the FMR1 mutation are, however, much more diverse. Because FMRP binds to messenger RNAs, it can be assumed that its absence affects the translation of genetic messages from a number of sites on the human genome. Within the brain, there is evidence that FMRP also “recognizes” mRNAs associated with GABA receptors. FXS animal models (FMR1 knock-out mice) show reorganization of GABAergic neocortical inhibitory circuits: there is altered GABA-A subunit expression and a marked reduction in the density of GABAergic interneurons (Hagerman et al., 2010). In addition, the pattern of congenital anomalies and the connective tissue abnormalities seen in FXS can most likely be attributed to insufficient FMRP, altered translation of a wide range of mRNAs and dysregulated production of their associated proteins.

FXS phenotype The physical phenotype of fragile X syndrome has been well-described, but can be highly variable, depending in part on FMR1 methylation and FMRP production. Classic facial features include a long face, prominent ears, and a prominent chin. Additional features may include puffiness around the eyes, narrow palpebral fissures, epicanthal folds, strabismus, a large head relative to the body, a prominent forehead, and hypotonia. The skin of patients with FXS is described as soft, and there is joint laxity attributable to elastin abnormalities. Large testicles occur in 80–95% of adolescent and adult males with FXS. Mitral valve prolapse is frequently found, and hypertension may be seen. The most common neurological abnormality in FXS is seizures, which occur in approximately 20% of patients. Volumetric brain MRI studies have demonstrated a smaller cerebellar vermis in both males and females, in addition to a larger caudate, thalamus, and hippocampus (Hagerman, 1999). Cognitive functioning in FXS can be quite variable, depending on genetic status, sex, and age. Fully affected males with little or no FMRP production tend to have IQ scores that fall in the mild to moderate range of intellectual disability. Some males with genetic mosaicism and others with only partially methylated FMR1 genes may have somewhat higher intellectual functioning. Cognitive level in affected females also depends on FMRP production, but this varies with which X chromosome is inactivated. If the normal X chromosome is inactivated in the majority of cells, a female will be more cognitively impaired; if the fragile X chromosome is inactivated she will be less so (Dykens et al., 2000). Of females with FXS, 25% meet criteria for intellectual disability (Boyle and Kauffman, 2010). There is some evidence from longitudinal studies that intellectual functioning in FXS declines with time. This decline appears to result not from loss of skills, but rather from difficulty maintaining a developmental course that is similar to same-aged peers (Reiss and Hall, 2007). A number of studies have investigated and described a characteristic cognitive profile in people with FXS. These studies are summarized by Dykens et al. (2000). Strengths associated with FXS are verbal skills, fund of knowledge, verbal long-term memory, expressive and receptive vocabulary, and adaptive living skills. Weaknesses include auditory and visualperceptual short-term memory, sequential processing, integrating information, and adaptive socialization skills. There is also some evidence that boys with FXS have difficulties with

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theory-of-mind tasks when compared to other children with intellectual disability. These deficits, however, have been attributed to problems with working memory, and differ from the theory-of-mind difficulties that characterize children with essential autism (Grant et al., 2007). There is also a characteristic behavioral phenotype that has been described in FXS. A behavioral phenotype is defined as “the heightened probability or likelihood that people with a given syndrome will exhibit certain behavioral and developmental sequelae relative to those without the syndrome” (Dykens and Cassidy, 1995). In males with FXS, the behavioral phenotype comprises stereotyped motor movements, such as hand-flapping and rocking, tactile defensiveness, perseveration, hand biting, and shyness. It also has a major social component: social withdrawal, anxiety, hyperarousal in interpersonal situations, and gaze avoidance. This social component is well described by Cornish et al. (2008): The majority of individuals with FXS, although tending to avoid social interactions, offer what is now classically termed the “fragile X handshake”, whereby an initial wish to communicate socially, with a “handshake,” socially acceptable remark or even brief initial eye contact, is coupled with active and even persistent gaze avoidance. Subsequent interactions with familiar persons may be marked by the same active gaze avoidance despite the growing relationship. The gaze avoidance persists even when attempts are made to extinguish it; it may, in fact, increase in intensity. It has been suggested that FXS is associated with a unique pattern of hyperarousal and social anxiety that can cause them to avert their eyes in a social situation (to avoid the sensory stimulation of eye contact), but may still wish to communicate socially. (p. 473)

In general, females with full mutations show fewer behavior problems than males. Common symptoms in females include shyness, social withdrawal, avoidance, isolation, awkwardness and some oddities of thinking. Although interested in social relationships, many females with fragile X lack the requisite skills to interact successfully and comfortably. There is some evidence for similar vulnerabilities in females with premutations (Dykens et al., 2000). As would be expected from the general behavioral phenotype, anxiety disorders, particularly social anxiety, are relatively common in FXS. Symptoms of ADHD are also frequently encountered, with one report of 66% of males and 30% of females receiving treatment for hyperactivity (Boyle and Kauffman, 2010).

FXS and autism Because of the large number of “autistic-like” behaviors that comprise the FXS behavioral phenotype, especially in males, it was initially believed that there was an especially large overlap between autism and FXS. Early studies in the 1970s and 1980s focused on features such as poor eye contact, language delay, perseveration, echolalia, self-injury, sterotypies, hypersensitivity to noises, tactile defensiveness, narrow preoccupations, and poor peer relationships as they occur in males with FXS. As many as 50–60% of individuals with FXS were said to have autistic disorder, while FXS was said to be found in up to 16% of autistic populations who were screened for the fragile X chromosome (Dykens et al., 2000). These early studies were problematic in terms of variable sample size, the criteria for diagnosing autism, and the absence of a DNA test for FXS. More recent research has suggested that the prevalence of autistic disorder in males with FXS is closer to 30%, with an another 30% meeting criteria for PDD-NOS (Hagerman et al., 2010). In addition, 1–3% of females with FXS meet diagnostic criteria for an ASD (Moss and Howlin, 2009). When considering the

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broad population of children with ASDs, those with FXS appear to account for 2–6% of cases of autism (Cornish et al., 2008). The high prevalence of ASDs among males with fragile X syndrome raises questions about the nature of autism in FXS. Is it somehow unique to the disorder, with FXS symptoms directly contributing to the diagnosis? Or does it resemble the kind of autism one encounters in primary or idiopathic ASDs? Attempts to address these questions have been made in a number of research studies, comparing people with fragile X and an ASD (FXS + ASD) with those who have FXS but are not autistic (FXS – ASD), and comparing people with FXS and autism with people with primary or essential autism. Bailey and colleagues, in several studies using the Childhood Autism Rating Scale (CARS; Schopler et al., 1988), repeatedly found one quarter of subjects with FXS to score above the cut-off for autism, and the overall pattern of reported behaviors in this group was similar to that found in children with idiopathic autism (Bailey et al., 1998; Hatton et al., 2006). These researchers also found a relationship between higher mean score on the CARS and both cognitive delay (Bailey et al., 1998) and lower levels of FMRP (Hatton et al., 2006). Although the CARS is an instrument that measures autistic symptoms, it does not conform to contemporary diagnostic criteria for ASDs. A group of researchers at the Kennedy Krieger Institute at Johns Hopkins have studied a cohort of boys with FXS using the ADI-R, which is based on DSM-IV criteria, along with a number of other cognitive, behavioral, and adaptive measures. In each of several reports, they compared boys with FXS + ASD with boys with FXS – ASD. They found that there was a range of severity of autistic symptoms within the fragile X population, with social interaction being the primary differentiator (Kaufman et al., 2004). They also found that social withdrawal measures were a predictor of ASD status, but measures of adaptive skills, such as recognition of the rules of social behavior and identifying emotions, were the primary predictor (Budimirovic et al., 2006). This last finding is important, because it suggests that it is not just the characteristic social avoidance associated with the FXS behavioral phenotype that leads to a diagnosis of autism, but specific deficits in social cognition and adaptive behavior. The same research group confirmed these distinctions between FXS + ASD and FXS – ASD in a longitudinal study and found that the ASD diagnosis remains stable over 3 years, suggesting that FXS + ASD is a “distinct, stable phenotype” among boys with fragile X syndrome. At all time points in this study, adaptive socialization and peer relations were the primary determinants of both autistic status and the severity of autistic behaviors. The authors commented: “Contrary to some assumptions about the FXS neurobehavioral phenotype, basic nonverbal social behaviors (e.g. gaze avoidance) and stereotypic repetitive behaviors do not have a major influence on [autism] diagnosis or severity of autistic behaviors” (Hernandez et al., 2009). In addition to studies of autism within the FXS population, there has also been some research comparing the FXS + ASD subgroup with other autistic populations. Rogers et al. (2001) found global similarities between 2- and 4-year-old boys with FXS + autism and those with idiopathic autistic disorder on the ADI-R and the ADOS-G. Demark et al. (2003) used the CARS to distinguish an FXS + autism group from an FXS – autism group and compared them to a sample of boys with idiopathic pervasive developmental disorder. They found that the FXS + autism group was similar to the PDD group on a measure of maladaptive behavior. The Kennedy Krieger group examined a sample of boys with idiopathic autism, with and without language delay, and compared them with a group of boys with FXS + ASD and a group with FXS – ASD. The FXS + ASD group were less impaired than the other autistic

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groups in reciprocal social interaction. They resembled the autistic children with language delays in problem/aberrant and adaptive behaviors (Kau et al., 2004). In a large Australian sample, Dissanayake et al. (2009) compared individuals with FXS and autistic disorder (FXS + AD) with people with idiopathic autism (AD). Parents were also studied. Researchers found that the FXS + AD and AD groups showed similar profiles on the ADOS-G communication and reciprocal social interaction domains, once IQ was adjusted for. (The FXS + AD group was significantly lower on nearly all Wechsler IQ subtests.) There was a paternal effect on combined ADOS communication and social interaction scores for the AD group, but not for the FXS + AD group. Not surprisingly, there were parental effects on IQ for the AD group, but not for the FXS + AD group. Taking a somewhat different approach with a combined Australian and American sample, Loesch et al. (2007) looked at autistic behavior in 147 males and females with fullmutation FXS, 59 individuals with the premutation and 42 non-FXS relatives. They found a strong relationship between ADOS communication and social interaction scores and FMRP deficits, but noted that these became insignificant when full-scale IQ was included in the analysis. Other significant predictors of ADOS scores were measures of executive functioning related to verbal fluency. Of note was the distribution of ADOS scores on a continuum that included individuals with the premutation. This finding was consistent with an earlier report from the same research group that 14% of males and 5% of females with the premutation met criteria for an ASD on the ADOS-G (Clifford et al., 2007). The authors concluded that cognitive impairment, especially of verbal skills, is the best explanation for the occurrence of autism in FXS. Fragile X syndrome is the best studied of the neurogenetic disorders that are associated with autism, and the studies reviewed in this section have some common themes. The first is that, although it is frequently asserted (cf. Moss and Howlin, 2009) that autism in FXS is qualitatively different from essential autism, deriving from the social anxiety, gaze avoidance, and stereotypies that are part of the syndrome rather than from diminished social interest, there is considerable evidence that autism in FXS is phenomenologically similar to essential autism – particularly when groups are matched by IQ. Also, the social deficits that lead to a diagnosis of an ASD among people with FXS are likely to be complex, reflecting difficulties or delays in understanding social interaction. Second, autism in FXS is similar to essential autism in terms of behavior problems and symptom severity. Third, lower amounts of FMRP correlate with the occurrence of autism in FXS, but this effect is likely mediated by IQ. Indeed, there is robust evidence that level of intellectual functioning plays a major role in whether an individual with FXS meets criteria for an ASD. The factors that contribute to the occurrence of autism in FXS thus appear to include features of FXS syndrome itself, like social withdrawal, along with lower IQ and its manifestation in social-cognitive and verbal deficits. It is worth noting, however, that autism occurs at an increased rate in FXS not only in affected males with low IQs, but also in higherfunctioning affected females. There is evidence, moreover, for ASDs (and symptoms of a broader autism phenotype) among people with the premutation, suggesting a connection between decreased FMRP and autism that is not necessarily mediated by low IQ or by severity of other FXS symptoms. Can what we know about fragile X syndrome inform our understanding of autism? At the very least, our growing knowledge of the molecular, synaptic, and neurodevelopmental processes in FXS provides one model of how autism can occur. In FXS, absence or nearabsence of FMRP leads to cellular changes, abnormal synaptic connections, altered regional .

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anatomy and errant pathways in the developing brain, and these brain changes are associated with a high risk for autism. There are, in addition, neurobiological similarities between the behaviorally defined condition autism and the genetically and biologically defined condition fragile X. Hagerman (2006) noted some of these. (1) Both conditions are associated with increased head circumference, and young children with FXS + autism have a larger head circumference than FXS children without autism. (2) Neuroimaging studies have revealed a smaller cerebellar vermis in both conditions. Neuropathology has indicated that there is Purkinje cell dropout in both FXS and autism. (3) White matter abnormalities and impaired neuronal connectivity have been described in both conditions. (4) Enhanced autonomic reactivity to sensory stimuli is seen in both FXS and autism. Perhaps most compelling is the recent research that identifies synaptic dysfunction as a common etiology in FXS and in a number of other neurogenetic conditions that are associated with high rates of autism. This finding suggests that the altered synaptic development and plasticity that occur in FXS could also play a role in other types of autism (Abrahams and Geschwind, 2008). It is also a strong possibility that pharmacologic agents presently being evaluated to address synaptic pathology in FXS may some day prove to be more broadly useful in autism. These agents include several mGluR5 antagonists and GABA-A and GABA-B agonists that are presently being tested in clinical trials (Hagerman et al., 2010; Wetmore and Garner, 2010). Also being investigated in fragile X patients is minocycline, an antibiotic that inhibits activity of MMP-9 (an enzyme involved in synaptic plasticity that is overproduced in FXS). Minocycline reduced excess MMP-9 activity in the FMR1 knock-out mouse (Wang et al., 2010). It was also well-tolerated and had positive behavioral effects in an open-label trial with 20 FXS patients (Paribello et al., 2010).

Tuberous sclerosis complex Tuberous sclerosis complex (TSC) is a neurocutaneous disorder with characteristic developmental abnormalities of the brain and skin and variable involvement of a large number of other body organs. The condition was originally called tuberous sclerosis by Bourneville in 1880, but has been recently renamed as tuberous sclerosis complex to emphasize the multiple organs that may be affected. It occurs in approximately 1 in 6000–10 000 individuals and has an autosomal dominant pattern of inheritance, with a high rate of spontaneous mutations (Rosser et al., 2006). The cardinal feature of the syndrome is the cortical tuber, which is a distinctive, benign growth within the brain. Similar benign growths (hamartias and hamartomas) occur in the skin, kidneys, heart, lungs, and retinas. Tuberous sclerosis is associated with high rates of intellectual disability, epilepsy, and autism.

Clinical description and phenotype The diagnosis of TSC is based on the presence of a specified number of major and minor features of the disorder. Major features include facial angiofibromas, forehead plaques, subungual fibromas, hypermelanotic macules, shagreen patches, cortical tubers, subependymal nodules, subependymal giant cell astrocytomas, retinal hamartomas, cardiac rhabdomyomas, lymphangiomas, and renal myelolipomas. Minor features include a number of other dental, bone, cutaneous, gum, intestinal, and renal lesions. TSC is unequivocally diagnosed when either two major features or one major feature plus two minor features are present (Rosser et al., 2006).

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Several of the major features of TSC constitute distinctive pathological findings within the central nervous system (CNS). Cortical tubers are pale “potato-like” lesions in the cerebral cortex and cerebellum, ranging in size from a few millimeters to several centimeters. A second type of lesion, subependymal nodules (SENs), appear as small protrusions into the walls of the lateral ventricles and tend to calcify after 10 years of age. Both tubers and SENs are detected on MRI scans, which might also show white matter migration lines, indicating neurons and glial cells that halted in their progress toward the cerebral cortex during cell migration. Subependymal giant cell astrocytomas (SEGAs) are benign tumors that occur in 5–10% of people with TSC and are clinically significant because they can enlarge and obstruct the flow of cerebrospinal fluid, causing hydrocephalus (Kwiatkowski, 2004; Rosser et al., 2006). The cutaneous manifestations of TSC are readily identified. Hypo-pigmented macules (“ash-leaf” patches) are common and typically have the shape of a pointed oval. They increase in number over time. Also common are facial angiofibromas, or adenosebaceum – numerous, small, discrete, red to pink macules or nodules that are spread over the nose and cheeks in a butterfly pattern. These first appear between ages 2 and 6. Shagreen patches – irregular, rough, raised nevi composed of connective tissue – are present in 50% of patients and can be seen in early childhood. Subungual fibromas can be seen in 90% of people with TSC by age 30 (Kwiatkowski, 2004; Rosser et al., 2006). The most common lesion in TSC is the renal angiomyolipoma (AML). AMLs are present in 80% of patients and are usually benign, but can bleed or encroach upon kidneys and compromise function. Renal cysts may also be present. Other common and clinically significant manifestations of TSC are cardiac rhabdomyomas, retinal hamartomas, and lymphangiomatosis – a progressive disease that occurs primarily in females and results from proliferation of atypical smooth muscle cells in peribronchial, perivascular or perilymphatic tissues (Rosser et al., 2006). The most important central nervous system consequences of TSC are intellectual disability and seizures. ID has been reported in 44–66% of TSC cases, and there is a connection between the number and location of tubers and the development of ID (Asato and Harden, 2004; Rosser et al., 2006). Multiple bilateral tubers, particularly in frontal and occipital regions, are associated with more severe cognitive impairment. Early onset seizures are also a risk factor for the development of intellectual disability in TSC (Zaroff et al., 2004). Epilepsy is present in 70–90% of individuals with TSC (Asato and Harden, 2004). All seizure types may occur, including clonic, tonic, tonic–clonic, myoclonic, atonic, atypical absence, and partial complex. Approximately two-thirds of TSC patients first present with infantile spasms, usually between 3 and 9 months of age (Kwiatkowski, 2004). Most patients with infantile spasms go on to develop other types of seizures. Infantile spasms are also a risk factor for ID, particularly if they are prolonged, if treatment is delayed, and if subsequent seizures are poorly controlled (Rosser et al., 2006). Generally speaking, there have been no systematic descriptions of a behavioral phenotype associated with TSC. This may be because the disorder is so varied in its presentation that there are no syndrome-specific behavioral features. It may also be because the question of a behavioral phenotype – other than that associated with a high prevalence of autism – has not been investigated by psychological researchers. In his discussion of behavior in TSC, Harris (1995) mentions that hyperactivity, aggression, self-injury, and obsessive or ritualized behavior may each occur in approximately one-third of patients. He also reports a high rate (60%)

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of sleep disorders, but these tend to correlate with the presence of seizures. According to Harris, some behaviors – social aloofness, obsessiveness, and hyperactivity – may improve over time.

Molecular genetics and neurobiology of TSC Tuberous sclerosis results from mutations that inactivate one of two genes: TSC1 (on chromosome 9), which encodes the protein hamartin, and TSC2 (on chromosome 16), which encodes the protein tuberin. De-novo mutations are responsible for most of these genetic errors, although they may be inherited, following a dominant pattern. Spontaneous mutations occur five times more often in TSC2 than in TSC1 (Napolioni et al., 2009). There is also evidence that TSC2 disease is less severe than TSC1 disease, with lower mean number of tubers and SENs, fewer angiomyelolipomas, fewer retinal hamartomas, and less cognitive impairment (Kwiatkowski, 2004). The cellular function of hamartin and tuberin is incompletely understood. Recent evidence indicates that the two proteins work together to regulate activity of a molecule called mTOR, within a complex signaling pathway that appears to play a role in controlling cell growth and proliferation. Loss of hamartin–tuberin leads to increased protein synthesis and cell growth and proliferation, the likely explanation for the hamartomas that characterize TSC. Messenger RNAs that result from TSC1 and TSC2 transcription have been detected in many brain regions in both the immature and mature CNS. Increased activation of the mTOR pathway that results from the loss of function of the hamartin–tuberin complex has multiple effects on neuronal development and function. These include enlarged neuronal cell bodies, altered dendritic arborization, abnormalities in axonal outgrowth and targeting, disruption of GABA-ergic interneuron development, and enhanced glutamatergic transmission in some neurons (altering the balance of excitation and inhibition in the cerebral cortex). Thus, in addition to the appearance of abnormal growths within the CNS, decreased hamartin and tuberin function has downstream effects on synaptic structure and function, neurotransmission, and interneuronal networks (Napolioni et al., 2009; Orlova and Crino, 2010).

TSC and autism The prevalence of ASDs among clinical populations with TSC has been reported in the range of 17–61% (Curatolo et al., 2004), and appears to be around 50% when DSM-IV criteria and the ADI or ADOS are used (Jeste et al., 2008). In an epidemiologic cohort study in Hong Kong, Wong et al. (2006) found the incidence of autistic disorder (not the broad spectrum of ASDs) to be 16%. TSC accounted for 1% of cases of autistic disorder in that population. Of note in many studies of autism in TSC is that the male to female ratio is close to 1 : 1, in contrast to the male predominance seen in autism generally (Wiznitzer, 2004). There have also been a few studies addressing the phenomenology of autism in TSC. Gutierrez et al. (1998) compared a sample of children and adolescents with TSC and autism (TSC+aut) with a TSC group without autism (TSC–aut) and a sample with PDDs and no TSC (aut only). TSC+aut subjects were found to be similar to aut only subjects on all domains of the ADI. The two autistic groups shared similar histories with regard to motor development and speech acquisition. Presence of intellectual disability was the primary risk factor for autism among TSC patients, although some patients without ID met criteria for a PDD diagnosis. The TSC+aut group also differed significantly from the TSC–aut group in history of infantile spasms and in psychiatric disorders in first-degree relatives.

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Jeste et al. (2008) followed 20 clinic-referred children with TSC at intervals from 18 months to 5 years of age. They used the Mullen Scale of Early Learning and the ADOS to investigate the relationship between ASD diagnosis and cognitive functioning, and to describe the pattern of impairments in social skills, communication, and play in young children with TSC. These researchers found a consistent trend associating developmental quotient and ASD diagnosis at all ages, reaching statistical significance at 24 months and 36 months. (The entire population had developmental delays, but those with autistic disorder showed the most delay, those with PDD-NOS less, and those without an ASD the least.) Autism diagnosis was relatively stable over time, but three children not diagnosed with an ASD prior to age 3 years went on to meet diagnostic criteria later. Play skills were deficient in children with TSC, regardless of whether they had autism. Although the evidence is limited, there thus appears to be some suggestion that autism in TSC resembles essential autism and that its occurrence is associated with lower levels of intellectual functioning. However, why does autism occur so frequently in this neurocutaneous disorder? If decreased intellectual functioning and history of infantile spasms correlate with occurrence of autism in TSC, are there neuropathologic features that can account for all these phenomena? These questions have been addressed in a number of studies. One approach has been to investigate the association between number and anatomic location of tubers and occurrence of autism. Weber et al. (2000) found that full-scale IQ and adaptive functioning negatively correlated with the number of tubers on MRI scans in 29 patients with TSC, 19 of whom had a history of infantile spasms. Autistic symptoms, as measured by the CARS, did not correlate with the number of tubers or location in the cerebral cortex, but did have an association with the number of tubers in the cerebellum. In contrast, Bolton and Griffiths (1997) reported a strong association between temporal lobe tubers and autism. Curatolo et al. (2004) argued that tuber localization is too crude a measure and ignores the underlying nature of TSC as a global disorder of the developing brain. Other researchers have applied a number of neurophysiologic measures to address the occurrence of autism in TSC. Seri et al. (1999) reported that autism correlated with prolonged latency on brainstem auditory evoked responses. Asano et al. (2001) used PET scans to study subjects with TSC and intractable epilepsy. Using several measures, including the Gilliam Autism Rating Scale (GARS), they compared three groups: TSC with autism; TSC with ID, but without autism; TSC without autism or ID. The autism group showed decreased glucose metabolism in the lateral temporal lobes on both sides and increased glucose metabolism in deep cerebellar nuclei when compared to the non-autistic group with ID. Numis et al. (2011) compared a cohort of TSC+ASD and TSC–ASD patients on a number of clinical measures. TSC patients with ASD were less likely to have mutations in the TSC1 gene, had an earlier age of seizure onset and had more frequent seizures. On EEG they had a significantly greater amount of interictal abnormalities over the left temporal lobe. There were no differences in regional burden of tubers. Based on the then-available evidence, Wiznitzer (2004) offered three hypotheses to explain the high rate of autism in TSC. The first is that absence of TSC1 and TSC2 have a direct effect on the development of brain regions that are dysfunctional in ASDs. This hypothesis is supported by evidence that TSC2 is highly expressed in the brain regions identified in the PET scan studies of Asano et al. – regions that are required for facial recognition, language, and cognition. A second hypothesis is that the association between autism and TSC is the result of nonspecific brain dysfunction in TSC, including intellectual disability and seizures, and that disease factors that correlate with autism, such as tuber

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location and number and history of infantile spasms, may be manifestations of underlying encephalopathy. A third hypothesis is that there may be a proximity on chromosomes 9 and 16 for the genes for TSC1 and TSC2 and purported ASD-susceptibility genes. Bourgeron (2009) suggested a more direct neurodevelopmental correlation between TSC and autism and provided a model that utilizes common findings in the two disorders. In this model, susceptibility to autism in TSC derives directly from increased activity in the mTOR pathway that could lead to abnormal synaptic function, secondary to excessive protein synthesis at synapses. There is evidence from neurobiologic studies for an excess of cellular and synaptic growth in the early years of life and early macrocephaly in both TSC and essential autism. Over-activation of mTOR also occurs in other conditions associated with autism – in PTEN mutations (discussed in Chapter 6), FXS, and neurofibromatosis type 1 (de Vries, 2010; Ehninger and Silva, 2011). Although its molecular genetics and neurobiology are less completely understood, TSC resembles FXS in that both syndromes display pathology in early neurodevelopment, with excessive protein synthesis at synapses and abnormalities in dendritic structure. The high prevalence of autism in both conditions suggests that these developmental abnormalities may underlie ASD symptoms. Also, as in FXS, there is a consideration that pharmacotherapy directed at molecular pathology in TSC may have broader applicability in autism. The mTOR inhibitor, rapamycin, has been shown to reverse abnormalities in synaptic plasticity in a mouse model for TSC and is presently being studied in humans (de Vries, 2010; Ehninger and Silva, 2011).

Rett syndrome Rett syndrome (RS) is a neurogenetic disorder with a unique developmental course, characterized by rapid early regression and later moderating of functional decline. It is the second most common genetic cause of intellectual disability in girls. RS was initially described by Andreas Rett in 1966, but gained broad recognition following an article by Hagberg et al. (1983) describing 35 girls with “a progressive syndrome of autism, dementia, ataxia and loss of purposeful hand movement” that they named Rett’s syndrome. The diagnosis of classic or typical Rett syndrome is presently based on a clinical course of regression followed by stabilization and presence of several required features: partial or complete loss of purposeful hand skills and acquired spoken language; dyspraxic gait or inability to walk; and stereotypic hand movements such as wringing/squeezing, clapping/ tapping, washing/rubbing and mouthing of hands. A number of additional features support, but are not required for, diagnosis. These include breathing disturbances while awake; bruxism; impaired sleep pattern; abnormal muscle tone; peripheral vasomotor disturbances; kyphosis/scoliosis; growth retardation; small, cold hands and feet; spells of inappropriate laughing and/or screaming; diminished response to pain and intense eye communication (Neul et al., 2010). In classic RS, girls appear to develop normally in the early months of life, then experience deceleration of head growth, which may not be detected at first and usually results in microcephaly. Beginning at approximately 6 months, characteristic symptoms appear, proceeding in four stages (Ben Zeev Ghidoni, 2007). In Stage I, starting between age 6 months and 28 months and continuing for about a year, there is decreased eye contact, reduced interest in toys, delays in motor development, and hypotonia.

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In Stage II, beginning between 1 and 4 years and lasting weeks to months, there is rapid developmental regression. Communication and motor skills are lost. Most striking is the deterioration of manual motor abilities and the appearance of hand-wringing and hand-tomouth stereotypies. There is diminished social interest. Insomnia, crying or screaming spells, feeding difficulties, breathing abnormalities, and seizures may occur. Gait and truncal apraxia and limb spasticity appear. It is at this stage that RS may be diagnosed as autistic disorder. Stage III begins between 2 and 10 years of age and lasts for years. This is a stage when regression slows and some symptoms stabilize. Behavior tends to improve, as do social skills, communication, and attention. Apraxia, ataxia, spasticity, and rigidity may worsen. Seizures are prominent at this stage. Stage IV is marked by further motor deterioration: increased rigidity and spasticity, dystonic posturing, increasing scoliosis. Ability to ambulate independently may be lost. There is no further decline in manual skills, communication or cognition. Since the description of classic RS, a number of variant forms of the disorder have been identified. These have been recognized both clinically and as a consequence of the discovery of the genetic basis for RS: mutations in MECP2, a gene on the X chromosome. Among the variant forms of RS are subgroups with congenital onset: females with intrauterine microcephaly; males with MECP2 mutations and severe intellectual disability; and patients whose clinical presentation resembles Angelman syndrome. There are less severe forms of RS that do not meet clinical criteria for the classic presentation of the disorder: females with later symptom onset; females who retain the ability to talk (preserved speech variant or PSV); a forme fruste variant with generally milder symptoms; and normal female carriers of MECP2 mutations. There is also a rare, male form of classic RS, often associated with a 47XXY karyotype (Erlandson and Hagberg, 2005). As discussed in Chapter 1, RS was included in DSM-IV and ICD-10 as one of the pervasive developmental disorders. Labeling RS a PDD has made clinicians more aware of the condition and more likely to include it in the differential diagnosis of young girls with developmental regression in communication and social skills, accompanied by stereotypies. The discovery of the genetic etiology of RS in 1999 has had the effect, however, of making “Rett’s Disorder” the only DSM-IV mental disorder caused by a recognized mutation in a single gene. RS is not likely to be designated a PDD in DSM-5 and ICD-11.

Molecular genetics and neurobiology of RS MECP2 is located in the Xq28 region of the X chromosome. Mutations in this gene have been detected in 96% of females with classic RS. Numerous mutations have been identified: missense, frameshift, nonsense, and intragenic insertions and deletions (Moretti and Zoghbi, 2006). Most mutations are de novo, with a high spontaneous occurrence rate. Most occur in the paternally derived X-chromosome, which may be a contributing factor to the rarity of RS in XY boys, who do not inherit this chromosome (Ben Zeev Ghidoni, 2007). The relationship between type of mutation and severity of the resulting RS phenotype has been frequently investigated, but not yet clarified. Severity of symptoms in girls also depends on which Xchromosome is inactivated in the majority of body cells (Neul and Zoghbi, 2004). MECP2 codes for methyl-CpG-binding protein 2 (MeCP2). This protein binds to methylated DNA and regulates gene expression by repressing transcription. Its role is epigenetic, permitting or repressing the expression of other genes during development (Lopez-Rangel

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and Lewis, 2006). When MeCP2 is bound to the promoter region of genes, it represses gene expression. Neuronal activity, however, induces a cascade of intracellular processes, leading to the phosphorylation of MeCP2, its dissociation from methylated DNA, and its release from the promoter region, permitting gene transcription. Among the genes regulated by MeCP2 are several in the 15q11–13 region of chromosome 15. (See discussion of Angelman syndrome and Prader–Willi syndrome, below.) This action may explain the occurrence of cases of MECP2 mutation with an Angelman-like phenotype (Hogart et al., 2007). Most of the genes regulated by MeCP2 remain to be identified. Better understood are the regional expression of MECP2 in the brain during development and the neuronal pathology that results from diminished MECP2 activity. This information comes largely from mouse studies. In mice, levels of MeCP2 increase prenatally from the spinal cord and lower brainstem to the cerebellum, deep cortical neurons, thalamus, and caudate; and postnatally from deep cortical to superficial layers as development progresses. This suggests that MECP2 transcription increases after neurons have migrated to their target locations in the outer cortex, at the time that they are developing axonal projections and extending their dendrites to form synaptic connections. There is also evidence for alteration in the number of cortical glutamatergic synapses in a mouse model for RS. MeCP2 thus appears to be necessary for modulating synaptic function and plasticity and in regulating the number of excitatory synapses during postnatal development (Chahrour and Zoghbi, 2007). Additionally, Chao et al. (2010) recently reported deficiency of MeCP2 in inhibitory, GABAergic cortical neurons, with associated alterations in synaptic physiology, in a similar mouse model for RS. In MECP2-deficient mice, the cortex is reduced in thickness and dendritic arborization is decreased. In human postmortem specimens from patients with RS, there is also less complex dendritic arborization and smaller neuronal size (Neul and Zoghbi, 2004; Kishi and Macklis, 2005). Gross brain tissue in girls and mice with RS is relatively normal – except for decreased neuronal size and increased neuronal packing. There is no abnormality in myelin, no cell loss, and no abnormalities in neuronal migration (Neul and Zoghbi, 2004; Ben Zeev Ghidoni, 2007). It thus appears that the primary effect of MECP2 mutations associated with RS is on postnatal neuronal connectivity and synaptogenesis, and that this effect is mediated by altered expression of genes directly involved in axonal projection, dendritic extensions and synapse formation. Clinically, the fact that regression in RS occurs in late infancy fits with the notion that MeCP2 plays a role in activity-dependent postnatal maturations of synaptic connections (Gonzales and LaSalle, 2010).

RS phenotype The clinical presentation and natural history of the classic form of RS have already been discussed. Cognitively, girls with classic RS tend to function in the severe to profound range of intellectual disability. In expressive language, the majority acquire single words and may be able to achieve word combinations, only to lose this ability during regression. In the preserved speech variant of RS, single words are acquired by age 2, regression is later and slower, and language may be lost for a time, then recovered. Expressive language is said to be echolalic and repetitive. Girls with PSV are also severely functionally impaired (Zappella et al., 1998). Males with classic RS are extremely rare. Boys with a 47XXY karyotype and classic RS have been described. There are, however, males with MECP2 mutations who have been detected when populations with intellectual disability are screened for this genetic defect.

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Phenotypic presentation is variable, but, overall, MECP2 mutations are a rare cause of intellectual disability in males (Psoni et al., 2010). There have been few studies investigating the behavioral phenotype associated with RS. Mount et al. (2002) found that mood fluctuations, signs of fear and anxiety, inconsolable crying, screaming at night, mouth and tongue movements, and facial grimaces – along with hand stereotypies and breathing problems – distinguished a group of girls with RS from a group with a comparable level of intellectual disability. Matson et al. (2008) compared three small groups of female adults in residential placement on a caregiver-report measure: those with RS, those with autistic disorder, and an ID control group. The RS group was between the other two groups in socialization and closer to the autistic group on a measure that reflected negative interactions with caregivers. Individuals with Rett syndrome had higher rates of hand skill deficiencies and repetitive tongue movements. Given these limited observations, one would have to conclude that the features that define a behavioral phenotype in RS are essentially those that establish the diagnosis.

Rett syndrome and autism “Autistic features” were recognized in RS in the initial report of Hagberg et al. (1983) that named the disorder. It was soon emphasized, however, that RS and classic autistic disorder have many distinguishing features: developmental course, head circumference, level of intellectual disability, motor development, language abilities, types of stereotypic movements, frequency of seizures, and quality of eye contact (Gillberg and Coleman, 1992). The true overlap between autism and RS, moreover, has been obscured by the designation of RS as a PDD in DSM-IV and ICD-10. Diagnostic guidelines preclude making a diagnosis of AD in RS, and all cases of RS are, by definition, on the autistic spectrum. Nevertheless, there has been some research on the occurrence of autism in populations with RS. Mount et al. (2003a, 2003b), in their effort to define a behavioral phenotype for Rett syndrome, compared a large population of girls with RS with a group of girls with comparable intellectual functioning. On a parent-report behavioral measure, the RS girls had more “autistic-relating deficits” and less antisocial behavior than the comparison group. Scores for the RS girls on an “autistic-relating” subscale were indistinguishable from those for a small sample of boys and girls with autistic disorder and severe intellectual disability. However, the autistic group scored higher on items related to core features of autism (e.g. “aloof,” “doesn’t respond to others’ feelings,” “avoids eye contact”) (Mount et al., 2003a). Using a measure designed specifically to assess autistic symptoms, the Autism Behavior Checklist (ABC), these researchers again compared an RS group with a group of girls with similar intellectual functioning. The groups were significantly different in total ABC score, on the sensory subscale, and the relating subscale – but not on subscales measuring language and social behavior (Mount et al., 2003b). These studies tend to confirm that girls with RS have “autistic features,” but that their social relatedness and communication do not differ significantly from non-autistic girls of a comparable level of cognitive functioning. They also suggest that the presence of autistic features in RS may not be attributable to low IQ. This inference is supported by research in the preserved speech variant of RS, a milder form of the disorder, in which 29 of 32 girls with PSV met diagnostic criteria for autism (Zappella et al., 1998). In a 2009 Dutch study, Wulffaert and colleagues sought to determine how often autistic disorder (AD) might be found in RS and what role age, or stage of RS, plays in the appearance

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of autism. They used the autism screening algorithm from a parent-report measure (the Dutch version of the Developmental Behavioral Checklist, Primary Care) and a structured diagnostic interview (DISCO-10, Wing et al., 2002) to evaluate 52 girls with RS. Of their sample, 42% scored above the cut-off for autism on the DBC. Thirty subjects (58%) met criteria for an ICD-10 diagnosis of childhood autism on the DISCO-10, and an additional 10 subjects had a history consistent with having met criteria for the diagnosis in the past. Their findings suggest that even though AD and RS are different syndromes, there is overlap between the two conditions that goes beyond the “autistic features” that characterize RS, particularly in young girls in Stage II of the disorder. Kaufmann et al. (2011) also addressed the question of autistic symptoms in RS and their relationship to patient age. In a study of 80 girls with RS (1.6–14.9 years of age), using measures of social interaction, adaptive behavior, and RS symptoms, they found that autistic behavior persists throughout childhood and is not uniquely associated with the period of developmental regression. Neurologic symptoms and autistic features appeared to be relatively independent – with the exception of hand functioning, which correlated positively with social skills. Does the overlap between RS and AD and the initial appearance of characteristic autistic symptoms in the regressive phase of RS have implications for our understanding of autism more broadly? Is there reason to believe that aspects of the pathophysiology of RS are relevant to that of essential autism? At the genetic level, recognition of MECP2 and its function appears to have implications for autism. As new variants of MECP2 have been discovered, they have been found to occur in populations of individuals with essential autism. Loat et al. (2008) reported on polymorphisms in the DNA sequence of MECP2, including single nucleotide variations, and found an association between several MECP2 variants and risk for ASDs. Cukier et al. (2010) looked at mutations both in MECP2 and in similar genes coding for other methyl-CpGbinding proteins. These researchers found 46 alterations in 226 autistic individuals, some of which were unique to the autistic population. They suggested that some of these mutations might be a rare cause of autism. At the epigenetic level, some of the genes known to be regulated by MeCP2 are implicated in autism. Among these are several within the 15q11–13 region of chromosome 15, including UBE3A and GABRB3. Deficient UBE3A transcription appears to be a fundamental cause of Angelman syndrome, and altered GABRB3 expression has been implicated in essential autism along with altered expression of genes for other GABA receptor subunits. (See Figure 5.2 and discussion later in this chapter.) The altered expression of UBE3A resulting from absent MeCP2 may account for those cases of phenotypic Angelman syndrome resulting from MECP2 mutations (Lopez-Rangel and Lewis, 2006; Hogart et al., 2007). At the level of neurodevelopment, there are many common features between RS and nonsyndromic AD. The gross structure of the brain is relatively normal in both, although brains may be larger (especially in the early years of life) in autism and smaller in RS. In both conditions, there is no significant cell loss, atrophy or myelin abnormalities. Common deficits occur in dendritic arborization in both conditions (Ramocki and Zoghbi, 2008). Thus, as with TSC and FXS, RS appears to share with essential autism fundamental, postnatally acquired abnormalities in synaptogenesis – in the establishment and refinement of interneuronal connections. As more target genes for MeCP2 become identified and the mechanism by which deficiency of MeCP2 leads to the neuronal pathology of Rett syndrome becomes better understood, this knowledge is likely to contribute to understanding the genetics and pathophysiology of other types of autism.

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Figure 5.2 The sequence of genes in the 15q11–13 region affected in Prader–Willi and Angelman syndromes. Genes that are active on the paternally inherited chromosome are depicted in black; genes expressed on the maternally inherited chromosome are in white; non-imprinted genes are in gray. The location of the imprinting center is indicated.

Our present conceptualization of RS is that of a unique neurogenetic syndrome with a classic presentation and a number of recognized clinical variants. Since its initial description 45 years ago, researchers and clinicians have observed the presence of certain autistic-like features. These must be kept in mind when evaluating young girls for an autism spectrum disorder, as the diagnosis of RS has implications for genetic counseling, clinical management, and prognosis – and should not be missed. This is particularly the case in milder forms of RS. Young et al. (2008) have recommended that all females who are diagnosed with autism be carefully monitored for the evolution of signs and symptoms of RS. Although RS was included as a PDD in DSM-IV and ICD-10, it is not likely to be so designated in DSM-5. RS can best be conceptualized as a syndrome in which all individuals display some autistic symptoms and in which a high percentage of individuals can also be diagnosed with co-occurring ASDs.

Angelman and Prader–Willi syndromes Angelman and Prader–Willi are two dissimilar neurogenetic disorders that are linked because the genes whose altered expression produces these conditions are located within the same region of the long arm of chromosome 15: 15q11–13. These genes, moreover, are differentially expressed, depending on whether the chromosome on which they reside was inherited from the father or the mother – a phenomenon called genomic imprinting. (In Angelman syndrome, genes that are normally expressed only on the chromosome 15 that is inherited from the mother are absent or unexpressed. In Prader–Willi syndrome, genes that are normally active only on the chromosome 15 that is inherited from the father are silent.) Both conditions have an association with autism: syndrome-specific, autistic-like features and – in Angelman syndrome – an increased prevalence of co-occurring ASDs. Genes in the 15q11–13 region, moreover, appear to play a role in cases of non-syndromic autism: mutations in this region constitute the most common chromosomal disorder found when screening large populations with essential ASDs.

Angelman syndrome: clinical description In 1965, Harry Angelman, a British pediatrician, described three children with severe intellectual disability, ataxia, jerky movements, inability to speak, easily provoked laughter, seizures, and distinctive facial features: prominent chin, deep-set eyes, wide mouth, and microcephaly with a flat occiput (Clayton-Smith and Laan, 2003).

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Clinical features that are found in all cases of Angelman syndrome are severe intellectual disability; a movement or balance disorder, characterized by gait ataxia and/or tremulous movement of the limbs; behavioral features of frequent laughter, excitability, apparent cheerfulness; motor stereotypies and hyperactivity; and speech impairment with minimal or no use of words. Frequently present (in more than 80% of cases) are early-onset seizures, a characteristic EEG pattern, and microcephaly. Variable, associated features include a flat occiput, a wide mouth with widely spaced teeth, feeding problems and truncal hypotonia during infancy, hypopigmentation, an uplifted and flexed arm position during ambulation, and sleep disturbances (Jedele, 2007). The behavioral phenotype of Angelman syndrome is quite consistent and striking. Pelc et al. (2008), in a comprehensive review of neuropsychiatric features of the disorder, noted that 60 of 64 studies made specific mention of laughing, smiling and happy demeanor, and that these features were more likely to be associated with Angelman syndrome than with Down or Prader–Willi syndrome or nonspecific intellectual disability. Bouts of laughter were noted to be frequent, occurring in both context-appropriate and inappropriate situations. Motor hyperactivity, impulsivity, and distractibility were frequently reported. Levels of hyperactivity were said to exceed those found in controls with moderate to profound intellectual disability. Stereotypies, compulsions, and rituals were all described, with hand flapping and waving the most common. Stereotypies also involved the mouth, including mouthing of inedible objects. Features in Angelman syndrome that resemble those found in autistic disorder include absence of speech, impaired nonverbal communication, and repetitive sensory and motor behaviors. Children with the syndrome may also display a fascination with reflective surfaces and play with water (Clayton-Smith and Laan, 2003). Speech does not develop in most Angelman patients, and there is some suggestion that oral-motor apraxia may play a role in this deficit, together with cognitive impairment (Dykens et al., 2000). A minority can communicate using some signs and picture exchange methods; others use gestures. Self-help skills are usually at a level associated with severe to profound intellectual disability (Clayton-Smith and Laan, 2003). Hypotonia is present in infancy and is succeeded by increased motor tone past 3 years of age, with brisk reflexes. Some children are non-ambulatory secondary to spasticity (Hagerman, 1999). Seizures are frequent and all seizure types may occur. EEG abnormalities are similar in patients with and without epilepsy. Paroxysms of laughter are not correlated with EEG findings (Clayton-Smith and Laan, 2003).

Angelman syndrome: molecular genetics and neurobiology The genetic basis of Angelman syndrome is fascinating and is usually discussed with reference to Prader–Willi syndrome (PWS). The two conditions are best understood with reference to the sequence of genes located at 15q11–13. Figure 5.2 provides a partial illustration of this sequence (including some, but not all, of the genes along this chromosomal segment). Within this region, there are genes that are normally active on the paternally inherited chromosome (toward the centromere or to the left in the illustration) and genes that are active on the maternally inherited chromosome (toward the telomere). In a typically developing child, with two intact chromosomes 15, both sets of genes are expressed: the genes in the paternal domain are transcribed on the copy inherited from the father; the genes in the

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maternal domain on the copy inherited from the mother. Genomic imprinting is the epigenetic process by which these genes are determined to be available or unavailable for transcription, a process that occurs during gametogenesis and involves selective methylation of specific DNA loci. In addition, the 15q11–13 region contains a number of break points – places in the genome where breaks may occur during meiosis, leading to deletions and interstitial duplications of DNA (Hogart et al., 2010). In Angelman syndrome, one or more genes in the maternal expression domain are absent or unexpressed because of a mutation. The mutation may be one of four types. The first and most common is a deletion of the 15q11–13 region on the maternally derived chromosome. The second is uniparental disomy, which occurs when two copies of chromosome 15 are inherited from one parent, with no contribution from the other. In Angelman syndrome, people with uniparental disomy have two copies of chromosome 15 inherited from the father, none from the mother. The third type of mutation involves an imprinting defect, which probably occurred in the mother’s oocyte when the paternally derived chromosome 15 (inherited from her father) failed to be switched to a maternal imprint. A possible cause of an imprinting defect is a mutation in the imprinting center on the chromosome. The fourth type of mutation associated with Angelman syndome is within the UBE3A gene on the maternally inherited chromosome, so that this particular gene is not transcribed (Wagstaff, 2004; Venkitaramani and Lombroso, 2007). In Prader–Willi syndrome, some of the same mutational mechanisms occur, but involve the copy of chromosome 15 inherited from the father so that genes in the paternal expression domain are effectively silenced. Of these cases, 70% result from a deletion of 15q11–13 on the paternally derived chromosome; 25% result from uniparental disomy; the remaining cases are caused by imprinting defects. No single-gene mutations have been associated with PWS (McCandless and Cassidy, 2004). The molecular pathogenesis of Angelman syndrome is largely unknown, but evidence suggests that deficiency of the product of the UBE3A gene – E3-ubiquitin protein ligase, or Ube3A – plays a central role. This molecule is needed for proper functioning of ubiquitin, which helps regulate protein turnover in cells by tagging proteins for degradation. This turnover is essential for normal cellular function. Decreased levels of ubiquitin protein ligase lead to accumulation of damaging molecules within neurons and these interfere with normal synaptic functioning and neocortical plasticity (Venkitaramani and Lombroso, 2007; Yashiro et al., 2009). One protein that is ubiquitinated and degraded by Ube3A is Arc. The accumulation of Arc in neurons has been shown to affect the activity-dependent expression of a class of glutamate receptors (AMPAR) and to alter synaptic function (Greer et al., 2010). As already mentioned, mutations in UBE3A are sufficient in themselves to cause Angelman syndrome. Also, transcription of UBE3A is regulated, in part, by MeCP2, and this mechanism is likely responsible for cases with the Angelman phenotype that are attributable to MeCP2 deficiency (Lalande and Calciano, 2007). With regard to genotype–phenotype correlations, there is some evidence that more severe forms of Angelman syndrome (higher incidence of seizures, microcephaly, and hypopigmentation) are associated with 15q11–13 deletions, as compared to uniparental disomy and UBE3A mutations. Deletions eliminate genes other than those in the maternal expression domain, including three for GABA-A-receptor subunits, and these may play a role in altered neurodevelopment and severity of epilepsy (Clayton-Smith and Laan, 2003; Wagstaff, 2004).

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Angelman syndrome and autism A few studies have investigated the occurrence of autism in Angelman syndrome. Steffenburg et al. (1996), in a large population study in Sweden, identified four individuals with Angelman syndrome, all of whom also met criteria for autistic disorder. Trillingsgaard and Østergaard (2004) used the ADOS-G to evaluate 16 children with Angelman syndrome and found that 13 of them met criteria for an ASD (10 for AD and 3 for PDD-NOS). When contrasted with cases of essential autism, however, the children with Angelman syndrome and autism showed better social interaction. The authors suggested that autism in the Angelman group may have been overdiagnosed because of low cognitive functioning. Peters et al. (2004) used the ADOS and ADI-R to evaluate 19 young children with Angelman syndrome. Eight of the 19 were diagnosed with autistic disorder. These children scored lower on measures of language, adaptive behavior, and cognition than the Angelman children without autism, but they also had significant deficits in social interaction, took little pleasure in it, and focused preferentially on objects. The authors stated that these deficits could not be explained solely by cognitive level. In a similar study, an Italian group (Bonati et al., 2007) found that 14 of 23 children with Angelman syndrome met criteria for an ASD diagnosis. There was some suggestion of a connection between genetic subtype and autism, with 8 out of 8 children with 15q11–13 deletions meeting criteria for autism, but only 6 of 15 with uniparental disomy, imprinting defects, and UBE3A mutations. Walz (2007) surveyed parents of 248 individuals with Angelman syndrome between the ages of 3 and 22, utilizing the Gilliam Autism Rating Scale (GARS, Gilliam, 1995). Features of autism that were reported by more than half their sample included inappropriate use of toys and objects, inappropriate laughter, non-imitative play, a variety of stereotyped movements, and self-stimulating vocalizations. There was no significant association between autistic features and type of genetic mutation. Although relatively few, these studies suggest a significant overlap between Angelman syndrome and autism – despite the superficial sociability and desire for communication displayed by many individuals with the syndrome. Some of the high prevalence of autism in Angelman syndrome may be attributed to the effects of low cognitive functioning, but the prevalence of autism in this condition exceeds that found in general populations of severely to profoundly impaired individuals, and specific social deficits exceed cognitive deficits in those with Angelman syndrome and autism. Repetitive behaviors, stereotypies, narrow interests, and communication deficits are well-recognized features of the Angelman behavioral phenotype. The process by which the genetic defects associated with Angelman syndrome lead to autistic features and increased risk of autism remains unclear. Deficient UBE3A activity is likely implicated (Bonati et al., 2007). Various evidence suggests that quantitative alteration of the products of the three non-imprinted genes that code for GABA receptor subunits may also play a role. (There is only one copy, not two, of each of these genes when there are deletions of 15q11–13.) It is worth noting that autism has also been linked to conditions in which there are duplications in this region, i.e. extra (three or more) copies of these genes. GABA is the principal inhibitory neurotransmitter in interneurons in the cerebral cortex and, with glutamate, regulates the level of cortical excitability. Disruption of the proliferation and migration of GABA-ergic interneurons in mice leads to complex neurodevelopmental problems, with seizures and behavior with similarities to human ASDs. Imbalances in excitation and inhibition, moreover, can affect synaptic activity, leading to elimination of some synapses and retention of others, altering cortical connectivity. Genetic linkage studies

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in essential autism have repeatedly found evidence for a susceptibility locus near GABRB3; and reduced GABRB3 protein levels have been reported in postmortem brain specimens from autistic individuals (Hogart et al., 2010). The data of Bonati et al. (2007) suggesting that autism is more likely associated with maternal deletions than uniparental disomy in Angelman syndrome provides support for the notion that quantitative alterations in both UBE3A and the three GABA receptor subunits coded for in this region play a role in the occurrence of autism in the syndrome.

Prader–Willi syndrome: description Prader–Willi syndrome (PWS) was first described in 1956 when Prader and colleagues reported on a series of similar patients with obesity, short stature, hypotonia, and a history of failure to thrive during infancy. Diagnosis of PWS is presently based on genetic testing, but recognized major features of the syndrome include hypotonia and feeding problems during infancy, rapid weight gain between 1 and 6 years of age, characteristic facial features, hypogonadism, and intellectual disability. Commonly present are history of decreased fetal movement and infantile lethargy, short stature, hypopigmentation, small hands and feet, esotropia and myopia, viscous saliva, speech articulation difficulties, and compulsive skin picking (Dykens et al., 2000). PWS can be described as occurring in two developmental phases. The first phase (which includes gestation, with notable decreased fetal movement) is characterized by poor suck in infancy (secondary to hypotonia), lethargy, weak cry, and failure to thrive. Infants have trouble gaining weight and motor development is delayed. Between 1 and 6 years of age, the second phase begins. This is characterized by onset of hyperphagia – excessive and indiscriminate eating without satiety – along with food seeking, stealing and hoarding, and a general preoccupation with food. Vomiting does not occur despite excessive eating. Severe obesity ensues. This phase is lifelong and may be accompanied by cardiopulmonary compromise, sleep apnea, type II diabetes, and hypertension (Dykens et al., 2000; McCandless and Cassidy, 2004). Many of the clinical features of PWS are thought to be secondary to hypothalamic dysfunction. These include central hypotonia and early failure to thrive, subsequent hyperphagia without satiety, genital hypoplasia, infrequent vomiting, problems with temperature regulation, bradycardia, excessive daytime sleepiness, and short stature (Whittington and Holland, 2004). Dysmorphic features associated with PWS are dolicocephaly; narrow bi-frontal diameter; almond-shaped, sometimes upslanting palpebral features; a down-turned or tent-shaped mouth; and small hands and feet (Hagerman, 1999; McCandless and Cassidy, 2004). Most individuals with PWS function in the mild to moderate levels of intellectual disability (ID), although there is a broad range of cognitive abilities, with 6% having severe to profound ID and one-third with borderline or average intellectual functioning. Whittington and Holland (2004) reported a normal distribution of IQ scores for a large Prader–Willi population, with a mean IQ around 60, 40 points below the mean for the general population. There is some suggestion that visual processing is stronger than auditory processing in individuals with PWS. Of note is an unusual facility with jigsaw puzzles in many individuals (Dykens et al., 2000). Maladaptive behaviors associated with PWS, other than those involving food, include skin picking, disobedience, impulsivity, tantrums, obsessions, and compulsions. The latter

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often consist of hoarding, ordering and arranging items, concern with symmetry, and redoing things. Compulsive symptoms are reported to occur in PWS at a significantly higher rate than in other conditions with a comparable level of intellectual disability. Obsessive– compulsive disorder can be diagnosed in 50% of people with PWS (Hagerman, 1999; Dykens et al., 2000). There is evidence for a genotype–phenotype association in PWS, with regard to severity. Individuals with paternal 15q11–13 deletions appear to be more severely affected than those with uniparental disomy (UPD) in a number of areas: facial appearance, hypopigmentation, cognitive functioning, and maladaptive behavior. Core features of hyperphagia and obesity, however, do not differ between these two genetic subtypes (Milner et al., 2005; Dimitropoulos and Schultz, 2007). There is also evidence that individuals with UPD are considerably more prone to affective disorders with psychosis (Vogels et al., 2004; Whittington and Holland, 2004).

Prader–Willi syndrome and autism There have been few studies that have looked at the prevalence of autism in PWS. Veltman et al. (2005) combined data from five case series and found an overall rate for ASDs of 14.5%, with a higher rate (25%) among genetically confirmed paternal deletion and UPD cases. There are problems, however, with combining data from different studies, and diagnostic criteria for ASDs were not always specified. Descheemaeker et al. (2006), comparing 59 individuals with PWS with 59 matched controls, found comparable rates of PDDs (19% vs. 15%) when using a screening questionnaire. Dykens et al. (2000) asserted that PWS, unlike fragile X syndrome, is not accompanied by a higher risk for autism spectrum disorders other than that associated with intellectual disability. Of more interest to researchers have been the compulsive behaviors and social impairments that are found in PWS, the extent to which these phenotypic characteristics resemble similar symptoms in autism, and any association between these behaviors and specific Prader–Willi genetic subtypes. In a review article, Dimitropoulos and Schultz (2007) made the point that obsessive–compulsive symptoms in PWS resemble repetitive behaviors in autism more than they do those usually found in obsessive–compulsive disorder. They involve more features like hoarding, insistence upon routines, ordering and arranging objects, and repetitions; they are also not anxiety-producing, as are obsessions and compulsions in OCD. Descheemaeker et al. (2006) described a “striking, autistic-like phenotype” in their Prader–Willi subjects, based on quality of language, obsessive interests, stereotypies, and dependence or routines and rituals. They suggested conceptualizing compulsive behaviors in PWS within the broader spectrum of autism disorders. Greaves et al. (2006) investigated repetitive and ritualistic behavior in children with PWS and children with ASDs, using a parent-report measure. They found the two groups to be nearly indistinguishable on total and subscale scores and on frequency and intensity of behaviors. Differences between the groups were found on a few items, e.g. hoarding (higher in PWS) and food selectivity (higher, as one would expect, in autism). A post-hoc analysis of the PWS sample found no differences between UPD and paternal deletion groups. In contrast to this last observation, two large studies (using many of the same subjects) found significantly more autistic-like symptoms among PWS cases with UPD compared to those with 15q11–13 deletions. Veltman et al. (2004) used two parent-report measures [the Autism Screening Questionnaire (ASQ) and the Vineland Adaptive Behavior Scales] to

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compare 38 UPD cases with 38 age- and sex-matched cases with deletions and found that the UPD cases had a higher total score on the ASQ, with impaired social interaction contributing much of this difference. Also, the mean total ASQ score of the UPD group was above the threshold for a possible diagnosis of autism. The two groups did not differ on adaptive behavior composite scores. Milner et al. (2005), with a slightly larger sample, compared matched UPD and paternal deletion groups on ASQ, Vineland, ADI-R, ADOS, Yale–Brown Obsessive–Compulsive Scale, and measures of intellectual functioning. The UPD group had significantly more impairment in reciprocal social interaction than the deletion group on all three autism symptom measures, despite lack of difference in measures of intelligence and adaptive functioning. The groups also did not differ in communication or repetitive behaviors. These researchers hypothesized that the difference between the UPD and paternal deletion groups on a measure of a core symptom of autism – social impairment – is attributable to overexpression of maternally imprinted genes in the group with UPD. This finding is consistent with the association that has been reported between cases of essential autism and duplications of 15q11–13 genes on the maternally derived chromosome. However, paternally derived duplications have also been associated with neurodevelopmental disorders, including ASDs; and the connection between Angelman syndrome and autism is well established. One thing is clear: the high rate of autistic-like symptoms in PWS and the association between these symptoms and UPD in particular, reinforce the notion that genes in the 15q11–13 region appear to be a genomic “hot spot” with regard to risk for autism, and the number of copies of genes in this region appear to mediate this risk. (See discussion below.)

15q11–13 mutations in essential autism Genetic abnormalities associated with non-syndromic autism are discussed in the following chapter, but the issues raised in the previous paragraph invite discussion of other 15q11–13 mutations. These mutations occur in 1–2% of individuals with essential autism who are screened for genetic disorders (Schanen, 2006; Depienne et al., 2009). The genetic abnormalities in this region that are associated with autism are copy number variations: chromosomal duplications, translocations, deletions, and microdeletions (Coleman and Betancour, 2005; Miller et al., 2009; Burnside et al., 2011). Duplications of 15q11–13 are the most frequently reported chromosomal aberrations in ASDs. These include supernumerary as well as interstitial mutations that result in two, three or more copies of genes. Duplications of the maternally derived 15q11–13 region appear to confer an especially high risk for autism, although paternally derived duplications are also associated with ASDs (Dykens et al., 2004; Depienne et al., 2009). It is important to note that not all of these chromosomal duplications lead to autism. Bolton et al. (2001) studied 21 individuals from six families with proximal 15q duplications. Four subjects received a PDD diagnosis. Also, the number of copies does not automatically predict gene expression, as the process by which the transcription of genes in this region is epigenetically regulated is extremely complex (Hogart et al., 2009). The specific genes involved in the pathogenesis of autism in 15q11–13 genetic disorders remain to be determined. Linkage studies have found an association between a marker for GABRB3 and autistic disorder (Cook et al., 1998; Buxbaum et al., 2002) and for a marker between GABRB3 and GABRA5 and savant traits in autism (Nurmi et al., 2003). Genes for

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GABA subunits may thus be implicated in essential autism as well as in Rett syndrome, Angelman syndrome, and Prader–Willi syndrome. Imprinted genes, such as UBE3A and ATP10C, may also play a role (Schanen, 2006). Indeed, one may speculate that the ontologic reason for imprinting – the silencing of specific genes on one or the other of the two copies of chromosome 15 – is to tightly regulate the quantity of gene products: specific mRNAs and proteins. This regulation is disrupted by duplications, deletions, imprinting defects, and uniparental disomy, and this disruption may be the basis of the connection between the mutations in this region and risk of autism.

22q11 deletion syndrome (22q11DS) 22q11 deletion syndrome is the second most common chromosomal disorder after Down syndrome, occurring in 1 out of 4000 births. It is comprised of three historically described syndromes – velocardiofacial syndrome (VCFS), DiGeorge syndrome, and conotruncal anomaly facial syndrome. These syndromes share a common etiology: interstitial deletions of varying length within the q11 region of chromosome 22. The phenotypic presentation of 22q11DS is highly variable, but common features are well described and clinically recognizable. These include a characteristic facial appearance of a pear-shaped nose with a prominent nasal bridge and bulbous tip, narrow palpebral fissures, hypertelorism, a flattened malar region, malformed ears, and a recessed chin; cleft palate or various degrees of velopharyngeal insufficiency; lymphoid tissue hyperplasia; growth retardation; and conotruncal congenital cardiac defects (e.g. tetralogy of Fallot, VSD, truncus ateriosis) (Hagerman, 1999; Vorstman et al., 2006). The cognitive and behavioral phenotype is also highly variable. Niklasson and Gillberg (2010), reporting on a series of 100 consecutively referred patients with 22q11DS, found a mean full scale IQ of 71, with a normal distribution of scores around this mean. Ousley et al. (2007) noted significant delays in motor and speech/language development in the first 2 years of life, and a majority of individuals with intellectual disability. A frequently encountered neuropsychological profile is one of nonverbal learning disability, in which verbal IQ is significantly higher than performance IQ, visual–spatial abilities develop more slowly than verbal, and spelling skills exceed math skills. Speech and language disorders are also common in this population. Most individuals with 22q11DS have a specific learning disorder (Murphy, 2004; Ousley et al., 2007). An even more striking aspect of the behavioral phenotype of 22q11DS is the high comorbidity of psychiatric disorders, especially attention deficit hyperactivity disorder, mood disorders, and psychoses. Niklasson et al. (2009) reported a 30% prevalence of ADHD in their population of 100 consecutively referred cases; and Ounsley et al. cited a figure of up to 65% for comorbid ADHD. Psychotic disorders – schizophrenia and bipolar disorder with psychosis – have been found in 20–30% of adults with the syndrome, with the risk of psychosis increasing through adolescence and early adult life. Mood disorders, including bipolar disorder, major depression, and dysthymia, occur in 11–40% of people with 22q11DS (Vorstman et al., 2006; Ousley et al., 2007). The anatomic, immunologic, endocrine and neurologic features of 22q11DS appear to derive from early embryonic abnormalities involving abnormal or absent migration of neural crest cells (Hagerman, 1999). Resulting changes in brain structure include decreased total brain volume, with specific reductions in occipital, parietal, temporal, and cerebellar regions. The genes involved in producing these neuroanatomic changes and associated

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cognitive and psychiatric symptoms are not yet known. The available evidence suggests that 22q11 deletions disrupt the expression of multiple genes involved in the development and maturation of neurons and interneuronal connections (Simon et al., 2005). Structural abnormalities of dendrites have also been reported (Ramocki and Zoghbi, 2008). Given the phenotypic variation in 22q11DS and the high comorbidity with other psychiatric disorders, the relationship between this syndrome and autism is not easily delineated. The prevalence of ASDs in populations of people with 22q11DS has been reported as 15% (Fine et al., 2005), 23% (Niklasson et al., 2009), and 50% (Vorstman et al., 2006). In two of these studies, PDDs other than autistic disorder predominated; in one (Fine et al., 2005), autistic disorder was the most frequent diagnosis. The high rate of diagnosed ASDs in 22q11DS is not surprising, given certain common features of the syndrome: communication difficulties, withdrawn behavior, difficulties initiating social interactions and decreased repertoire of facial expressions (Feinstein and Singh, 2007). Niklasson et al. found a high overlap between ASDs and ADHD in their sample. In their study, Vorstman et al. found a high rate of psychotic symptoms and identified individuals with both autism and psychosis. The implication of these findings is that autism in 22q11DS may be associated with impulsivity and hyperactivity and may precede or underlie psychosis. One study (Antshel et al., 2007) looked at differences between 22q11DS children with and without autism. The groups did not differ significantly on IQ. The group with autism was significantly more socially impaired and had more comorbid psychiatric disorders. The only neuroanatomic difference on brain imaging was in the size of the amygdala, which was smaller in children with ASDs. Given the high prevalence of autism in 22q11Ds, it is of interest that a number of chromosome 22 abnormalities have been detected in essential autism, including trisomy 22, ring chromosome 22 and 22q11.2 duplications (Mukaddes et al., 2007). This observation may suggest that altering the number of copies of yet-to-be-determined genes on chromosome 22 may confer risk for autism. Certain allelic variants of individual genes within this region may also do so (Freitag et al., 2010).

Summary and conclusion This chapter has presented the concept of syndromic autism and has offered a detailed discussion of six neurogenetic disorders that are associated with ASDs. Each disorder is characterized by behavioral features that include so-called autistic traits and most overlap significantly with ASDs, i.e. the prevalence of ASDs among individuals with the syndrome is markedly higher than in the general population. Although the occurrence of autism tends to correlate with lower intellectual functioning in several of these syndromes, IQ is not the sole determiner of an ASD diagnosis. The clinical features of autism in these syndromes tend to resemble those that characterize primary or essential autism, even in those conditions with syndrome-specific autistic-like behaviors (e.g. gaze aversion in FXS and hand-to-mouth stereotypies in Angelman syndrome). One can conclude, therefore, that the neurogenetic syndromes discussed here predispose the individuals with them to autism, and the neurodevelopmental abnormalities associated with each syndrome constitute one path (among many) to the behavioral triad that defines ASDs. This conclusion is important because it underscores the fact that autism is etiologically heterogeneous. Described syndromes, however, occur in only 12% of total ASD cases

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(Coleman and Betancur, 2005). The importance of the six conditions discussed in this chapter derives, rather, from the research that has been done on their underlying molecular, genetic, and neurobiological processes and the postulated occurrence of similar mechanisms in non-syndromic cases. In our discussion of the molecular genetics and neurobiology of FXS, TSC, Rett syndrome, Angelman syndrome, PWS, and 22q11DS, we have repeatedly encountered certain phenomena. These are the epigenetic regulation of gene expression; synaptogenesis and the refinement of synaptic connections (selective strengthening and elimination of synapses during development and in response to environmental input), with particular reference to dendritic structure and density; and the effects on synapse formation of excitatory (glutamatergic) and inhibitory (GABA-ergic) neuronal activity. Epigenesis is the regulation of gene expression without alteration of the DNA code. It is a molecular mechanism that can mediate stable changes in brain function and is accomplished primarily by chromatin remodeling. Processes such as DNA methylation or the binding of various molecules to histones either make genes more available for transcription or repress their activity (Tsankova et al., 2007). FXS is an epigenetic disorder because hypermethylation of the DNA in the elongated segment of CGG repeats represses transcription of FMR1 and FMR2. MeCP2 binds to methylated DNA and regulates the remodeling of chromatin; its absence in Rett syndrome leads to the altered expression of certain genes. Imprinting (a process that includes DNA methylation) in the 15q11–13 region selectively represses certain genes on the maternal or paternal copies of chromosome 15. When the “dosage” of these genes is altered by mutation, Angelman syndrome, PWS or essential autism may result. The prominence of epigenetic dysregulation in these disorders suggests that susceptibility to autism may result from epigenetic factors (Schanen, 2006). In addition to epigenetic dysregulation, the syndromes discussed in this chapter share a common feature of altered synaptic structure and function. In FXS, there is an increased density of abnormally shaped dendritic spines and abnormal synaptic plasticity, presumably secondary to lack of FMRP, which would normally inhibit protein synthesis in dendrites that is triggered by glutamate binding to GluR5 receptors (Ogren and Lombroso, 2008). In Rett syndrome, there is decreased arborization of dendrites and, in a mouse model of the disorder, lack of MeCP2 leads to a major reduction in synaptic response to glutamate-utilizing neurons, with loss of synaptic plasticity (Tsankova et al., 2007; Ramocki and Zoghbi, 2008). In TSC, there is evidence for abnormal dendritic spines, enhanced glutamatergic transmission, and disruption of GABA-ergic interneurons during development. A model has been proposed whereby these changes result in an imbalance of cortical excitation and inhibition that predisposes to both epilepsy and autism (Napolioni et al., 2009). In Angelman syndrome, reduced amounts of the protein ubiquitin-protein ligase E3A leads to a decreased density of abnormally shaped dendritic spines. Neuropathology from a mouse model for the 22q11 deletion syndrome showed smaller dendritic spines and less complex dendritic trees in hippocampal and prefrontal cortical neurons (Ramocki and Zoghbi, 2008). The common features of synaptic pathology in most of the neurogenetic syndromes reviewed here suggest that altered synaptic structure and function may be a pathogenetic mechanism in autism more generally. It should be noted, moreover, that synaptic plasticity involves epigenetic processes. The activity-dependent strengthening of synapses depends on chromatin remodeling and transcription of genes to synthesize the proteins that constitute neurotransmitters, receptors, and the cytoskeletons of dendrites. To the extent that these

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processes may be amenable to therapeutic intervention – e.g. in experiments to block mGluR5 in a mouse model of FXS and to introduce MeCP2 in a mouse model of RS – they may also provide molecular targets for future therapeutic intervention in autism.

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Section 1 Chapter

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What We Know About Autism

Etiology: essential autism Mark E. Reber

The previous chapter reviewed the features of ASDs as they occur in several specific genetic disorders with well described syndromic presentations. By following molecular pathways from genes to neurodevelopment, we considered several mechanisms that could explain the emergence of autistic symptoms. These included the genetic defects themselves, abnormalities in the formation of synapses and interneuronal connections, alteration in the balance of neuronal excitation and inhibition, and abnormalities in activity-driven synaptic plasticity. Underlying some of these aberrant neuronal processes was presumed disruption of epigenetically regulated transcription of particular genes. The present chapter will continue to explore some of the possible etiologic mechanisms for ASDs, looking at the fundamental role of genetic variations, at some possible ways in which genes and the environment might interact, and at abnormalities in other physiologic processes that might mediate the link between genetic vulnerability and the emergence of autism. The discussion will focus on primary or essential autism – not associated with particular genetic and malformation syndromes. This is also a heterogeneous category: no single mechanism is likely to explain all ASDs.

Autism genetics It was not initially appreciated that autism could be caused by the action of human genes. The first individuals identified with autistic disorder were usually the only people in their families so affected, and there was no obvious transmission of autistic symptoms from grandparents or parents to patients. It was not until 1977, when Folstein and Rutter published a study showing that monozygotic twins were far more likely to share features of autism than dizygotic twins, that it could be clearly argued that autism had a genetic basis. Folstein and Rutter’s twin studies have been extended and replicated many times, as have studies on the risk among siblings of a child with autism. For ASDs as a whole, it appears that the concordance risk for monozygotic twins may be as high as 90%, compared to 10–30% for dizygotic twins (Rosenberg et al., 2009; El-Fishawy and State, 2010). Risk of autism in a sibling of an autistic child is 10–20 times that of the general population (Losh et al., 2008; Constantino et al., 2010; Ozonoff et al., 2011). These data form the basis of the frequent assertion that autism is the most heritable of the neuropsychiatric disorders. Szatmari and Jones (2007) have stated that the heritability of autism is greater than 90%, a statistic that attributes the occurrence of the autistic phenotype overwhelmingly to genetic factors. [This The Autism Spectrum, ed. Mark E. Reber. Published by Cambridge University Press. © Cambridge University Press 2012. 145

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assertion has recently been challenged by the results of a large twin study, which found a higher concordance rate for ASDs than had been previously reported among dizygotic twins (Hallmayer et al., 2011). An analysis based on these data suggested that shared environmental factors played a larger role than genetic factors in the co-occurrence of ASDs in twins. This analysis, however, made the assumption that environmental and genetic factors were entirely independent.] If genes play a major role in the occurrence of autism, do we know what genes and how many – either in an individual or in the entire ASD population – cause or confer risk for the disorder? One thing is clear: the ASDs, taken as a whole, do not conform to simple Mendelian or single-gene inheritance patterns, even though some of the syndromes that confer major risk for autism may do so (Szatmari and Jones, 2007). Autism is a complex condition, heterogeneous in its phenomenology and heterogeneous in its underlying genetic architecture. At present, 10–20% of ASDs can be attributed to specific genetic disorders such as fragile X syndrome and tuberous sclerosis, recognized chromosomal abnormalities and inborn errors of metabolism. The remaining cases – so-called idiopathic, primary or essential autism – are generally explained by two complementary, hypothetical models for the underlying genetic architecture. The first model is called the common disease–common variant hypothesis. Common variants are genetic polymorphisms (or variant forms of certain alleles) that confer risk for a disease and are widely distributed in the general population. Each allele by itself carries only a small risk, but the co-occurrence and possible interaction of several of these alleles in an individual produce the disease phenotype, perhaps with the additional action of environmental factors. This model fits certain familial features of autism, such as the existence of the broader autism phenotype (the presence in relatives of subclinical autistic traits); the tendency for the risk of autism to drop off dramatically the further away relatives are from the autistic proband; and the apparent independent inheritance of the traits that comprise the autistic triad. All these features could be readily explained by risk alleles acting together – particularly in multiplex families with many affected members (Abrahams and Geschwind, 2008; O’Roak and State, 2008). The second model is called the multiple rare variant hypothesis. This model conceptualizes autism as a collection of distinct conditions, each one caused by a rare variant in one or a very few alleles. These rare alleles individually confer a large risk for disease for an affected person and are highly penetrant within families. This model also fits certain features of autism: its occurrence in families with only one affected individual and no evidence of the broader autism phenotype, and the high rate of concordance in monozygotic twins – both of which could result from de-novo mutations. Such mutations would be individually rare in the general population because, by conferring considerable risk for a severe developmental disorder, they would affect reproductive fitness and tend not to be passed on through generations (O’Roak and State, 2008). The common variant–common disease and multiple rare variant models are not mutually exclusive. It is possible that some cases of autism – perhaps those from multiplex families – result from several common genes acting in concord, while others result from rare, causal single-locus genetic changes. Abrahams and Geschwind (2008) refer to these models as “two contrasting but valid and potentially compatible paradigms.” Some methods of genetic investigation are more likely to detect common variants; others, rare. In the past half decade, application of a variety of new techniques for exploring the human genome has led to the identification of a large and growing number of genes that

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appear to cause or confer risk for ASDs. Most of the variant alleles that have been implicated by this research are individually rare, but studies suited for finding common genes continue to be pursued. Moreover, with each discovery of new autism genes comes an opportunity to explore the molecular and cellular action of those genes and the alterations in biological processes that connect specific mutations or allelic variations with the autistic phenotype. Gene identification thus informs our larger understanding of what gets altered in the neurologic development of children with autism. This chapter section on genetics will review the research strategies that have been recently used for gene identification in autism and resultant findings. The immediately following section will review theories of gene–environment interactions in the development of ASDs.

Genetic research: approaches Linkage studies Linkage studies are the oldest of the gene identification strategies applied to autism. They make use of known DNA sequence variations that serve as markers throughout the human genome. When there is enough evidence of a pattern of genetic transmission of a disorder within a family (or group of families), a parametric linkage analysis can be undertaken. In this approach, genetic data from multiple generations are analyzed, looking for markers that appeared to be transmitted in accordance with the hypothesized model of inheritance. The assumption here is that a marker and the disease-causing allele are linked – proximal to each other on the same chromosome – if they are inherited together in a manner not attributable to chance. Linkage data are reported as logarithm of odds (LOD) scores. A LOD greater than 3.6 is significant evidence of linkage; a LOD score of 2.2–3.6 is suggestive evidence of linkage (O’Roak and State, 2008). Parametric linkage analysis was used with rare familial cases of Rett syndrome to identify the Xq28 region as the locus of the disease-causing gene, ultimately leading to the discovery of MECP2 (Chahrour and Zoghbi, 2007). An X-linked inheritance pattern in a large family affected with intellectual disability and ASD led to detection of a functional mutation in NLGN4X, which plays a role in other identified cases of autism. A parametric linkage study of a syndrome of intellectual disability, epilepsy, and autism among the Old Order Amish led to the discovery of a mutation in the CNTAP2 gene, whose association with autism has been replicated in a number of studies (O’Roak and State, 2008; Kumar and Christian, 2009). For most cases of autism, however, there is no simple Mendelian pattern of inheritance, and parametric analysis cannot be used. Most genetic linkage studies in autism have utilized genome-wide nonparametric, or model-free, analysis, which permits scanning the genome for disease-associated loci in the absence of any a-priori hypothesis. This approach looks at the frequency with which markers are shared among affected versus unaffected family members and determines whether that frequency is attributable to possible linkage with a diseasecausing allele or to chance. LOD scores are used. In more than a dozen genome-wide linkage studies of autism, nearly every chromosome has been found to have suggestive evidence of linkage, but findings have been inconsistent over studies, with few replications (Losh et al., 2008). Linkage to autism has been found, in replicated studies, in the following regions: 2q11– 33, 3q25–27, 3p25, 4q32, 6q14–21, 7q22, 7q31–36, 11p12–13, 17q11–21 (Freitag et al., 2010). Of the regions listed, only 17q11–21 and 7q have been linked with autism at the significant level (Abrahams and Geschwind, 2008).

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The inconsistency in the results of genome-wide linkage studies may be attributable to the small size of the family cohorts that were used. More recently, this type of study has been performed using data and biological material available through large consortia, such as the Autism Genetic Resource Exchange and the Autism Genome Project Consortium. In these studies, genome-wide linkage analyses are often combined with other research strategies. Efforts have also been made to improve the usefulness of linkage studies by targeting endophenotypes, in an effort to increase sample homogeneity, e.g. by focusing on traits such as macrocephaly or delayed speech, rather than on ASD diagnosis. However, there may be limitations inherent in applying this methodology to autism. Genome-wide linkage analyses are said to be most effective with highly penetrant, singlegene disorders, but lose their power with oligogenic conditions involving multiple risk alleles of small effect and with highly heterogeneous disorders involving many different genes and chromosomal loci (Losh et al., 2008, Betancur, 2011).

Cytogenetics: chromosomal banding and DNA microarrays Chromosomal rearrangements large enough to be seen in the microscope were the first genetic abnormalities unequivocally associated with autism. A number of these have already been discussed in the chapter on syndromic autism, including the triple base-pair repeats in FXS, 22q11 deletions, and 15q11–13 duplications. It is estimated that 5–12% of autism cases are associated with chromosome abnormalities that are identifiable on routine G-banding analysis, even though most of these abnormalities occur in very few individuals (Martin and Ledbetter, 2007). More recently, techniques have been developed to examine the structure of chromosomes at the submicroscopic level. DNA microarrays, such as the comparative genomic hybridization (CGH) array, permit genome wide scanning to detect micro-deletions and microduplications or unbalanced chromosomal rearrangements. These arrays use probes – hundreds or thousands of DNA segments representing known genes and non-coding regions of the human genome that are fixed to slides or chips. If DNA from a patient has a different fluorescence pattern from control DNA samples when hybridized to the array of probes, this suggests the presence of regional deletions, duplications, triplications or insertions (collectively referred to as copy number variations, or CNVs). Arrays can detect these variations at the 150 kilobase level of resolution. Some of these CNVs are benign, usually inherited from a parent. Others are de novo and may have phenotypic consequences (Shinawi and Cheung, 2008). CNVs have opened a new window on genetic variation and are emerging as potentially important causes of autism, in keeping with the multiple rare variant hypothesis. Sebat et al. (2007) used a CGH array and confirming tests to study the DNA of autistic patients and their parents. De-novo CNVs were significantly associated with autism, occurring in 10% of patients with sporadic autism, 3% of patients with an affected first-degree relative and only 1% of controls. Christian et al. (2008) reported finding 51 CNVs in 46 of 397 patients with autism. Three of these were maternal duplications of 15q11–13, known to be associated with autism; others were novel chromosomal rearrangements. Bucan et al. (2009) identified 150 loci harboring rare variant CNVs in 912 multiplex families from the Autism Genetic Resource Exchange, none in controls. In a Swedish population, Bremer et al. (2011) found clinically significant CNVs in 8% of ASD cases, plus another 9% with CNVs of uncertain clinical relevance. With regard to individual genetic loci, potentially important CNVs have been found at 16p11.2 by a number of research teams. These CNVs are 100 times more likely to occur in

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Table 6.1 Examples of genomic regions and genes identified in genome-wide CNV studies

Chromosome location

Candidate genes

1q21.1

No specifically tagged genes

2p16.3

NRXN1

3p25–26

CNTN4

7q36.2

DPP6

15q11–13

UBE3A

16p11.2

Six different candidate genes

22q11.21

No specifically tagged genes

22q13.3

SHANK3

Xq13

NLGN3

Sources: Freitag et al. (2010), Glessner et al. (2009), Kumar and Christian (2009).

autism cases than in controls and now constitute the second most frequently encountered genomic imbalance in essential autism (Kumar et al., 2008; Weiss et al., 2008; Marshall et al., 2008). Table 6.1 lists some genomic regions and specific genes that have been identified in several genome-wide CNV studies of autistic populations. To be sure, not all detected CNVs are necessarily clinically meaningful. CNVs are found in 1% of control subjects and many have no apparent biological effect. Whether identified by traditional cytogenetic methods or by microarrays, copy number variations must also be shown to have molecular and neurobiological effects that would make them plausible risk factors for ASDs.

Genetic association studies and SNP arrays A genetic association study is a cross-sectional, case-control study that inquires about the cooccurrence of a disorder (or phenotype) and a gene or genetic marker. In this type of study, genetic material from individuals with a disorder is compared to that of a control group. Historically, association studies have focused on candidate genes or regions: genes or chromosomal segments that were suggested to play a role in autism either by linkage analysis or by their known molecular action. Control groups can be healthy, matched subjects or the unaffected parents of a child with autism. Candidate gene association studies have proved to be useful in providing confirmatory evidence for a number of allelic variants and mutations in autism. Table 6.2 provides a listing of recent replicated candidate gene association studies in ASDs. Some of the variant alleles for the genes that are listed in Table 6.2 have been identified by making use of single nucleotide polymorphisms or SNPs. These represent single base-pair deletions, additions or substitutions in the DNA sequence. Altogether, the average human genome contains 10 million SNPs, which have been highly conserved throughout evolution and tend to be shared by people of similar ethnic background. SNPs may be located within the coding sequences of genes, or in non-coding and intragenic regions. They may have direct phenotypic effects themselves and can also be used as genetic markers, tagging regions of the genome to help identify disease-causing genes.

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Table 6.2 Examples of genes identified in replicated candidate gene association studies

Location

Gene

Findings

2q24

SLC25A12: codes for mitochondrial glutamate aspartate carrier

Different common variants associated with ASDs

3p24–26

OXTR: codes for oxytocin receptor

Several SNP alleles associated with ASDs

7q22

RELN: codes for a signaling protein that plays a role in neuronal migration, formation of cortical layers and synaptogenesis

Triple repeat polymorphism associated with ASDs

7q36

EN2: codes for Engrailed 2, which plays a role during brainstem and cerebellar development

Different alleles of two SNPs associated with autistic disorder

7q35

CNTNAP2: codes for member of neurexin family involved in cell-adhesion and neuronal migration

Common variants, as well as rare mutations, associated with ASDs

7q31

MET: codes for receptor kinase involved in neuronal growth and organization and gastrointestinal repair

Several common SNP alleles and rare mutations associated with autistic disorder

17

SLC6A4: codes for serotonin receptor

Common variants associated with autistic disorder in several studies – but not all with same polymorphism

22q13–3

SHANK3: codes for synaptic scaffolding protein

Several mutations associated with ASDs in family and population studies

Sources: Abrahams and Geschwind (2008), Freitag et al. (2010), Losh et al. (2008).

SNPs provide the basis for a second type of association study: the genome-wide association study (GWAS). For this type of research, an SNP array is used, containing oligonucleotides specific to known SNPs with between 300 000 and 1 million SNPs on an array. These SNPs were selected because they are linked with other nearby SNPs for which they act as proxies: they represent a subset of the 10 million SNPs in the genome, but convey information on most of them. The array thus constitutes an extremely dense set of DNA markers (Psychiatric GWAS Consortium, 2009). Unlike candidate gene association studies, genome-wide association studies are hypothesisfree. In a GWAS, a large population of people with a disorder can be compared with an ethnically matched control group to identify genes that are associated with the subject population across all SNP-labeled parts of the genome. GWA studies are well-suited for detecting common susceptibility alleles that are present in a large number of patients. Three recent GWA studies using SNP arrays have pointed to several chromosome regions and genes associated with autism. Wang et al. (2009) included both a family-based cohort (780 families) and a case-control cohort (1204 affected subjects and 6491 controls). They found an association between autism and six SNPs in a region of 5p14.1, located between the genes CDH10 and CDH9, which code for cadherins – neuronal adhesion molecules. This region was also identified as associated with autism by Ma et al. (2009) in a smaller GWAS. Weiss et al. (2009) used SNPs for both a linkage and an association analysis of 1031 multiplex autism families. Regions of suggestive and significant linkage were found on chromosomes 6 and 7. No significant genome-wide associations were found for the total population, but a

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second study with additional families that focused on the most frequently encountered SNPs found a significant SNP at 5p15.2, near the gene SEMA5A. Expression of SEMA5A was also shown to be reduced in autistic patients. These GWA studies provide some evidence that common variants can play a role in causing or conferring risk for autism. Additionally, SNP arrays can be used to detect CNVs. Glessner et al. (2009) performed a genome-wide CNV study by scanning for regions with consecutive SNPs. Utilizing two populations of autism cases and controls, they identified CNVs associated with autism in several previously recognized candidate genes (e.g. NRXN1 and CNTN4) and several novel genes, including four genes involved in ubiquitin pathways. Similarly, Pinto et al. (2010) used an SNP array to identify CNVs in 996 ASD individuals of European ancestry and 1287 matched controls. ASD cases were found to carry a high burden of rare CNVs, especially for loci previously implicated in autism and intellectual disability. Novel CNVs attributed to de-novo mutations associated with ASDs were found in the following genes: SHANK2, SYNGAP1, and DLGAP2, all of which code for proteins in postsynaptic densities.

Gene sequencing An important part of confirming the role of a candidate gene is to explore that gene’s specific molecular structure and functioning. To do this, the alleles that are assumed to carry the risk for disease need to be sequenced and their molecular effects studied in the brain, in tissue culture or in animal models. For example, a frameshift mutation was identified by sequencing the recessively inherited CNTNAP2 gene in the Amish families with the syndrome of epilepsy, autism, and intellectual disability – after this gene was detected by linkage analysis. Abnormalities in neuronal migration associated with this genetic mutation were also demonstrated (Abrahams and Geschwind, 2008). Not all genes identified as likely playing a causative role in autism have been studied and sequenced, but this is a goal for clearly demonstrating the pathological effects of variant alleles.

Employing endophenotypes Another approach to autism genetics has been to focus not on genes that may be directly associated with the full autistic syndrome, but on the genetic underpinnings of the separate components of the autistic triad: communication deficits, social disability, and rigid, repetitive behaviors. This approach is supported by some evidence that features of the triad are inherited independently (Happé et al., 2006; Ronald et al., 2006). Such studies may include non-autistic family members who demonstrate features of the broad autism phenotype, such as delayed language acquisition, pragmatic language deficits, delayed social development, reticence in social interaction, and rigid personality. When behaviorally or quantitatively defined and used as the focus of genetic research, such features are called endophenotypes and are thought to be associated with a smaller constellation of genes than the full autistic triad (Losh et al., 2008). It has been suggested that using endophenotypes may improve the statistical power of non-parametric linkage studies and genome-wide association studies (Abrahams and Geschwind, 2008). Alarcon et al. (2008) reported on a series of studies using a quantitative trait (age at first spoken word) as an endophenotype. These investigators first found a linkage between this trait and a region at 7q34–36. (See Table 6.1.) When fine mapping of this region in 291 families suggested linkage to autism, they carried out an SNP-based association study

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comparing autistic probands and their parents, finding significant association between autism and CNTNAP2.

Genetic research: some specific genes The genetic research strategies just discussed have thus far identified more than 100 genes whose variant forms appear to confer risk for autism (Betancur, 2011). Some of these genes are common variants of small effect; more of them are rare, with individually large effects but involving a range of different cellular pathways (Freitag et al., 2010). The genes that appear most clearly to play an etiologic role in autism have been identified in replicated studies utilizing more than one research approach. Some of their variant forms have been sequenced and there is some understanding of their molecular and cellular effects and how these may play a role in neurodevelopment. A few of these genes and the research that shows their connection to autism are presented here. Obviously, gene discovery is a story in progress.

RELN RELN was first identified as a candidate gene because of its location in a peak linkage region, 7q22. Both family and population-based association studies have indicated that RELN may confer risk for autism. Preferential transmission of a trinucleotide repeat polymorphism has been reported in autistic subjects, but not their unaffected siblings. RELN codes for a protein that guides brain development during neuronal migration and formation of cortical layers. Examination of postmortem brain tissue has indicated that RELN mRNA was reduced in the frontal cortex of autistic subjects compared to controls. Cytoarchitectural cerebral abnormalities in postmortem studies of autism are said to resemble those in reeler mice, who have a large deletion within RELN (Losh et al., 2008; Kumar and Christian, 2009; Freitag et al., 2010).

MET MET is also located in the 7q31–36 linkage region. In family and population-controlled association studies, SNP variants of the MET promoter had a strong association with autism, especially the common C-allele, which is associated with decreased MET promoter activity in a mouse model. In a postmortem study, individuals with autistic disorder showed lower levels of MET protein than controls. MET codes for a receptor tyrosine kinase that is involved in neuronal growth and organization. It also plays a role in gastrointestinal and immune system functioning (Abrahams and Geschwind, 2008; Kumar and Christian, 2009; El-Fishawy and State, 2010).

OXTR OXTR is located at a recognized linkage region for autism, 3p25, and codes for the oxytocin receptor. Two SNPs, representing two common alleles of OXTR, have been found to be associated with autism in a Chinese population (Jacob et al., 2007). Campbell et al. (2011) reported association with autism of 25 different polymorphisms within the OXTR locus in a large population of multiplex families with over 2000 ASD individuals. OXTR has also been associated with a quantitative trait – score on the Vineland Adaptive Behavior Scale – and level of gene expression in blood distinguishes autism cases from controls (Lerer et al., 2008; Abrahams and Geschwind, 2008). There is also some evidence for increased methylation of OXTR in individuals with autism and decreased gene expression in the temporal lobes of

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autistic subjects, suggesting possible suppression of epigenetically regulated transcription of this gene (Gregory et al., 2009). Oxytocin is a promotor of social interaction in mammals and has been studied as a therapy for social deficits in autism. (See discussion later in this chapter.)

SLC6A4 and 5-HTTLPR SLC6A4 is located on chromosome 17 and codes for the serotonin transporter. Both the transporter gene and its promoter region (5-HTTLPR) have been the object of candidate gene association studies, because of decades of research that have linked abnormalities in the serotonin system with autism. Of particular interest, in keeping with the common variant hypothesis, has been the connection of two common alleles of 5-HTTLPR (short and long) with risk for ASDs. Some evidence has indicated that a short form of the allele is preferentially transmitted in ASD families, but results have been inconsistent (Devlin et al., 2005; Huang et al., 2008). Kistner-Griffin et al. (2011) presented data that there may be an association between autism and maternally inherited copies of the short allele of 5-HTTLPR.

PTEN Located at 10q23.3, PTEN is a tumor suppressor gene like the genes whose mutation causes tuberous sclerosis, TSC1 and TSC2. The protein it encodes acts as a catalyst in the mTOR signaling pathway, upstream from tuberin and hamartin. Mutations in PTEN are the cause of Cowden syndrome, a rare autosomal dominant condition characterized by hamartomas and macrocephaly (Pardo and Eberhart, 2007). Because of the association with macrocephaly, PTEN has been studied in autistic children with this feature. Gene sequencing has identified a surprisingly high frequency of PTEN mutations in this selected population (Losh et al., 2008; Varga et al., 2009). Transgenic mice with PTEN mutations have impoverished social interactions and neuropathology that is characterized by neuronal hypertrophy and synaptic abnormalities suggesting overgrowth or impaired pruning (Pardo and Eberhart, 2007). PTEN mutations, like those of TSC1 and TSC2, may thus confer risk for autism by their neurodevelopmental and anatomic effects. In the model proposed by Bourgeron (2009), susceptibility to autism in these genetic conditions derives from increased activity in the mTOR pathway, leading to neuronal overgrowth, excessive protein synthesis at synapses, and consequent abnormal synaptic activity.

Neuroligin genes Neroligins are cell adhesion molecules that play a role in the formation of excitatory and inhibitory synapses. Connection of neuroligins with autism was first suggested by the discovery of deletions in the Xp22.1 region in three females with autism. Jamain and colleagues explored this region and found mutations in NLGN4X (which is important in specifying excitatory versus inhibitory synapses) in several cases of autism, but not in controls. NLGN3, a similar gene at Xq13, was also found to be associated with autism (Jamain et al., 2003). Four of 148 individuals with autism had a misssense mutation in NLGN4X versus no mutations in healthy and psychiatric ill controls (Yan et al., 2004). CNVs in NLGN3 were identified as associated with autism in two genome-wide CNV studies (Glessner et al., 2009). In addition, mutations seen in NLGN3 have been modeled in mice. Mice with an amino acid change that replicates the one found in the protein product of the variant NLGN3 in

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humans show impaired social interaction. Social deficits are also present in NLGN3 knockout mice. In the mouse, neuroligins are essential for proper synapse maturation and function (Betancur et al., 2009).

Neurexin genes NXRN1 codes for a presynaptic cellular adhesion molecule (CAM) that is a binding partner for postsynaptic neuroligins. In a genome-wide linkage and CNV study, Szatmari et al. (2007) identified a deletion in NRXN1 in association with autism. Cytogenetic and gene sequencing studies have found a number of different de-novo mutations of NRXN1 in individuals with ASDs, but not in controls (Losh et al., 2008; Betancur et al., 2009). Glessner et al. (2009) also identified NRXN1 as a susceptibility gene for autism in their genome-wide linkage and CNV analysis. CNTNAP2 is a gene that codes for a contactin-associated protein. This protein is structurally similar to neurexins, but its synaptic function is less clear (Betancur et al., 2009). CNTNAP2 has already been mentioned in a number of contexts: as the locus of the recessively inherited mutation that causes the syndrome of autism, ID, and seizures in Amish children; as a likely gene in the 7q peak linkage region; as a gene associated with ASDs in replicated candidate gene association studies; and as an SNP-tagged common variant linked to a quantitative developmental trait (age at first word) as well as to autism. Neuroligins and neurexins are CAMs that are necessary for synaptic function. Figure 6.1 illustrates a stylized glutamatergic synapse, showing these CAMs and their interaction. Also illustrated are other CAMs, shank scaffolding proteins, and two types of glutamate receptors (mGluR and AMPAR), whose production is dysregulated in FXS and Angelman syndrome and may also be etiologically linked to autism.

Figure 6.1 A glutamatergic synapse, showing several cellular adhesion molecules and other synaptic proteins whose genetic variants have been implicated in autism. (See discussion in text.) (For a color version of this figure, please see the color plate section.)

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SHANK genes SHANK3 codes for one of several postsynaptic scaffolding proteins that interact with neuroligins. The Shank 3 protein plays a role in dendritic spine morphogenesis and synaptic plasticity. Reduced Shank 3 protein function has been implicated in 22q13.3 deletion syndrome, which is characterized by ID, speech deficits, and autism. Cytogenetic and sequencing studies have found other SHANK3 mutations in families with autism, as have two CNV studies (Betancur et al., 2009; Freitag et al., 2010). SHANK3 variants have an estimated prevalence of 0.5% in ASDs (Betancur et al., 2009). SHANK2, a similar, synaptic scaffolding gene has been recently implicated in autism. Berkel et al. (2010) found CNVs associated with autism and with ID in a genome-wide study. They then sequenced the gene in individuals with autism, individuals with ID, and controls, finding a number of mutations in probands that were not present in controls. In their large, genome-wide CNV study, Pinto et al. (2010) identified SHANK2 as a gene with novel CNVs found in the autistic population, but not in controls. It is noteworthy that so many of the genes that have been identified with links to autism code for cell-adhesion molecules and other synaptic proteins. In addition to NLGN4X, NLGN3, NRXN1, CNTNAP2, SHANK3, and SHANK2, mention has been made in this chapter of genes for cadherin 9 and cadherin 10, and a contactin gene (CNTN4). To this number must be added PTEN and a number of the syndrome-associated genes reviewed in Chapter 5 that affect production of synaptic constituents. As a group, these genes play a role in cell–cell interaction, synapse formation, synaptic plasticity, dendritic structure, and the balance of excitatory and inhibitory neurotransmission. Disruption of synaptic pathways thus appears to be a common result of multiple different genetic abnormalities that predispose to autism and is a probable etiologic mechanism for at least a subset of cases.

Environmental effects and gene–environment interactions When environmental effects are discussed as an etiologic factor in autism, the word “environment” may be used in two ways: in the broad sense, referring to all events not attributable to the action of genes that might impinge on a developing organism, and in the narrow sense of exposure to harmful or toxic agents in the air, soil, or water. Obviously there is some overlap between these two meanings, but the distinction is important. In the first usage, environmental effects comprise the many prenatal and perinatal risk factors for autism that have been identified in epidemiologic research, e.g. maternal smoking, low birth weight, certain prenatal drug exposures, obstetric complications, neonatal jaundice. (See Chapter 2 for a review of this literature.) This usage would also encompass any changes in the prenatal and postnatal humoral environment of the brain that could affect processes like synapse formation and refinement of interneuronal connections, as well as agents that could alter DNA methylation, thus affecting the epigenetically regulated expression of certain genes. In the second usage, “environmental” effects are generally limited to known teratogens and neurotoxins and other chemicals with potentially deleterious biological activity. The epidemiologic literature showing any association between toxic agents and autism is, at present, quite sparse, but the possibility of such an association is being actively investigated in a number of comprehensive, large-population, longitudinal studies. Despite the preliminary nature of our knowledge, there is a commonly repeated surmise that environmental toxicants are playing a role in the increasing prevalence of autism, and there is much theorizing in the

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literature regarding the neurotoxic and systemic effects of certain chemicals, along with the potential mechanisms by which chemical agents could give rise to the neurodevelopmental abnormalities that underlie autism. Most of this literature is founded on the assumption that ASDs likely arise from some combination (and interaction) of genetic and environmental effects on prenatal and early postnatal brain development. Figure 6.2, adapted from Pardo and Eberhart (2007), presents one model of how genes and environmental factors could combine to alter developmental trajectories and lead to autism. It is worth noting that this model looks at environmental influences in the broad sense, with specific mention of toxins as one potential contributor to altered neurodevelopment. This chapter section will provide a brief review of some of the epidemiologic and clinical research on environmental etiologies in autism in the narrow sense, with discussion of some possible mechanisms for gene–environment interactions. Case-control and other epidemiologic studies have identified only a handful of chemical agents that are clearly associated with autism, all through prenatal (first-trimester) exposure: ethanol, thalidomide, valproic acid, and misoprostol (Arndt et al., 2005; Bromley et al., 2008; Landrigan, 2010). Other environmental toxins have been implicated in ASDs because they are known to have deleterious effects on early brain development: lead, methylmercury, polychlorinated biphenyls (PCBs), arsenic, manganese, organophosphate insecticides, and DDT (Landrigan, 2010). Some of these neurotoxicants and a few others have been suggested as risk factors in ecologic and retrospective case-control studies. As discussed in Chapter 2, a history of maternal residence at time of delivery in geographic regions with high concentrations of air pollutants was found more often among offspring with autism than among controls (Windham et al., 2006; Kalkbrenner et al., 2010). Implicated pollutants included heavy metals, methylene chloride, quinolone, and styrene. In a Danish study, Larsson et al. (2009) looked at questionnaire data on home environments and diagnosis of ASDs based on parent report, and found a correlation with low ventilation and polyvinyl chloride flooring in the home. Eskenazi et al. (2007), in an interim report of a longitudinal study of

Genes Neurobiological Trajectories

Maternal Factors

Intrauterine Brain Development

11–20 Weeks

Environment

21–30 Weeks 31–40 Weeks Infections

Toxins

Neurodevelopmental Trajectories

0–10 Weeks

Postnatal Brain Development

First Year

Brain Maturation

Second and Third Year

Neurons and Cortical Organization Synaptic/dendritic Modeling Cortical Networks Development

Social Cognition and Interaction Language and Communication Motor Development

Brain Growth

Altered Trajectories = Autism Spectrum Disorders

Figure 6.2 Model of how genetic and environmental factors that influence early brain development might interact to alter neurobiological and neurodevelopmental trajectories and lead to ASDs. (Adapted from Pardo and Eberhart, 2007, with permission of the publisher.) (For a color version of this figure, please see the color plate section.)

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Mexican-American agricultural workers and their families in California, found that both maternal (prenatal) and child (postnatal) exposure to organophosphates, as reflected in urine specimens, were associated with a risk of pervasive developmental disorder, based on a subscale of the Child Behavior Checklist (not a diagnostic measure). And – as discussed in Chapter 2 – an early Texas ecologic study reported an association between autism prevalence and exposure to mercury, inferred from proximity to coal-fired power plants (Palmer et al., 2006), but no such association was found in subsequent studies with later-born cohorts (Lewandowski et al., 2009). These epidemiologic studies provide some suggestion of a possible link between early exposure to environmental toxins and ASDs. This link has been further explored by clinical and laboratory researchers who have focused on possible biologic mechanisms to explain this link, with particular reference to gene–environment interactions. The key questions asked in this research are: How might toxicants exert their effects? Are there specific genetic vulnerabilities that place some children at unique risk of these toxic effects? And is there a particular stage of neurodevelopment that constitutes the time of greatest risk from xenobiotics? One hypothesis that addresses these questions is the redox/methylation hypothesis (Deth et al., 2008; James, 2008; Zecavati and Spence, 2009). An important aspect of cellular metabolism is maintenance of a balance between production and consumption of reactive oxidants, such as the superoxide anion (O2−) and peroxide. Certain molecules, such as glutathione, can act to reduce excessive reactive oxidants, thus preventing their toxic effects on cell components like proteins, lipids, and DNA. When there is a disturbance in this reduction/oxygenation (redox) balance – e.g. when there is insufficient antioxidant activity – the cell is said to be under oxidative stress. Many of the metabolic processes that lead to production of antioxidants involve transfers of methyl groups – processes that are themselves vulnerable to oxidative stress. The redox/ methylation hypothesis holds that in some cases of autism, genetically vulnerable individuals have a decreased capacity for methylation and for antioxidant activity in the presence of oxidative stress. Among the known triggers for oxidative stress are environmental chemicals such as heavy metals, solvents, and insecticides. There is some biochemical and genetic support for this hypothesis – largely from research by James and colleagues (James et al., 2006, 2008; James, 2008). This research is best understood with reference to the underlying metabolic pathways. (See Figure 6.3.) Homocysteine, a precursor of glutathione, is a key participant in, and product of, the methionine cycle: methionine to S-adenosyl methionine (SAM) to S-adenosyl homocysteine (SAH) to methionine. The step from methionine to SAM involves methyl donation (to other cellular methylation reactions). The step from homocysteine back to methionine involves tetrahydrofolate-dependent re-methylation. Homocysteine that leaves the cycle is converted in three steps to glutathione, which, in its reduced state (GSH), is capable of antioxidant activity. James et al. (2006) measured metabolites from these pathways in 80 autistic and 73 control children. They found that the children with autism had significantly lower levels of SAM, a decreased SAM/SAH ratio, decreased total glutathione, and a significantly reduced GSH/GSSG (redox) ratio. Using a somewhat larger sample of autistic children and SNP arrays, these authors also found some “borderline” differences between autistic subjects and controls in the frequency of certain known allelic variants in genes critical to the methionine cycle. Other investigators have reported an association between autistic disorder and SNPs near genes involved in glutathione metabolism (Ming et al., 2010). James et al. (2008) also

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Figure 6.3 Interaction of the folate cycle with the methionine cycle, producing methyl groups (for various cellular processes) and glutathione, which helps maintain cellular reduction/oxidation balance.

reported decreased SAM/SAH and GSH/GSSG in some parents of autistic children, when compared to controls. On the basis of these findings, James (2008) hypothesized that oxidative stress and abnormal methylation may play an etiologic role in autism and, further, may provide a common link among diverse metabolic and genetic disorders that have been associated with ASDs. According to this hypothesis, environmental toxicants exert their effect by inducing oxidative stress, which autistic individuals are genetically less able to control or resolve. During critical stages of perinatal and postnatal development, DNA may be damaged, and cells, as a result of this DNA damage, may fail to proliferate and differentiate. Deth et al. (2008) extended this hypothesis by pointing out that DNA methylation, which depends on SAM for methyl donation, is a key mechanism for epigenetic modulation of gene expression. Thus, disrupted epigenetic mechanisms – which have been hypothesized to play an etiologic role in autism – may possibly result from oxidative stress. A second hypothesis linking genes and environmental exposures is that toxicants cause denovo genetic mutations that give rise to cases of autism. In this model, pre-conception exposure of the parents is the critical event. Kinney et al. (2010) reviewed some of the ecologic studies discussed above along with studies that have identified mercury, cadmium, nickel, trichloroethylene, and vinyl chloride as mutagens. They offered the suggestion that these chemicals may be associated with autism by induction of de-novo mutations in parents’ germ cells. A third hypothesis linking environmental exposures to gene expression in autism involves epigenesis: the suggestion that specific environmental exposures can alter the epigenetically regulated expression of certain genes. Grafodatskaya et al. (2010) reviewed evidence that valproic acid may have this kind of effect on the developing brain, either by inhibiting acetylation of histones – leading to over-expression of certain genes – or by altering folate metabolism. The second effect would be mediated by the tetrahydrofolatedependent methionine cycle (see Figure 6.3), resulting in decreased production of SAM and decreased methylation of DNA and histones, which would also lead to gene over-expression.

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A fourth hypothesis with regard to gene–environment interactions is that certain variant alleles might confer risk for autism by rendering an individual more susceptible to deleterious effects of environmental exposures at particular developmental stages. RELN, for example – a gene previously discussed that is critical for neuronal migration and has variant alleles linked with ASDs – codes for the protein reelin. One of the cellular effects of reelin is inhibited by organophosphates. Susceptibility to organophosphate toxicity would therefore depend, in part, on RELN gene variant and reelin expression. Persico and Bougeron (2006) presented a model whereby the effect of prenatal exposure to organophosphates on neuronal migration could be shown to depend on the interaction of reelin production and activity of the PON1 gene, which encodes for an enzyme that inactivates organophosphates. PON1 has also been associated with autism in some populations. Herbert et al. (2006) made note of more than 100 “environmentally responsive genes” that are located in known autism linkage regions. In Herbert’s view, moreover, genetic vulnerability to environmental effects in autism is not limited to early neurodevelopment, but continues throughout life and is mediated by a number of somatic systems whose resulting dysfunction affects the brain (Herbert, 2010). Although this chapter section has focused on possible environmental etiologies of autism in the narrow sense of toxicant effects, the hypotheses regarding gene–environment interaction are also relevant to environmental effects in the broad sense. There is, as has been mentioned, good reason to argue that some sort of environmental influence must play an etiologic role in autism: although ASDs are largely hereditable, the phenotypic differences between monozygotic twins suggest that effects other than genetic play a role. Also, many of the genes associated with autism are subject to epigenetic control and many are involved in the formation and refinement of interneuronal connections. Pardo and Eberhart’s model, presented in Figure 6.2, is a comprehensive, plausible and useful heuristic. It bears repeating, however, that, at present, the research evidence for environmental toxins as a cause of autism is preliminary at best, and the mechanisms just reviewed remain hypothetical.

The immune system and autism A number of hypotheses have been advanced suggesting that altered immune function – including autoimmunity, trans-placental action of maternal antibodies and neuroinflammation – may play an etiologic role in autism. The evidence supporting these hypotheses is both indirect and direct. When altered immunity is advanced as a possible cause of ASDs, it is usually viewed as acting in concert with genetic and other neurobiologic processes and with environmental effects. Some of the indirect evidence that the occurrence of autism may in part be immunemediated derives from repeated findings of peripheral immune system abnormalities in some individuals with autism – as many as 15–60% in some clinical series (Zimmerman et al., 2006). Examples of these abnormalities include reduced levels of immunoglobulins (Heuer et al., 2008a); T-cell dysfunction (Ashwood et al., 2011a); altered cytokine profiles (Molloy et al., 2006; Ashwood et al., 2011b); and dysfunction of natural killer cells (Enstrom et al., 2009). Several reports have also noted quantitative correlations between measures of immune dysfunction and measures of autistic behavior (Heuer et al., 2008a; Ashwood et al., 2011a). There is also a suggestion that immune processes may be involved in the long-recognized connection between autism and prenatal infections, such as rubella and cytomegalovirus. It is

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possible that maternal immune activation in response to these infections could play a role in pathogenesis (Zimmerman et al., 2006). Support for this notion is found in experiments with pregnant mice who are infected with influenza mid-gestation. Their offspring have neuropathological and behavioral abnormalities, in association with alterations of cytokine expression in various brain regions, suggesting an immune-mediated neurodevelopmental disorder (Heuer et al., 2008b). There are, furthermore, several genetic variants associated with autism that affect immune function. The frequency of HLA-DRB1*04 – an allele for a histocompatability antigen that is a susceptibility marker for autoimmune disease – is reported to be higher in children with autism and their mothers (Pardo et al., 2005; Careaga et al., 2010). Similarly, a defective allele coding for a complement component that produces no protein – the C4B null allele – has been noted to occur slightly more frequently in autistic children than in controls (Ashwood et al., 2006; Mostafa and Shehab, 2010). Also, some of the genes whose variant forms have already been discussed as implicated in autism – MET, RELN, and PTEN – are important for immune system functioning. PTEN, for example, is a regulator in the mTOR pathway, which is involved in T-cell activation (Careaga et al., 2010). The fact that there are many links between the CNS and the immune system also provides indirect evidence for a possible etiologic role for immune dysfunction in ASDs. Throughout life, there is a continuing communication between the immune system and brain, with many of the same peptides playing a role in both (Ashwood et al., 2006). In addition, during prenatal and early postnatal life, immune mediators such as chemokines, cytokines, and complement participate in mechanisms of brain development – including neuronal migration, axonal growth, neuronal positioning, cortical lamination, and dendritic and synaptic formation (Pardo-Villamizar, 2008). The primary immune cells of the CNS – astroglia and microglia – facilitate migration and positioning of neurons in the cerebral cortex, mechanisms that are synchronized with the action of certain cytokines and chemokines (including both pro-inflammatory cytokines, such as IL-6 and TNF-alpha, and anti-inflammatory cytokines, such as TNF-beta). Astroglia and microglia also contribute to modulation of synaptic and dendritic function in an elaborate interaction with neurons that varies by brain region (Pardo-Villamizar, 2008). Thus, there is reason to assume that factors that affect CNS immune function early in life will have an impact on neurodevelopment. More direct evidence is needed, however, to establish an etiologic role for immune mechanisms in ASDs – to separate pathogenic from secondary effects and from epiphenomena. With this goal in mind, a number of researchers have investigated immune dysfunction in autism, focusing largely on autoimmune processes, including transplacental transfer of maternal immunoglobulins, and on neuroinflammation.

Autoimmunity and autism Autoimmunity involves immune dysfunction in which there is failure to recognize one’s own cells and proteins as self, with resultant formation of an immune response toward these components. A number of studies have detected autoantibodies to brain proteins in the sera of individuals with autism. These targeted proteins include a serotonin receptor, myelin basic protein, glial fibrillary acidic protein, gliadin, brain endothelial cell proteins, and brainderived neurotrophic factor (BDNF) (Careaga et al., 2010). Other researchers have looked at circulating antibodies to specific brain regions. Using adult brain tissue, Singer et al. (2006) found higher levels of specific antibodies to the cerebellum and cingulate gyrus in autistic

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subjects and their siblings than in controls. Ashwood and his colleagues found antibodies to human thalamus proteins in 29% of autistic subjects, but 0% of controls. In a later study, using a macaque monkey preparation, these researchers found specific reactivity to Golgi cells of the cerebellum in 21% of subjects with autism, compared to 0% of typically developing and developmentally delayed controls (Heuer et al., 2008b). It should be noted, however, that the presence of these autoantibodies is insufficient by itself to make the case that autism is an autoimmune disease. For one thing, the targets of antibody activity are inconsistent over studies. For another, the formation of antibodies could be in reaction to a preceding brain insult and not the first or primary event producing the brain changes that resulted in autism. Also, autoantibodies to brain tissue and proteins have been found to occur in typically developing children and in children with developmental delays without autism (Goines et al., 2010). Indeed, Rossi et al. (2011) found autoantibodies to cerebellar Golgi neurons in both ASD subjects and controls, with no differences between the groups in rate of occurrence. Most importantly, there is as yet no evidence that links these serum antibodies to brain pathology, such as demyelination resulting from the presence of antibodies to myelin basic protein. Such pathological evidence is required for designation of an autoimmune disease. When the focus moves beyond autistic subjects themselves to other family members, particularly mothers, the theorized role of autoimmune processes shifts. What is of concern here is that autoimmune activity in the mother may have an effect during gestation and thus contribute to early changes in brain development that result in autism. There is some epidemiologic evidence for increased occurrence of systemic autoimmune diseases in family members, particularly mothers, of autistic children. Atladóttir et al. (2009), studying a large population-based Danish cohort, found elevated rates of rheumatoid arthritis and celiac disease among mothers of children with ASDs. Using three Swedish registries, Keil et al. (2010) found a significant, but weak, association between parental autoimmune disease and ASDs in offspring. In a California case-control study, Croen et al. (2005) found no overall increased risk of autoimmune disease in mothers of children with ASDs, but these mothers had higher rates of psoriasis when compared to mothers of typically developing children. Thus, there is some suggestion from epidemiologic data that maternal autoimmune disease might possibly be linked to autism. If autoimmune disease, or other maternal immune activation, plays a causative role in autism, a likely mechanism for this effect would be transplacental transfer of maternal antibodies (e.g. to brain proteins and brain regions) or other immune system molecules, such as cytokines, that have a direct effect on the developing brain. These effects have been studied. Braunschweig et al. (2008) found certain plasma antibodies to human fetal brain proteins in 7 of 61 mothers of autistic children, but not in mothers of typically developing children and mothers of children with developmental delays. Singer and colleagues found that sera from mothers of children with autistic disorder (MCAD) had a different pattern of reactivity to human fetal and rat embryonic brain tissue than sera from mothers of unaffected children (Zimmerman et al., 2007; Singer et al., 2008). Both Braunschweig and Singer noted a correlation between the presence of maternal antibodies to fetal brain and a regressive pattern of onset of autistic symptoms. This last finding is of interest because it suggests that possible inflammatory processes in the fetal brain, caused by maternal antibodies crossing the placenta, resulted in developmental changes that were not manifest until the second year of postnatal life.

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To demonstrate that maternal antibodies actually reach the fetal brain and cause inflammatory and neurodevelopmental changes, Singer’s lab took the additional step of introducing serum from MCAD and mothers of unaffected children into a pregnant mouse model. Pregnant mice were injected with purified human IgG (pooled from MCAD or from control mothers) or with saline throughout gestational days 14–18. Some mouse embryos were killed before birth and their brains immediately dissected; others were carried to term and followed into adolescent and adult life. Neuropathological studies of sacrificed embryos showed microglial activation in the MCAD IgG-exposed mice. Adolescent and adult mice who were exposed prenatally to MCAD IgG had more anxiety-like behavior and increased startle response compared to both control groups; adult mice exposed to MCAD IgG had decreased sociability (Singer et al., 2009). In a similar experimental approach, Martin et al. (2008) injected human IgG pooled from two groups of mothers into pregnant rhesus monkeys. Four monkeys received IgG from mothers of children with autistic disorder; four received IgG from mothers of typically developing children; five additional monkeys were used as controls. Rhesus monkeys who were exposed prenatally to IgG from MCAD demonstrated increased stereotypic movements and motor hyperactivity when compared to the other two groups of offspring. These experiments provide some direct evidence for transplacental transfer of maternal immune system molecules as a possible etiologic mechanism in autism, at least in some individuals with an ASD. It should be noted, however, that the maternal sera used in these studies were obtained at least 2 years after birth, only after the children had grown old enough to be diagnosed with autism. In contrast, Croen et al. (2008) were able to obtain midpregnancy serum specimens from a county-wide prenatal screening program in California and use these for a case-controlled study looking at patterns of antibody reactivity to human fetal brain protein in a cohort of mothers who gave birth in 2000–2001. The cases were mothers whose offspring were later diagnosed with autism; two control groups were mothers whose children were diagnosed with intellectual or other developmental disabilities and mothers whose children never received special services. Although differences in pattern of mid-gestational antibody reactivity were found among the three groups, with a specific band of reactivity seen more often in sera from mothers of autistic children, these differences did not reach statistical significance. In a related study, the same research group looked at immunoglobulins in newborns, comparing levels in children who later developed autism with controls. No antigen-specific IgG antibodies were elevated in ASD cases and overall IgG levels were lower in autistic subjects (Grether et al., 2010). More studies are needed of both antibodies and other molecules produced by maternal immune system activation to clarify the pathogenic role of this mechanism in autism. Also, the triggers for the maternal immune response – e.g. a possible autoimmune reaction or an exogenous infection – remain to be identified.

Neuroinflammation and autism Neuroinflammation is a mechanism that involves elements of the immune and nervous systems in response to injury, infection or dysfunction of the CNS. It includes immune system cellular responses (T- and B-cells, monocytes, macrophages), chemical mediators such as cytokines and chemokines, neuroglia, and neurotransmitters. It is probable that neuroinflammation plays both protective and deleterious roles in the setting of neurologic dysfunction and injury (Pardo-Villamizar, 2008).

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As has been noted above, the primary cells of the CNS that are involved in neuroimmune response – astroglia and microglia – also play a role in development, in processes of neuronal migration, cortical organization, and synaptic plasticity. Cytokines and chemokines are similarly involved in brain development. Given the connection between neuroinflammatory activity and neurodevelopment, it is of note that two studies of postmortem brain tissue have detected neuroinflammation in brains of autistic individuals that was not present in control specimens. Vargas et al. (2005) performed neuropathological analysis on brains of 11 people with autism (age 5–44 years at death), finding an active and ongoing neuroinflammatory process in the cerebral cortex and white matter, notably in the cerebellum, and immunocytochemical evidence of marked activation of microglia and astroglia, when compared to controls. Analysis of cytokines and chemokines in brain tissue revealed increases in MCP-1, which is pro-inflammatory, attracting monocytes and macrophages; and TGF-beta-1, which is derived from microglia and is anti-inflammatory. There were also region-specific elevations of other cytokines, most notably IL-6. Li et al. (2009), using tissue from the cerebral cortex, compared brains from eight deceased ASD patients and eight age- and gender-matched controls. They found increased presence of several pro-inflammatory cytokines, including TNF-alpha, IL-6, and GM-CSF in the brains from individuals with ASDs. They attributed these findings to a heightened immune response that was possibly associated with localized brain inflammation and tissue necrosis. In addition to looking at brain tissue, Vargas et al. (2005) also examined CSF from living autistic patients and found increases (when compared to controls) in MCP-1, with lesser increases of other pro-inflammatory cytokines, such as IL-6. In a study comparing cytokines in CSF and cytokines in serum from 10 children with autism, Chez et al. (2007) found significant elevation of TNF-alpha in cerebrospinal fluid. Pardo-Villamizar (2008) concluded that neuropathological studies of postmortem brain tissue from patients with autism demonstrate “an active and ongoing neuroinflammatory process,” supporting a role for neuroimmune responses in the pathogenesis and persistence of abnormalities in ASD. He suggested that genetic, maternal, and early environmental factors influence neuroimmune pathways, ultimately altering developmental trajectories, resulting in the behavioral characteristics of autism spectrum disorders. Once begun, neuroimmune activation may then persist into the “post-pathogenic period” and continue to affect brain maturation and adaptation in childhood and adulthood. One may question, however, whether neuroinflammatory processes that may occur in some individuals with autism are in fact pathogenic and if they represent persistence of an immune response that was initiated in prenatal and early postnatal development – as opposed to their being a secondary response to brain dysfunction or a process such as epilepsy, or to an independent event occurring later in childhood. Even if pathogenic, it remains unclear at present how many children with ASDs have brain inflammation. As with direct evidence involving maternal immune activation, the findings with regard to neuroinflammation provide some support for the hypothesis that altered immune function plays an etiologic role in autism, but further research is needed.

Mitochondrial dysfunction and autism Mitochondrial disorders are a group of multi-organ diseases caused by abnormalities in mitochondria – the organelles that contain essential pathways for cellular energy production. Mitochondrial disorders can result from genetic variations or mutations in both cellular and

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mitochondrial DNA. Most mitochondrial diseases progressively impair neurologic and neuromuscular function, with varied involvement of other organs and organ systems (e.g. kidney, heart, liver, pancreas). Even within a single disorder, clinical presentation and severity of symptoms can be highly variable. Cases of autism have been reported in association with mitochondrial disorders (Lerman-Sagie et al., 2004; Haas, 2010). Most of these cases can best be conceptualized as a form of syndromic autism. The clinical picture of children with this type of syndromic autism is presented by Weissman et al. (2008), who conducted a medical record review of 25 patients with ASDs who had unequivocal evidence of mitochondrial disease, specifically abnormalities in oxidative phosphorylation (the electron transfer or respiratory chain that produces ATP). All these children were originally diagnosed with ASDs and then referred for genetic or metabolic evaluation. Biochemical evidence of mitochondrial disease included increased blood lactate and pyruvate; increased serum AST, ALT, and CPK; increased fibroblast lactate/pyruvate ratio; and abnormal urine organic acid analyses. Cranial MRIs were abnormal in 48% of the patients who had them. Muscle biopsies revealed a number of abnormalities. Twenty patients had deficient respiratory chain enzyme activity. Clinically, these patients differed from most children with essential autism in having significant motor delays (present in 64%) and unusual patterns of regression, involving both losses of gross motor function and developmental regression after 3 years of age. Co-occurring nonneurological disorders (e.g. cardiac, hematologic, hepatic, and pancreatic abnormalities and growth retardation) were present in all patients. The sex distribution in this population was approximately 1 : 1, quite different from the male predominance in essential autism. Weissman et al. did note that one of their 25 patients had no clinical features that would distinguish her from children with “typical autism.” This type of observation and several clinical reports have led to the suggestion that some form of mitochondrial dysfunction – perhaps less severe than that seen in recognized mitochondrial disorders – could play an etiologic role in essential autism. Or stated another way: mitochondrial dysfunction may be present in individuals other than those who have clinical metabolic disease, and this dysfunction may be the cause of the autistic phenotype in these individuals. Holtzman (2008) expressed an opinion that mitochondrial cytopathies may contribute to the pathogenesis of ASDs in “an appreciable percentage” of people, even without metabolic disease. Haas (2010) asserted that at least 5–8% of ASDs can be attributed to mitochondrial dysfunction, with another 4% caused by definite mitochondrial disease. The evidence for mitochondrial dysfunction as a possible cause of essential autism derives from a limited number of case reports and clinical series that have looked at metabolic measures that reflect respiratory chain activity and at mutations in mitochondrial DNA (mtDNA) in autistic subjects. Filipek et al. (2004), in a chart review study of carnitine levels in 100 autistic patients, made note of other metabolic parameters that had been concurrently obtained in about half their sample. When compared to control values, mean carnitine and pyruvate levels were reduced; mean alanine and ammonia were increased. The authors considered a number of possible causes for low carnitine levels in their patients and suggested that the hypothesis that best fit their findings was a primary block in respiratory chain function, with secondary carnitine deficiency. They interpreted their findings as consistent with mild mitochondrial dysfunction in these patients. As part of a large epidemiologic study of autism in Portuguese schoolchildren who were born in 1990–1992, Oliveira et al. (2005) identified 120 children with clinically confirmed

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ASD diagnoses, of whom 102 had non-syndromic autism. Of these 102 subjects, 69 had studies of plasma lactate and pyruvate, and 14 demonstrated elevated lactate levels. Muscle biopsies were obtained from 11 of these 14 patients, with a resulting diagnosis of definite mitochondrial disorder in 5 patients (7.2% of the 69 subjects who had metabolic testing). In a follow-up study, Correia et al. (2006) affirmed a similar high prevalence of increased plasma lactate in a larger, recruited sample of Portuguese patients with nonsyndromic ASDs. Thirtysix of 210 patients had elevated lactate, and 53 of 192 patients had elevated lactate/pyruvate ratios. Two studies have looked at mitochondrial activity in lymphocytes. Holtzman (2008) cited a finding in his laboratory that respiratory rates were higher in lymphoblasts from autistic subjects than in near, same-gender relatives without autism. This finding was associated with inhibition of electron transport in the first segment of the respiratory chain (Complex I). Giulivi et al. (2010) reported on a small, exploratory study using a subset of subjects from the CHARGE study – a major longitudinal, case-control investigation of genetic and environmental contributions to autism. Oxidative phosphorylation capacity, number of copies of mtDNA and mitochondrial peroxide production were compared in 10 children with autistic disorder and 10 controls, as were plasma lactate and pyruvate measures. Mitochondrialdependent oxygen consumption, as measured by NADH activity, was found to be impaired in children with autism when compared to controls. Six of 10 autistic children had Complex I activity below control values. Children with autism had higher rates of hydrogen peroxide production compared to controls. There were no significant differences between groups in plasma lactate, but plasma pyruvate levels were higher in autistic subjects. There were no significant group differences in mtDNA copy number. Other research has looked at mtDNA, which differs in many ways from nuclear DNA. (Each mitochondrion has 2–10 copies of DNA and the mutation rate in mtDNA is higher than in nuclear DNA. Mitochondrial DNA is maternally inherited, but most mutations are de novo. With regard to proteins in the respiratory chain, only 13 proteins are encoded by mtDNA, with most others encoded by genes in the cell nucleus.) Filliano et al. (2002) described 12 children with severe mitochondrial disease and syndromic autism. Eight of these 12 had muscle biopsies and 7 were found to have reduced levels of enzyme subunits encoded by mtDNA, and 5 exhibited large mtDNA deletions. Pons et al. (2004) studied five patients with ASDs and a family history of mitochondrial disorders. Two patients had a common mitochondrial mutation; two others did not have this mutation “in accessible tissues,” but it was present in their mothers; the fifth patient had mtDNA depletion. Kent et al. (2008) investigated mtDNA haplotypes in 162 autistic subjects and two case-control groups: their fathers and a group of birth-cohort controls. According to the authors, 98% of Europeans belong to one of 10 distinct haplotype groups based on SNPs in the mtDNA coding region. There were no differences found between autistic subjects and their fathers in mitochondrial gene variation; differences from the birth cohort control group were attributable to lack of ethnic matching. In a case-control study of 148 autistic patients and controls, Álvarez-Iglesias et al. (2011) also found no association between mtDNA polymorphisms and ASDs. Associations have also been reported between nuclear genes and mitochondrial dysfunction in autism. Filipek (2003) described two autistic children with motor delays, hypotonia, acidosis, and respiratory chain block. Both had duplication of 15q11–q13 – the most common chromosomal abnormality in essential autism. Marui et al. (2011) found an association between two SNPs in a gene for a mitochondrial respiratory chain subcomplex and autism in

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a study of 235 ASD patients and controls. This gene is located at 7q32, within a peak linkage region for autism. The empirical threads tying mitochondrial dysfunction to essential autism are thus quite disparate, and the research is far from consistent. The fact that mitochondrial disease is a cause of syndromic autism is not in doubt. However, children with ASDs secondary to diagnosable mitochondrial disorders can be expected to have features like hypotonia, developmental motor delays, episodic regression, multi-organ involvement, and metabolic abnormalities. The findings of Oliveira and colleagues of frequently encountered elevations of plasma lactate – and a high rate (7%) of subsequently diagnosed mitochondrial disorders – in Portuguese schoolchildren with ASDS and no previously identified metabolic disease are striking and need replication. The findings of Holtzman’s group and the exploratory study by Giulivi et al. (2010) suggesting that respiratory chain inhibition may occur in a significant percentage of randomly selected children with autism also require confirmation. One further issue needs to be discussed before concluding this chapter section. A feature that essential autism and mitochondrial disease have in common is that some children with these disorders experience developmental regression. As discussed in Chapter 1, the phenomenon of developmental regression in autism in the second year of life, occurring in approximately 1 of 3 autistic patients, has been the subject of considerable inquiry and debate, especially with regard to its relationship to environmental events. Parents of children with autism have reported regression following febrile illness and vaccination. Regression following infections with fever is also known to occur in mitochondrial disease. Shoffner et al. (2010) recently reviewed charts of 28 patients who met diagnostic criteria for both ASDs and mitochondrial disease. Seventeen experienced autistic regression, and 12 of these 17 regressed with fever. None showed regression with vaccination in the absence of a febrile response. If mitochondrial dysfunction can be postulated to be a cause of autism and if regression in mitochondrial disease can follow vaccine-induced fever, opponents of childhood immunizations could continue to invoke “risk of autism” as their reason to refuse life-saving vaccinations – despite clear evidence from epidemiologic studies that no connection exists between vaccine use and autism. Indeed, assertions of this kind followed a decision by the US vaccine court in the Hannah Poling case in 2008. Hannah Poling is a girl who received five vaccines (catching up for missed immunizations) at age 19 months. Two days later, she was lethargic and febrile; thereafter, she experienced autistic regression. Months later she was diagnosed with encephalopathy attributable to mitochondrial enzyme deficiencies. When Hannah was 9 years of age, the vaccine court granted her compensation for a vaccine-related injury: exacerbation of her mitochondrial disease. Thereafter, the Poling case was cited as support for the idea that vaccines can lead to autism. The obvious point to be made here is that Hannah Poling had syndromic autism that was caused by her mitochondrial disease. She experienced developmental regression following a febrile illness, whose relationship to vaccinations was temporal. She might have had such a regression at another time, following any infection. The fact that she received multiple vaccines may have been a factor in her regression, but even this is questionable because of the limited number of immunologic components in modern vaccines (Offit, 2008). In any case, it was Hannah Poling’s primary mitochondrial disease that predisposed her to regression, and this regression was a manifestation of her primary disease, as were the resultant behavioral features of autism. All children, moreover, need to be vaccinated to prevent serious childhood infectious disease – most especially children with mitochondrial disorders,

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precisely because they are severely at risk for serious developmental regression were they to become ill with one of these preventable infections.

The endocrine system and autism Several endocrine factors have been mentioned as possibly playing an etiologic role in autism. Oxytocin (OT) and arginine-vasopressin (AVP) have been studied because these two peptide hormones have well-documented effects on social behavior. Androgens, particularly prenatal testosterone, have been investigated, in part because of male predominance in ASD populations, and in part because of a theory advanced by Baron-Cohen and colleagues that autism may be a manifestation of an “extreme male brain” (Knickmeyer and BaronCohen, 2006; Auyeung and Baron-Cohen, 2008). Insel et al. (1999) proposed that an abnormality in activity of oxytocin and vasopressin could account for central features of autism: social impairments, nonverbal communication difficulties, and rituals. Both hormones are peptides consisting of nine amino acids, structurally differing from each other at only two positions. Both are produced in hypothalamic nuclei and released into the blood by way of projections to the posterior pituitary. Systemic effects of OT include uterine contraction during labor and modulation of milk ejection during lactation. AVP regulates water retention by the kidneys. In addition to those that project to the pituitary, hypothalamic neurons expressing these peptides make connections with other areas of the CNS, and the brain is rich in receptors for both OT and AVP. The social effects of OT and AVP have been demonstrated in a number of animal and human studies, which were reviewed by Harony and Wagner (2010). In most mammals, the postpartum onset of maternal behaviors such as nest building and licking, grooming, and retrieving offspring has been linked to central and peripheral levels of oxytocin. Differences in pair bonding between two species of vole – prairie voles who form pair bonds and are monogamous, and meadow or mountain voles who show no preferences toward a mate – are attributable to species differences in patterns of distribution of OT and AVP receptors in the brain. In rats and mice, the ability to recognize a familiar individual depends on both OT and AVP. In human laboratory studies with healthy volunteers, administration of intranasal oxytocin has been shown to improve ability to infer the mental state of others from facial social cues; to increase “trust,” as measured by willingness to accept a simulated investment risk on the basis of social interaction; and to increase eye-to-eye gaze. A functional MRI study showed that amygdala activation by fear-inducing visual stimuli was strongly modulated by intranasal oxytocin (Kirsch et al., 2005). These findings provide indirect evidence for the hypothesis that dysfunctional or deficient oxytocin (and possibly AVP) in the brain could play a role in causing the social and communication deficits in autism. More direct evidence can be found in the reported associations of autism with genetic variations in the oxytocin receptor gene and with increased methylation in the promoter region of this epigenetically regulated gene. (See discussion earlier in this chapter.) In addition, a number of pharmacologic trials have been undertaken using one-time experimental doses of oxytocin and measuring various social responses. Hollander et al. (2007) gave separate intravenous infusions of OT and placebo in a crossover design to 15 adult subjects with ASDs, testing their ability to recognize emotions conveyed by speech. All subjects showed immediate improvement during their initial infusion (whether OT or

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placebo), but those who received oxytocin retained this improved ability over an interval of at least one week, while those who received placebo reverted to baseline skill. In a doubleblind, placebo-controlled crossover study, using intranasal oxytocin and a visual emotionrecognition task (“Reading the Mind in the Eyes”), Guastella et al. (2010) reported improvement on OT in teenage patients with ASDs. In another crossover study, Andari et al. (2010) gave single doses of intranasal oxytocin and placebo to 13 adults with ASDs and observed effects using a computer-simulated, ball-tossing game with fictitious partners who had different personalities and intents. Subjects’ interactions and play were observed and they were asked to report their emotional reactions to play partners. After oxytocin, subjects exhibited increased interaction with the most socially cooperative partner and reported enhanced feelings of trust and preference. Taking a somewhat different approach to the relationship between oxytocin and autism, Gale et al. (2003) asked the question of whether or not exposure to Pitocin (synthetic oxytocin) during labor could lead to ASDs in offspring, e.g. by down-regulating OT receptors in the developing brain. They conducted a case-control study with 41 autistic boys and 25 age- and IQ-matched boys without autism. No difference was found between the groups in history of Pitocin-induced labor. The hypothesis that alterations in central transmission of the social hormones OT and AVP could lead to autism thus has some limited support. Experimental trials with singledose oxytocin suggests that there may be therapeutic benefit from use of this agent, but safety must be established and longer-term studies are needed. These trials, however, provide little information on oxytocin deficiencies as an etiology of autism, because positive effects of oxytocin on social cognition and behavior have also been demonstrated in healthy volunteers. Research on elevated testosterone levels as a possible cause of autism is limited. Most studies have been done by Baron-Cohen and colleagues and derive from the theory that autism spectrum conditions are an extreme manifestation of characteristics that are typically male. According to this theory, cognitive and behavioral differences between males and females can be conceptualized as lying along two measurable dimensions: empathizing and systematizing. Empathizing involves identifying another person’s emotions and thoughts and responding to them with an appropriate emotion. Systematizing comprises analyzing and constructing rule-based systems, both mechanical and abstract. An individual’s “brain type” depends on where she or he stands on these two quantitative dimensions. The brain type of typical females tends to be more empathizing than systematizing (E > S); the brain type of typical males is S > E. Most individuals with autism would look like “extreme males,” with S >> E (Auyeung and Baron-Cohen, 2008). Because prenatal levels of sex hormones appear to affect gender-typical aspects of behavior, as well as to stimulate differentiation of male and female genitalia and other physical characteristics, Baron-Cohen and colleagues have undertaken a longitudinal study of children in Cambridge, England whose mothers had amniocentesis during pregnancy, looking at correlations between fetal testosterone levels in amniotic fluid and children’s subsequent social behavior, language use, and cognitive style. As part of this larger project, two studies focused on “autistic traits.” Auyeung et al. (2009) examined autistic features in 6- to 10-year-olds in this population using two parent-report autism screening instruments. (No effort was made to identify and diagnose children with ASDs.) For the population as a whole, fetal testosterone levels were positively correlated with higher scores on both instruments, in males and in females. In a second study, Auyeung et al. (2010) evaluated autistic

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traits in toddlers using the quantitative checklist for autism and toddlers (Q-CHAT). Boys as a group scored higher on the Q-CHAT, and a significant correlation was found between autistic traits and fetal testosterone level in both girls and boys. The authors concluded that autistic traits are “already sexually dimorphic” by 18 months of age and that fetal testosterone level is a significant predictor of autistic traits. These two studies are the only ones that have utilized actual measures of fetal androgen exposure. Other researchers, however, have made use of proxy measures for such exposure. One of these is the sex ratio of siblings. This measure derives from a recognition that both parents’ testosterone levels at the time of conception contribute to determining the sex of offspring. As elevations of testosterone tend to persist in women, a predominance of male offspring would suggest that the woman who bore them had elevated testosterone and that the fetuses she carried were exposed to increased levels of androgens. W. H. James (2008) undertook a review of published studies of neurodevelopmental disorders with male predominance: reading disorder, ADHD, and ASDs. She found a higher proportion of male siblings for these disorders as a group, but not specifically for autism. Mouridsen et al. (2010) studied the sex ratio in siblings of 326 individuals with ASD who were consecutively referred to Danish specialty clinics over a 25-year period. The proportion of males was 0.585, significantly higher than the Danish live-birth sex ratio over the same period, 0.514. The authors concluded that these results possibly provided indirect confirmation of the androgen theory of autism. The same authors also looked at another proxy measure in this population: subsequent diagnosis of testosterone-related cancer in mothers of probands. In a case-control study, Mouridsen et al. (2009) found similar rates of cancer in mothers of autism cases and controls and no association between testosterone-related cancers and autism. A key feature of the extreme male brain theory of autism is the emphasis it places on “traits” that can be measured and are roughly normally distributed in the general population. ASDs in this conception (renamed autism spectrum conditions by Baron-Cohen) are seen as extreme variants of these normally distributed traits. In none of the studies of “autistic traits” reviewed here, however, was a clinical diagnosis of autism made in any of the subjects. Only the Danish sex-ratio study provides a link between actual ASD cases and a proxy measure for fetal androgen exposure, and this connection is indirect. The theory of the extreme male brain does provide a possible explanation for male predominance in essential autism: testosterone, which peaks in males at 18–24 weeks of gestation, correlates with later development of systematizing, restricted interests, lesser interest in social relating, and decreased empathy. If fetal testosterone is, in fact, the cause of these and other “male” characteristics, and if it also makes boys more vulnerable than girls to developing extreme versions of these traits – traits that are labelled “autistic” – it follows that boys are more likely to be over-represented in a population of children with ASDs. There are, however, other, simpler explanations for there being many more boys than girls with autism. One is that, genetically, there is a protective effect of a second X-chromosome. A second is that girls have an increased ability – precisely because they are by nature more social – to compensate for autistic impairments (Skuse, 2009). Another possible explanation is that males are more vulnerable than females to prenatal environmental insults (Kinney et al., 2008). Until there are studies showing a direct link between prenatal androgen exposure and clinically diagnosed ASDs, one cannot confidently assign testosterone an etiologic role either in the occurrence of autism or in the predominance of males in ASD populations.

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Epilepsy and autism The idea that epilepsy might play an etiologic role in autism derives from two observations: the high prevalence of both epilepsy and epileptiform EEG abnormalities in people with ASDs; and overlapping phenomenology in autism and several epileptic encephalopathies. It is commonly stated that approximately 30% of people with autism develop epilepsy (at least two unprovoked seizures), with reported ranges of 5–38% in population-based studies and 13–46% in clinic-based studies (Spence and Schneider, 2009; Tuchman et al., 2009). The highest rates are associated with autistic disorder, but epilepsy also occurs in Asperger’s syndrome and PDD-NOS at rates far exceeding those of the general population (Mouridsen et al., 2011). In addition, epileptiform discharges not necessarily associated with seizures have been reported to occur in up to 60% of patients with ASDs who have had long-term EEG monitoring, including 30% of patients without a history of clinical seizures (Chez et al., 2006; Tuchman et al., 2009). Epileptic encephalopathies are clinically described disorders, characterized by seizure type, age of onset, EEG abnormality, and cognitive disturbance, often associated with neuropsychiatric symptoms. In epileptic encephalopathies, epileptiform abnormalities are believed to contribute to progressive disturbance in cerebral function (Kelley and Moshe, 2006). Examples of epileptic encephalopathies are West syndrome (infantile spasms), Lennox–Gastaut syndrome (LGS), and syndromes associated with electrographic status epilepticus of sleep (ESES), such as Landau–Kleffner syndrome. West syndrome refers to infantile spasms accompanied by a unique, high-voltage, multifocal and chaotic pattern on EEG called hypsarrhythmia. The onset of infantile spasms is in the first year of life and is frequently accompanied by neurodevelopmental regression. West syndrome has many causes, and children with a history of infantile spasms usually go on to develop other seizure types. In a population-based study, 35% of children with a history of infantile spasms were subsequently diagnosed with ASDs (Saemundsen et al., 2007). Persistence of hypsarrhythmia and frontal abnormalities on the EEG have been tied to development of autism in patients with West syndrome (Kayaalp et al., 2007). Lennox–Gastaut syndrome is characterized by frequent seizures of different types. Its onset is most frequently between 2 and 6 years of age and it is difficult to treat. Twenty per cent of patients with LGS have a history of prior infantile spasms. The prevalence of autism in LGS has not been reported, but Besag (2004) noted that behavior problems in these patients include autistic features. Autistic features have also been reported in patients with Landau–Kleffner syndrome (LKS), but this condition is most often cited in connection with ASDs as a differential diagnosis and as a potential model for how epilepsy might lead to autistic regression. (See discussion in Chapter 1.) LKS is a syndrome of acquired epileptic aphasia (primarily receptive) in which a child experiences onset of language loss associated with an epileptiform EEG. ESES is often present. The peak age of onset for a LKS is 4–5 years, but some cases can arise in the first 2 years of life. Not all children with LKS have clinical seizures, but treatment with anticonvulsants and/or steroids may reverse the developing aphasia in these patients (Kelley and Moshe, 2006; Deonna and Roulet, 2006; Besag, 2009). ESES may also be associated with regression in expressive language and executive function and global developmental delays. The onset of this symptom cluster is from 1 to 10 years of age, with a peak at age 4–5 years (Kelley and Moshe, 2006).

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Given that epilepsy occurs frequently in autism and that seizures and epileptiform discharges on EEG apparently contribute to symptoms seen in specific epileptic encephalopathies – symptoms that overlap with those of ASDs – it seems reasonable to hypothesize that epilepsy, or epileptiform abnormalities, may cause or contribute to some cases of autism. Alternatively, one could postulate that the co-occurrence of ASDs and epilepsy is a consequence of a common abnormality in brain development, with both being symptomatic of the same underlying pathology. The connection between epilepsy (and epileptiform abnormalities) and autism has been most extensively studied with regard to the phenomenon of autistic regression. This literature is reviewed in Chapter 1. Most authors have concluded that developmental regression occurring in the second year of life in ASDs is rarely attributable to epileptic phenomena and is usually clinically distinguishable from LKS (Hrdlicka, 2008; Tuchman, 2009). The issue, however, is still debated. Besag (2009) has pointed out that children with regression are often not studied with EEGs until long after the regression occurred, and that the characteristic EEG abnormality of LKS (ESES) may diminish with age. Deonna and Roulet-Perez (2010) have argued that longitudinal studies are necessary to determine if, and to what extent, bioelectric abnormalities play a causal role in the subgroup of children with social and language regression and epilepsy. With regard to the larger population of children with ASDs and seizures, one problem in trying to clarify whether epilepsy plays an etiologic role is the enormous heterogeneity of both conditions. Studies looking at differences between autistic children with and without epilepsy have yielded some consistent findings. Those with epilepsy have lower intellectual functioning, more motor deficits, lower social maturity, and are more likely to be on psychotropic medication than those without epilepsy (Hara, 2007; Amiet et al., 2008; Tuchman et al., 2010). These phenomena point to a generally more neuropsychiatrically impaired subgroup, but do not suggest that epilepsy – as opposed to underlying neurologic dysfunction – is the reason for this impairment. Similarly, EEG patterns in the ASD population are not informative regarding etiology: there is no consistent pattern of epileptiform discharges associated with autism. Some studies suggest that temporal foci are more common, but others do not. ESES is rare in individuals with ASDs (Spence and Schneider, 2009). There appears to be a consensus developing, however, that epilepsy and autism are both the consequence of alterations in interneuronal connectivity in cortical and subcortical systems. Epilepsy is the byproduct of this underlying network disruption and also makes a further contribution to this disruption (Tuchman et al., 2009). Brooks-Kayal (2010) has offered a model whereby genetic conditions cause abnormalities in synaptic plasticity and neuronal inhibition/excitation, leading to early-life seizures and the developmental abnormalities that characterize ASDs. Early-life seizures, in turn, impact synaptic plasticity and may thereby contribute to ASD symptoms, intellectual disability and further seizures – by a process called epileptogenesis. Specifically, early-life seizures alter the function of neurotransmitter systems, such as GABA and glutamate, and affect a number of molecules that are essential for intrinsic neuronal function, such as CREB, a protein that binds cyclic AMP. The utility of this model is that it unites many different genetic etiologies for ASDs in a common mechanism causing both autism and epilepsy. It explains the frequent emergence of autism following infantile spasms. It also explains the high rate of intellectual disability among children with autism and epilepsy. One can also infer from this model that early identification and treatment of seizures might have a positive effect on subsequent behavioral

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symptoms in young children who are genetically predisposed to both epilepsy and autism. At present, the evidence for this model derives from animal studies and the frequent co-occurrence of autism and epilepsy in syndromes like tuberous sclerosis complex, Rett syndrome, and fragile X syndrome, and in association with mutations in genes for neuroligins and neurexins (Brooks-Kayal, 2010). The relationship between epilepsy and autism is complex. Genetic variations give rise to both conditions and to co-occurring intellectual disability. At least in some cases, seizures and the process leading to epileptogenesis may contribute to neurologic changes that also result in autistic symptoms. This connection is clearest in West syndrome and may also occur in some cases of autistic regression accompanied by seizures. Much more research is needed: to identify relevant genes; to clarify relationships among epilepsy types, EEG abnormalities, and ASD symptoms; and to determine what role – if any – anticonvulsant therapy may have in preventing or ameliorating autistic symptoms in children with epilepsy who are at risk for ASDs or in autistic children with epileptiform EEGs.

Summary and conclusion This chapter has reviewed several proposed etiologic mechanisms for autism – best conceptualized as a heterogeneous collection of disorders with many different causes, all resulting in common, early-onset behavioral features. ASDs are highly heritable, and genetic causes have been clearly linked to 20–25% of cases, with significant new genetic variants being discovered every year. Most of the genes identified in ASDs affect early brain development, but the link between genetic vulnerability and autistic symptoms may involve other physiologic systems. We have considered several of these, including immune system activation (individual and maternal), abnormalities of energy production (mitochondrial dysfunction), endocrine abnormalities, and epilepsy. Effects of environmental exposures, possibly mediated by genetic mutation or later metabolic and epigenetic mechanisms, have also been discussed. There appears to be a developing consensus that most cases of essential autism result from a combination of genetic vulnerability and environmental influences, but this has not yet been clearly demonstrated. Most of the etiologic mechanisms discussed in this chapter represent reasonable – even compelling – hypotheses based on the available research. Obviously, in a heterogeneous condition, any or all of these mechanisms could be operative, but empirical support for most of these hypotheses is preliminary. Families of individuals with ASDs and the public at large often express frustration that the “cause” of autism has yet to be found. People should derive hope, however, from the range of etiologic mechanisms that are being explored, the rich variety and quality of research that is being done, and the advances that have been made in understanding some of the causes of ASDs. The task is difficult and complex, but the effort is ongoing.

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Chapter 6: Etiology: essential autism

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Neuropathology in Autism 30 (a) Williams et al. (1980)

a

(b) Bauman & Kemper (1985−91)

25

(c) Coleman et al. (1985)

b (d) Kemper & Bauman (1993) (e) Rivto et al. (1986)

d Number of subjects

20

(f) Hof et al. (1991)

e

(g) Fehlow et al. (1993) (h) Raymond et al. (1996)

g 15

(i) Rodier et al. (1996)

a

j 10

b k l m

5

a c

d i

b

j

d f

h

f j

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(j) Bailey et al. (1998)

c

(k) Fatemi et al. (2002a)

j

(l) Araghi-Niknam & Fatemi (2003)

l

(m) Lee et al. (2002)

n

(n) Casanova et al. (2002a)

o

(o) Wegiel et al. 2010

o

n

o

o

0 Cerebellum

Brain Stem

Amygdala

Hippocampus

Temporal

Orbitofrontal

Areas of the Brain

Figure 4.1 This figure represents a compilation of the number of brains that have been investigated in postmortem histopathological studies of individuals with autism. Along the x-axis are the different areas of the brain that have been implicated in autistic disorder. The y-axis represents the number of brains in which abnormalities were identified in the specified area.

Figure 4.2 (A). This figure depicts a lateral view of the human brain. Color-shaded areas indicate those brain regions thought to be functionally compromised in individuals with AD. Areas shaded in red are associated with problems with communication. These problems have been related to dysfunction of a complex distributed neural network involving the following areas and connecting pathways: inferior frontal cortex, superior temporal gyrus, superior temporal sulcus, supramarginal gyrus, insula, basal ganglia, thalamus, and cerebellum. Some structures are not visible given the plane of the image. Areas shaded in green reflect cortical regions that have been implicated in social impairment. These areas comprise a complex distributed network involving the orbito- and medial frontal cortex, frontal pole, cingulate cortex, superior temporal sulcus, temporal pole, fusiform gyrus, and amygdala. Problems with repetitive and stereotyped behaviors (blue) have been related to dysfunction of a complex distributed neural network involving the following areas and connecting pathways: orbitofrontal cortex, posterior parietal cortex, supplementary motor cortex, cingulate gyrus, basal ganglia, thalamus, and cerebellum. Areas depicted in alternating green and red stripes represent regions of the brain that appear to be involved in mediating social behavior and communication. (B). Some of the medial cortical and subcortical structures involved in mediating behaviors in each of these three domains are shown.

Figure 6.1 A glutamatergic synapse, showing several cellular adhesion molecules and other synaptic proteins whose genetic variants have been implicated in autism. (See discussion in text.)

Toxins

Infections

Maternal Factors

First Year

Second and Third Year

Brain Maturation

31–40 Weeks

21–30 Weeks

11–20 Weeks

0–10 Weeks

Postnatal Brain Development

Intrauterine Brain Development

Motor Development

Language and Communication

Social Cognition and Interaction

Neurodevelopmental Trajectories

Altered Trajectories = Autism Spectrum Disorders

Brain Growth

Cortical Networks Development

Synaptic/Dendritic Modeling

Neurons and Cortical Organization

Neurobiological Trajectories

Figure 6.2 Model of how genetic and environmental factors that influence early brain development might interact to alter neurobiological and neurodevelopmental trajectories and lead to ASDs. (Adapted from Pardo and Eberhart, 2007, with permission of the publisher.)

Environment

Genes

Section 2

Assessing and Treating Children with Autism Spectrum Disorders

Chapter

Autism screening and diagnostic evaluation

7

Mark E. Reber

In order to maximize the benefits of treatment, children with autism should be identified and diagnosed as early as possible. This chapter will address screening for autism spectrum disorders in community and high-risk populations and the components of a comprehensive diagnostic evaluation. Instruments used for screening and diagnostic assessment will be described. Discussion will also touch upon two other aspects of evaluation: investigating the etiology of autism in an individual child, and identifying comorbid conditions that occur commonly in children with ASDs.

Screening Screening of young children to detect those who might have, or be at risk for, ASDs may be implemented in a number of settings: in primary care physicians’ offices and clinics, in developmental diagnostic centers and in early intervention programs. Such screening can be described as being of two types, depending on setting and purpose. Level 1 screening takes place in primary care or community settings and aims to differentiate children who are at risk for autism from the rest of the population. Level 2 screening generally takes place in early intervention programs and diagnostic centers and aims to distinguish children with ASDs from those with other developmental disabilities. Level 2 screening may thereby also contribute to establishing a diagnosis of autism. The primary assumption that motivates early screening for ASDs is that early identification, diagnosis, and intervention lead to better outcomes. There is a consensus that this is, in fact, the case. Zwaigenbaum and Stone (2008) cited a half-dozen studies supporting a connection between early intervention and outcome in autism, as reflected in IQ gains, greater likelihood of being in fully integrated programs at school entry and reduced total lifetime cost of care. Howlin (2007) noted that the two factors most clearly associated with better long-term outcome in ASDs are early speech development and higher intellectual functioning. Both appear to be affected by timely early intervention (National Research Council, 2001; Warren et al., 2011). A second assumption underlying early screening, particularly at Level 1, is that use of standardized screening instruments can identify more children, at an earlier age, than primary care physicians will pick up by attending to parents’ complaints and performing routine developmental assessment of their patients. Although this assumption still needs empirical support from community studies, it is the basis of the recommendation by the The Autism Spectrum, ed. Mark E. Reber. Published by Cambridge University Press. © Cambridge University Press 2012. 179

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Developmental Disabilities Council of the American Academy of Pediatrics that all children be evaluated at their 18- and 24-month health maintenance visits with an ASD-specific screening tool – in addition to pediatric surveillance throughout childhood for autismrelated developmental concerns (Johnson et al., 2007). This chapter section will review the phenomenologic underpinnings of ASD screening tools that can be used with children as young as toddlers and the specifics of the AAP recommendations for surveillance and screening for autism. Specific Level 1 and Level 2 screening instruments, their validation, and data supporting their use will also be discussed. Certain features of autism can be recognized in the first and second years of life. These include abnormalities of social interest and social communication, such as decreased eye contact; delayed or absent social smiling; impairments in joint attention (e.g. following a parent’s gaze, which should be evident at 8 months, following a point, which begins at 10–12 months, and initiating a point, which is usually apparent around 14 months); not seeking to share objects and wants with parents; failure to imitate gestures; and lack of interest in interactive and pretend play. Early language deficits found in autism include lack of alternating back-and-forth vocalizations with parents; delayed onset of babbling and verbal speech; and failure to respond to one’s own name. Slightly older children who do acquire speech may demonstrate persistent echolalia, stereotypic intonation patterns, and “pop-up words” – spontaneous and repetitive verbal utterances that are completely out of context (Zwaigenbaum and Stone, 2008; Johnson et al., 2007). Many of these observable behaviors are the basis of specific items on interactive, interview, and questionnaire-based screening tools that seek to identify children at risk for ASDs. For example, the Checklist for Autism in Toddlers (CHAT) – a screening instrument designed by Baron-Cohen and colleagues for use in primary care and studied with 18-month-old children – has nine parent-interview items including such questions as: 

Does your child take an interest in other children?  Does your child enjoy playing peekaboo?  Does your child ever pretend?  Does your child use an index finger to point, to ask for something? The CHAT also has five interactive items to structure observations by the primary care provider, e.g. pointing at an object to see if the child follows the point, presenting the child with a cup and asking him to make a cup of tea, and attempting to elicit a point by shining a light and saying, “show me the light” (Baron-Cohen et al., 1992, 2000). Similarly, a parent-report questionnaire derived from the CHAT – the modified CHAT (M-CHAT) – has 23 items, asking, for example, if the child enjoys peek-a-boo, pretends to talk on the phone, plays properly with small toys, brings objects to show, imitates the parent, responds to his/her name, and understands what people say (Robins et al., 2001; DurmontMathieu and Fein, 2005). One benefit of using autism-specific screening tools in primary care is that these instruments help to focus the practitioner on specific developmental features and behaviors that are frequently associated with ASDs, thereby improving the likelihood of early identification and referral. It should be noted that most of the behaviors selected for screening instruments are in the domains of social development, communication, and language – not in the third realm that defines ASDs, repetitive behaviors, and restrictive interests. There are two reasons for this. The first is that stereotypies and repetitive behaviors may emerge relatively late in

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autistic children, not appearing until the third or fourth year of life. The second is that stereotypies occur with some frequency in children with intellectual disability who are not autistic. For both reasons, these behaviors – although clearly observable when present – are not reliable discriminators of ASDs in 18-month- and 2-year-old children (Charman and Baron-Cohen, 2008; Johnson et al., 2007). As already mentioned, the Council on Children with Developmental Disabilities of the AAP has recommended a combination of general surveillance for autism risk throughout childhood and targeted screening for ASDs at the 18- and 24-month health maintenance visits. In this context, surveillance refers to ongoing and continuous assessment of a child’s developmental progress and behavior. It occurs at every preventative pediatric visit and comprises eliciting and attending to parents’ concerns, making observations of the child and identifying the presence of risk and protective factors. The AAP Council has also incorporated the suggestions of the American Academy of Neurology and Child Neurology Society that certain “red flags” be noted during surveillance (Filipek et al., 2000). These include: 

no babbling by 12 months, no communicative gesturing by 12 months,  no single words by 16 months,  no two-word phrases (excluding rote or echolalic phrases) by 24 months, and  any loss of language or social skills at any age. 

When conducting surveillance, the primary care practitioner should use a simple formula, adding together the presence of any red flags or other practitioner concerns, parents’ concerns, any concern relevant to ASDs on the part of other caregivers, and any history of an ASD in a sibling. With each of these four sources of concern assigned a score of one, any score of two or more would necessitate referral of the child for comprehensive ASD evaluation, plus simultaneous referrals to an early intervention or early childhood education program and to an audiologist. If the total score is one, then the practitioner should administer an ASD-specific screening instrument. If responses on this instrument are indicative of a risk for an ASD, then referrals need to be made for comprehensive evaluation and to early intervention and an audiologist. At each step in the surveillance, screening, and referral process, the parents need to be educated about the nature of ASDs, the difference between being at risk for an ASD and an actual diagnosis, and the possible presence of other developmental disorders and hearing deficits (Johnson et al., 2007). Surveillance is supplemented by systematic screening of all children at the 18- and 24month visits. Screening is intended to identify at-risk children who might be missed through surveillance only. Screening at 18 months permits very early identification of children at risk for autism. Screening at 24 months allows one to find those children whose symptoms emerge late in the second year. Level 1 screening instruments, which are designed for use in primary care, are most suitable for this purpose, but Level 2 instruments may also be used. Table 7.1 lists examples of both kinds of screening tools.

Level 1 screening tools Level 1 ASD screening instruments are of two types: questionnaire-only or a combination of parent interview (or questionnaire) and structured observation of the child.

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Table 7.1 Summary of selected ASD screening measures

Level 1

Level 2

Measure

Age range

Format

Administration time

CHAT (Baron-Cohen et al., 1992)

18–24+ mos

Parent interview (9 items) and interactive (5 items)

5 min

M-CHAT (Robins et al., 2001)

16–48 mos

23-item parent questionnaire

5–10 min

PDDST-II (Siegel et al., 2004)

18–48 mos

22-item parent questionnaire

10–15 min

ITC (Wetherby et al., 2008)

9–24 mos

25-item parent questionnaire

5–10 min

STAT (Stone et al., 2000)

24–36 mos

Interactive by trained examiner, 12 scored items

20 min

SCQ (Rutter et al., 2003)

4 years and older (M.A. > 2 yr)

40-item parent questionnaire

10–15 min

SRS* (Constantino and Gruber, 2005)

4–18 years

65-item parent or teacher questionnaire

15–20 min

CARS-2* (Schopler et al., 2010)

24 mos and older

Interactive by trained examiner, 15 scored items

Variable

GARS-2* (Gilliam, 2006)

3–22 years

Parent or teacher questionnaire, 42 scored items

5–10 min

* May also be used for diagnosis or as a measure of symptom severity.

CHAT The CHAT (Baron-Cohen et al., 1992) is an example of combined interview and observation: nine yes/no questions to be asked of the parent, plus five interactive items. It can be administered in 5–10 minutes and is the only Level 1 screen to be tested in a large general population with long-term follow-up (Baird et al., 2000; Baron-Cohen et al., 2000). The results of this testing showed the CHAT to have an acceptable positive predictive value and specificity, but sensitivity was low: many ASD cases were missed. (Sensitivity is the proportion of children with ASD who are correctly identified by a screen; specificity is the proportion without an ASD who are correctly excluded; positive predictive value is the proportion of identified children who subsequently receive an actual diagnosis of an ASD.) Baron-Cohen et al. (2000) argued that the false negative rate of the CHAT is not a serious drawback because autism is not life-threatening. A low sensitivity, however, appears to undermine the fundamental purpose of a screening tool: to identify cases that would otherwise be missed.

M-CHAT The M-CHAT is a 23-item parent questionnaire that was developed to screen children from 16 to 30 months of age and can be filled out in 5–10 minutes. It expands upon the parent-interview part of the CHAT, adding items that address a broader range of developmental domains, including sensory and motor abnormalities, imitation, and response to name (Zwaigenbaum and Stone, 2008). A positive screen on the M-CHAT is defined as an

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“at-risk” response on any 3 items of the 23, or on 2 of 6 critical items: takes an interest in other children; ever uses an index finger to point, ask or indicate interest; brings objects to show; imitates parent; responds to name; looks when parent points. The initial population studied for validation of the M-CHAT consisted of 1293 children drawn from (low-risk) primary care settings and (high-risk) early intervention sites. Of the 1293 subjects, 58 were referred for evaluation based on both a positive M-CHAT screen and a follow-up telephone interview. Thirty-nine were diagnosed with an ASD, at a mean age of 28 months, yielding a positive predictive value of 0.80 (Robins et al., 2001). There was insufficient follow-up of the original sample to determine true sensitivity and specificity (Robins, 2008). Kleinman et al. (2008) reported on a larger study with the M-CHAT with 3792 subjects drawn from both primary care and developmental intervention settings. When combined with a follow-up parent interview, the M-CHAT had a positive predictive value (PPV) of 0.74. PPV was, however, lower for the low-risk population – a finding also noted by Pandey et al. (2008). Two studies have presented data on use of the M-CHAT as a Level 1 screening instrument with community populations only. Robins (2008) reported on administration of the M-CHAT at the 18- and 24-month pediatric visits of 4797 children in the Atlanta area. Of this sample, 466 (9.7%) had positive screens, and their parents were subsequently interviewed by phone. Based on this interview, 61 were offered evaluation; 41 were evaluated; and 21 were identified as having an ASD. (Seventeen of the 20 children without ASDs had other developmental disorders.) As in the initial M-CHAT validation study, there is no information as yet on true sensitivity and specificity. In a Canadian study, Yama et al. (2011) reported on a telephone screening with the M-CHAT of a prospective cohort of 1604 children. They found that the instrument was useful from 20 to 40 months of age and that additional questions at the time of the initial interview yielded the same proportion of children at risk for an ASD as earlier M-CHAT studies that utilized a follow-up telephone interview. Thus, the M-CHAT appears to have acceptable screening properties and the advantage of ease of administration. It can identify a significant number of toddlers at risk for ASDs, and false positives tend to include children with other developmental disabilities. However, specificity of the instrument appears to depend on corollary information such as follow-up interview, and the true sensitivity of the instrument for general population screening has not yet been determined.

PDDST-II Another English-language Level 1 screening instrument is the Pervasive Developmental Disorder Screening Test-II (PDDST-II; Siegel, 2004). The PDDST-II is designed to be used in three stages, with Stage 1 suitable for primary care and the other two stages intended for use as a Level 2 screen in developmental clinics and autism programs. Stage 1 is called the PDDSTII Primary Care Screener and consists of a 22-item questionnaire to be completed by the parent. Its intended use is with children 18–48 months of age. The initial sample for standardizing this instrument consisted of 681 children at risk for ASDs and 256 children with other developmental disorders. With this sample, the instrument had a sensitivity of 0.92 and a specificity of 0.91 (Durmont-Mathieu and Fein, 2005). It has yet to be studied in a community setting.

Infant–Toddler Checklist The Infant–Toddler Checklist (ITC; Wetherby and Prizant, 2002) is a 24-item parent questionnaire that also includes one open-ended question about developmental concerns.

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It is one component of a larger assessment tool, the Communication and Symbolic Behavior Scales Developmental Profile. The ITC is not actually a specific screening instrument for ASDs, but a broad screen for developmental and language delays. It might, therefore, be less than ideal as a screening instrument at the 18- and 24-month health visits. The ITC does, however, provide standard scores for social communication milestones between 6 and 24 months and a cut-off score. It has been studied as a broadband screen for communication delays in a general population of 5385 children. Of the 60 children in this population who were subsequently diagnosed with an ASD by 4 years of age, 56 had previous positive screens on the ITC, between 9 and 24 months of age – a sensitivity of 0.93 (Wetherby et al., 2008). The ITC can be especially useful when pediatric surveillance indicates a need to administer a screening test, but the child is less than 18 months old – the minimum age for most Level 1 and Level 2 ASDs screening tools. Miller et al. (2011) used the ITC together with the M-CHAT to screen 796 toddlers (age 4–30 months) in a community-based pediatric practice. The two measures led to identification of ASD cases that were not picked up by pediatric surveillance, but both instruments were overly inclusive and follow-up telephone interview was needed to hone the at-risk population. Both instruments also missed cases that were subsequently diagnosed with ASDs.

Level 2 screening tools Level 2 screening tools are designed to detect a subgroup of children at risk for ASDs among those who have already been identified as having some form of developmental delay or disability. They are usually used by multi-disciplinary evaluation teams for this purpose and may be included in a comprehensive diagnostic assessment. Questionnaire-based Level 2 screening tools can also be used in doctors’ offices and other primary care settings. Examples of Level 2 instruments are the screening test for autism in 2-year-olds (STAT; Stone et al., 2000, 2004) and – for slightly older preschoolers – the Social Communication Questionnaire (SCQ; Rutter et al., 2003). Also used for screening at this level are several diagnostic measures, such as the Social Responsiveness Scale (SRS; Constantino and Gruber, 2005), the Childhood Autism Rating Scale (CARS; Schopler et al., 1988; now in its second edition, CARS-2, Schopler et al., 2010) and the Gilliam Autism Rating Scale–Second Edition (GARS-2; Gilliam, 2006).

STAT The STAT is a structured observational tool designed to differentiate young children with ASDs from others with language and developmental delays. It is intended for children aged 24–36 months and requires specific training to use. Children’s behaviors are assessed during a 20-minute interactive session. Twelve specific items are scored, covering the domains of motor imitation, play skills, requesting, and redirecting attention (Zwaigenbaum and Stone, 2008; Gamliel and Yirmiya, 2009). The STAT has been shown to have reasonable sensitivity and specificity when used with high-risk 2-year-olds; it has a sensitivity of 0.95; a specificity of 0.73; and a PPV of 0.56 (Zwaigenbaum and Stone, 2008). Psychometric properties are also adequate when used with high-risk children between 12 and 23 months of age, as long as a sufficiently high cut-off score is utilized (Stone et al., 2008).

SCQ The SCQ is a 40-item, caregiver-report questionnaire designed as a screening tool for autism in children aged 4 years and above, with a minimum mental age of 2 years. Each item can be

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answered “yes” or “no,” and the questionnaire can be completed in 10–15 minutes. The specific items derive from the ADI-R – a much longer, parent-interview diagnostic instrument widely regarded as the “gold standard” in establishing an ASD diagnosis. (See discussion below.) In the original validation study of the SCQ, sensitivity was 0.85 and specificity 0.75 when using a cut-off score of 15 to differentiate ASDs from non-ASDs (Berument et al., 1999). When tried as a Level 2 screening test in younger children (as young as 17 months), sensitivity of the SCQ was low, but improved if a lower cut-off score of 11 was used (Wiggins et al., 2007). The SCQ is the most validated screening instrument for ASDs, based on the number of studies that have been published; it performs best with older children. The SCQ has also proved to be a useful diagnostic screening tool for ASDs when prospectively following former preterm infants (Johnson et al., 2011).

SRS The SRS is a measure of reciprocal behavior, not strictly a diagnostic instrument. It is a 65item questionnaire that can be filled out by parents or teachers (on separate forms) with individual items rated on a four-point Likert scale (“not true,” “sometimes true,” “often true,” and “almost always true”). As has been discussed in Chapter 1, total SRS scores from ASD and non-ASD populations appear to be normally distributed. Thus, an individual’s total SRS score can generate a t-score, and t-scores greater than 75 are strongly associated with an ASD diagnosis. t-Scores of 60–75 represent deficiencies in reciprocal behavior that interfere with everyday social interactions. Scores in this range are described as typical of individuals with high functioning autism and Asperger’s syndrome (Constantino and Gruber, 2005). The SRS is thus a reasonable choice for use as a Level 2 screening instrument for ASDs. It can also serve as a clinical assessment tool. The target age population for the SRS is 4–18 years.

CARS The CARS is a structured interview and observation instrument that requires some training to administer. It has been used for over two decades as a diagnostic instrument and measure of autism symptom severity, but it was developed before DSM-IV and ICD-10, and a score on the CARS that is suggestive of an autism diagnosis does not necessarily mean that a child will meet standard diagnostic criteria. When compared to the ADI-R, the CARS appeared to over-identify autism (Ozonoff et al., 2005). The second edition of the CARS (CARS-2; Schopler et al., 2010) includes a “standard version” which is equivalent to the original CARS and is intended for use with children under 6 years of age, plus a new “high-functioning version,” which can be used with higherfunctioning, verbally fluent individuals over age 6. The standard CARS consists of 15 items scored by the examiner based on a 20–30-minute observation and play interview. Each item is scored on a four-point Likert scale, but if a child falls between two points, that can be specified, so that there are seven possible scores per item. For example, with the imitation item, a score of 2 would be for “mildly abnormal” imitation, which would indicate a capacity to imitate simple motor acts and single words most of the time; a score of 3 would be for “moderately abnormal” imitation, which would be used for imitating only some of the time or after long delays; but the examiner also has the option of assigning an intermediate score of 2.5 for this item. The CARS is appropriate for children over 24 months of age. Total scores above 36 indicate severe autism. Chlebowski et al. (2010), however, recommended a cut-off score of 25.5 for distinguishing ASDs from other developmental disorders and from typical

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development. In their study, this criterion yielded good sensitivity and reliability with populations of 2- and 4-year-old children.

GARS-2 The GARS-2 is a caregiver interview instrument consisting of 42 questions that can be answered on a four-point Likert scale (from “never observed” to “frequently observed”), 25 yes/no items that focus on a child’s behavior after age 3, and 11 open-ended questions. It is intended for use with individuals who are between 3 and 22 years of age. The GARS-2 is similar to the original Gilliam Autism Rating Scale (GARS; Gilliam et al., 1995) in the content of the first 42 items, which are divided into three conceptually derived subscales: stereotyped behaviors, communication, and social interaction. Independent studies of the psychometric properties of the original GARS have yielded mixed results (Pandolfi et al., 2010; Norris and Lecavalier, 2010). Pandolfi et al. (2010) conducted a factor analytic study of the GARS-2 and found that its subscales had limited clinical utility. The original validation study of the GARS-2 indicated adequate sensitivity, specificity, and PPV when the total Autism Index score was used to distinguish children with ASDs from both a normative sample and a group with developmental disabilities (Gilliam, 2006). The GARS-2 may therefore have some utility as a Level 2 screening tool for children 3 years of age and older. Whatever instrument is chosen to screen young children for possible ASDs, it is incumbent upon practitioners to explain to parents the nature of screening and the meaning of a positive screen. Most parents of children with positive screens will have already expressed concerns about their children’s development and the screening results suggest that their concerns were well-founded. However, parents need to understand that a positive screen indicates, at most, that their child is at risk for an autism spectrum disorder and, possibly, other non-autistic conditions. It should also be explained that screening tests, in order to be effective, have to be overly inclusive, hence false positive screens do occur. Also, a positive screen says nothing about prognosis. What must be emphasized is the need to proceed as soon as possible with a careful, comprehensive diagnostic assessment and for the child to be followed over time. Parents will have anxiety, and will need ongoing consultation from their primary care providers. They will also need to be directed to informative resources that they can consult to learn more about ASDs and other developmental disorders. Referral to an early intervention program even before a diagnosis is established, as recommended by the AAP Council on Children with Disabilities, should help to address many of these concerns on the part of parents and other family members.

Comprehensive diagnostic assessment A comprehensive diagnostic assessment serves many purposes. First, it determines whether a child has an ASD diagnosis, based on DSM-IV or ICD-10 criteria. Second, it provides an assessment of the child’s linguistic, cognitive, and adaptive functioning, so that appropriate planning for educational interventions and related services can proceed. Third, it seeks to determine, whenever possible, the etiology of a child’s autism. Fourth, it seeks to identify comorbid medical and psychiatric conditions that may need treatment. Ideally, comprehensive evaluations should be carried out by a multidisciplinary team of trained professionals or by collaborating individual clinicians in the same community. These professionals would typically include psychologists, early childhood or special educators, speech-language pathologists, occupational therapists, and early intervention specialists

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(Levy et al., 2009). They may also include developmental-behavioral pediatricians, child psychiatrists, pediatric neurologists, and geneticists. All these individuals bring their own expertise to the evaluation process; they also provide an opportunity for multiple, separate observations of a child, which are needed – together with a detailed history from parents and other caregivers – to obtain an accurate picture of the child’s performance. All professionals must also take a developmental approach to assessment, recognizing that the form and quality of autism symptoms change over time and that early developmental lags and achievements impact later skill acquisition (Ozonoff et al., 2005).

Parent interview An effective comprehensive diagnostic process combines a detailed interview of the parents with multiple observations of the child. The interview should cover the parents’ concerns; developmental history, with specific reference to developmental deviations characteristic of autism; the child’s behavior; and the child and family’s medical history. Structured interviews facilitate history-taking that can lead to a diagnosis. As has already been mentioned, the Autism Diagnostic Interview-Revised (ADI-R; Lord et al., 1994) is widely used in autism research for establishing a DSM-IV diagnosis. The ADI-R requires training and takes up to 1.5–2.5 hours to administer. It consists of 93 items, probing 8 content areas: a review of the subject’s behavior; family education and medical background; early development and developmental milestones; language acquisition and any history of language regression, or loss of other skills; current language and communication functioning; social development and play; interests and behavior; and clinically relevant behaviors, such as self-injury, aggression, and epileptic features. Individual items are coded by a trained examiner based on parent responses. Diagnostic algorithms are generated from selected items that best distinguish children with autistic disorder from those with other developmental disabilities and conform to the three DSM-IV domains of qualitative abnormalities in reciprocal social interaction, qualitative abnormalities in communication, and restricted, repetitive, and stereotyped patterns of behavior. There is a specified cut-off score for each domain (Lord et al., 1994). The ADI-R is designed to be used with children whose mental age is greater than 24 months. It focuses on age 4–5 years as a significant developmental time period in many of its questions. The ADI-R has good sensitivity and specificity as a diagnostic instrument, but its sensitivity for milder ASDs is low at age 2, better by age 3.5 (Ozonoff et al., 2005). Given the training and length of time required to administer and score the ADI-R, some clinicians may prefer to conduct their own interviews with parents and supplement these with some of the caregiver questionnaire and interview instruments previously discussed, such as the SCQ (which correlates well with the ADI-R), the SRS, the GARS-2 and – for children younger than 30 months – the M-CHAT. Using multiple instruments may contribute to diagnostic accuracy. Instruments that yield quantitative scores can also be used as measures of symptom severity and can be re-administered at later times to assess progress.

Interactive observation The observational component of diagnostic assessment is also enhanced by the use of structured tools. The STAT (for toddlers) and the CARS-2 have already been mentioned in this regard. The former, however, is a screening instrument and the latter cannot be used to determine a reliable DSM-IV or ICD-10 diagnosis. More suitable for this purpose is the

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Autism Diagnostic Observation Schedule (ADOS; Lord et al., 2002). Like the ADI-R, the ADOS is widely used in research and considered to be a gold standard instrument for this purpose; it utilizes DSM-IV diagnostic criteria. The ADOS is a semi-structured observational assessment that takes 30–45 minutes to administer. Examiners must have extensive training in its use. The ADOS was initially developed to diagnose autistic disorder, and it also permits differentiation of autistic disorder from other ASDs (i.e. the diagnostic algorithm provides separate cut-off scores for “autism” and “autism spectrum” in the domains of communication and reciprocal social interaction). The ADOS comes in four modules, with choice of module dependent on a patient’s verbal abilities and age. Module 1 is used primarily with toddlers and young children who are preverbal or whose expressive language consists of single words or two-word phrases. Coding for diagnostic algorithms is based on observations made during 10 activities that utilize toys, e.g. bubble play and a pretend birthday party. The standardized materials and situations created by the examiner provide multiple opportunities to observe and elicit social interaction and other behaviors, such as joint attention, shared enjoyment, making requests. If the elicited social behaviors are not observed after several opportunities to display them, then they are assumed to be absent or too difficult for the child (Ozonoff et al., 2005). Module 2 is used with young children who speak, at least in flexible three-word phrases, but are not verbally fluent. It consists of 14 activities, e.g. a construction task, describing a picture. Module 3 is used with verbally fluent children; module 4, with verbally fluent adolescents and adults. The ADOS has excellent interrater reliability for all four modules and excellent sensitivity and specificity for diagnosing ASDs (Naglieri and Chambers, 2009). Diagnostic accuracy is enhanced by combining ADOS diagnosis with parent interview, thereby adding information on past symptoms to present observations. Risi et al. (2006) found that sensitivities and specificities were improved when the ADI-R and ADOS were used together; the two instruments made independent, additive contributions to the judgment of clinicians in diagnosing ASDs. As already noted, the collaboration of a multi-disciplinary team or the combining of separate evaluations by different clinicians contributes to diagnostic accuracy. Speechlanguage pathologists, occupational therapists, and psychologists may be primary evaluators of a child suspected of having autism, conducting parent interviews and diagnostic observations, but their assessments are more likely to be primarily focused on a child’s specific skills and deficits in speech, communication, adaptive functioning, and cognition. In performing focused assessments, however, these professionals also make observations and gather information on the child’s qualitative impairments in social communication and on restricted, repetitive interests and behaviors. Thus, an ASD diagnosis is most firmly established when a full and detailed history has been obtained from the parent, multiple observations have been made of the child by different professionals, and standardized diagnostic interview and observational instruments have been employed. The information thus obtained can be synthesized by the team or by the clinician managing the case to determine if a specific diagnosis can be made, in accordance with DSM-IV or ICD-10 criteria.

Assessments of cognitive, linguistic, and adaptive functioning As will be discussed in subsequent chapters, the primary treatments for the core symptoms of autism are educational and habilitative. Interventions that have been shown to be effective are based on principles of child development and behavioral psychology and are

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individualized. In addition to determining diagnosis and providing a description of the child’s unique manifestation of ASD symptoms, a comprehensive evaluation facilitates treatment planning by providing a profile of the child’s strengths and weaknesses as a learner. Cognitive, or intellectual, assessments are done by psychologists or neuropsychologists. Selection of an assessment instrument is a clinical decision to be made by the psychologist, based on the purpose of the evaluation and the judgment of what measures might best reflect an individual’s abilities. Table 7.2 lists some measures of intellectual functioning that may be used by psychologists when assessing preschool and school-age children with ASDs. Children with ASDs present particular challenges to psychologists. Their limited social abilities, unusual or absent use of language, off-task behaviors, distractibility, and variable motivation make them difficult to engage with testing materials. Examiners must be familiar with these challenges and must find ways to motivate children and have them complete assessment tasks, without altering the standardized procedures of test instruments (Ozonoff et al., 2005). When deciding on a specific test, examiners need to take into consideration the developmental level and verbal abilities of the child. If a child’s developmental age or linguistic abilities are significantly below his chronological age, his inability to complete items on an assessment instrument selected only for chronological age could result in his receiving the lowest standard score provided by the instrument. His actual level of intellectual functioning, if below this score, would not be determined and the assessment would provide limited information on his individual pattern of strengths and weaknesses (Klinger et al., 2009). Some of the considerations that go into the choice of a cognitive measure, therefore, depend on factors such as the targeted age range of the instrument, the time required for administration, whether the order of the test items can be varied, whether the test provides separate measures of verbal and nonverbal intelligence, and the complexity of verbal directions. Ideally, any test selected should be appropriate to a child’s chronological and mental age, have a full range of standard scores, and include separate measures of verbal and nonverbal skills (Filipek et al., 1999). A cognitive assessment provides direct information on a child’s language skills, spatial skills, attention, and fine motor skills that can be used in developing his individualized Table 7.2 Selected tests of intellectual functioning

Test

Age range

Mullen Scales of Early Learning (Mullen, 1995)

Birth–5 years, 8 mos

Wechsler Preschool and Primary Scale of Intelligence, 3rd edition (WPPSI-III, 2002)

2 years, 6 mos–7 years, 3 mos

Wechsler Intelligence Scale for Children, 4th edition (WISC-IV, 2003)

6–16 years, 11 mos

Stanford Binet Intelligence Scale, 5th edition (SBS-V) (Roid, 2003)

2–85+ years

Differential Ability Scales, 2nd edition (DAS-II) (Elliott, 2007)

2 years, 6 mos–17 years, 11 mos

Leiter International Performance Scale – Revised (Leiter-R) (Roid and Miller, 1997). Completely nonverbal measure.

3–18 years

Kaufman Assessment Battery for Children, 2nd edition (KABC-II) (Kaufman and Kaufman, 2004)

2–20 years

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intervention or education plan. It also provides a measure of overall intellectual functioning that has prognostic value and can help school placement decisions. When testing reveals significantly subaverage intellectual functioning, it suggests a diagnosis of developmental delay in preschool children; and, along with significant deficits in adaptive functioning, establishes a diagnosis of intellectual disability in school-age children. When an ASD and ID both occur, a child must be given both diagnoses. The ID diagnosis will have life-long implications in terms of eligibility for educational, vocational, and support services. It may also have implications with regard to etiology. Along with level of intellectual functioning, an autistic child’s expressive verbal abilities are the best predictor of long-term outcome (Howlin et al., 2007). Comprehensive assessment of skills and deficits in receptive and expressive language, speech, and social communication is fundamental to the development of a treatment plan. This assessment is performed by speech-language pathologists and is described in Chapter 9. Evaluations of sensory and motor functioning and daily living skills are also essential to the treatment planning process. These evaluations are usually performed by occupational therapists and are also described in Chapter 9. An additional domain that should be assessed in a comprehensive evaluation is adaptive behavior, which must be measured in order to determine a diagnosis of intellectual disability. Measures of adaptive behavior also provide a useful way to assess an autistic individual’s strengths and weaknesses beyond those in cognitive skills. It is not unusual for children with ASDs to demonstrate adaptive behavior levels that are significantly lower than their cognitive levels. The most commonly used measure of adaptive behavior is the Vineland Adaptive Behavior Scales, now in a second edition, Vineland-II (Sparrow et al., 2005). These scales include a parent/caregiver rating form (checklist), a teacher rating form, and two caregiver interviews – a shorter survey form and an expanded form. The interview forms require some training to administer, and the survey takes 20–60 minutes. The Vineland-II can be used with individuals from birth to age 90. Functioning is assessed in three domains: communication, daily living skills, and socialization. A fourth domain of motor skills is added for children under 7 years of age. Results for each domain are expressed in standard scores, with a mean of 100 and a standard deviation of 15. There is also an adaptive behavior composite score. Another measure of adaptive behavior is the Adaptive Behavior and Assessment System (ABAS-II; Harrison and Oakland, 2003). This instrument consists of caregiver and teacher rating scales that take around 20 minutes to complete. Specific skill areas that are assessed are labeled as follows: communication, community use, functional academic or pre-academic, home/school living, motor, health and safety, work, leisure, self-care, and social. ABAS-II scores have been reported to correlate with Vineland-II scores, and both instruments were standardized with populations that included individuals with ASDs (Klinger et al., 2009). Moving beyond the essential components of a comprehensive evaluation for diagnosis and treatment planning, one may consider neuropsychological assessment. Such an evaluation would be performed by a certified neuropsychologist and address those domains of neuropsychological functioning that are known to be affected in ASDs: attention, sensory functioning, language, memory, executive functioning, and visual-spatial processing – in addition to cognitive, adaptive, social, and academic functioning. For discussion of neuropsychological testing of children with ASDs, the reader is referred to the review by Corbett et al. (2009) and the book by Deborah Fein (2011).

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Etiologic evaluation By the time the child has been referred for a comprehensive diagnostic assessment, the primary care doctor should also have made a referral for audiologic evaluation. The child likely has had periodic developmental assessments and physical examinations with an emphasis on detecting any developmental delays and dysmorphic features. These preliminary examinations – along with the cognitive testing and speech-language evaluation performed during a comprehensive assessment – provide information that can help with the next decision: to what extent to pursue an investigation of the cause of autism in a particular child. If the child has developmental delays or multiple congenital anomalies, further evaluation for a neurogenetic or metabolic disorder is indicated. The American Academy of Pediatrics Committee on Genetics (Moeschler et al., 2006) and several groups of geneticists (Gijsbers et al., 2009; Miller et al., 2010) have made specific recommendations for the diagnostic evaluation of children with developmental disorders or congenital abnormalities, and these can be followed for ASD cases with these features. Components of the evaluation recommended by the AAP Committee on Genetics are a cytogenetic evaluation, a FISH subtelomere study, fragile X or other indicated molecular testing, and targeted metabolic testing and targeted brain imaging (MRI). Selection of what studies to obtain depends on clinical features. If a specific genetic diagnosis is suspected, based on history and examination, then cytogenetic, subtelomere, molecular, and metabolic investigations are undertaken, as indicated, to confirm the diagnosis. If no specific diagnosis is suggested by history and examination, then the recommended procedure is to obtain fragile X and routine cytogenetic testing, and, if negative, to follow up with FISH subtelomere and metabolic testing. (The purpose of the subtelomere study is to identify the approximately 7.4% of children with moderate to severe intellectual disability who have clinically significant deletions and duplications in subtelomeric regions of chromosomes, but normal results on routine chromosome analysis.) Only if these studies fail to yield a diagnosis are further metabolic testing and an MRI to be considered, and then only if indicated by a history of regression (for metabolic testing) and an abnormal head size or neurologic examination (for MRI). These recommendations have been amended by geneticists who cite recent evidence that chromosomal microarray studies offer a much higher diagnostic yield in individuals with unexplained developmental delay, intellectual disability, ASDs or multiple congenital anomalies than traditional cytogenetic studies. Miller et al. (2010) recommended a chromosome microarray as the initial laboratory investigation, with follow-up fragile X and targeted single gene testing if the microarray results are negative. When the microarray identifies a genetic mutation of known significance, follow-up studies are undertaken to confirm this diagnosis and evaluate the parents. If – as often occurs with microarray studies – a genetic variant (copy number variation) of uncertain significance is discovered, then a protocol is followed for obtaining and interpreting genetic investigations of the parents. Gijsbers et al. (2009) recommended a similar approach. Another frequently co-occurring condition in children with autism is epilepsy. When taking a history and examining a child with an ASD, physicians must attend to symptoms and signs of a seizure disorder. If clinical epilepsy is suspected, a neurologic evaluation is indicated and likely to include an EEG and neuroimaging. If a child has a history of significant developmental regression, particularly in language, the possibility that seizures or an epilepsy syndrome may be playing a role should be considered. A sleep-deprived EEG

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or video EEG may help to clarify the clinical picture. Metabolic evaluation may also be indicated, particularly if there is a history of episodic regression, lethargy, unusual odors, or cyclic vomiting. In ASD cases where there is no evidence of developmental delays, dysmorphology, epilepsy, or significant regression, recommendations with regard to etiologic evaluation are less clear, with no clear consensus on this issue. Miles (2011) has recommended a DNA microarray and fragile X molecular testing for all children with autism. The Professional Practice Guidelines Committee of the American College of Medical Genetics (Schaefer et al., 2008), offering recommendations for “medical geneticists and other health care professionals,” has suggested a tiered approach to evaluating referred children with autism, both with and without congenital anomalies. The first tier would be to focus on possible syndromes, as indicated by history and physical examination, and obtain DNA for fragile X, a chromosome analysis, and a standard (i.e. newborn) screen for metabolic disorders. If a metabolic diagnosis is suspected, urine for mucopolysaccharides and organic acids and serum lactate, pyruvate, ammonia, and acyl-carnitine profile would also be obtained. Second-tier studies would include a chromosomal microarray, MECP2 gene testing in females, and PTEN gene testing if a child has macrocephaly. Third-tier studies would involve further metabolic investigation and brain imaging. This careful, step-wise approach could possibly yield a genetic etiology in as many as 40% of referred ASD cases (Schaefer et al., 2008). A similar tiered approach is recommended by the AAP Council on Developmental Disabilities (Johnson et al., 2007), but studies of children without clear intellectual disability (or developmental delay) or evidence of neurologic or metabolic disease are not recommended. Schiff et al. (2011) has recommended against routine metabolic screening in non-syndromic ASDs. There does, however, seem to be a consensus that, at present, there is insufficient evidence to recommend screening EEGs in children with ASDs, despite the high rate of co-occurring epilepsy and epileptiform abnormalities (Kagan-Kushnir et al., 2005; Johnson et al., 2007). Also, routine brain imaging is not recommended (Levy et al., 2009). The primary reason to pursue an etiologic diagnosis is to provide meaningful information and advice to families. Although outcome in ASDs, in general, depends on factors like a verbal ability and IQ, in an individual case, clinical course may be determined by etiology primarily. Risk of recurrence of autism in subsequent offspring also depends on the cause of a child’s ASD. The overall chance of a couple’s having a second child with autism is 8–18% (Szatmari and Jones, 2007; Constantino et al., 2010; Ozonoff et al., 2011), but certain genetic disorders have a 50% chance of occurring with each subsequent pregnancy. Having an explanation for a child’s autism also reduces some of the uncertainty and anxiety associated with an ASD diagnosis. Parents who know why their child is autistic are less prone to accept speculative theories and seek unconventional treatments. In addition, if the cause is a known syndrome or genetic disorder, then the child has two diagnostic homes; and the second home may come with its own knowledge base, resources, and support groups. It may also provide specific interventions and experimental treatment protocols that confer hope for progress – in an individual’s development and in our understanding of autism.

Consideration of comorbid disorders At the time of comprehensive diagnostic assessment or shortly thereafter, inquiry should be made with regard to certain disorders that are frequently associated with ASDs. The most

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important of these comorbid conditions – in terms of etiologic implications, rate of occurrence, treatment needs, and lifelong impact – are intellectual disability and epilepsy, which have already been discussed. Other problems frequently encountered in autistic children are gastrointestinal complaints, sleep disturbance, and psychiatric disorders.

Gastrointestinal problems Gastrointestinal problems have been reported in 9–70% of children with ASDs (Myers et al., 2007). In a cross-sectional comparison study, Valicenti-McDermott et al. (2006) found that 70% of children with ASDs had a lifetime prevalence of gastrointestinal symptoms, compared to 28% of typically developing children. The most common symptoms were food selectivity and abnormal stool pattern. Nikolov et al. (2009) reported that 22.7% of subjects in the RUPP Autism Network studies had GI problems, primarily constipation and diarrhea, and that these children had greater symptom severity on measures of irritability, anxiety, and social withdrawal. However, Mouridsen et al. (2010), when looking at GI disease, rather than symptoms, in a case-control study, found no evidence that patients with infantile autism were more likely than controls to have gastrointestinal diseases over a 30-year observation period. Similarly, Ibrahim et al. (2009), following autistic cases and controls to age 18, found a higher cumulative incidence of food selectivity and constipation in autistic cases, but no other associations between autism and GI diagnoses. And when comparing children with ASDs to those with other developmental and neurological disorders, Smith et al. (2009) found no differences in frequency of bowel symptoms, such as constipation, diarrhea, and flatulence. It can be inferred from these various studies that children with ASDs have an increased rate of gastrointestinal symptoms, which may affect mood and behavior, but many of these symptoms are attributable to GI motility problems that are also common in other neurodevelopmental conditions and are not rare in typically developing children. There does not appear to be a significant association between autism and other types of gastrointestinal disease. These observations are worth noting because, in the years since the theory of a “leaky gut” and its association with intestinal lymphoid hyperplasia and the MMR vaccine was first advanced by Wakefield, there have been repeated claims that there is a type of inflammatory bowel disease that is specific to, and may play an etiologic role in, autism. (See discussion in Chapter 2.) A multi-disciplinary panel recently reviewed the medical literature on gastrointestinal disorders in individuals with ASDs and issued a consensus statement with recommendations for evaluation and treatment (Buie et al., 2010a). Among their conclusions were that the GI conditions reported to be common in individuals with ASDs are the same as those encountered in individuals without ASDs. Signs and symptoms of these conditions are chronic constipation, abdominal pain, with or without diarrhea, and encopresis. GI abnormalities include gastroesophageal reflux disease (GERD) and disaccharidase deficiencies. Possible behavioral presentations of GI symptoms were described by the panel in detail: disturbed sleep and nighttime awakening (often in association with GERD and esophagitis); unexplained irritability; constant eating/drinking/swallowing, facial grimacing, and gritting teeth; screaming, crying for no reason; unusual posturing such as arching back; agitation, pacing; increases in stereotypic and self-injurious behaviors; and certain verbalizations, e.g. delayed echolalic statements making reference to the belly or to pain. The consensus statement recommended that care providers consider that a GI symptom, particularly pain, may underlie serious problem behaviors in children with ASDs. Suspicion

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of significant GI symptoms constitutes an indication for medical investigation, and children with autism should have the same thorough gastrointestinal evaluation as any other child. Also, caregivers and healthcare providers need to learn to recognize signs and symptoms of GI diseases. The consensus statement also recommended: (1) careful attention to the nutritional status of children with ASDs; (2) obtaining a history to consider potential association between exposure to food allergens and GI or behavioral symptoms; (3) evaluation of children with ASDs for comorbid allergic disease; and (4) involvement, as needed, of specialists – allergists, gastroenterologists, nutritionists, and feeding therapists – in the management of children with ASDs. Eight gastroenterologists who participated in the consensus statement also issued a number of guidelines for the diagnostic evaluation and treatment of abdominal pain, chronic constipation, and GERD in children with ASDs (Buie et al., 2010b). Discussion of these guidelines is beyond the scope of this chapter, but the reader is referred to this report, particularly with regard to the initial approach to diagnostic assessment. The child with ASD who has GI symptoms – or changes in mood and behavior that may be attributable to GI symptoms – needs a careful diagnostic history, nutritional assessment, and physical examination, with referral, when indicated, to a gastroenterologist, nutritionist, allergist or feeding specialist. It is especially important that these symptoms be inquired about and parents’ complaints addressed. There is a strong, presently unsubstantiated belief among many parents and advocacy groups that gastrointestinal disease is unique and highly prevalent in autistic children, that it causes some of the core features of ASDs, and that its treatment will improve their social and cognitive development and ultimate outcome. Many of the treatments from complementary and alternative medicine that parents try are targeted at purported gastrointestinal disease, and some of these treatments are highly controversial. When a child with autism is being evaluated or treated for gastrointestinal symptoms, it is essential that parents receive accurate information about the nature of GI disorders in autism, the goals and limitations of therapy, and the actual relationship, if present, between the child’s abdominal discomfort and his behavior.

Sleep disturbance Sleep problems are another frequently encountered comorbidity in children with ASDs, with a prevalence of 40–80%. The most common sleep problem is insomnia, including difficulties initiating and maintaining sleep. Night awakenings in autistic children are frequently prolonged, as long as 2–3 hours per night (Bauman, 2010). Other intrinsic sleep problems reported in children with ASDs include circadian rhythm dysfunction, obstructive sleep apnea, non-REM parasomnias, and REM sleep behavior disorders (Johnson et al., 2009). Sleep problems may be secondary manifestations of pain; psychiatric symptoms, such as anxiety and depression; GERD and other GI disorders; a seizure disorder; or upper respiratory conditions. It can be anticipated that most parents will spontaneously report significant sleep disturbance in their children with ASDs. Systematically inquiring about sleep problems is still likely to be helpful, especially when obtaining a clinical history that addresses symptoms of possible gastrointestinal disease and epilepsy. After sleep problems have been identified, their nature and severity can be evaluated using parent-report screening questionnaires, such as the Children’s Sleep Habits Questionnaire (CSHQ; Owens et al., 2000) and the Pediatric Sleep Questionnaire (PSQ; Chervin et al., 2000). Both instruments provide a number of

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subscales measuring different aspects of sleep. They both have been found to have adequate validity and reliability in discriminating a sleep disorder group from a community group of children along medical dimensions (e.g. disordered breathing, parasomnias) and behavioral dimensions (e.g. bedtime resistance) of sleep disturbance. Based on clinical history and response to questionnaires, a decision can be made as to whether a child needs referral to a sleep specialist for further evaluation. As Johnson et al. (2009) have emphasized, a diagnosis of obstructive sleep apnea should not be missed. Children with syndromic autism and dysmorphic features may be at increased risk for this problem. Ultimately, most autistic children with sleep difficulties will be found to have insomnia. Once pain and medical causes have been addressed, the primary treatments will be behavioral: parent education, sleep scheduling, and sleep hygiene procedures. Pharmacotherapy may be considered. (See Chapter 11.)

Psychiatric disorders Accompanying psychiatric symptoms are also quite common in individuals with ASDs – beyond the restricted, repetitive behaviors and interests and social communication problems that constitute the core symptoms. What is unclear, however, is whether symptoms of anxiety, hyperactivity, oppositional behavior, aggression, and self-injurious behavior should be conceptualized as variably manifested symptoms of ASDs, or whether they should be considered symptoms of co-occurring, comorbid psychiatric disorders (e.g. of ADHD when criteria for both an ASD and ADHD are met). Different investigators have approached this problem in different ways. Taking the symptoms-imply-comorbidity approach, Joshi et al. (2010) reported that 95% of children with ASDs referred to a pediatric psychopharmacology clinic had three or more comorbid psychiatric disorders. Simonoff et al. (2008), in a population-derived cohort of 10- to 14-year-olds with ASDs, found that 70% had at least one comorbid psychiatric disorder and 41% had two or more. The most common additional diagnoses were social anxiety disorder, ADHD, and oppositional-defiant disorder. In a combined community and clinic-based sample of 9- to 16-year-olds with high-functioning autism and Asperger’s syndrome, Mattila et al. (2010) found 74% with present comorbid psychiatric disorders, 84% with a lifetime history. The most common lifetime disorders were ADHD, phobias, OCD, and tic disorders. One can easily argue with this approach. Oppositional behavior and obsessive–compulsive features overlap with one of the core features of autism – rigid and repetitive behaviors. Social anxiety would be expected in an individual whose primary difficulties are in social communication. Parents and teachers frequently observe labile moods, generalized anxiety, hyperactivity, and attention deficits in children with ASDs. DSM-IV-TR, in fact, excludes making a diagnosis of ADHD or obsessive–compulsive disorder in an individual whose symptoms can be attributed to a PDD. Even if one does not accept the notion of a high prevalence of comorbid psychiatric disorders in ASDs, there is no doubt that many children and adolescents with autism have significant mood and behavioral disturbances that require evaluation (and likely referral to a mental health professional). Some of this psychopathology does constitute true comorbidity: disorders whose symptoms do not have a major overlap with symptoms of ASDs, such as depression, bipolar mood disorders, and psychoses. Identifying these disorders in minimally verbal individuals who lack insight or cannot report their thoughts and feelings can be especially challenging. In a review of the literature on comorbid psychopathology in ASDs,

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Matson and Nebel-Schwalm (2007) found that the strongest evidence for clear comorbidity was with phobias and depression. They suggested that patterns of waxing and waning symptoms can be useful for identifying cyclic mood disorders, phobias, depression, and psychosis. They also lamented the absence of ASD-specific measures that address comorbid psychiatric disorders. Scahill (2005) has recommended the inclusion of several instruments that measure behavior problems and psychiatric symptoms in the optimal diagnostic evaluation for ASDs – even though most of these measures were not developed for use with autistic individuals. These instruments can provide a quantitative measure of problematic behaviors and specific psychiatric symptoms. They are as follows. The Child Symptom Inventory-4 (Gadow and Sprafkin, 1998) or the Early Childhood Inventory-4 (Gadow and Sprafkin, 2000). The former is for children 5–12 years of age; the latter for children from 3 to 5 years. Both are checklists, to be filled out by parents and teachers, that screen for DSM-IV disorders. The Aberrant Behavior Checklist (ABC; Aman et al., 1985) or the Developmental Behavior Checklist (DBC; Einfeld and Tonge, 1995). The ABC and DBC were developed for use in individuals with intellectual disability and provide a quantitative measure of problematic behaviors, disturbed communication, and social relatedness. While not diagnostic, such measures can document the severity of emotional and behavior problems and can be used to measure response to treatment interventions. Together with the Vineland Adaptive Behavior Scales, they can provide valuable information to treatment teams developing individualized intervention programs. The Children’s Yale–Brown Obsessive Compulsive Scale – Pervasive Developmental Disorders (CYBOCS-PDD; Scahill et al., 2006). The CY-BOCS-PDD is an adaptation of the CYBOCS (Scahill et al., 1997), a semi-structured interview that is used to diagnose obsessive– compulsive disorder. The assessment instruments recommended by Scahill can be a useful component of a comprehensive evaluation by an interdisciplinary team or a specialist to help determine when referral for further evaluation of an autistic child by a psychologist or psychiatrist is indicated. Ideally, such a person would have clinical experience in treating children with ASDs and be familiar with the types of treatments that are provided in early intervention and special education programs. Such professionals should also be comfortable collaborating with other providers. As with intellectual disability, epilepsy, gastrointestinal disease, and sleep problems, the identification of significant accompanying psychiatric symptoms and possible comorbid psychiatric disorders is an important component of a comprehensive evaluation. Treating these symptoms can have a salutary effect on the comfort, adjustment, and availability for learning of children with ASDs.

Summary This chapter has reviewed the identification, diagnosis, and clinical evaluation of children with autism spectrum disorders. The goals and methods of each of these endeavors have been discussed, with particular reference to procedures recommended by various physician panels and to assessment instruments. We now have an ever-increasing wealth of knowledge on ways in which the early development of children with autism differs from that of their peers. Diligent application of surveillance and screening based on that knowledge should lead to

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identification and referral of children at risk for ASDs by the late second or third year of life. Application of that knowledge by multi-disciplinary teams and trained professionals using valid and reliable diagnostic instruments should permit ever earlier diagnosis of autism and provision of autism-specific treatment interventions. The next four chapters will discuss those treatments and the roles of the various clinicians who deliver them.

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Section 2 Chapter

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Assessing and Treating Children with ASDs

Educational treatments for children with ASDs Judy L. Horrocks

This chapter will review several topics in the educational treatment of children with autism spectrum disorders. The discussion will provide a broad overview of preschool and school-based interventions, some of which will be addressed in greater detail in the next two chapters. Specific topics to be covered are early intervention services, special education for children of school age, comprehensive and focused interventions that are used in the classroom, transition to adult services, and inclusion of ASD children in regular education.

Early intervention services (birth until age 3) When a child is diagnosed with autism or a developmental delay, parents endeavor to find educational services as early as possible to address their child’s needs. Most European countries and the United States offer educational services to developmentally delayed children and their families prior to school age. The purpose of early educational services is to identify children who have, or are at risk for, a specific disability or general developmental delay, and to design a program to meet their needs before the typical start of public educational services. Research has demonstrated that children who receive early special services have better outcomes, and families who are provided with early support are better equipped to advocate for their children later in life (Guralnick, 1997). The delivery of these services, known as “Early Intervention Services” or “Early Childhood Intervention,” can differ among countries. Some countries have center-based programs (such as Head Start in the US), homebased programs (such as Portage in the UK), or a mixed program (such as Lifestart in Australia). Most of these countries have services beginning at birth; however, parents often need to seek referral to those systems through their medical doctors. Most programs are funded entirely by the government; however, some programs may require fees for specific services (“Special Education,” 2010). Early intervention services in the US focus on five developmental areas: physical development, cognitive development, communication, social or emotional development, and adaptive development. The funding for these services was established in 1986 as a part of the revision of the Education for All Handicapped Children Act. The revision to this federal legislation lowered the age at which children can receive special services to 3 years and established the Handicapped Infants and Toddlers Program for children in need of help from birth to their third birthday. State governments must designate a lead agency to receive the funds and administer the program. States have some discretion in setting the criteria for The Autism Spectrum, ed. Mark E. Reber. Published by Cambridge University Press. © Cambridge University Press 2012. 201

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eligibility and the agency designed to administer this program, so there can be significant differences from state to state. Under the federal regulations, timely comprehensive evaluations and assessments are required to be provided at no cost to the parent. This evaluation is conducted by a multidisciplinary team (professionals with training and experience in areas of education, speech and language, physical development, hearing and vision, etc.) to find out the nature of the child’s strengths, delays or difficulties and determine whether the child is eligible for early intervention services under each state’s criteria (National Dissemination Center for Children with Disabilities, 2010). Following the evaluation, the family members and service providers meet as a team to determine the necessary services and plan the implementation of the child’s program. Early intervention services may be simple or complex depending on the child’s needs. An Individualized Family Service Plan (IFSP) is written outlining the services to facilitate the child’s development and document how the services will be evaluated. Sometimes services are provided in the child’s home; however, they may also be provided in other settings, such as a neighborhood daycare center, hospital, or medical facility. To the maximum extent appropriate, the services are expected to be provided in natural environments or settings (National Dissemination Center for Children with Disabilities, 2010). Many European countries have early intervention service models similar to those in the US, although their implementation may vary from country to country. The European Agency for Development in Special Needs Education provides detailed information on the services in many European countries (cf. http://www.european-agency.org) (European Agency for Development in Special Needs Education, 2005).

Early intervention for autism Children at risk for autism are often referred to – and may initially be identified in – early intervention programs for the developmentally disabled. However, once diagnosed, they are most likely to benefit from the earliest possible provision of specific services for young children with ASDs. Characteristics of effective interventions for young children with autism have been summarized by the National Research Council (2001) and Myers et al. (2007). These include:      

enrollment as soon as a suspicion of a developmental problem is considered, even before establishment of a diagnosis of an ASD; time-intensive programming of at least 25 hours per week; repeated, planned teaching opportunities, incorporating predictable routines and one-toone attention; inclusion of a family component, including parent training; low student-to-teacher ratio; curricula that focus on:      

functional spontaneous communication; social skills, including joint attention, imitation, and reciprocal social interaction; functional adaptive skills; reduction of maladaptive behaviors; cognitive skills, such as symbolic play and perspective taking; school readiness skills;

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ongoing program evaluation and assessment of children’s progress;  interaction with typically developing peers; and  strategies for generalizing learned skills to new environments. A comprehensive intervention that was recently developed by Sally Rogers and her colleagues for very young children with autism is the Early Start Denver Model (ESDM) (Smith et al., 2008). This manual-based program combines intensive instruction by a trained therapist for 20 hours a week with parent training and parent integration of the teaching model into daily routines and play. Instruction utilizes techniques from both applied behavior analysis and developmentally based interventions, with an emphasis on interpersonal exchange, shared engagement with real-life materials, parents’ responding to children’s cues, and verbal and nonverbal communication. Dawson et al. (2010) reported on a 2-year randomized comparison of the ESDM with commonly available early interventions in a population of toddlers (18–30-months-old at enrollment) with autistic disorder and PDD-NOS. At 1 and 2 years there were significantly better gains in IQ in the ESDM group. After 2 years, the ESDM group had higher adaptive functioning.

Special education services (age 3–21) The history of special education actually dates back to the work of Valentin Hauy, who opened a school for the blind in Paris in 1784 and developed a way to print books with raised letters that could be read using the fingers. Hauy demonstrated the skills of some of his blind students to Louis XVI and Marie Antoinette in 1786. The king and queen were impressed with the children’s accomplishment, and the school became known as the Institution Royale des Juenes Aveugles (Royal Institute for Blind Youth). In 1799, a child of approximately 11–12 years of age was found by a local woodsman in France. It appeared that this child, later named Victor, had survived in the woods without assistance for many years. Victor was taken to Paris to be studied as an example of the primitive mind. Notable physicians in Paris found the boy to be an incurable idiot, a term used for the mentally deficient. Jean Marc Gaspard Itard disagreed and attempted to provide intensive individualized instruction to the young boy. Itard’s instruction did demonstrate success through improvement in Victor’s abilities, but he never approached normalcy. This was the beginning of special education as we know it today (Itard, 1802). In the US, nationwide special education was first mandated in 1975 by Public Law 94–142, the first Education for All Handicapped Children Act (EAHCA). This was a monetary appropriations law, so this statute had to be reauthorized by Congress every few years. In 1990, the federal laws were consolidated into new legislation named Individuals with Disabilities Education Act (IDEA), then it was changed to Individuals with Disabilities Education Improvement Act (IDEIA), but most people still refer to it as IDEA (Exkorn, 2005). Like its previous versions, IDEA (2004) requires schools to provide a free and appropriate education in the least restrictive environment for all children with disabilities between the ages of 3 and 21. Such education is based on an Individualized Education Plan (IEP), which must include – when needed – speech therapy, occupational therapy, assistive technology, and other services. The UK and France have similar systems to the US. The local school authorities identify children with special needs, make an assessment of those needs, develop an educational program, and specify the provisions necessary to meet them. The preferred option for

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disabled students is always the local mainstream school. However, if the home school is not suitable, the student will be placed in a school environment as close to home as possible. In the UK, students with disabilities participate in an Annual Review that assesses progress towards special education goals and determines the need for ongoing support and services. Some European countries such as Germany and The Netherlands have a system of identifying special schools for disabled students to attend based on their specific diagnosis. The teachers are specifically trained to address the needs of these populations. The school programs are often regional centers or consortiums within a geographic area. The students are eligible for special education if they meet certain criteria of specific vision or hearing loss, IQ, or a diagnosis based on the Diagnostic and Statistical Manual of Mental Disorders (DSM) published by the American Psychiatric Association. Each country has its own procedures for identification of special students and for the funding of special programs. The European Agency for Development in Special Needs Education is a good source of information regarding the programs and funding sources in various countries throughout Europe.

Autism and special education services Autism was not identified as a specific disability under the first US federal legislation for children with disabilities, EAHCA. Children diagnosed with autism were served under the category, “other health impairment.” This required a medical diagnosis of autism. Later legislation, The Individuals with Disabilities Education Act, in 1997, included autism as a new category in the definition of children with disabilities (IDEA, 1999). IDEA defines autism as: A developmental disability significantly affecting verbal and nonverbal communication and social interaction, generally evident before age 3, that adversely affects a child’s educational performance. Other characteristics often associated with autism include engagement in repetitive activities and stereotyped movements, resistance to environmental changes or changes in daily routines, and unusual responses to sensory experiences. (p. 12 421)

As discussed in Chapter 2, there has been a significant increase in identified cases of autism in the past two decades. This increase is also evident in educational services. For example, in 2001, 3969 students between the ages of 6 and 21 were being served in schools in the state of Pennsylvania under the diagnostic category of autism; by 2008, this number increased to 13 790 school-age students in the same category (Pennsylvania Department of Education, 2008). This dramatic rise in the population of school-age students with autism suggests that autism is becoming more common; however, other factors such as changes in the definition of autism and how cases are reported have also influenced this growth. (See discussion in Chapter 2.) In the US, the medical community relies on the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV; APA, 1994) to define and categorize mental health disorders. This manual places autism under a broad category of Pervasive Developmental Disorders (PDD). There are several disorders found in this category, including Pervasive Developmental Disorder not otherwise specified (PDD-NOS), Autistic Disorder, and Asperger’s Syndrome. These three diagnoses are not easy to distinguish from one another. The DSM-IV provides specific criteria for autistic disorder; however, the diagnostic label of PDD-NOS is reserved for children demonstrating many of the characteristics of autism, but

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not meeting the full criteria for autism. Asperger’s syndrome includes children who demonstrate characteristics of autism in the social and behavioral domains without the same degree of impairment in language. (See further discussion in Chapter 1). Under the new autism category in IDEA, the educational definition of autism diverged from criteria for the medical diagnoses of PDDs. This has caused some confusion in the terminology. School psychologists are not held to the specific criteria defined in the medical diagnoses of Autistic Disorder and Asperger’s Syndrome, as presented in the DSM and used by medical professionals. This may have contributed to some of the rapid rise in the number of children given this label in special education.

Educational treatments for children with autism Children with a diagnosis of autism have unique characteristics that can create challenges in their education and set them apart from other learners. Some of these characteristics affecting educational progress include: an uneven profile of learning, often demonstrating an understanding of tasks without the underlying concept; an excessive focus on detail that can make generalization of skills difficult; problems of attention and distractibility in the classroom; language impairments, including both receptive and expressive language; problem behaviors; and fixation on routine. There are numerous treatment programs designed for children with autism, making it extremely difficult for professionals and parents to choose the best approach. Many intervention programs market directly to parents with products that may have little or no proof of efficacy. In the US, the passage of the national No Child Left Behind Act of 2001 required public school programs to have a new focus on scientifically based educational methods for children, although there is still disagreement among professionals as to what the criteria for “scientifically based” should be. The scientifically supported methods in education generally derive from empirical research, qualitative research, and evidence-based practice. The term “empirically supported treatment” refers to experimental studies using randomized sampling for both treatment and control groups that have provided evidence of statistically significant effectiveness. Qualitative studies are generally based on larger samples that may involve observational data that also demonstrate statistically significant results. Evidence-based practice involves the integration of the best and most current research evidence with professional/educational expertise and parent perspectives to make the best possible decisions for educational practices. Continued practice must be documented with data to objectively support the decisions. A difficulty in using evidence-based practice is the shortage of teaching staff qualified to work with individuals with autism (Simpson, 2004). Educational professionals working with these students should be trained to understand how the characteristics of autism affect learning. Currently, there is no consensus on the best educational practices for children with autism. There are numerous approaches that all have some forms of research to support their use, but no single method has demonstrated effectiveness for all children with autism. The use of evidence-based practice can include a variety of approaches as determined by the outcome result for individual children. It is highly recommended that educational professionals and parents learn about all these methods and use an eclectic approach to each student. Specific educational interventions for children with autism are of two types – comprehensive and focused.

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Comprehensive interventions Applied behavioral analysis Applied behavioral analysis (ABA) is an approach that involves systematic manipulation of antecedents and consequences to affect an individual’s behavior (Baer et al., 1968; Darch et al., 1999). It derives from the approach developed by Ivar Lovaas at UCLA in the 1960s. (See Chapter 10.) The methodology calls for a one-on-one treatment model. Each “discrete trial” includes a discriminative stimulus or prompt, followed by the child’s behavioral response, and a consequence (both positive and negative) based on the child’s responses. The purpose of the discrete trial is to break instruction down to clear, precise steps, reducing relevant words and stimuli (Sullivan et al., 1998). To teach new skills, this intervention typically begins with mass discrete trials (repetition of each trial multiple times) for each specific objective; then the therapist systematically removes the prompts. The underlying philosophy is that behavior changes slowly and in small increments. ABA programming can also eliminate behavior that interferes with the child’s learning. A child’s problem behavior is analyzed to determine its function and then the therapist focuses on teaching a new behavior to replace the problematic one. Research has demonstrated this intervention to be most successful when provided in an intensive one-on-one format by trained individuals. This method has evolved over the years and, currently, most practitioners do not use the original Lovaas curriculum; instead, they focus on instructional techniques that have been developed based on the behavioral principles. A classroom using this approach typically has a very high staff-to-student ratio; some programs have one adult for every student. In an ABA classroom, adults are instructing and monitoring student behavior at all times, using data sheets to record directives, cues, responses, etc. Teacher directions to students are scripted in short directives, and correct student responses are followed by specific reinforcers. Adults sit next to, or directly across from, each student, so the arrangement of furniture does not resemble a typical classroom set-up, but may be more like that in an office space. Although the entire class may all be working on reading or math instruction, it is carried out in independent discussions with each teacher and student pairing. Group instruction is seldom used; however, if a group session is conducted, each student is assisted by an adult during the session. Hundreds of published studies have shown that ABA techniques can help individuals with autism learn specific skills (LeBlanc et al., 2003; Shabani et al., 2002; Buffington et al., 1998; Lee et al., 2002; William et al., 2000; Hagopian et al., 2001; McComas et al., 2000). Used correctly, ABA is not a set program of drills for all children with autism. The intervention consists of an analysis of a particular child’s behavior and then an intervention that is individualized and designed to target specific behaviors to change.

Pivotal response trainings Drs. Bob and Lynn Koegel at the University of California in Santa Barbara developed the Pivotal Response Intervention Model for children with autism. (See Chapter 10.) This model also utilizes the techniques of ABA. It incorporates a more naturalistic intervention that targets pivotal areas of child development, one at a time (Koegel et al., 1999). The researchers have defined four goals for intervention: responsivity to multiple cues; motivation to initiate and respond appropriately to social and environmental stimuli; and self-direction of

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behavior, which includes self-management and increasing the frequency of initiation (Simpson et al., 2008). Research on Pivotal Response Trainings has documented improvement in language skills (Koegel et al., 1987; Pierce and Schreibman, 1997) and improvement in play and social skills (Thorp et al., 1995; Koegel and Frea, 1993; Hupp and Reitman, 2000; Schreibman et al., 1991). This approach supports the natural environment, as opposed to the structured, oneto-one format of most ABA programs. Pivotal Response Trainings do not offer a complete curriculum package; they can be combined with other curricula and strategies (Prizant and Rubin, 1999).

Treatment and Education of Autistic and Related Communication Handicapped Children Treatment and Education of Autistic and related Communication handicapped CHildren (TEACCH) was started in the 1970s by Dr. Eric Schopler at the University of North Carolina School of Medicine in Chapel Hill. This program involves adapting the environment to address the unique learning needs and characteristics of individuals with autism spectrum disorders (Schopler et al., 1995). The philosophy of the program focuses on understanding autism, teacher training, and parent–teacher collaboration. An assessment of the child’s learning profile identifies potential strengths and interests such as visual memory, visual– spatial skills, and adaptive routines, to support their deficits in auditory processing, verbal expression, attention, abstract thinking, generalization, and organization (Simpson et al., 2008). The child’s strengths and interests determine the necessary educational supports. The approach identifies four major components of structure: physical organization, visual schedules, work systems, and task organization. A TEACCH classroom is environmentally organized, with separate, defined areas for each activity, instruction with the teacher, individual work, group activities, and play. The children use visual schedules made up of pictures and/or words to help them understand the sequence of activities that occur during the day and help them move smoothly between activities. Visual directions or work systems assist the child’s understanding of the teacher’s expectations, promote independence, and require the child to use learned skills in a functional context. Finally, instructional tasks are made visually clear and have a definite beginning and end (Simpson et al., 2008). A classroom using this approach looks different from the traditional classroom because of its environmental organization. A large classroom is divided into smaller areas and each area is labeled with a sign. Students have individual desks, but these desks are separated from other students’ by some distance and are not grouped in traditional rows. Desks may also face the wall to minimize visual distractions. Group and individual instructional areas are distinct, and often a break or reinforcement area is also included in the classroom. Students transition between areas independently by following an individualized schedule. Instructional tasks are presented as separate discrete activities, often packaged separately as well. Students may work at their desks independently, at the same time that others are receiving one-to-one instruction. Visual directions, cues, and highlighting provide additional assistance during instruction. In comparison to an ABA classroom, the staff-to-student ratio is often lower and the emphasis is on student independence, instead of continuous engagement with an adult. Long-term use of the TEACCH program has demonstrated positive outcomes (Lord and Venter, 1992; Mesibov, 1997; Schopler et al., 1981, 1982). There are also numerous studies

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that have demonstrated improvement in specific skill areas such as behavior and independence (Persson, 2000); gross motors skill, eye–hand coordination, and imitation (Panerai et al., 2002); expressive and receptive language and social skills (Panerai et al., 1998). One component of the method, the use of visual schedules, has also been reported to result in increased play behavior (Morrison et al., 2002), increased compliance, and decreased problem behavior (Dooley et al., 2001), increased on-task behavior (Bryan and Gast, 2000), and decreased response latency (Dettmer et al., 2000). Comprehensive interventions, such as the TEACCH program, that are developed specifically for individuals with autism spectrum disorders appear to lead to improved learning by addressing the unique needs and characteristics of this population. Large-group, randomized controlled studies of this approach are, however, still needed (Volkmar et al., 2011).

Focused interventions: communication Picture Exchange Communication System Picture Exchange Communication System (PECS) is an augmentative language development program that was designed for children with autism by Andrew Bondy and Lori Frost in the 1980s. PECS is based on ABA principles and is designed to teach children with limited language and/or social skills to communicate in functional contexts (Frost and Bondy, 2002). Communication is enhanced by the use of pictures and the exchange demonstrates the social exchange in communication between two people. This system specifically addresses the difficulty children with autism have in initiating social interaction by teaching them to approach an adult or peer to make a self-initiated request (Bondy and Frost, 1998). The training begins with a child making a request for something she wants by giving the picture to a communication partner, who then provides the requested item. The program consists of six key phases. 1. The child learns to exchange a picture for desired items within reach of the partner. 2. The child has to travel increasing distances to exchange pictures by retrieving cards or approaching the conversation partner. 3. Discrimination is required between several pictures, including “distractor” pictures. 4. Requests are expanded to complete sentences. 5. The child learns to answer questions. 6. Vocabulary is expanded, based on all previously mastered skills (Simpson et al., 2008). Although some parents fear that the use of PECS may inhibit the development of speech, the contrary is true: training is correlated with increased speech production (Bondy and Frost, 1994; Schwartz et al., 1998). Research has demonstrated the ability of this program to promote functional communication skills (Bondy and Frost, 1994; Chambers and Rehfeldt, 2003; Charlop-Christy et al., 2002; Ganz and Simpson, 2004). In addition, a recent study also suggested that the PECS system is preferable to sign language for this population, which includes children with developmental deficits in the severe range. Each exchange is clearly intentional and easily understood by the general population; the child is given an effective means to quickly and easily meet his needs (Chambers and Rehfeldt, 2003). In a meta-analysis of research on PECS in ASDs, Flippin (2010) concluded that this intervention is promising, but not yet established, as an evidence-based practice for promoting communication. Gains in speech are less evident.

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Applied Verbal Behavior Applied Verbal Behavior is a variant program of ABA that focuses on teaching verbal behavior or language. Based on B. F. Skinner’s book, Verbal Behavior (1957), this intervention assumes that systematic reinforcement can influence verbal behavior as well as other behavior. Skinner separated language development into a set of functional units, with each type of operant serving a different function. These functions include echoics, mands, tacts, and intraverbals. The purpose of this therapy is to encourage children to learn new language or behavior to influence those around them. As with other applications of ABA, the teaching procedures are very specific; all skills are broken down into components and then taught through a system of prompting and reinforcement. As the child begins to master a skill, prompts are faded until the child can do the skill independently. The current techniques in this program are based on the work of Vincent Carbone and Mark Sundberg (Carbone et al., 2006; Sundberg and Michael, 2001). (See discussion in Chapter 10.)

Focused interventions: social skills Floortime Dr. Stanley Greenspan, a child psychiatrist, developed a form of play therapy that focuses on social interaction rather than behavior. This approach bears two names: the Developmental– Individual Differences–Relationship Model (DIR) or, simply, “floortime.” The latter name derives from use of the general approach – following a child’s natural interests in order to mobilize engagement – with young children, with a parent or caregiver joining a child as he plays on the floor. The program engages the child as he pursues his daily activities, and it uses spontaneous interactions and relationships to teach social skills. Floortime is based on the developmental sequence in social ability that is demonstrated by normally progressing children. It takes into consideration each individual child’s motivation to communicate and present level of social functioning. It is the caregiver’s role to follow the child’s lead, developing “circles of communication” to help him move on to more advanced interaction skills. When the child says or does something, the adult responds in a way that increases the child’s pleasure and reinforces his relating to others. The program has three levels. Floortime, the first level, refers to the time spent on the child’s level, typically the floor, where the child is able to lead the adult to an activity of choice as the adult encourages social interaction around the preferred activity. The next level of interaction includes Semi-Structured Play, where the adult interacts with the child to create highly motivating situations in which to engage in problem-solving. The third level involves Motor, Sensory, Spatial Play, where the child is involved in physical activities such as running, jumping, and spinning. In many ways, the techniques in this program are the opposite of the adult-directed approach in ABA programs because this program is childdirected. Floortime is most popular at the preschool level because many of the techniques do not fit well into a school-age classroom.

Social Stories™ Carol Gray, an educator with Jenson Public Schools in Michigan, developed Social Stories in 1991 (Gray, 2010). Ms. Gray used story formats to assist children with autism to understand social situations and provide information on appropriate behavior. The stories attempt to

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provide the answers to questions that children with autism may need in order to interact appropriately in social contexts. The stories consist of four types of sentences: descriptive, directive, perspective, and control. The descriptive sentences provide information about social situations and why people may respond the way they do. Directive sentences give the child directions as to how to behave in positive terms. The perspective sentence provides the child with autism an understanding of the situation from another person’s perspective, a concept that many children with autism find difficult. The control sentence provides information on how to remember the strategies suggested. Not all sentence types are used in every story. Social Stories work best with verbal, school-aged children. They are written by teachers and parents and may be repeated and memorized by the child. They help prepare a child for new experiences, remind him of proper social behavior, and enable him to recall what he has done well. Box 8.1 presents an example of a Social Story. My Place in Line

It’s my teacher’s job to give my class directions. It’s our job to try to follow those directions. Sometimes, many students move from one place to another. To walk safely, and to allow other groups to walk through the hall at the same time, it’s important to try to walk in a line. Many students like to be first in line. The teacher decides which student is first in the line. Sometimes, I will be the first student in line. Most of the time, another child will be first. When this happens, I will be at another place in the line. This helps the teacher give each student a chance to be first. My teacher decides which student is first in the line. Once in a while, I will be first in line. Most of the time, another student will be first in line. That’s how lines work at my school. That’s Life on Planet Earth. Reprinted with permission from The New Social Story Book: 10th Anniversary Edition by Carol Gray (Copyright © 2010). Future Horizons, Inc.

Many parents and teachers have found Social Stories to be helpful in explaining unwritten social rules to children with autism. In comprehensive reviews of research on the efficacy of Social Stories in teaching social skills, Reynhout and Carter (2006) stated that they constitute a promising intervention, with further studies needed; Ferraioli and Harris (2011) concluded that evidence-based research supporting this approach is emerging, awaiting replication. Of the comprehensive and focused interventions reviewed in this chapter section, Lovaas’s approach has the strongest empirical support – primarily as an early intensive behavioral intervention for young children. For the school-age child, there is no compelling evidence-based research that favors one intervention over another. As has already been stated, special educators who work with children need to be familiar with all these interventions and to take an eclectic approach, choosing the methods that can best serve an individual child, continuously assessing their effectiveness and revising as needed.

Inclusion in general education: history Many children with autism receive their education in special, self-contained classes, but, in the US, an increasing number are being “included” in regular classrooms. The concept of inclusion began when a federal district court ruled on behalf of mentally retarded children’s rights in the case of Pennsylvania v. Pennsylvania Association of Retarded Children in 1971 (Pennsylvania

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Association for Retarded Children v. Commonwealth, 1971). This case decreed that children with mental retardation in Pennsylvania were entitled to a free public education and further stipulated that whenever possible they should be educated in regular classrooms rather than segregated from the normal school population. In 1972, Mills v. Board of Education of District of Columbia expanded this decision to include all disabled children. These cases were followed by federal legislation: the Education for All Handicapped Children Act (EAHCA) in 1975, the Individuals with Disabilities Education Act (IDEA) in 1999, and finally No Child Left Behind in 2002 (NCLB). As a result of this legislation, the public school systems in every state are required to provide a continuum of services in the least restrictive setting for all children with disabilities. In the 1980s, advocates for children with developmental disabilities began to assert that those with milder disabilities should remain within the general education setting. Known as the Regular Education Initiative, this advocacy movement grew rapidly to suggest the full inclusion of all children, no matter what their disabling condition, in neighborhood schools (Villa et al., 1996). The inclusion of students with severe disabilities into general education classrooms has become increasingly prevalent (United States Department of Education, 2000). Although IDEA does not mandate the inclusion of all students with disabilities, the legislation strongly encourages consideration of appropriate placement in general education settings.

Inclusion of children with autism Although inclusion is not a legal mandate, implementing the Individual Education Plan in the least restrictive environment is a component of the IDEA. Full inclusion programs typically offer students with disabilities services in the general education classroom with little or no time in special education settings. Many parents and professionals insist that full inclusion be the only option, despite the lack of empirical evidence indicating its benefits for this population (Rimland, 1993). IDEA mandates that the placement decision be made by a multi-disciplinary team and that a continuum of service delivery options be maintained. Evaluating and providing appropriate programming for individuals with autism requires specialized knowledge and training. Unfortunately, many otherwise skilled and competent regular educators frequently report that they consider themselves to be less than fully capable of serving the needs of students diagnosed with autism (Simpson, 1995). Children with autistic characteristics demonstrate significant deficits in basic areas of functioning, including social interaction, communication, and learning, as well as difficult behaviors, which contribute to the challenge for educators who endeavor to serve them effectively in included settings. Selecting appropriate placement for children with autism is a challenge for multidisciplinary teams because of limited information on valid placement strategies. Parents’ requests for inclusion in regular education classrooms can become a source of contention between multidisciplinary teams and families, sometimes requiring specialized “due process” hearings to be resolved.

Transition services An important aspect of education of individuals with disabilities is preparing them to function as adults. The transition process from school to employment necessitates instruction within the education system that enables young people with special needs to become

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economically active by providing the skills and training needed in everyday life. Transition services within special education were established to address concerns among disabled adults, such as a higher percentage of educational drop outs, high rate of unemployment, low level of vocational training, low level of access to employment, limited qualifications, difficulties facing changing work conditions, underestimation of abilities, negative attitudes of employers, vocational training not related to actual job practice, and limited contact between the educational community and employment community (European Agency for Development in Special Needs Education, 2002). In England, transition services were first mandated in 1993 and revised in 2001. According to the code of practice, professionals are responsible for written notice about transition plans, including details about the services likely to be needed by a young person when he leaves children’s services. A discussion between the children’s services personnel, adult services personnel, and the parents to ensure the quality of care and support is held to provide a foundation for the student to participate in education, training, or employment. The nationwide “Connexions” Service is designed to provide all 13–19-year-olds with access to advice, guidance, and support through a network of personal advisers. The advisers are responsible for identifying young persons with intellectual disabilities and attending annual reviews of all secondary students who have Statements of Special Education Needs. The key elements for a successful transition were clarified by Russell Viner as the following: a flexible policy on the timing of the transfer; a preparation period and education program to identify the necessary skills to enable the student to function in the adult world; a coordinated transfer process identifying the professionals who will assist and coordinate access to appropriate adult services; active participation from adult services; and administrative support from both children’s and adult services (Viner, 1999). In the US, the primary purpose of the Individuals with Disabilities Education Act of 2004 was to ensure that all children with disabilities have available to them a free appropriate public education that emphasizes special education and related services designed to meet their unique needs and prepare them for further education, employment, and independent living (IDEA, 2004). To facilitate an adolescent’s movement from school to postschool activities, a coordinated plan must be developed to focus educational services on the academic and functional achievement necessary for her to take part in vocational education, integrated employment, and participate in community activities. These transition services must be addressed in the IEP of the student at age 14, with additional requirements mandated at age 16. There are several steps to the development of a Transition Plan. It begins with an assessment to identify the student’s present levels on a number of post-secondary goals. The team identifies measurable goals for the new IEP, which includes the course of study and related services, then determines if partnerships can be developed with community and adult programs. The final step is to determine how progress will be monitored and to set annual goals. The student can participate in the IEP meeting, but if he is unable to do so, the team must consider the student’s preferences and interests. Parents need to advocate for students unable to communicate their preferences clearly. An important step in this process is to confirm the date of graduation. According to law, a child can graduate with a regular diploma, typically at age 18, or when the child reaches the age of eligibility under state law, typically at age 21 (Graham and Wright, 2010). To determine appropriate goals for the transition plan, the team needs an understanding of the adult services available in the student’s community. This requires contact with

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representatives from adult services such as vocational education, supported employment, community/recreational opportunities, and/or transportation services. These representatives can be invited to the student’s IEP meeting to assist in planning and identifying the skills necessary to participate fully in their services. (See Chapters 12 and 13.)

Summary and conclusion In the past, proponents of ABA, TEACCH, Floortime, and Applied Verbal Behavior held that intervention programs should rigidly adhere to their specific protocols and denied the effectiveness of other methods. There are examples in the research literature of arguments and counter-responses advocating one approach and minimizing the value of others. Today, more and more educators are making use of a variety of techniques that derive from all of these approaches. The most effective classrooms for children with autism incorporate the visual strategies from TEACCH and PECS with the behavioral analysis and principles of ABA to teach new skills and eliminate problem behavior. The eclectic approach demands that the teacher be well versed in several comprehensive interventions and flexible in the application of specific techniques in the classroom. The concern with recommending an eclectic approach is that many teachers will be introduced to various approaches but not given the depth of knowledge in any given area. The goal is for all teachers of children with autism to gain a full understanding of the various approaches and to have the ability to implement necessary strategies as needed. The most essential training would be an understanding of the characteristics of autism and how those characteristics affect educational progress. Teachers need to be able to recognize basic behaviors related to autism and be ready to teach new skills to help overcome deficits. For example, children with autism adhere to routine, which is a characteristic basic to many students with ASDs. Teachers will be more successful presenting a new stimulus and teaching a new routine, rather than trying to break the cycle of a longstanding routine that is firmly established. Otherwise, the student and teacher may struggle, as the teacher introduces successive rewards and/or punishments without success. This example uses an understanding from both TEACCH and ABA, recognizing that a child’s behavior is related to intrinsic characteristics of autism (the TEACCH approach) and identifying a new behavior to systematically introduce and reinforce (ABA principles). As new strategies and approaches are developed, a teacher who knows the characteristics of children with autism and is comfortable with an eclectic approach can introduce innovations into the classroom, without having to make a radical change in basic teaching methods.

References American Psychiatric Association. (1994). Diagnostic and Statistical Manual of Mental Disorders (4th ed.).Washington, DC: American Psychiatric Association. Baer, D. M., Wolf, M. M. and Risley, T. R. (1968). Some current dimensions of applied behavior analysis. J Appl Behav Anal, 1, 91–7. Bondy, A. S. and Frost, L. A. (1994). The picture exchange communication system. Focus on Autistic Behavior, 9, 1–19.

Bondy, A. S. and Frost, L. A. (1998). The picture exchange communication system. Semin Speech Lang, 19, 373–89. Bryan, L. C. and Gast, D. L. (2000). Teaching ontask and on-schedule behaviors to high functioning children with autism via picture activity schedules. J Autism Dev Disord, 30, 553–67. Buffington, D., Krantz, P., McClannahan, L., et al. (1998). Procedures for teaching

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appropriate gestural communication skills to children with autism. J Autism Dev Disord, 28, 535–45. Carbone, V. J., Lewis, L., Sweeney-Kerwin, E. J., et al. (2006). A comparison of two approaches for teaching VB functions: total communication vs. vocal-alone. J Speech Lang Pathol Appl Behav Anal, 1, 181–92. Chambers, M. and Rehfeldt, R. A. (2003). Assessing the acquisition and generalization of two mand forms with adults with severe developmental disabilities. Res Dev Disabil, 24, 265–80. Charlop-Christy, M. H., Carpenter, M., Le, L., et al. (2002). Using the picture exchange communication system (PECS) with children with autism: assessment of PECS acquisition, speech, social-communicative behavior, and problem behavior. J Appl Behav Anal, 35, 213–31. Darch, C., Miller, A. and Shippen, P. (1999). Instructional classroom management. A proactive model for managing student behavior. Beyond Behavior, 9, 12–16. Dawson, G., Rogers, S., Munson, J., et al. (2010). Randomized, controlled trial of an intervention for toddlers with autism: the Early Start Denver Model. Pediatrics, 125, e17–23. Dettmer, S., Simpson, R. L., Myles, B. S., et al. (2000). The use of visual supports to facilitate transitions of students with autism. Focus Autism Other Dev Disabil, 15, 163–9. Dooley, P., Wilczenski, F. L. and Torem, C. (2001). Using an activity schedule to smooth school transitions. J Posit Behav Interv, 3, 57–61. European Agency for Development in Special Needs Education. (2002). Transition from School to Employment. http:// www.european-agency.org/publications/ ereports/transition-from-school-toemployment/transition-fromschool-to-employment (accessed 15 September 2010). European Agency for Development in Special Needs Education. (2005). Early Childhood Intervention: Analysis of Situations in Europe Key Aspects and Recommendations, http:// www.european-agency.org/publications/ ereports/early-childhood-intervention/earlychildhood-intervention-analysis-ofsituations-in-europe-key-aspects-and-

recommendations (accessed 15 September 2010). Exkorn, K. S. (2005). The Autism Sourcebook. New York, NY: HarperCollins. Ferraioli, S. J. and Harris, S. L. (2011). Treatments to increase social awareness and social skills. In B. Reichow, P. Doehring, D. V. Cicchetti, et al. (Eds.), Evidence-based Practices and Treatments for Children with Autism (pp. 171–96). New York, NY: Springer. Flippin, M., Reszka, S. and Watson, L. R. (2010). Effectiveness of the Picture Exchange Communication System (PECS) on communication and speech for children with autism spectrum disorders: a meta-analysis. Am J Speech Lang Pathol, 19, 178–95. Frost, L. A. and Bondy, A. S. (2002). The Picture Exchange Communication System Training Manual (2nd ed.). Cherry Hill, NJ: Pyramid Educational Consultants. Ganz, J. B. and Simpson, R. L. (2004). Effects on communicative requesting and speech development of the Picture Exchange Communication System in children with characteristics of autism. J Autism Dev Disord, 34, 395–409. Graham, J. and Wright, P. (2010). Transition Planning: Setting Lifelong Goals. http://www. wrightslaw.com/info/trans.plan.graham.htm (accessed 15 September 2010). Gray, C. (2010). The New Social Story Book: 10th Anniversary Edition. Arlington, TX: Future Horizons. Guralnick, M. J. (1997). The Effectiveness of Early Intervention. Baltimore, MD: Brookes. Hagopian, L. P., Wilson, D. M. and Wilder, D. A. (2001). Assessment and treatment of problem behavior maintained by escape from attention and access to tangible items. J Appl Behav Anal, 34, 229–32. Hupp, S. D. A. and Reitman, D. (2000). Parent-assisted modification of pivotal social skills for a child diagnosed with PDD: a clinical replication. J Posit Behav Interv, 2, 183–8. Individuals with Disabilities Education Act (IDEA). (1999). Rules and regulations. Federal Register, 64(48), 12421. Individuals with Disabilities Education Act (IDEA) (2004). 20 U.S.C. §1414(d)(1)(A) (VIII).

Chapter 8: Educational treatments

Itard, J. M. G. (1802). An Historical Account of the Discovery and Education of a Savage Man. London: Printed for Richard Phillips. Koegel, L. K., Koegel, R. L., Harrower, J. K., et al. (1999). Pivotal response intervention I: overview of approach. J Appl Behav Anal, 25, 174–85. Koegel, R. L. and Frea, W. D. (1993). Treatment of social behavior through the modification of pivotal social skills. J Appl Behav Anal, 26, 369–77. Koegel, R. L., O’Dell, M. C. and Koegel, L. K. (1987). A natural language teaching paradigm for nonverbal autistic children. J Autism Dev Disord, 17, 187–200. LeBlanc, L. A., Coates, A. M., Daneshvar, S., et al. (2003). Using video modeling and reinforcement to teach perspective-taking skills to children with autism. J Appl Behav Anal, 36, 253–7. Lee, R., McComas, J. J. and Jawor, J. (2002). The effects of differential and lag reinforcement schedules on varied verbal responding by individuals with autism. J Appl Behav Anal, 35, 391–402. Lord, C. and Venter, A. (1992). Outcome and follow-up studies of high-functioning autistic individuals. In E. Schopler and G. Mesibov (Eds.), High-functioning Individuals with Autism (pp. 187–99). New York, NY: Plenum Press. Mesibov, G. B. (1997). Formal and informal measures on the effectiveness of the TEACCH programme. Autism, 1, 25–35. McComas, J., Hoch, H., Paone, D., et al. (2000). Escape behavior during academic tasks: a preliminary analysis of idiosyncratic establishing operations. J Appl Behav Anal, 33, 479–93. Morrison, R. S., et al. (2002). Increasing play skills of children with autism using activity schedules and correspondence training. J Early Interv, 25, 58–72. Myers, S. M., Johnson, C. P. and the American Academy of Pediatrics Council on Children With Disabilities. (2007). Management of children with autism spectrum disorders. Pediatrics, 120, 1162–82. National Dissemination Center for Children with Disabilities (2010). Overview of Early Intervention. http://nichcy.org/babies/ overview (accessed 30 September 2010).

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(Volume 1) (pp. 293–301). Baltimore, MD: University Park Press. Schopler, E., Mesibov, G. B. and Hearsey, K. (1995). Structured teaching in the TEACCH system. In E. Schopler and G. B. Mesibov (Eds.), Learning and Cognition in Autism (pp. 243–67). New York, NY: Plenum Press. Schreibman, L., Kaneko, W. M. and Koegel, R. L. (1991). Positive affect of parents of autistic children: a comparison across two teaching techniques. Behav Ther, 22, 479–90. Schwartz, I. S., Garfinkle, A. N. and Bauer, J. (1998). The picture exchange communication system: communicative outcomes for young children. Topics Early Child Spec Educ, 18, 144–59. Shabani, D. B., Katz, R. C., Wilder, D. A., et al. (2002). Increasing social initiations in children with autism: effects of a tactile prompt. J Appl Behav Anal, 35, 79–83. Simpson, R. L. (1995). Children and youth with autism in an age of reform: A perspective on current issues. Behav Disord, 21, 7–20. Simpson, R. L. (2004). Finding effective intervention and personnel preparation practices for students with autism spectrum disorders. Except Child, 70, 135–44. Simpson, R. L., Smith Myles, B. and Ganz, J. B. (2008). Treatments and interventions. In R. Simpson and B. Smith Myles (Eds), Educating Children and Youth with Autism (pp. 477–512). New York, NY: Plenum Press. Skinner, B. F. (1957). Verbal Behavior. Englewood Cliffs, NJ: Prentice Hall. Smith, M., Rogers, S. and Dawson, G. (2008). The Early Start Denver Model: a comprehensive early intervention approach for toddlers with autism. In J. S. Handelman and S. L. Harris (Eds.), Preschool Education Programs for Children with Autism (3rd ed.) (pp. 65–101). Austin, TX: Pro-Ed Corporation, Inc.

“Special Education.” (2010). In Wikipedia, the free encyclopedia. http://en.wikipedia.org/ wiki/Special_education (accessed 15 September 2010). Sullivan, M., Sundberg, M., Partington, J., et al. (1998). Teaching Children with Language Delays: A Handbook of Strategies and Techniques for the Classroom (2nd ed.). Danville, CA: Behavior Analysts. Sundberg, M. and Michael, J. (2001). The benefits of Skinner’s analysis of verbal behavior for children with autism. Behav Modif, 25, 598–724. Thorp, D. M., Stahmer, A. C. and Schreibman, L. (1995). Effects of sociodramatic play training on children with autism. J Autism Dev Disord, 25, 265–82. United States Department of Education. (2000). Twenty-second Annual Report to Congress on the Implementation of the Individuals with Disabilities Education Act. Washington, DC (ERIC Document Reproduction Service No. ED444333) Villa, R., Thousand, J., Meyers, H., et al. (1996). Teacher and administrator perceptions of heterogeneous education. Except Child, 63, 29–45. Viner, R. (1999). Transition from paediatric to adult care. Bridging the gaps or passing the buck? Arch Dis Child, 81, 271–5. Volkmar, F. R., Reichow, B. and Doehring P. (2011). Evidence-based practices in autism: where we are now and where we need to go. In B. Reichow, P. Doehring, D. V. Cicchetti, et al. (Eds.), Evidence-Based Practices and Treatments for Children with Autism (pp. 365–96). New York, NY: Springer. William, G., Donley, C. R. and Keller, J. W. (2000). Teaching children with autism to ask questions about hidden objects. J Appl Behav Anal, 33, 627–30.

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Assessing and Treating Children with ASDs

Habilitative treatments for children with ASDs: speech and occupational therapy, assistive technology Joseph E. Campbell and Kathleen M. Mears

In addition to social and communication impairment and restricted, repetitive behaviors, children and adults with autism may experience other symptoms that contribute to disability. These concomitant symptoms can include motor and motor planning deficits, sensory deficits such as impaired hearing and vision, sensory processing and modulating disorders, and specific learning disabilities. These impairments can limit an individual’s ability to function and participate in home, leisure, school, and work activities. In early intervention, preschool and school programs, children with ASDs are often referred to speech-language pathologists, occupational therapists, and other related service providers to remediate core and co-occurring symptoms. The present chapter will review some characteristics of core communication deficits and frequently accompanying motor and sensory deficits in autism. It will also describe interventions that are conducted by speech-language pathologists and occupational therapists in contexts of comprehensive and focused treatment programs, and address the use of assistive technology.

ASD characteristics affecting communication skills Impairments in communication are a core deficit in individuals with autism spectrum disorders. These impairments affect a person’s ability to communicate in a variety of contexts and limit adaptive functioning. Speech and language impairments in ASDs may manifest in how an individual is able to form words and to understand and produce meaningful language. Many individuals with autism never develop the ability to speak, and a large number of those who do acquire speech do not use it in a meaningful way. Some studies have suggested that one-third (Bryson, 1996) to one-half (Lord and Paul, 1997) of children diagnosed with autism may fail to develop functional speech (Bosserler and Massaro, 2003). Even for those who do use language meaningfully, social skills and conversational skills can be areas of great difficulty, and communication milestones are often delayed.

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These communication deficits usually become apparent in the first 3 years of life. In typically developing children, these are years of immense language development. Some children with autism start to develop language in these first years and then regress or lose their language skills. Other children fail to develop language at all. Research using retrospective studies of first-year videos has provided clues to where language is breaking down and has identified processes that may help in earlier diagnosis. In these videos, children with autism show differences from their typically developing peers by 1 year of age in the behaviors of showing, pointing, orienting to name, and, most notably, in how often a child looks at the face of another person (Osterling and Dawson, 1994). A review of video tapes of children with early onset autism, aged 8–10 months, found that these children were much less likely to orient when their name was called than typically developing children and, to a lesser degree, were less likely to look at another person while smiling (Werner et al., 2000). These skills are not only important as predictors of a later diagnosis of autism but are significant in themselves, because these early skills are built upon to form more complex language and social skills.

Joint attention The behaviors of following eye gaze, showing, pointing, and point-following were some of the more notable behaviors followed in first-year videos that distinguished typically developing children from those with autism. These same behaviors are core components necessary for the development of joint attention. Joint attention has been studied extensively because it is a critical behavior for, and predictor of, later language development. Children with ASDs have been found to have impairments in this pivotal skill. Joint attention involves the shared attention between a child and another person to an object or event. It takes two forms. The first to develop, at about 9 months of age or earlier, involves the child’s response to the gaze or gestures of an adult. This may present as a child following the eye gaze or point of an adult to an object of interest. Following at about 1 year of age, children begin to engage in gaze alternation. In this behavior the child will direct their gaze to an adult and then to an object of interest and then shift their gaze back to the adult. This form of joint attention involves the child initiating or calling on the attention of another person to focus on an object or activity of their interest. The child may use eye gaze alone or in conjunction with a pointing gesture. Charman (2003) studied joint attention and found that the frequency of gaze switches correlated with later language ability. He found that the greater the child’s ability to engage in gaze-switching between an object and another person at 20 months of age, the greater the child’s abilities in social and communication skills at age 42 months. Swettenham et al. (1998) suggested that because joint attention requires engagement with another person, those children with higher frequencies of joint attention bids would have higher levels of practice in social communication and would be more likely to become adept social communicators. Conversely, those children with lower rates of joint attention might never develop the skills to become social communicators. Murray et al. (2008) found that increased responsiveness by children with autism to another person’s bids for joint attention was associated with use of longer utterances and higher receptive language scores. Joint attention is also an important skill in the acquisition of vocabulary. To correctly map a word onto a novel item or action, a child must correctly follow the eye gaze of the speaker. For example, if a child were at the zoo with his parent and had never seen a zebra before, he would use this strategy to follow the parent’s eye gaze to map “zebra” onto the

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correct animal. Baron-Cohen et al. (1997) studied children with autism and found that only 29.4% correctly used the eye gaze of the speaker to map a novel word onto a novel object. They suggested that this deficit in joint attention may be responsible for delayed vocabularies and word usage.

Theory of Mind To have Theory of Mind (TOM) is to understand that others have mental states that differ from your own. TOM allows you to understand that other people’s thoughts, knowledge, beliefs, and feelings are different from yours. It allows you to predict and understand the intentions and behavior of others. A growing body of research suggests that many children with autism have impaired TOM. It is also believed that TOM is essential to the development of social communication. There are many children with autism who are able to learn to request things they want or need, but have much greater difficulty learning social language. TOM is tested by false belief tasks. False belief tasks require the child to infer another person’s mental state or belief. Two classic false belief tasks involve the prediction of the contents of a deceptive container, the “Smarties” task, and the prediction of the location of an item that has been placed elsewhere, the “Sally-Anne” task. In the Smarties task, the child is asked to guess the contents of a container that typically contains Smarties, a type of candy. Most children guess “Smarties.” After the contents are revealed to be pencils, the child is then asked what another child will guess is in the container. Children with a developed TOM will recognize that other children do not have access to their knowledge of what is in the container and will predict that the other child will also say “Smarties.” However, children who lack TOM will assume others know what they know and answer that the other child will guess there are pencils in the deceptive container. The Sally-Anne task involves two dolls, Sally and Anne, both of whom have containers. A marble is placed in Sally’s container. In the task scenario, Sally goes for a walk leaving her container with the marble in it. While Sally is gone, the marble is moved to Anne’s container. Upon Sally’s return, the child is asked to predict where Sally will look for the marble. Once again, children who lack TOM will assume that Sally knows what they know and will predict that Sally will look in Anne’s container. Many children with autism have difficulty with false belief tasks because, without a welldeveloped TOM, they can only see their own point of view and lack the ability to recognize that other people may have different knowledge from theirs, e.g. lack knowledge that they have. Without these skills, social language will not be well developed. Increasingly, research is suggesting that joint attention is a precursor to the development of TOM, because in order for TOM to develop, a child needs to be able to recognize intentionality and the existence of mental states of others (Charman, 2003; Adamson et al., 2009).

Lack of communication intent Communication intent is the ability to communicate with meaning. Many children with ASDs use repetitive or stereotypical language. They may be able to repeat conversations they have heard or lines from favorite videos, but are unable to greet family members or ask for what they want or need. They may demonstrate excellent rote memories, but fail to generate novel language. Also of concern is the tendency of children to become prompt-dependent. When a child is prompt-dependent, he relies on the cue of another person to initiate communication, rather than being able to spontaneously initiate language. Such a child may be hungry and have a favorite snack within sight but be unable to ask for it until first

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asked by a parent or caretaker if he would like the snack. Impaired communicative intent and lack of spontaneity affect a child’s daily living throughout their daily routines.

Echolalia: immediate and delayed Echolalia is the repeating back of what is heard. There are two basic forms of echolalia: immediate, and delayed. In immediate echolalia, a child will repeat back what is heard directly after hearing it, either in whole or part. When greeted and asked “How are you?” a child may repeat back exactly what is heard. A person with echolalia may not discriminate between what is spoken directly to him from what is overheard from others. Someone exhibiting delayed echolalia will repeat back what they have heard in the past. It can be something heard hours, days, or even months before. Some individuals with delayed echolalia have the ability to recite lengthy segments of movies or conversations. Mitigated echolalia takes the form of repeated responses that have been slightly altered. Schuler (1979) suggested that mitigated echolalia, if deliberate, may be a positive indication of cognitive linguistic abilities. Echolalia is not exclusive to autism. It has been observed in people with aphasia and schizophrenia (Weverick, 1986), and may be a feature of normal language development in young children. Because echolalia is observed over such diverse contexts, it is unlikely that there is a single cause for this behavior. A more important discussion may be whether a child’s echolalia carries a function and whether it is meaningful or meaningless. Prizant and Duchan (1981) analyzed the immediate echolalic utterances of four boys with autism and found that the majority of their echolalic responses fell into six categories: turn-taking, declarative, rehearsal, self-regulatory, yes-answer, and request – all of which served either a communicative or cognitive function. Only a small percentage of their echolalic utterances fell into a non-functional category, referred to as non-focused. In a second study, Prizant and Rydell (1984) categorized delayed echolalic utterances as either non-interactive or interactive. The non-interactive group included non-focused, situation association, rehearsal, selfdirective, and label (non-interactive). The interactive categories were turn-taking, verbal completion, label (interactive), providing information, calling, affirmation, request, protest, and directive. This study suggested that there may be communicative intent and use of symbolic language in the delayed echolalic utterances of children with ASDs. In developing individualized language programs, it is important to distinguish between functional echolalia and non-functional echolalia. Many language programs focus on reduction or elimination of echolalia regardless of the function. These programs may be disregarding an effective means of shaping echolalia into more effective forms of language. Although it may be appropriate to reduce non-communicational or non-interactive forms of echolalia, those forms of echolalia that hold communication intent and are the result of deliberate mitigation should possibly be encouraged. Schuler (2003) used play-based therapy to shape the echolalia of a child with autism by facilitating peer play with typically developing peers. He coached the peers to respond to the child’s communicative attempts, progressively including him in their play. Over a period of 6 months, the autistic child’s echolalia transformed from stereotypic and solitary to interactive and linguistically varied. Although some forms of echolalia are believed to be functional and hold communication intent, other forms of echolalia are characterized as self-stimulatory or automatic. These echolalic responses are marked by short response latencies (as in the case of immediate echolalia) and are believed to occur when comprehension skills are low.

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Effective intervention techniques can be developed with consideration of the different dimensions of echolalic behavior. Automatic, non-communicative, and non-interactional delayed echolalia that does not show any signs of syntactic productivity may have to be discouraged, while less rigid and more interactional forms should possibly be encouraged, particularly if the repetitions can become functional and discriminate. Some forms of echolalia may, through productive imitative processes, be mitigated into linguistically more advanced utterances.

ASD characteristics affecting sensory processing and motor function Studies suggest that as many as 45–95% of people with autism spectrum disorders experience sensory modulation disorders (Ben-Sasson et al., 2009). A sensory modulation disorder is present when a child manifests atypical behavioral responses to sensory input. These may be described as hyperactive, hypoactive, or craving responses to certain sensory stimuli or experiences. A hyperactive response is a quick and exaggerated reaction to sensory inputs such as sound, touch, and movement. Hyperactive behavioral responses can be aggressive, self-injurious, or stereotypic in nature. A hypoactive response is a slow reaction to sensory input and may look like unawareness. For example, a child may not respond when his name is called. Craving is the seeking of prolonged or intense sensory experiences. Severity of sensory modulation disorders may be dependent on the age of the child and the severity of autism symptoms. Studies have shown a correlation between sensory processing disorders and stereotypic behaviors in children with ASDs (Dawson and Watling, 2000). There has also been a link reported in children with ASDs between sensory-avoiding behavior and fine motor performance (Jasmin et al., 2009). Independent of sensory processing difficulties, other research has shown that some children with ASDs have difficulty with motor planning (apraxia), imitating body movements, and postural control, and may have general gross and fine motor delays (Jasmin et al., 2009).

Services Speech-language pathologists and occupational therapists provide services to people with autism at different ages and in a variety of mandated programs and settings: in early intervention programs, in special education (in accordance with individual education plans), and in home and community-based service programs for children and adults. In the US, the American Speech-Language-Hearing Association (ASHA) sets out clear guidelines on the role of speech-language pathologists in the delivery of services to individuals with autism. Although all speech-language pathologists (SLPs) receive training in the evaluation and treatment of speech and language disorders, some SLPs go on to receive further training in the area of autism through workshops, certificate programs, and hands-on training and experience with this population. Based on ASHA’s Code of Ethics, SLPs may only provide those services in which they are competent. One of the roles of SLPs is provision of direct services to people with ASDs: performing screenings and referrals to facilitate early detection and initiation of intervention services, conducting diagnostic assessments (by those SLPs with proper training, although this is

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typically done as part of a multi-disciplinary team), and providing assessments and interventions based on empirically supported approaches. The goals of direct interventions encompass initiation of spontaneous communication, both verbal and nonverbal, comprehension of verbal and nonverbal communication, social communication, and literacy skills. SLPs should also work closely with families when possible. They work with professionals and other interested parties in a collaborative manner to build a service plan that leads to functional outcomes. Additional roles are the provision of training designed to prepare and enhance the knowledge and skills of other professionals, participation in research, and advocacy for individuals with ASDs. ASHA standards also mandate a focus on outcomes that lead to generalization of skills that are meaningful and functional for each individual child. The Practice Framework of the American Occupational Therapy Association (AOTA) describes the common terminology and therapeutic approaches employed by occupational therapists regardless of arena of practice (Roley et al., 2008). At the core of the occupational therapy profession is the use of engagement in meaningful activities, or occupations, to improve health, wellness, and participation in life roles and pursuits. Since the founding of the profession in 1917, occupational therapists have used the tools of the day to engage patients to participate in activities in order to improve physical, mental, or cognitive health. Early practitioners used looms, sanders, kilns, and other arts and crafts and vocational tools to engage people as a means of promoting health. Today, therapists continue to use some of those same tools, but also incorporate the use of computers and other forms of technology, sensory integration equipment, classroom tools, and other modalities in their clinical practices. Occupational therapists require an understanding of neuroanatomy and physical anatomy, biomechanics, the influence of environmental factors, and the components of tasks that contribute to, or hinder, successful participation in meaningful life activities by people with disabilities. Occupational therapists strive to develop a thorough understanding of each individual’s strengths and needs, areas in which the person is, or is perceived to be, struggling, the environmental contexts in which the person functions and the tasks the person needs to complete.

Service delivery models There are many models of service delivery that support children with speech-language and occupational therapies. This chapter section describes some of the models that can be utilized in school, home, clinic, and community settings. The service delivery model and the frequency of service employed by therapists may be blended or may change over time to meet the dynamic needs of individuals with autism or to take advantage of environmental opportunities and events. However, the service delivery model and frequency of service is typically defined by a child’s individual education planning (IEP) process, by physician orders, and/or by third-party funding agencies.

Direct service Direct service is a model in which therapists work with one child or group of children. Group therapy works best when all children are working toward improving similar or complementary skills. This type of service delivery might include therapy provided within the classroom, using teacher-directed curricular activities, and/or therapist-directed activities designed to teach or reinforce targeted skills. Another variation might include multiple therapists

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providing services to one child, or a group of children, at the same time. Therapists might use this variation of the direct service model when it is beneficial for children to work simultaneously on skills presented by the multiple therapists. For instance, a child may be working with an occupational therapist to improve self-help skills while also working with a speechlanguage pathologist to use an augmentative communication system during the self-help task. Individual, or “pull-out,” therapy is a direct service model that can be most beneficial for emerging skills that can best be taught away from the distraction of classrooms and other natural environments. A drawback to the pull-out model is that the child may not generalize these skills across persons and environments. This can be alleviated by quickly moving therapy services out of the therapy room and into natural group settings.

Consultative services When providing consultative services, therapists may work with the child with autism, his family, school personnel, or others (Tomchek et al., 2009). A child with autism might be referred for consultative services when the team decides that the most appropriate form of service delivery is one in which a therapist is available as a resource for the child or team members to address issues that can be resolved quickly, or when needs or demands change over time or in different environments. Consultative services are ideal for insuring that all team members understand how to effectively meet the needs of a particular child and to facilitate the carry-over and generalization of skills across environments and situations.

Rehabilitation services The service models described above usually relate to needs deriving from an autism diagnosis. Therapy services may also be provided to people with autism in the context of rehabilitation for an injury or illness. Such services cannot be delivered independent of the diagnosis of autism, and health care providers in a rehabilitation setting may need to have previous training or experience in serving people with autism. For example, when working with a person with autism who has sustained an arm injury requiring intervention, the therapist who provides services must be familiar with the patient’s expressive and receptive communication needs, stereotypic behaviors that might involve the injured arm, resistance to change, resistance to touch, and other autistic behaviors that might interfere with the recovery process.

Staff and family support A large part of what SLPs and occupational therapists (OTs) do as they support the needs of children with ASDs is consult with and train parents, caregivers, and education staff to generalize treatment strategies across environments. SLPs may need to train family and staff to encourage, recognize, and respond appropriately to communication attempts; to use augmentation and alternative communication systems with children; and to utilize other therapeutic strategies in school and in the children’s homes. OTs work with family and school personnel to develop appropriate programming based on children’s sensory, motor, and life skills, educational and other functional and adapted equipment needs. Additionally, the input of family and staff is important for developing and fine tuning an effective

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treatment plan. Sound collaborative approaches are necessary to ensure consistency in programming for children across environments.

Evaluation process Whether completed by OTs or SLPs, there are two basic types of formal assessment: standardized, and criteria-based. Standardized tests have age norms and have been evaluated with a large population of children. The advantage of using one of these tests is that the scores can be compared against the abilities of other children of the same age. A disadvantage is that these tests are usually normed for typically developing children and therefore may not be applicable to children with autism. The results of tests of this type can, however, still be useful in revealing patterns of strengths and areas of need. Some of the commonly used tests that would fall into this category are the Oral and Written Language Scales (OWLS; CarrowWoolfolk, 1995) and the Pragmatic Language Skills Inventory (PLSI; Gilliam and Miller, 2006). These tests probe skills in the areas of receptive and expressive language, vocabulary, and social skills. Standardized tests that OTs might use with children with autism include the Bruininks Oseretsky Test of Motor Proficiency (BOT2™; Bruininks and Bruininks, 2005), which assesses gross and fine motor skills; the Peabody Developmental Motor Scales II (Folio and Fewell, 2000); and the Sensory Integration and Praxis Tests (SIPT; Ayres, 1989), an instrument that assesses sensory processing and motor planning skills in young children. Criteria-based tests do not provide age equivalencies as do standardized tests. Instead they probe for the presence of certain skills. The results do not allow the tester to compare the skills of one child against another. The strength of this type of test is that one can easily review the results and see what skills are present or absent, and identify basic skills that are missing and need to be strengthened – to ensure a firm foundation to be built upon for more advanced skills. Tests used by speech-language pathologists that fall into this category are the Assessment of Basic Language and Learning Skills-Revised (ABLLS-R) (Partington, 2006) and The Verbal Behavior Milestones Assessment and Placement Program (VB-MAPP) (Sundberg, 2008). Occupational therapists might work with others on the IEP team to use tests such as the Callier-Azusa Scale to assess the overall development of students with ASDs (Stillman, 1998).

Assessment of communication Speech-language pathologists play an essential role in the diagnosis and assessment of communication skills of children with autism. The goal of a communication assessment is to provide meaningful and reliable information regarding the skills and deficits of a person with autism across communication domains, leading to the development of a treatment plan for functional skill acquisition. A communication assessment is typically conducted in several contexts. Assessments will include a caregiver interview. This interview should document skills that are typical of the child’s communication performance; skills that are emerging should be documented as such. A strength of the caregiver interview is that skills may be documented that may not be directly observed because of the child’s unfamiliarity with the tester and the testing situation. Caution must be taken, however, with regard to the caregiver’s subjectivity in reporting the child’s skills and potential to either exaggerate or underestimate abilities. A second context of the communication assessment is informal observation of the child and caregiver in a typical play or interactive situation. This should occur in a setting rich with manipulatives and toys, encouraging communication

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opportunities. Such observations give the SLP an opportunity to observe both the communication skills of the child, as well as the communication style of the caregiver. A third context of the communication assessment may include informal interaction with the child by the SLP. This allows the SLP to probe for communication skills in a functional natural setting. Most communication assessments will also include some testing with standardized or criteria-based instruments. Because so many children with autism also have a motor planning deficit or apraxia, it is important to probe the area of sound production. An oral peripheral examination should take place to assess the structure and function of the articulators, including the lips, tongue, and jaw. The strength, range of motion, and rate of movement should be noted. A formalized test may also be administered to assess the articulation of specific speech sounds and phonological processes.

OT assessment Occupational therapists evaluate a person with autism to gain an understanding of the person’s motor skills, sensory abilities (in terms of acuity, perception, and processing), psychosocial abilities, functional cognition, learning style, and emotional regulation (Tomchek et al., 2009) and of how these systems contribute to the person’s ability to participate in meaningful activities. The evaluation process includes interview; testing and observation of the person with autism; gathering of information through caregiver interview, questionnaires and inventories; and a review of records and previous work samples of the person.

Ecological assessment Although a child with autism will receive initial assessments to determine strengths and needs in communication skills, fine and gross motor skills, social skills, and ability to perform self-care and activities of daily living, it is important to recognize that these early assessments are only a starting point. Assessment is not a one-time procedure. It should be an ongoing process throughout a child’s treatment program, both formally and informally. This allows fine tuning of the program to meet the child’s ever evolving development and needs. Assessments may also have an ecological component. In ecological assessments, evaluators look at a child’s performance in specific skills in real time, within natural environments. One such assessment is the School Function Assessment (SFA) (Davies et al., 2004). The SFA measures students’ abilities to perform functional tasks that allow them to engage in educational and social school activities. These tasks include getting on/off the school bus, accessing the cafeteria, participating in recess, moving through the school, and using the bathroom. The SFA and other ecological assessments often require input from multiple school personnel. The information obtained allows the education team to develop appropriate interventions and supports. A good assessment is only useful if it leads to a well-constructed treatment plan. Ogletree (2007) described some of the essential features of such a plan. First, it must set goals for meaningful change; i.e. goals that: 

aim to develop skills that promote independence and social competence; lead to success upon completion or serve as the prerequisite skills of other, functional goals;  lead to generalization of skills for use in everyday situations. 

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Additionally, the treatment plan must:  

be based on the individual needs and unique learning attributes of the child; and fit the needs of the family and those implementing it.

Lastly, a treatment plan should follow the guidelines of the National Research Council (2001) whenever feasible and applicable:       

Start intervention by age 3 years. Provide active engagement for 25 hours per week or more. Provide planned teaching opportunities that occur with low student-to-teacher ratios. Use systematic and developmentally appropriate instruction. Include families in the assessment and intervention process. Provide ongoing assessment that informs the need for programming changes when necessary. Provide instruction with typical children.

Interventions Most treatment models fall into two categories, those based on applied behavioral analysis (ABA) and those based on a developmental framework. All ABA programs use teaching strategies of prompting, shaping, reinforcement, and discrete trials. Developmental treatment programs focus on social relationships and developmental stages. Treatment programs that fall into this category would include DIR/Floortime, and the SCERTS Model. Although most treatment programs fall into one category or the other, there is overlap between the elements of many of these programs. (See Chapters 8 and 10 for further discussion.) There are many treatments available for developing a program of intervention for children with ASDs. Up until recently the criteria for choosing a treatment were not always evidence-based. To fill this void in the US, the National Autism Center conducted The National Standards Project in 2005. A panel of respected scientists and practitioners gathered to systematically review intervention research and rated both comprehensive and focused interventions as either established, emerging, unestablished, or ineffective/harmful. Established treatments are those for which there are well-controlled studies that have demonstrated effectiveness. Of the established treatments, the majority were based on ABA. Emerging treatments are those for which the evidence suggests they are effective. However, additional research is needed before it can be said with confidence that they are effective. It is important to keep in mind that further research may place some of these treatments in either the established or not effective categories. Unestablished treatments have little or no solid evidence to firmly support their effectiveness or to disprove that they are not ineffective or harmful. The last category is ineffective/harmful. To fall into this category, there must be several well controlled studies backing this conclusion. No education or habilitation treatments were found at this time to belong in this category (National Autism Center, 2009). Interventions can be further broken down into two more categories: comprehensive treatment models (CTM) and focused intervention practices. A CTM consists of a set of practices designed to achieve a broader learning or developmental impact on the core deficits of ASDs. CTMs tend to be implemented over a long period of time; they address a broad range of skills and behaviors; and they require high levels of staff involvement. Comprehensive treatment models may be implemented for 40 or more hours each week,

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and include both school and home programs that require active involvement of families and caregivers for best results and generalization. Focused interventions are often incorporated within CTMs to target specific skills or behaviors. Some examples of CTMs are ABA, DIR, and TEACCH. Focused intervention practices are designed to produce specific behavioral or developmental outcomes for individual children with ASDs by using a prescribed set of procedures. They can target skills in communication, social skills, and adaptive behavior. Examples of focused intervention practices include prompting, reinforcement, discrete trial teaching, Social Stories™, or peer-mediated interventions. Focused intervention practices are used for a more limited time period than comprehensive treatment models. Before the National Standards Project was published, there were no clear guidelines for selecting treatments. Therapists relied on their education, experience, and clinical/professional judgment. Often, valuable time and resources were lost on the use of ineffective treatments. Because it is imperative that children with autism receive effective treatment at an early age in order to achieve the best outcome, this situation was not optimum. With the results of The National Standards Project, therapists now have the beginnings of a roadmap to guide them on best practices and evidence-based treatments. It is important to remember that this is still just a starting point and that, over time, with continued research, some treatment models that now fall into the emerging or unestablished category may later prove effective. Although all CTMs have defined theoretical and procedural guidelines that stand alone, many elements of these models can be used to complement each other when planning a treatment protocol for a child. When blending components of different CTMs, therapists must be familiar with the theoretical foundations of the CTMs and maintain fidelity to each selected approach.

Comprehensive treatment models Because SLPs and OTs work with classroom teachers and other professionals, they often provide their services within the framework of comprehensive treatments models. With the growing need for evidence-based practices and treatment fidelity it is recommended that therapists become familiar with all of these models. (See Table 9.1.) The most widely used CTMs are discussed in Chapters 8 and 10 and include ABA (Lovaas, and Applied Verbal Behavior, Pivotal Response Trainings), TEACCH, and DIR. In addition to these four approaches, one other developmental model needs mentioning: SCERTS. The SCERTS approach, based largely on the work of Barry M. Prizant, is a developmental model that focuses on three domains: Social Communication, Emotional Regulation, and Transactional Support (Prizant et al., 2004). It strives to develop spontaneous functional communication, first focusing on the development of joint attention, reciprocity, and symbol use. It also attends to children’s emotional self-regulation, supporting their coping with individual daily challenges by maintaining optimal states of arousal, to promote learning, interpersonal relating, and the experience of positive emotions. Transactional supports are provided in the four areas of interpersonal, learning and education, family, and professionals and service providers. Learning supports include schedules and fulfilling sensory needs to enhance the learning of children with ASDs. SCERTS also allows other approaches such as ABA, DIR, and TEACCH to be incorporated into its program.

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Table 9.1 Comprehensive treatment models.

Name of intervention

National standards project rating

Brief summary

Target population

TEACCH Eric Shopler

Uses visual strategies to provide classroom structure and develop educational routines

School-age children with ASDs

Emerging

ABA, both Lovaas and Applied Verbal Behavior based on B. F. Skinner

Teaches skills by shaping behaviors and through reinforcing correct or desired responses

All ages with persons with developmental disabilities including ASDs

Established

Pivotal Response Training Koegel, Koegel, Harrower, and Carter

Based on the principals of ABA, children are taught key “pivotal” skills that cause progress in related skills in social skills, communication, and behavior

Preschool through high school for persons with developmental disabilities including ASDs

Established

DIR/Floortime Stanley Greenspan

A developmental model that focuses on building healthy social, emotional, and intellectual foundations

Preschool through high school for persons with developmental disabilities including ASDs

Emerging

SCERTS Barry M. Prizant, Amy M. Wetherby, Emily Rubin, Amy C. Laurent

A developmental model that focuses on the three domains of Social Communication, Emotional Regulation, and Transactional Support

Preschool through high school for persons with developmental disabilities including ASDs

Emerging

Focused interventions Focused interventions are developed to target more specific behaviors or skills within a broader context. A focused intervention can be a procedure implemented throughout skill teaching, such as a prompting hierarchy, or it can be a specific program designed to address a deficit, such as the use of Social Stories to teach social skills. Focused interventions may complement and be utilized concurrently with CTMs.

Prompting There are different types of prompts that are used to teach children with ASDs. These include verbal, modeling, physical, gestural, and positional. Verbal prompts use partial words, whole words, sentences, and intonations to elicit a desired response from the child. Modeling involves demonstrating desired behaviors for the child to imitate. Physical prompting involves touching the child to move them toward a desired response. Gestural prompting involves providing a nonverbal body movement, e.g. pointing, to elicit a desired response. Positional prompting involves arranging the environment so that a correct response is placed in a location that increases the likelihood of the child to select it. There are two general schools of thought in the use of prompts. The first is to use a least (e.g. verbal instruction) to most (e.g. physically directing the child) prompting hierarchy; the second is to use a most-to-least prompting hierarchy. Because children with autism

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tend to become very prompt-dependent, it is critical to consider carefully which hierarchy best serves a child. Using least-to-most prompting hierarchies decreases the intrusiveness of prompting and starts with prompts that are easiest to fade. However, if the initial prompting used is not adequate, this strategy leads to more errors, adding to the child’s confusion and consuming time. Most-to-least prompting hierarchies limit child errors and make better use of teaching time; however, in this approach, prompting must be faded as quickly as possible to decrease prompt dependency. This approach is often referred to as errorless learning. Generally, the most intrusive prompts for motor skills are physical or hand-over-hand prompting, followed by partial physical prompts, visual prompts, and finally verbal prompts. However, in the case of some verbal skills, verbal prompting can be the most intrusive and most difficult to fade. Failure to fade verbal or in-vivo prompting can lead to continued reliance on family and staff to complete education, self-care, and vocational tasks.

Discrete trial teaching (DTT) Discrete trial teaching is used most often in ABA programs. Discrete trials take skills and break them down into their smallest unit for ease of learning. These units are then taught in an ABC format (antecedent, behavior, and consequence). In other words, the teacher presents a request such as “touch your nose,” the child responds, and then that response is reinforced if correct or possibly ignored if incorrect. Some programs use mass trials – the repetition of the same discrete skill multiple times consecutively. In contrast, some practices have abandoned mass trials, arguing that they constitute an unnatural scenario and can cause the child to repeat the same behavior without assimilating it. These practitioners prefer to mix other mastered skills in between trials of an emerging skill to promote flexibility of learning. Both groups agree that newly emerging skills must be repeated multiple times to ensure acquisition.

Shaping to elicit speech Shaping is a technique by which a therapist elicits a skill and reinforces better and better approximations of that skill to produce the best form of the particular skill being targeted. This is a technique often used by SLPs when functional speech skills are being taught. Vocal imitation is a prerequisite skill needed for this technique. The first step in this technique is to take an inventory of all the speech sounds that the child can presently produce. In a therapy session, the SLP will then produce the target sound paired with a reinforcing item. If bubbles are used as a reinforcer, the SLP will produce “ba” and then pause for the child to imitate the sound; when the child imitates the “ba” sound, the SLP will blow soap bubbles for the child. Once “ba” is mastered, the SLP will then only accept “ba-ba” before delivering the bubbles. This will continue until the child is able to say bubbles or its best approximation. If the child is having difficulty verbally imitating the “ba” sound in the initial stages of this technique, the SLP may start with motor imitation before then attempting vocal imitation. This technique should always be paired with a reinforcing activity or item so that the child attaches meaning to the words that he produces. For those children who exhibit some echolalia, this behavior can be helpful in initiating and shaping speech. This may be particularly true for those children whose echolalia is more discriminate and interactive. However, autistic children with very automatic, non-communicative echolalia may repeat speech but have more difficulty in using it functionally (Schuler, 1979).

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Visual strategies Because many children with autism are thought to be more effective as visual learners, using strategies that take advantage of this strength can be very effective. One of the most effective strategies is the use of visual schedules and task analyses. Because many children with autism have difficulty with transitions, visual schedules can allow a child to preview the next activity in order to prepare for that transition. Such schedules can be set up as a series of pictures, drawings, and representative objects mounted on a page, posted on a wall in the classroom or home, or set up on a computer. A visual schedule can also be a simple, written list of the day’s planned activities. Similarly, task analyses take advantage of the visual strengths of children with autism by breaking up tasks into multiple small steps and displaying them visually to aid in learning. Skills that lend themselves to task analysis are daily living skills such as showering and dressing; vocational skills, such as packaging, assembling, and cleaning; and home care tasks such as laundry. Task analyses, whether in written or picture form, help to ensure consistency among family, educators, and other support personnel on how a student should approach a task. They can also be developed into effective documentation tools to aid students, parents, therapists, teachers, and paraprofessionals in collecting data to assess the effectiveness of teaching strategies and therapeutic interventions. Picture schedules and task analyses are utilized in individual work systems. These systems increase student and worker independence and skill generalization by increasing self-initiation of tasks and decreasing the need for prompts from others (Hume et al., 2009). Individual work systems are designed to communicate to the student what he is supposed to do, the amount of work to be completed, and what to do once the current task is completed.

Sensory integration Sensory integration (SI) is a treatment method with a growing body of evidence to support its use for people with autism spectrum disorders. It is based on the assumption that impaired processing of sensory information leads to a disruption in development and in the execution of purposeful behaviors, i.e. to sensory modulation disorders (Pfeiffer et al., 2011). Therapists evaluate sensory modulation disorders using standardized questionnaires that are completed by children, caregivers or teachers, such as the Sensory Profile developed by Winnie Dunn (Ermer and Dunn, 1998). These questionnaires seek to gather information about how a child responds to stimuli received through each of his senses. The results of sensory assessments allow therapists to develop and recommend individualized activities that provide children with controlled sensory experiences, the aim of which is to help them participate in functional educational, social, and life-skills tasks. SI interventions include both targeted sensory integration treatments and inclusion of sensory-oriented strategies within educational and life-skills activities. Therapists may use tools such as swings and scooters to provide vestibular input; fluorescent light covers to adjust environmental lighting; specialized vests made of weighted or elastic materials to provide or sustain body pressure; and textured materials to provide tactile and deep-pressure input. The use of these tools and strategies is intended to help children make sense of, and more effectively respond to, information received through their senses (Schaaf and Miller, 2005). While some studies do show effectiveness of sensory integration as an intervention for people with ASDs, empirical research of SI is difficult to conduct. There are challenges for

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this research that derive from the heterogeneity of ASDs and from differences in how therapists interpret assessment data and develop and implement intervention strategies. Another challenge for researchers is that the application of sensory integration treatment requires therapists to individualize treatment strategies, often in a dynamic manner that might vary with each treatment session (Parham et al., 2007). Although studies show that sensory integration approaches will not necessarily teach desired behaviors, they have been reported to help children with autism to be more attentive and thus better able to benefit from behavioral intervention programming (Baranek, 2002). Case-Smith and Arbesman (2008) reported that sensory integration treatments resulted in improvement in social interaction and play and decreased sensory hypersensitivities. Sensory massage techniques, applied daily, have also been shown to reduce stereotypic behaviors, reduce aversion to touch, and improve attention, at least in the short term (Case-Smith and Arbesman, 2008). Smith et al. (2005) found that individualized sensory integration treatments resulted in a decrease in self-injurious behavior in adolescents with ASDs and intellectual disabilities. Sensory integration therapy has become a frequently requested service for children with ASDs. Efforts to standardize how the approach is defined and how the theory is applied in practice will help researchers to further evaluate its effectiveness as a focused intervention.

Social Stories™ Social Stories were developed by Carol Gray in 1991 to meet the social needs of children and adults with ASD who have deficits in understanding and responding to social situations. (See discussion in Chapter 8.) Social Stories can include pictures and may be presented using computer software such as Microsoft PowerPoint.

Picture Exchange Communication System (PECS) PECS was developed for children with social communication deficits and is widely used by children with autism. It is a pictorial system that uses the principles of shaping, chaining, and differential reinforcement. (See description in Chapter 8.) As a child progresses through the phases of PECS, he is taught to communicate with a variety of communication partners across contexts, and then progress to discriminate between pictures and to form sentences. In later phases of PECS, a child learns to answer questions and make comments. It is advised that all educational, community, and family members who interact with the child be trained in this system to increase the speed of acquisition and carryover.

Augmentative and alternative communication For those children who do not develop speech or lack adequate speech to meet their communicative needs, another form of communication is needed. Augmentative and alternative communication (AAC) is any modality of communication that is used in place of, or in support of, speech. It can include modalities such as sign language, gestures, PECS, picture boards, and high- and low-tech speech-generating devices (SGDs). AAC falls into two categories: aided and unaided (Mirenda, 1999). Aided systems require specific equipment or materials. Examples of aided AAC are SGDs, PECS, and language boards. Sign language and gesture are examples of unaided AAC. The selection of an AAC system is highly individualized and must be tailored to the strengths and needs of the child using it.

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When choosing an AAC system, there are several factors to consider. The system selected should be evaluated for its ease of acquisition, generalization, and use across communication partners and communication environments (Sigafoos and Drasgow, 2001). It is also important to consider any limitation of the individual using the system. Many children with autism do not have any physical, visual, or auditory limitations; however, for those who do, the use of unaided or some aided systems (e.g. those with small icons) may pose a problem. Children with motor difficulties or apraxia may have difficulty replicating the specific hand shapes required of sign language or have difficulty manipulating the pictures used in PECS. Sometimes these limitations can be overcome by using adapted hand shapes or modifying the pictures used in PECS or picture-based systems. It is also important to consider who will be the communication partners of a person using AAC. Some systems, such as those using pictures or voice output, can be easily understood by any communication partner. These systems can be used across a large number of communication partners and environments. Sign language, on the other hand, may only be understood by those trained to work with that child and may not be generally understood. The more an AAC system is used with different people and in different environments, the more functional it becomes. It is therefore desirable, with AAC, to have the child use the system throughout the day, at school, home, and on outings in the community. For this to be successful, the AAC system must have practical utility in different settings and must offer the child the capacity to control his environment and make choices. Although speech acquisition is not the primary goal of AAC, the question of whether its use might hinder the development of speech has been raised as a theoretical concern. However, studies suggest that the use of AAC might actually result in just the opposite – an increase in speech production. Schlosser and Wendt (2008) reviewed the current research and found that there was no decrease in the use of speech as a result of the use of AAC intervention in any study. They went on to note that, for those who did acquire speech, the amount of speech acquired varied greatly among individuals, but was typically small in magnitude. There were some children who did make large gains in speech, but little is known about the characteristics of these children that might suggest positive prognostic indicators for speech acquisition. Presence of speech prior to initiating the use of AAC is the only strong predictor of better speech acquisition that has been identified at this time. A review by Millar et al. (2006) found positive effects across a range of AAC intervention approaches, including highly structured, clinician-directed instruction grounded in behavioral theory and childcentered approaches implemented in play contexts. With regard to specific AAC modalities, PECS has been noted to be especially easy to teach, with many children learning the first phases quickly (Bondy and Frost, 1993). The PECS protocol also promotes generalization to different communication partners and environments. Pictures are kept in a notebook and attached with Velcro, making it both lightweight and easy to carry so that it can be easily accessible. PECS is also inexpensive and easy to maintain. The motor movement required to make the picture exchange is fairly simple, and modifications can be made for those children who have difficulty grasping the traditional pictures. Also, as the pictures resemble items requested, PECS can be used for this purpose with a large number of communication partners, even those who have not been specifically trained. In a review of current research, Sulzer-Azaroff et al. (2009) reported that PECS is an increasingly used method of teaching functional communication. Speech-generating devices are often a suitable option for children with autism who need AAC to communicate. These devices vary significantly in complexity and cost. The simplest

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devices consist of a large switch allowing the recording of a single message that can be issued by activating a switch. Mid-level devices allow the recording of many more messages, at multiple levels of complexity, that can be recorded to generate expressive vocabulary for use in specific contexts. Different levels can be dedicated to particular activities or environments, such as snack time, gym, school, or home. The most advanced devices use synthesized speech and touch screens. These devices have unlimited vocabularies and include the capacity for grammatical expansion. For those with physical or visual limitations, a scanning option or eye gaze system can be used. Such devices may also provide environment controls and other non-communicative functions such as access to the Internet. SGDs provide many advantages. One of the main benefits is voice output. Many providers prefer the more natural communication mode of having “a voice.” SGDs, however, come with a trade-off. Programming can be cumbersome and time-consuming. SGDs can break down, requiring costly repairs, resulting in periods when the child does not have a means to communicate. When SGDs are used, many therapists continue to ensure that children remain proficient in PECS or other alternate communication strategies. Children also need alternate communication methods in environments that do not support the use of high-tech, electronic communication devices, such as a bathtub or pool. Lancioni et al. (2007) did a comprehensive review of the literature evaluating the effectiveness of PECS and SGDs in promoting requesting in children with autism. The results supported use of both systems in providing a functional system for communicating requests. The vast majority of participants were successful to some degree in using both systems to request preferred items. When the use of PECS was compared to that of an SGD, no specific or consistent differences were found between the two systems in making requests or in daily use.

Assistive technology Speech-generating devices and other augmentative communication systems fall within the category of interventions based on assistive technology (AT). Assistive technology is defined in the Individuals with Disabilities Act of 2004 as “any item, piece of equipment, or product system, whether acquired commercially off the shelf, modified, or customized, that is used to increase, maintain, or improve functional capabilities of a child with a disability” (IDEA, 2004). Assistive technology devices are often categorized by costs and complexity of the technology and range from low- to high-tech. Low-tech devices are relatively inexpensive or easy to make, comprising items such as pencil grips and pictures for PECS. Mid-tech devices include static communication display devices and some peripherals that aid in computer access (e.g. a switch that can be used with scanning computer software). High-tech devices include dynamic display communication devices and tablets, such as Apple’s iPad. The use of AT products and services should be considered for each child with autism during the development of his Individualized Education Plans. IDEA 2004 requires a student’s IEP team members to inquire if AT is necessary for the student to receive a free and appropriate education in the least restrictive educational environment. There are a number of AT products and services to address the needs of people with ASDs. The challenge for educators, related service providers, and assistive technology practitioners is to develop a process by which they can effectively match the right AT service and/or device to the educational and functional needs of students with ASDs in keeping with IDEA 2004 and other mandated requirements. To that end, there are a number of assessment tools

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available to IEP team members. These instruments evaluate each functional educational skill area from the perspective of potential AT users. These tools are also applicable in non-school environments. Two of the many AT evaluation/consideration tools are the SETT (Zabala, 2011) and the WATI (Reed and Lahm, 2004). The SETT (an acronym for Students, Environments, Tasks, and Tools) is a model developed by Joy Zabala, Ed.D. that is used to assess the student, the environments in which the student is expected to interact, the tasks in which the student is expected to engage, and the devices or services that might be necessary in order for the student to complete those tasks (Bugaj and Norton-Darr, 2010). The WATI is the consideration guide of the Wisconsin Assistive Technology Initiative. The WATI is complementary to the SETT. It takes an exhaustive look at the needs and abilities of students, the tasks in which they participate and then – through its accompanying handbook – lists possible AT solutions to consider (Reed and Lahm, 2004). Neither of these assessment guides identifies AT products and services specifically for people with autism; their recommendations are based on an individual’s functional needs rather than their diagnosis. There are technology solutions that do meet the functional needs of some people with autism. Students with graphomotor needs might benefit from low-tech solutions such as pencil grips and specially lined paper, or from high-tech options such as a keyboarding device or computer. There are specific computer software programs that are designed to be used with students with ASDs, e.g. DT Trainer, by Accelerations Educational Software, a computer software program that is based on ABA principles. AT can be combined with other treatment approaches. Social Stories, task analyses, academic instruction, and organizational strategies can be enhanced by incorporating assistive technology. Social Stories can be presented on computers that students can read themselves or they can have the computer read it for them. Graphics, including pictures of familiar people, objects, and environments, and other multimedia items, such as preferred voices or music, can be inserted into the stories to match the students’ individual learning needs and preferences. When presented on a computer or handheld device, students with limited reading skills can review the stories at their own pace and at their own desired frequency, without dependency on others. There are a number of AT devices that can be programmed to identify the sequence of steps necessary to complete a task. Task analyses, lists of steps included within a task, can be captured and/or presented on handheld devices, such as Apple’s iPad or Google’s Android, or single-switch, sequential message devices, such as Enablemart’s Step-by-Step, to help students learn the steps of a task or to inform them of what it is they need to do following the completion of a discrete step. The application of AT can help overcome challenges in spelling and letter and number production when producing written work (Cook and Hussey, 2002). Students with ASDs can use presentation software, other computer-based software, as well as other low- and hightech devices and strategies to meet written communication and graphical output needs. Lowtech options include adapted or specialized writing utensils that makes the utensil easier to hold, and/or paper, that provide visual or tactile boundaries for writing, word bars and sentence strips, name stamps, and manipulatives that represent mathematical concepts. Higher-tech options include computers and adaptive hardware devices, e.g. touch screens and specialized computer software that allows emerging writers to use pictures and other graphics. Graphic organizing software programs allow writers to organize their writing by

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creating visual connections between related concepts within a topic. These connections can be expressed as a concept map or a written outline. Moore and Calvert (2000) found that using computer technology with teacher instruction enabled students with ASDs to be more attentive and to learn and retain more vocabulary words than with teacher instruction only. Another technological approach used in the treatment of students with autism is video modeling. Video modeling involves the video recording of an instructor or peer performing desired behaviors for repeated viewing by students with ASDs to teach a variety of functional skills and behaviors. Verbal narration adds a multi-modal dimension to video modeling. After one or multiple viewings of the video, the student is given the opportunity to perform the skill. Bellini and Akullian (2007) conducted a meta-analysis of the use of video modeling and video self-modeling as intervention methods for people with ASDs. They concluded that these approaches are effective in teaching students with autism a range of skills and behaviors, including pragmatic social and communication skills, functional self-care skills (e.g. face and hand washing), and homemaking skills (e.g. making a sandwich and mailing a letter). The authors also found moderate maintenance of skills 6 months post-intervention. In another study, researchers combined video modeling with other treatment methods, including the use of flash cards and positive reinforcements, to teach toileting skills to five boys with autism. The combination of teaching methods increased in-toilet elimination and other toileting behaviors (e.g. pulling down/pulling up pants); there was greater success for those participants who viewed a toileting video early in the study. In-toilet elimination generalized to other settings and there was maintenance of the behavior for most participants (Keen et al., 2007).

Summary Autism is a neurobiological disorder that impairs both verbal and nonverbal communication and social behavior. The number of children diagnosed with ASDs is rapidly growing, with some researchers suggesting a prevalence of 1 in 110. Many of these children also have concomitant symptoms that are not a part of the diagnostic criteria. The totality of these deficits severely impairs a child’s ability to learn and function within the home, school, and community. To meet the needs of children with ASDs, speech-language pathologists and occupational therapists develop and provide individualized programming based on their deficits, strengths, and the educational, social, and community demands placed on them. Effective programming must begin with a thorough evaluation of the child and identification of the child’s strengths and needs. These evaluations rely, in large measure, on the involvement of the child, parents, and other team members in determining priority needs that should be addressed immediately and those needs that can be addressed less formally or at a later time. It is incumbent on speech-language pathologists, occupational therapists, and other related service providers to use interventions that have been researched and that offer some evidence of efficacy in meeting the needs of children with ASDs. There are a number of comprehensive treatment methods (e.g. ABA, SCERTS, DIR) that range from promising to well-supported in the available research and that serve as a basis for the individualized intervention programs utilized by therapists. Comprehensive treatment models provide a framework for service providers to address core deficits of ASDs. Within the comprehensive treatment models, therapists and other team members implement focused intervention strategies to address specific needs.

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As with comprehensive treatments, focused interventions vary in the degree to which they are supported by empirical research. Focused interventions include theoretical approaches such as sensory integration, and interventions that are based on using specific tools, such as Social Stories, PECS, and assistive technology. Team members, families, and, whenever possible, ASD children themselves decide on which intervention strategies to employ. Speech-language and occupational therapists thus provide treatment in a context that is defined by professional collaboration; the treatment itself comprises a broad range of direct and consultative services.

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Technology in Public Schools: Building or Improving your District’s AT Team. Washington, DC: International Society for Technology in Education. Carrow-Woolfolk, E. (1995). Oral and Written Language Scales. Bloomington, MN: Pearson Assessments. Case-Smith, J. and Arbesman, M. (2008). Evidence-based review of interventions for autism used in or of relevance to occupational therapy. Am J Occup Ther, 62, 416–29. Charman, T. (2003). Why is joint attention a pivotal skill in autism? Phil Trans R Soc Lond B, 358, 315–24. Cook, A. M. and Hussey, S. M. (2002). Assistive Technologies: Principles and Practice (2nd ed.). St. Louis, MO: Mosby. Davies, P. L., Soon, P. L., Young, M., et al. (2004). Validity and reliability of the school function assessment in elementary school students with disabilities. Phys Occup Ther Pediatr, 24, 23–43. Dawson, G. and Watling, R. (2000). Interventions to facilitate auditory, visual, and motor integration in autism: a review of the evidence. J Autism Dev Disord, 30, 415–21. Ermer, J. and Dunn, W. (1998). The sensory profile: discriminant analysis of children with and without disabilities. Am J Occup Ther, 52, 283–90. Folio, M. R. and Fewell, R. R. (2000). Peabody Developmental Motor Scales: Examiner’s Manual (2nd ed.). Austin, TX: PRO-ED. Gilliam, J. and Miller, L. (2006). Pragmatic Language Skills Inventory. Austin, TX: PRO-ED. Hume, K., Loftin, R. and Lantz, J. (2009). Increasing independence in autism spectrum disorder: a review of three focused interventions. J Autism Dev Disord, 39, 1329–38.

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Sigafoos, J. and Drasgow, E. (2001). Conditional use of aided and unaided AAC: a review and clinical case demonstration. Focus Autism Other Dev Disabl, 16, 152–61. Smith, S. A., Press, B., Koenig, K. P., et al. (2005). Effects of sensory integration intervention on self-stimulating and self-injurious behaviors. Am J Occup Ther, 59, 418–25. Stillman, R. (Ed.). (1978). The Callier-Azusa Scale. Dallas, TX: South Central Regional Center for Services to Deaf–Blind Children. Sulzer-Azaroff, B., Hoffman, A. O., Horton, C. B., et al. (2009). The picture exchange communication system (PECS): what do the data say? Focus Autism Other Dev Disabl, 24, 89–103. Sundberg, M. L., (2008). Verbal Behavior Milestones Assessment and Placement Program: The VB-MAPP. Concord, CA: AVB Press. Swettenham, J., Baron-Cohen, S., Charman, T., et al. (1998). The frequency and distribution of spontaneous attention shifts between social and nonsocial stimuli in autistic, typically developing, and nonautistic developmentally

delayed infants. J Child Psychol Psychiatry, 39, 747–53. Tomchek, S. D. and Case-Smith, J. (2009). Occupational Therapy Practice Guidelines for Children and Adolescents with Autism. Bethesda, MD: AOTA Press. U.S. Department of Education, Office of Special Education Programs Building the Legacy: IDEA 2004. Retrieved from http://idea.ed. gov/explore/view/p/%2Croot%2Cdynamic %2CTopicalBrief%2C13%2C (accessed 23 May 2011). Werner, E., Dawson, G., Osterling, J., et al. (2000). Brief report: Recognition of autism disorder before one year of age: a retrospective study based on home videotapes. J Autism Dev Disord, 30, 157–62. Weverick, P. (1986). The role of echolalia in children with various disorders: an overview and treatment considerations. Human Communication Canada/Communication Humaine Canada, 10, 25–9. Zabala, J. (2011). Sharing the SETT. Retrieved from http://www.joyzabala.com (accessed 23 May 2011).

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Behavioral treatments for children with ASDs Scott Spreat

Behavioral treatments for Autism Spectrum Disorders (ASDs) are derived from learning theory, or more specifically, from B.F. Skinner’s work in the area of operant conditioning (Skinner, 1938). Under Skinner’s theoretical model, a behavior that is followed by what he called a reinforcer is more likely to recur. The basic premise of Skinner’s work was stated by Edward Thorndike in 1911, when he put forth in his Law of Effect that a behavior that leads to a positive or desired outcome will tend to be repeated. While it is not necessary to postulate the subjective experiences of positive or desired, Thorndike’s presentation of the lawfulness of behavior is illustrative. Future behavior is influenced by the outcomes of current behavior in a lawful, predictable manner. Learning theorists typically say that one may infer that learning has occurred from an observed change in behavior. Thus, lawful, predictable changes in behavior are thought to reflect learning. Spreat and Spreat (1982) noted that because learning occurs in a lawful manner, the question is never whether to use or not use the laws of learning to promote growth and change, but whether to use them in an effective and consistent manner. A considerable body of literature exists regarding the application of operant conditioning and learning theory-based strategies both to teach new skills and to modify or eliminate undesirable behaviors. The reader is referred to a basic behavioral textbook, such as Richard Foxx’s Decreasing Behaviors of Severely Retarded and Autistic Persons (1982) or Repp and Singh’s (1990) review of aversive and non-aversive behavior modification strategies. This chapter will more specifically address behaviorally oriented treatment packages that are used to address behavioral symptoms of individuals with autism. While it is certainly possible to carve out distinctions among behavioral approaches to the treatment of autism, it must be recognized that the similarities across approaches may very well outweigh the dissimilarities. Clinicians working directly with the different models will certainly recognize the differences, while the more casual reviewer will be more likely to note the similarities. All of the approaches trace their lineage back to Skinner, and ultimately, Thorndike. All rely heavily on various forms of reinforcement to shape desired behaviors. All eschew intrapsychic processes or hypothetical constructs as explanations for behavior or as the basis of treatment. All approaches employ intensive training strategies, often encompassing most of the child’s waking hours. All rely heavily on the repetition of small, discrete behaviors. All are committed to basing instructional decisions on student performance data, and each approach presents an intensive integrated intervention strategy that is supported by empirical research. The use of a single integrated model, rather than an eclectic grab-bag of

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programs, has been demonstrated to be more effective in promoting growth in communication, cognition, and adaptive behavior (Howard et al., 2005).

Who are we treating? What are we treating? The primary focus of this chapter will be on three integrated, intensive behavioral treatments for autism. Before undertaking a review of these treatments, however, it seems pertinent to address the key questions of “what are we proposing to treat?” and “who are we proposing to treat?” Autism Spectrum Disorders are characterized by a collection of behaviors, typically in the domains of communication, socialization, and deportment (American Psychiatric Association, 2000). There is considerable latitude within these three domains, allowing researchers and clinicians flexibility in selecting targets for intervention. Most published behavioral research does not seek to treat autism itself, but some of the specific behavioral indicators or correlates of ASDs. In some studies, the selected outcome measures, such as IQ and adaptive behavior, are not even uniquely related to ASDs. With the possible exception of the work by Lovaas and his colleagues at UCLA, behavioral researchers have not focused on attempting to “cure” autism. Complicating matters is the fact that more people are being diagnosed with autism now than in the relatively recent past. The cause for this increase in prevalence of autism has not yet been clearly determined; however, one evident contributor to this trend is a broadening of the diagnostic criteria for disorders on the autistic spectrum. From a researcher’s perspective, the implications of changing diagnostic practice are major. The disorder is inferred from behavioral correlates, and these behavioral correlates have changed over time. This means that while target behaviors of a given intervention may very well be correlated with a diagnosis of ASDs, many of them can also be indicative of other disorders. The implication is that it is conceivable that treating a given behavior, such as stereotypy, may or may not be related to treating the underlying condition. Further, the evolving definition of autism means that research participants in 2012 might not have met inclusion criteria for research done as recently as 1980. Thus, contemporary research results will not really be comparable with earlier research. And because longitudinal research is necessary for studies that seek to address the “cure” question or any other test of long-term impact, those studies could only have been started in the past under more stringent definitions of autism. Evaluating the effectiveness of these comprehensive interventions is, therefore, particularly challenging. In this chapter, we will review the three most popular of the comprehensive behavioral approaches to the treatment of individuals who have autism spectrum disorders. These approaches and their originators are: 1. Discrete Trial Training – O. Ivar Lovaas 2. Pivotal Response Trainings – Robert Koegel 3. Verbal Behavior Training – Vincent Carbone Following this review, two additonal topics will be discussed: the behavior analytic approach to treating specific problematic behaviors in autistic individuals, and methodological challenges in autism behavioral research.

Lovaas and discrete trial training Perhaps the most frequently cited form of comprehensive behavioral treatment for children who have ASDs derives from the work of O. Ivar Lovaas at the University of California at Los Angeles (UCLA). Referred to as discrete trial training, DTT, ABA, the UCLA model, or

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Lovaas style training, this approach uses key elements of repetition and reinforcement. In contrast to other methodologies, Lovaas’ approach appears to seek to treat autism itself, rather than discrete behaviors, despite the discrete approach. Outcome measures tend to be larger, more socially valid measures of real-world functioning. To gain some perspective on Lovaas’ approach, it is necessary to consider its beginnings. Early behavioral research in autism was designed to test Ferster’s (1961) theory that autism was a function of some sort of deficiency in responsiveness to social reinforcers. That is, the individual with autism was hypothesized to be unable to respond to what constitutes social reinforcement for other individuals (praise, complements, etc.). It was reasoned that if one could develop a greater responsiveness to social reinforcers in an individual with autism, that individual might learn to become more socially interactive, and in turn, autistic symptoms might be diminished. Researchers (Lovaas et al., 1965, 1966) demonstrated the ability to establish social reinforcers for children with autism by pairing the planned social reinforcers with either a primary reinforcer (such as food) or with the removal/termination of an aversive stimulus. Despite this seemingly positive response to intervention, there appeared to be no accompanying change in socially appropriate behavior. The responsiveness to the social reinforcers did not generalize from the treatment setting, nor did it result in any sort of overall diminution of autistic symptoms. The failure of approaches designed to enhance receptivity to social reinforcement led Lovaas and others at the UCLA Autism Project to attempt, instead, to directly train specific, discrete behaviors in young children with autism. In adopting this more reductionist approach, they generally relied on primary reinforcers (such as food) in an effort to circumvent the limitations noted with regard to social reinforcers. Although it is somewhat challenging to attempt to dissect an integrated treatment approach, it is possible to discern four specific steps in Lovaas’ approach. These are described below. Step 1. The first step involves the use of behavioral strategies to reduce the frequency of interfering behaviors such as self-stimulation, self-injury, and tantrums. It was reasoned that these behaviors effectively prevent the child from becoming actively engaged in any sort of learning process. Some of the strategies first employed in this component of treatment (in the early 1970s) included contingent withdrawal of reinforcement, contingent aversive stimulation (including response-contingent electrical shock), and reinforcement of incompatible behaviors. With the exception of the no longer accepted use of aversive consequences (Spreat et al., 1989; Griffith and Spreat, 1989), the approach is consistent with contemporary behavior therapy practice. Step 2. The second step could occur simultaneously with the first, and could actually be part of the procedure to eliminate interfering behaviors. It involved the establishment of stimulus control over the child. The therapist would make simple requests of the child and would reinforce the child’s compliance with the request. In more contemporary terms, this component might be called compliance training (Russo et al., 1981) or momentum training (Mace and Belfiore, 1990; Nevins, 1996). Typically, a three-step prompting strategy was used to elicit the desired performance from the child. The child was not permitted to escape or avoid requests, but this was not truly designed to be errorless learning. Step 3. The third step is the central component of the training package: language training. The training is individualized to the needs and abilities of the child. For example, for children who do not speak, the therapist focuses on verbal initiation. This is done in a five-step training approach, in which vocalizations are reinforced and ultimately shaped into communicative language. For children with greater language abilities, the treatment focus is to

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make speech more meaningful and functional. Critics of this approach to language instruction argue that little attention appears to have been given to Skinner’s analysis of verbal behavior, and in particular, to the importance of mands. (See discussion below.) Step 4. The fourth step is designed to occur simultaneously with step 3. Step 4 focuses on the acquisition of social and self-help skills. Underlying this step is the common theme of rewarding “normal” behavior and punishing what Lovaas referred to as “psychotic” behavior. Outlined as four steps or four components, the treatment approach seems relatively simple and straightforward; however, it must be noted that it is also exceptionally laborintensive. Rather than teaching skills that might enable transcendent jumps in performance, Lovaas’ approach builds through discrete, individual building blocks. Training sessions with a behavior therapist typically consume 40 or more hours each week, and these sessions are supplemented in the home by trained family members. Behavior therapy, at this intensive rate, can continue for 3 to 4 years and sometimes longer. Lovaas et al. (1973) reported treating 20 children who had autism using the UCLA treatment package. They reported that inappropriate behaviors decreased in frequency, appropriate behaviors increased, spontaneous social interactions became evident after about 8 months of treatment, and both IQ and adaptive behavior improved during treatment. The authors also reported that, post-therapy, parents were able to maintain the newly learned skills, whereas regression was noted for children in congregate care settings. Design limitations prevent the inference of causality with respect to these findings, but the data certainly suggested that further investigation was warranted. Lovaas (1987) reported findings from a study that compared the intensive UCLA behavioral approach with what he described as a minimal treatment control group. Nineteen young (less than 4 years of age) children with autism formed the experimental group, whereas 19 other young children with autism constituted the control or comparison group. While assignment to group conditions was not random, Lovaas offered a reasonable argument regarding the general equivalence of the groups. Individuals in both groups received treatment for at least 2 years, with the experimental group receiving roughly 40 hours of therapy per week and the control group receiving about 10 hours per week. Follow up after completion of first grade revealed that 47% of the children in the experimental group had achieved normal intellectual and educational functioning, and another 40% were assigned to classes for children with language delays. In contrast, only 2% of the comparison group children achieved normal IQ and educational functioning. Forty-five percent had mild intellectual disability and were placed in language delay classes. Fifty-three percent had severe intellectual disability and were placed in classes for children with autism and/or intellectual disability. While it should be noted that there were some significant methodological shortcomings in this study, it is difficult to discount the social validity (Wolf, 1978) of the different outcomes for experimental and comparison groups. McEachin et al. (1993) reported on longer-term outcomes for the children from the 1987 Lovaas study. Assessing the same children at a mean age of 11.5 years, they found that the experimental group had maintained its gains over the comparison group. Of greater interest: those children who had exhibited the most positive response to behavior therapy were now indistinguishable from “average” children with regard to intelligence and adaptive behavior. The authors suggested that the findings may indicate that behavior therapy may produce long-lasting and significant improvements for children treated at an early age.

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Smith et al. (2000) compared intensive ABA training with parent training in a randomized, controlled study. Children in the ABA group evidenced greater growth than the comparison group in most areas except adaptive functioning and social emotional development. About a quarter of the ABA children achieved average post-treatment scores and attended regular school classrooms. Eikeseth et al. (2002) compared Lovaas’ ABA approach with an eclectic comparison group. Children in the behavioral cohort (n = 13) made significantly greater gains on standardized tests than did peers (n = 12) in the eclectic group. Of all comprehensive approaches to treating children with autism, the Lovaas approach has the best empirical support. Not only do most studies evidence at least an amelioration of symptoms, there are reports of children ceasing to meet critera for a diagnosis of autism and blending in with the mainstream school population. Jacobsen et al. (1998) suggested that the adoption of Lovaas-style early intervention strategies, although extremely expensive, could be justified by savings resulting from reduced support needs over a child’s lifetime. While the logic of the analysis is on target, one must be cautious in overgeneralizing from the reported results. Although the published results on Lovaas’ approach have been most encouraging, one must keep in mind that the validation of such an intensive, long-term treatment approach is challenging. The labor demands involved with the treatment appear to have limited the number of studies and the number of study participants. The real-world demands of a treatment environment have also resulted in significant experimental design flaws in many studies. In particular, Lovaas’ 1987 outcome study has been criticized for its sampling procedure. Children’s assignment to the intensive treatment group or the less-intensive treatment group was based on a number of practical factors, such as the availability of therapists and willingness of family members to travel daily to UCLA. Baer (1993) addressed this concern, arguing that quasi-experimental designs, such as those employed by Lovaas, can under some circumstances be as convincing as designs that employed random assignment. He noted that only about 1 in 64 children with autism (as defined in 1993) got better without treatment, and that data such as those would seem to make it impossible for a researcher to bias results in a manner to favor a treatment strategy. On the other hand, Foxx (1993), while clearly an advocate of behavioral approaches, wrote that the assignment process in the Lovaas work was of sufficient concern to withhold judgment of the treatment strategy pending the results of replication. While the Lovaas approach does meet most standards for empirical validation (National Autism Center, 2009), more research is clearly needed. One cannot say that this treatment approach is the cure for autism. As more intervention studies have been completed, a number of authors have sought to integrate their findings, utilizing literature review and/or meta-analysis. Lord et al. (2005) concluded that the amount and quality of autism research was insufficient to recommend any approach as effective for the treatment of autism. In contrast, Eikeseth (2009) reviewed a number of existing studies and concluded that ABA-based programs showed the greatest treatment effects for children with autism, while other approaches showed limited effectiveness. At the core of the problem is research methodology. The groups used in typical studies are too small to permit sufficient power to detect differences. Dependent measures are often not directly related to autism. Assignment to treatment and comparison conditions tends to be non-random. These factors combine to lead one to question whether the validity of the approach is really established. On the other hand, in each study reviewed, it seems that the behavioral approach is at least slightly favored, a consistency in pattern that cannot be easily dismissed.

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Koegel and Pivotal Response Trainings Pivotal Response Trainings constitute an approach developed largely by Dr. Robert Koegel and colleagues at the University of California at Santa Barbara (UCSB). This approach incorporates both a developmental perspective and behavioral treatment strategies, provided within the context of the child’s natural environment. Treatment with Pivotal Response Trainings focuses on what are called pivotal areas of behavior. A pivotal area of behavior is an area which, when targeted and successfully enhanced, is associated with large collateral changes in other desired but untargeted behaviors. To some extent, the treatment extends the arguments put forth by Ferster (1961) by incorporating an increased awareness of developmental progression. Rather than attempt to discretely train each and every new behavior, this approach attempts to teach skills that permit the child to more easily access existing learning opportunities. Despite what might appear to be the adoption of clinical short cuts, Pivotal Response Trainings are, like discrete trial training, a labor-intensive approach to treatment. These interventions typically include family involvement in the design and implementation of the services, treatment in the natural environment (both home and school), and treatment of key pivotal target behaviors. In contrast to the discrete trial approach, which typically employs skilled behavior therapists working 8 hours per day with the child in a controlled setting, Pivotal Response Trainings typically require a coordination of treatment across environments and therapists. The term therapist is more broadly used, with the recognition that the parents, siblings, and other significant stakeholders all become therapists for the child. When one considers that Pivotal Response Trainings are implemented across all environments by all in contact with the child, it becomes evident that Pivotal Response Trainings are every bit as labor-intensive as the Lovaas approach, although the financial costs may be less. A distinctive feature of Pivotal Response Trainings is that they use and rely upon the natural environment as well as elements within that environment as instructional tools. In a sense, the instructor follows the lead of the student in order to capitalize on the student’s own motivations. It is not an isolated series of instructional drills at the direction and absolute control of the teacher, but rather multiple teachers building on the interests of the child. Delprato (2001) reviewed a series of 10 controlled trials in which these sorts of naturalistic interventions were compared with more formalized behavioral instruction, and he found that normalized language instruction was more effective in producing language-criterion responses than was discrete trial training. Normalized language instruction was characterized by loosely structured sessions of indirect teaching within everyday situations, incorporating response to the child’s initiations, use of natural reinforcers, and a liberal criterion for the delivery of reinforcers. Several core pivotal areas have been identified and researched. The number of such areas varies somewhat across studies, but consistent across most delineations are: (1) motivation, particularly the motivation to engage in social communicative interaction; (2) the ability to initiate activities; (3) the ability to sustain joint attention; and (4) self-regulation or selfmanagement. Leading proponents of Pivotal Response Trainings (Koegel and Koegel, 2006) hypothesize that early intervention in these pivotal areas will permit a child to follow a more normal course of development and to avoid many of the negative side effects of abnormal development. In turn, failure to address these key areas will only result in a compounding of the challenges faced by the child as he/she ages. Most proponents of Pivotal Response Trainings are committed to working with younger children, typically elementary school age or younger. This

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is because the literature suggests that earlier intervention with Pivotal Response Trainings will maximize benefit. Several of the core pivotal areas will be discussed below. Motivation. Early Pivotal Response Trainings involve helping a child to establish a greater competence in understanding the relationship between the child’s behavior and receiving some sort of reinforcer. Koegel and Egel (1979) had noted that traditional instructional approaches often resulted in a low density of reinforcement for children with autism. To address this basic pivotal area and to increase the density of reinforcement, therapists reinforce the child’s attempts to perform a behavior, and they will directly prompt the child to task completion, following immediately with reinforcement. There is no unreinforced prompting as in discrete trial training. Trainers or therapists capitalize on errorless learning by prompting a response immediately, rather than using the stepwise prompting procedure employed in discrete trial training. The intent in Pivotal Response Trainings is to increase the density of reinforcement to ensure continuing motivation. A key component to the motivation pivot is the recognition of the child’s own interests and motivations. Early research (Koegel, Dyer, and Bell, 1987) demonstrated that children will less frequently engage in escape/avoidance behaviors when they select their own activities than when the activities are selected by the instructor. Capitalizing on the child’s own motivation enables the instructor to progress more quickly and with less resistance. This is part of the reason that Pivotal Response Trainings typically occur in the natural environment, rather than in a sterile study carrel. The instructor monitors the child’s interests and incorporates these interests into the instruction. If a child shows interest in a ball, the ball is incorporated into the training. If the child says “ball,” he is given a ball, rather than an M&M or similar unrelated reinforcer. Motivation-based approaches have been shown to increase imitative and spontaneous speech and to lead to generalization across settings, and can be done by parents (Koegel et al., 1995; Koegel, O’Dell, and Koegel, 1987; Laski et al., 1988; Moes, 1995). Also noted are collateral decreases in behavior problems (Koegel et al., 1994, 1995, 1992), thereby attesting to the pivotal role of motivation. Self-initiation. It has been suggested that self-initiation is a primary vehicle for selflearning and independence (McTear, 1985). Children with autism tend to have difficulties with initiation during early developmental periods (Hauck et al., 1995), and if the skill is not developed sufficiently, the child loses ground in what amounts to a significant retrogression. It follows that self-initiation may be an area for pivotal response training; that is, that teaching a child to self-initiate behavior like communication or interaction may have collateral benefits. Koegel et al. (2003) demonstrated that teaching children who have autism to ask questions within a naturalistic environment was associated with a generalized increase of self-initiated activities in other forms of behavior. They noted increases in variation of the form of questions, longer utterances, and greater diversity in verb use. This generalization of skills also extended to the home environment. Self-initiation of other social behavior was also found to occur and was related to the use of embedded social reinforcers (Koegel et al., 2009). Joint attention/responsivity to multiple cues. Joint attention refers to the ability to alternate one’s attention between an object and a communication partner. Deficits in this area are thought to underlie deficits in language, play, and social development (Mundy, 1995). Children with autism have been shown to respond to only limited portions of stimuli, and this limits their ability to learn in a given situation. Schreibman (1975) demonstrated that focused prompting can reduce this tendency in children with autism. Whalen and Schreibman (2003) used behavioral instruction methods to teach joint attention skills. Specifically targeted were responding to showing, pointing, and gaze shifting,

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coordinated gaze shifting, and initiating pointing. Positive results were obtained, and the behaviors generalized to other settings. Naïve observers of the children reported positive changes on social validation measures, suggesting that these behaviors were in fact pivotal. Self-management/self-regulation. Self-management strategies have been a key component of Pivotal Response Trainings. The ability to regulate one’s own behavior can help eliminate interfering behaviors such as stereotypy and allow a child to learn more from his/her environment. The actual teaching strategy for self-management will vary based on the abilities of the child, but the intent is to provide the child with a means of recognizing and reinforcing his/her own behavior. Timers and recording the presence or absence of a target behavior during a time interval are commonly used. Koegel and Koegel (1990) demonstrated the utility of a self-management strategy to decrease stereotypy, and Koegel and Frea (1993) demonstrated that this strategy led to generalization of improvement to untargeted communication skills. To these four areas, one might add empathy. The inability to recognize or gauge the emotions or feelings experienced by others has often been attributed to persons who have autism spectrum disorders (Rutter, 1978; Sigman et al., 1992). Children with autism are sometimes noted to lack empathy and to be insensitive to the feelings of others. This inability to recognize and/or understand the feelings of others would reasonably stand as a barrier to meaningful socialization. At the same time, one might reasonably question why a behavior analyst would consider addressing a presumably non-empirical construct like empathy. The term must be recognized as one that summarizes one’s ability to detect and respond to relatively subtle stimuli emitted by other persons. Behaviorists have been able to quantify empathy, and Saltmarch (1973) demonstrated that a behaviorally oriented, programmedinstruction approach could be used to teach empathic responding. While the term empathy is itself subjective, it appears that empathy can be objectified and quantified with little difficulty. Schrandt et al. (2009) were able to rapidly teach empathic behaviors to four children with autism using a vignette approach with dolls and behavioral instruction strategies. The empathic responding generalized from the training to non-training situations for each of the four children, and generalization from dolls to humans occurred for two of the four children. A number of other studies have directly trained and demonstrated generalized appropriate social or affective behavior (Gena et al., 1996; Reeve et al., 2007); however, there seems to be less empirical demonstration that the development of empathic behavior is a pivotal behavior. Perhaps the point to take from the above four (or five) pivotal areas is that clinicians and researchers have demonstrated an ability to promote and develop behavioral changes in these specific areas, and that changes in these areas tend to be associated with collateral changes in other positive behavior. In a sense, the areas are pivotal because they lead to broader improvements, not just in an immediate target behavior. Koegel and his colleagues, in some ways, have refined and substantially expanded upon Ferster’s (1961) postulation that the development of responsiveness to social reinforcers could more broadly mitigate the symptoms of autism. In contrast with Lovaas’ UCLA project, Pivotal Response Trainings appear to be more focused on specific targets associated with autism than on providing a cure for autism. In a sense, this treatment approach seeks to enable the child to become better prepared to participate in and learn from his/her environment. One might even speak of promoting developmental readiness. Pivotal Response Trainings include treatment strategies for promoting communication and socialization, and decreasing socially devalued behaviors, in

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addition to strategies for promoting initiation and self-monitoring. It appears in the literature that many of the components of the package have been submitted to evaluation and found successful; however, only limited summative evaluation work has been done. Koegel et al. (1999) did report the results of a 6-year follow-up study of four children who had been treated with Pivotal Response Trainings methods with the specific intent of increasing selfinitiations. After 6 years, three of the four children were in regular education classes, and none of the four children continued to qualify for the diagnosis of autism. While these are admittedly small numbers, they are encouraging. Note that Koegel and Koegel refer to Pivotal Response Trainings in the plural. This would suggest that it is not a treatment package, but rather a collection of empirically supported strategies that may be selected and implemented based on the assessment of the individual being treated (Koegel and Koegel, 2006). This is not to suggest a grab-bag approach to clinical treatment, but rather an approach that recognizes the individuality of the child and the need to tailor instruction to each child. The general approach of Pivotal Response Trainings is captured in a trainer’s manual, and Pivotal Response Trainings must be considered a comprehensive treatment package because it attempts to overcome behavioral deficits of autism in multiple settings and across multiple core deficits (Carbone, 2009). It should be recognized that the specific methods of this approach have more support than does the overall approach (Carbone, 2009).

Carbone and verbal behavior training Critics of Lovaas and discrete trial training (Sundberg and Michael, 2001) note that while Lovaas is to be credited for advancing the treatment of children with autism, his approach failed to incorporate the concepts and principles of verbal behavior outlined by Skinner (1957). In particular, these criticisms would focus on the failure to emphasize the importance of early mand training (Sundberg and Michael, 2001) and procedures to promote learning across classes of verbal operants. (See discussion below.) Carbone (2004) has suggested that both contribute to the ability to participate in conversations. The verbal behavior approach (sometimes called the Analysis of Verbal Behavior or AVB) is largely a response to these criticisms. While the verbal behavior approach incorporates many elements of discrete trial training, it relies more heavily on Skinner’s classification and description of language processes. Kates-McElrath and Axelrod (2006) provide a thorough comparison of the operational differences between the two approaches. In particular, a significant difference is in the increased emphasis on teaching children the power of communication. Teaching children to use mands (think of demand) enables them to gain increased control over their environment, as opposed to merely teaching them names of objects. One might argue that discrete trial training focuses more on receptive identification and expressive labeling while verbal behavior training focuses on the power and utility of language. Verbal behavior training adds the focus on teaching children to request desired objects. This, of course, implies that instruction in verbal behavior is somewhat under the control of the child’s motivation for “desired” objects. Barbera (2007) suggests that proponents of verbal behavior see expressive language as a skill that can be taught early in the training sequence. Within the verbal behavior approach, it is possible to delineate four specific foci (Burk, 2010). They are echoic language, mands, motivation, and tacts. Each will be briefly discussed below.

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Echoic language refers to the ability to repeat a spoken or signed word. Echoic behavior is seen as an essential building block for the development of higher levels of language. While spoken language requires the ability to mimic the spoken word, the use of signed language requires the motor skills that permit physical mimicry. It is the ability to echo language (either spoken or signed) that enables a child to learn new language without a laborious shaping process. The mother repeatedly says “momma” to her baby, and eventually the child says something like “momma” and receives reinforcement. Shaping of the response is used to refine the verbal performance. Mand. The mand is essentially a demand or request, and it is reinforced by satisfaction of the demand or request. For example, the child who says “Juice” is reinforced by getting the juice. Like most operant behaviors, a mand is under the control of the child’s state of deprivation. That is, the child generally has to be thirsty to request (or mand) juice. The mand is that part of language in which the child learns to exert power and control the environment. Because of its power and because it is based so directly on the child’s own motivation, specialists in verbal behavior typically teach the use of mands as a first step in teaching language. Motivating operations/establishing operations are linked directly to the mand. Michael (1982) wrote that the motivating/establishing operations constitute a set of environmental events that temporarily alter the reinforcement value of other stimuli, thereby eliciting the child to engage in behaviors (mands) that achieved the reinforcers in the past. An example of this type of operation would be the common procedure in a freshman psychology lab of decreasing a rat’s body weight to 85% of its baseline weight in order to motivate it to work for food. The reinforcement valence for food is thus increased; the rat highly motivated. In contrast, sated rats do not work for food. This is not to equate children who have autism with lower lifeforms, but rather to illustrate the impact of deprivation on the motivation to emit behavior. The hungry child will mand for “cookie,” but the child who has just devoured 24 cookies is unlikely to do so. Note that the reinforcement in mand training should be what the child manded. Thus, if the child manded for dirty socks, dirty socks would be used as the reinforcer rather than more traditional forms of reinforcement. Ultimately, the child selects the reinforcer. Verbal behavior instruction requires the instructor to be attuned to the motivations of the child at each given moment. Tact refers to verbal behavior that typically labels something else in the environment. It is not, however, limited to nouns, but may include adjectives, actions, and modifiers. A tact differs from a mand in terms of the motivating/establishing operation. The word “juice” is functioning as a mand for a thirsty child, and the utterance of the word will be reinforced by presentation of juice. For the child who has just guzzled four juice boxes, the word “juice” is more likely to be functioning as a tact, in naming the object. There is no deprivation, so it is unlikely to be a mand. Tacts are typically taught when motivation for a given object is low, and the reinforcer likely to be used by the instructor is praise or confirmation. Mands, on the other hand, are best taught in a state of deprivation. There is a fifth element to verbal behavior instruction, and that refers to verbal behavior that is conversational in nature and typically strengthened by social reinforcement. In addition to their reliance on Skinner’s analysis of verbal behavior, practitioners of verbal behavior training differ from discrete trial trainers and pivotal response trainers in a number of other ways. For example, verbal behavior trainers generally refrain from the use of breaks as a reinforcer (i.e. negative reinforcement). Because verbal behavior training is a bit

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more focused on the interests of the child, their interests set the occasion for training opportunities. This, in turn, implies that much but not all of the instruction is done in the natural environment, rather than in an isolated teaching carrel. In addition, verbal behavior training institutes an immediate prompt for incorrect responses. Every student response is either reinforced or prompted and then reinforced. This results in an errorless learning approach, in constrast with the the traditionally used three-step prompt hierarchy which permits several failures. To date, there have been no large-scale longitudinal evaluation studies of verbal behavior training in autism; however, it must be recognized that the general approach has been built upon a firm scientific foundation.

Treatment of behavior problems Each of the three approaches described above is comprehensive in nature and designed to promote positive changes in many life areas among children with autism. Behavior analysts, however, are often called upon to address more narrowly defined problems that may occur in individuals with ASDs – problems such as aggression, self-injury, property destruction, tantrums, and other such barriers to successful social group living. Because behavior analysts are frequently consulted on these more narrowly defined targets, it is pertinent to summarize the likely approach. The behavior analytic approach, with its roots back to Skinner and Thorndike (and perhaps even Aristotle), is based on the assumption that learned behavior can be modified or replaced with other behavior through the application of basic learning principles. Even physiologic parameters that are unlearned (such as blood pressure or heart rate) have been demonstrated to be subject to modification by application of basic learning principles (DiCara and Miller, 1968, 1969). While learning theory may not explain the genesis of autism or associated behaviors, it is believed to underlie strategies that can ameliorate symptoms of the condition. The approach to functional behavioral treatment consists of four basic steps. Step 1 – Define the target behavior. A target behavior is a behavior that someone wants changed. As suggested above, likely target behaviors for a behavior analyst would include aggression, self-injury, property destruction, and tantrums. It is essential that any target behavior be defined in an objective and measurable manner. Thus, “having angry thoughts” would not be a good target behavior; however, “punching someone in face with fist” would be acceptable. A target behavior must be something that is easily observable by someone outside of the client, and it must be reliably quantifiable. A data collection system must accompany this behavioral definition. That is, there must be a method to estimate the frequency with which the target behavior occurs. Unless the behavior occurs at exceptionally high rates, simple frequency counts are the preferable way of estimating the frequency of target behaviors. All decisions in behavioral treatment are based on data, and subjective opinions are not considered to be data. Note that subjective impressions are likely to be impacted by recency and primacy phenomena, selective memory, and other forms of bias. Step 2 – Analyze the behavior. Behavior analysts generally believe that behavior has purpose, and that the ascertainment of this purpose is essential to treatment. Functional analysis (Iwata et al., 1982) is essentially a behavioral diagnostic process that is designed to

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estimate this purpose. It represents an empirical effort to determine why an undesirable behavior occurs, or more precisely, to determine what reinforcer is maintaining this socially undesirable behavior. The underlying assumption is that all behavior is adaptive, and that without some sort of reinforcement, the behavior would undergo extinction. Therefore, even unacceptable forms of behavior must be receiving some sort of reinforcement if they continue to occur. The intent of functional analysis is simply to determine the reinforcer for a target behavior. Note that it is possible, particularly among older children, for a behavior to have multiple reinforcers and multiple functions. The initial description of functional analysis – and the model typically employed at acute behavioral treatment settings such as the Neurobehavioral Unit at the Kennedy Krieger Institute at Johns Hopkins – involves actually assessing the rate of the individual’s identified target behavior under different reinforcement conditions. Example conditions might include: a positive reinforcement condition, in which the client receives praise or attention after each emission of a target behavior; a negative reinforcement condition, in which a task or unpleasant demand is removed after each target behavior; and an “alone” condition, with no demands or external reinforcements, testing for self-stimulation. There can be variants on these conditions, but they all constitute an hypothesis-testing paradigm in which data are used to support or disconfirm hypotheses regarding the function of a behavior. Although this approach can be extremely powerful, it is time-consuming, and it is not really practical for low-frequency behaviors. A psychometric alternative, such as the Motivation Assessment Scale (Durand and Crimmins, 1988), is sometimes used in such situations, often supplemented with Antecedent-Behavior-Consequences charts and observation. Step 3 – Develop hypothesis-driven interventions. Hypothesis-driven interventions are therapeutic strategies that are based upon the behavior therapist’s empirical assessment of the variables that are currently reinforcing the target behavior (Repp et al., 1988). The use of hypothesis-driven interventions is sometimes referred to as functional treatment. That is, the behavior therapist is treating the function, rather than the form (or topography), of the behavior. Thus, one does not treat aggressive behavior per se, but rather escape-based aggression or attention-based aggression. This distinction is important. Two topographically similar behaviors may be maintained by entirely different reinforcers or environmental contingencies, and thus warrant distinct treatment approaches. For example, time-out would be an example of functional treatment for attention-based aggression, but an entirely dysfunctional treatment for escape-based aggression because the time-out would, in effect, authorize the escape. The key component of functionally derived treatment is that treatment procedures must have some rational connection to the variables hypothesized to control or reinforce the target behavior. The task of the behavior therapist is to either identify a means with which to reinforce an alternative, more acceptable behavior (to replace the target behavior) or a means with which to withhold reinforcement for the target behavior and permit its being extinguished through nonreinforcement. Note that punishment or the application of aversive consequences is not considered a form of functional treatment. Azrin and Holz (1966) demonstrated that while punishment will certainly change behavior, it must be of significant levels to eliminate an unacceptable target behavior. Step 4 – Evaluate the data. Data on the frequency of the target behavior must be examined longitudinally to determine whether behavioral improvement is evident. If change is not empirically evident, then the behavior therapist must go back to steps 2 and 3, make necessary modifications, and repeat the process.

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Methodological dilemmas for researching behavioral treatments Despite the encouraging outcomes that have resulted from the application of behavioral treatment strategies with persons who have autism, some degree of caution must be exercised in interpreting the published research. Research in the area of behavioral treatments for children who have autism is complicated by a number of unique challenges. Several key challenges will be reviewed below. Evolving definition of autism. As noted earlier in this chapter, the definition of autism has changed over time, and most professionals have come to recognize the disorder as one in which an individual’s behavior may vary widely along the three key diagnostic dimensions. While this may reflect a growing clinical sophistication, the net result is that children being evaluated today are diagnosed using different criteria from those used in the previous generation. Because of the wide variability recognized on the autism spectrum, the typical person diagnosed with autism in 2012 is probably significantly less challenged than the typical person so diagnosed in 1975. Science is largely built on the accumulation of knowledge and the iterative revision of theory pertaining to an area of interest. If the definition of that area of interest, in this case autism, is evolving, there can be limited comparability of earlier and more recent work. The earlier work cannot reliably serve as a foundation for more contemporary research because the earlier and more contemporary research may have been studying different types of people. Even the applicability of the pioneering work of Ivar Lovaas, started in the early 1970s, must be questioned today. Do Lovaas’ results pertain to the children with autism today? Or just to those children with autism who look like Lovaas’ study participants? Perhaps of greater concern is that the “cure” question remains central to families with children who have autism spectrum disorders. To ascertain whether one has achieved a “cure,” longitudinal data must be collected over a long period of time, and under the condition of an evolving definition of autism, longitudinal data will have limited generalizability. The people who have been around long enough to contribute data for a significant longitudinal study were probably diagnosed under different guidelines than those applied to children today. Thus, the applicability of those findings would be limited to persons who would meet the diagnostic criteria applied at the start of the longitudinal study. One might, by logical extension, apply those findings to persons diagnosed under the broader contemporary standards, but it would be a logical application rather than an empirical one. With the broadening of the definition comes the labeling or identification of individuals with autism who are more capable of expressing and sharing the phenomenological experience of their lives. The observations of people like Temple Grandin (1995) are truly inspirational, but it is not clear that the observations of Dr. Grandin and other such individuals will generalize to others on the autism spectrum with greater degrees of challenge. Caution must be exercised to avoid generalization from the observations of Dr. Grandin and similar individuals to all or even most children diagnosed with autism. Small sample sizes. Most of the behavioral approaches to dealing with children who have autism tend to be labor intensive. Lovaas and Pivotal Response Trainings both seem to expect in the neighborhood of 40 hours per week of instruction. Studies comparing longer-duration training with shorter-duration training generally favor the longer-duration training, and this is entirely what would be expected from basic experimental laboratory research. The intensity of

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instruction, combined with the level of expertise needed to implement, maintain, and monitor a treatment regimen, makes these treatment approaches very expensive. Ultimately, the high costs translate into small sample sizes. Lovaas’ classic “cure” study involved only 19 children. The follow-up study of Pivotal Response Trainings by Koegel et al. (1999) had only four children. While behavioral approaches have developed experimental designs capable of demonstrating causality within single subjects (Hersen and Barlow, 1976), Sidman (1960) reminded all that multiple replications are the essential key to behaviorally oriented research. From the perspective of either group-research or single-subject approaches to science, these numbers are not really large enough to inspire overwhelming confidence in the reported results. Absence of random assignment to conditions. The intensity and demanding nature of the behavioral treatments for autism not only keep sample sizes small, they tend to limit the ability of the researcher to use random assignment. One cannot randomly assign someone to 40 hours of therapy per week, sometimes in a distant location, if the family does not support that. There may very well be a tendency for the less-committed families to be placed in the comparison groups rather than the treatment groups. In fairness, Baer (1993) offered reasonable arguments that, while elements such as random assignment are desirable, an acceptable standard of proof can be met without them. Nevertheless, the absence of random selection and random assignment remains a criticism often leveled at behavioral research. Unreliability of early IQ scores. IQ score has been used as a “real-world” outcome measure in a number of autism treatment studies, but there may be significant problems with the use of IQ scores in this manner. Behavioral autism treatment studies have involved preschool children, and IQ scores have long been known to be unreliable among such young children (Honzik et al., 1948). In most research, an unreliable outcome measure merely means that it will be more difficult to obtain a statistically significant finding. In longitudinal autism research, however, there is a tendency to attempt to distinguish between the gainers and losers. Based on classical test theory, one would expect extreme low scores to increase simply as a function of error (regression to the mean). Thus, many children with autism and low IQ scores will have higher IQ scores on subsequent test administrations as a function of greater measurement error on the earlier test. Generalization of findings. Group research generally relies on random selection and assignment to bolster the generalizability of study findings. If study participants were randomly selected from some population and then randomly assigned to treatment conditions, the findings can be generalized to the population from which they were drawn. In reality, there is almost no random selection and little random assignment in most psychological research, and particularly in autism research. Instead, we need to rely on the logical comparison of the study group to some other larger group. In a sense, one is left attempting to guess from which population the study sample could have conceivably been drawn. Ultimately, the best approach is to use large sample sizes. Single-subject research addresses the topic of generalization in a different way. Singlesubject research relies on many direct and systematic replications of studies (Sidman, 1960). Behavior analysts are largely schooled in the study of single subjects, and it follows that their published research tends to utilize the single-subject approach. The problem is that there is considerable challenge in finding journals willing to publish replications. Again, we encounter difficulty assessing the comparability of the study participants with the children we propose to treat right now. Limitations of research methodology. It must also be recognized that the very structure of our research processes places limits on our ability to detect things. Spreat and Behar (1994)

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had an NIMH grant to study the utility of lithium carbonate to treat aggressive behavior in persons with intellectual disability. About 60 subjects were evaluated in a cross-over design open trial, and the overall conclusion was that there were no significant differences between groups. One individual, however, clearly demonstrated a causal linkage between serum lithium level and aggression. Her aggressive behavior could be controlled by manipulating the serum lithium level above and below 1.0 mEq/l. The procedure was reversed several times to prove the causal relationship between lithium and this individual’s aggressive behavior. A colleague had a similar experience in the late 1970s evaluating the Feingold diet for children with ADHD (Leander Ellis, personal communication). Overall, the diet did not work, but, for one client, the reduction in ADHD symptoms was both impressive and confirmed by reversing the conditions of the study. So what do these sorts of findings mean? The mere fact that group research fails to find a significant mean difference does not necessarily mean that a treatment does not work. It may mean that we have been unable to distinguish the likely responders from the likely non-responders. With respect to autism, this task is all the more complicated because the autism spectrum disorder population is so heterogeneous.

Conclusions Despite the methodological limitations described above, comprehensive behavioral approaches to the treatment of young children with autism have empirical support. It should be noted that this support is for improvement, rather than “cure.” Behavioral approaches, whether derived from Lovaas, Koegel, or Carbone, seem to have the ability to help improve the conditions of peoples’ lives. Further, there appears to be reason to believe that early intervention is a key component, although clearly not a guarantee, of success. The National Autism Center (2009) released a national standards report on evidencebased practice guidelines for working with people who have autism spectrum disorders. Thirteen specific treatments were identified as “established,” and 12 of these 13 are clearly behavioral in orientation. Twenty-one therapies were classified as emerging (as in not-yetestablished, but acquiring data). Most of the primarily communication and developmental approaches fall within this category. Treatments described as lacking empirical support included auditory integration training, facilitated communication, various dietary approaches, and sensory integration. This thorough review of autism treatment strategies clearly supports the various behavioral approaches over nearly all others. It would appear at this point in time that the best advice a clinician can offer to the family of a preschool child with autism is to get the child involved in an intensive behavioral approach to treatment. In the absence of significant “horserace” research comparing the various behavioral approaches, there can be little empirically derived advice as to which behavioral approach to select. Treatment selection may be governed more by the orientation of the clinicians in one’s given geographic area. Although behavior analysts are not required to have this certification, parents in the United States may want to look for clinicians with credentials as a Board Certified Behavior Analyst, which usually indicates a reasonable degree of expertise in the use of behavioral treatments with people who have autism. It should be noted that many licensed psychologists also have this expertise. Advice for parents of older children is more complicated simply because the most encouraging research has been done on younger children. That behaviorally oriented strategies promote learning among older individuals is beyond question, but there is no evidence that such strategies lead to elimination of core symptoms or major developmental

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gains. In some degree of contrast with the field of autism, the intellectual disabilities field has gradually come to recognize disability (Steuernagel, 2005) as the product of the interaction of people and the social and physical environment in which they live. Individuals have disabilities to the degree defined by the social and physical context in which they live. This social constructivist approach to disability emphasizes the acceptance and accommodation of individuals, rather than specific treatment efforts to change them and make them fit in with society. It is possible to minimize the problems associated with disability by removing numerous social or environmental barriers. For example, it has been argued that if access to all buildings was unfettered, the impairment requiring the use of a wheelchair would no longer be considered a disability. The term “treat” is regarded by some as something one does to “sick” people to ameliorate a condition and, it is believed, should not be used synonymously with “educate” or “rehabilitate.” Some disability advocates have even argued that efforts to correct an individual’s impairment may functionally serve to devalue that person. While this is perhaps an extreme perspective, it is clear that the intellectual disabilities field has moved from the medically influenced model of the 1970s (focused intervention in order to fix the “broken” individual) to one that more closely embraces the notions of choice, selfdetermination, quality of life, and inclusion. The pressing question may be whether adolescents and young adults with autism should be treated under a variant of the model presently used with children who have autism or whether they should be supported via the accommodation and support model used with many adults who have intellectual disabilities.

References American Psychiatric Association. (2000). Diagnostic and Statistical Manual of Mental Diseases, Fourth Edition, Text Revision. Washington, DC: American Psychiatric Association. Azrin, N. H. and Holz, W. K. (1966). Punishment. In W. K. Honig (Ed.), Operant Behavior: Areas of Research and Application (pp. 380–447). New York, NY: AppletonCentury-Crofts. Baer, D. (1993). Quasi-random assignment can be as convincing as random assignment. Am J Ment Retard, 97, 373–5. Barbera, M. L. (2007). The Verbal Behavior Approach. Philadelphia, PA: Jessica Kingsley Publishers. Burk, C. (2010). What is applied verbal behavior? http://www.christinaburkaba.com/AVB.htm Accessed 13 May 2010. Carbone, V. J. (2004). Clinical applications of verbal behavior research with children with autism. Paper presented at International Association of Behavior Analysis Conference, Boston, MA. Carbone, V. J. (2009). Behavior analytic research in autism. Presentation at 2009 National Autism Conference. Pennsylvania

Department of Education and Pennsylvania State University. State College, PA, August 3–7. Delprato, D. J. (2001). Comparisons of discretetrial and normalized behavioral language intervention for young children with autism. J Autism Dev Disord, 31, 315–25. DiCara, L. V. and Miller, N. E. (1968) Instrumental learning of systolic blood pressure responses by curarized rats: dissociation of cardiac and vascular changes. Psychosom Med, 30, 489–94. DiCara, L. V. and Miller, N. E. (1969). Heart-rate learning in the noncurarized state, transfer to the curarized state, and subsequent retraining in the noncurarized state. Physiology and Behavior, 4, 621–4. Durand, V. M. and Crimmins, D. B. (1988). Identifying the variables maintaining selfinjurious behavior. J Autism Dev Disord, 18, 99–117. Eikeseth, S. (2009). Outcome of comprehensive psycho-educational interventions for young children with autism. Res Dev Disabil, 30, 158–78. Eikeseth, S., Smith, T., Jahr, E., et al. (2002). Intensive behavioral treatment at school for

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Koegel, R. L., O’Dell, M. C. and Koegel, L. K. (1987). A natural language teaching paradigm for nonverbal autistic children. J Autism Dev Disord, 17, 187–200. Koegel, R. L., Vernon, T. W. and Koegel, L. K. (2009). Improving social initiations in young children with autism using reinforcers with embedded social interactions. J Autism Dev Disord, 39, 1240–51. Laski, K. E., Charlop, M. H. and Schreibman, L. (1988). Training parents to use the natural language paradigm to increase their autistic children’s speech. J Appl Behav Anal, 21, 391–400. Lord, C., Wagner, A., Rogers, S., et al. (2005). Challenges in evaluating psychosocial interventions for autistic spectrum disorders. J Autism Dev Disord, 35, 695–708. Lovaas, O. I. (1987). Behavioral treatment and normal educational and intellectual functioning in young autistic children. J Consult Clin Psychol, 55, 3–9. Lovaas, O. I., Freitag, G., Gold, V., et al. (1965). Experimental studies in childhood schizophrenia: analysis of self-destructive behavior. J Exp Child Psychol, 2, 67–84. Lovaas, O. I., Freitag, G., Kinder, M. I., et al. (1966). Establishment of social reinforcers in two schizophrenic children on the basis of food. J Exp Child Psychol, 4, 109–25. Lovaas, O. I., Koegel, R., Simmons, J. Q., et al. (1973). Some generalization and follow-up measures on autistic children in behavior therapy. J Appl Behav Anal, 6, 131–66. Mace, F. C. and Belfiore, P. (1990). Behavioral momentum in the treatment of escapemotivated stereotypy. J Appl Behav Anal, 23, 507–14. McEachin, J. J., Smith, T. and Lovaas, O. I. (1993). Long-term outcome for children with autism who received early intensive behavioral treatment. Am J Ment Retard, 97, 359–72. McTear, M. F. (1985). Children’s Conversations. Malden, MA: Blackwell Publishers. Michael, J. (1982). Distinguishing between discriminative and motivational functions of stimuli. J Exp Anal Behav, 37, 149–55. Moes, D. (1995). Parent education and parenting stress. In R. L. Koegel and L. K. Koegel (Eds.), Teaching Children with Autism: Strategies for Initiating Positive Interactions and Improving

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11

Assessing and Treating Children with ASDs

Medication and nutritional treatments for children with ASDs Shivani Upendra Mehta, Raghavendra Rao Siragavarapu, and Mark E. Reber

Generally speaking, the goal of medication treatment in autism is to ameliorate symptoms or symptom domains, such as compulsive behaviors, hyperactivity, mood disturbance, and temper outbursts that can interfere with an individual’s development and learning. At present there are no standard medications that effectively treat major core symptoms of autism – the social deficits, speech and language abnormalities, and restricted interests that define the ASDs. As suggested in previous chapters, core symptoms are best approached with educational, behavioral, and habilitative interventions. This chapter will review the research on pharmacologic treatment of specific symptom domains associated with autism. Based on this research and on clinical experience, recommendations will be made with regard to ways in which medications can best be used to treat children with these symptoms. The chapter will conclude with a discussion of pharmacotherapy and nutritional interventions from the realm of complementary and alternative medicine. These treatments are rarely symptom-specific, and advocates for their use sometimes claim a broader aim: to reverse core symptoms or even “cure” autism.

Symptom domain of repetitive and restricted behaviors Although technically a core feature of autism, repetitive, and restricted behaviors have historically been an independent target of pharmacologic intervention. These behaviors are highly variable among children with ASDs and are of concern if they limit a child’s availability for learning, cause distress within the family, or are associated with recurrent selfinjury or tantrums (e.g. when routines and rituals are interfered with). At the same time, repetitive behaviors and routines can be a source of comfort for an autistic child, a way to gain emotional equanimity and to cope with sensory overstimulation and the unpredictable nature of the social environment. The choice of when to try to intervene to reduce the frequency or intensity of such behaviors is thus highly individual. DSM-IV and ICD-10 list four categories of restricted, repetitive, and stereotyped patterns of behavior, interests, and activities: (1) encompassing preoccupation with one or more stereotyped and restricted patterns of interest that is abnormal in intensity or focus; (2) compulsive adherence to specific non-functional routines and rituals; (3) stereotyped and repetitive motor mannerisms; and (4) preoccupations with parts of objects. The phenomenology covered by these criteria is quite broad and could range from body-rocking to putting clothing on the same way every day to an all-encompassing interest in Aztec culture and religion. Some repetitive motor behaviors appear to be similar to the “stereotypical The Autism Spectrum, ed. Mark E. Reber. Published by Cambridge University Press. © Cambridge 258 University Press 2012.

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movements” seen in low-functioning, non-autistic individuals with intellectual disability; others resemble the tics and motor mannerisms seen in Tourette’s disorder. Some stereotypies have a sensory component, e.g. repeatedly waving a string in front of one’s eyes. Repetitive routines such as closing car doors in a specific order or insisting on a ritualized verbal exchange are not unlike phenomena seen in obsessive–compulsive disorder (OCD). They also resemble OCD in the intensity with which they are performed and the anxiety that can accompany any interruption. Some authors have suggested that there is an underlying neurophysiology that unites all these disorders and types of repetitive and restricted behaviors (Lewis and Bodfish, 1998). Others have made a distinction between “lower-level” repetitive behaviors, such as stereotyped movements, and “higher-level” behaviors, such as rituals and circumscribed interests (Turner, 1999). Both “levels” have been shown to occur across the autistic spectrum and to be a prominent source of distress to individuals and families (South et al., 2005). The medications that have been explored for repetitive and restricted behaviors and autism have been chosen largely because of their use in other psychiatric disorders with somewhat similar symptom domains. A primary example of this are the serotonin reuptake inhibitors, medications that have long been shown to be an effective treatment for OCD, in both children and adults, and have therefore been studied and used in autism. This use has also been supported by some research that has implicated the serotonin system in the pathophysiology of repetitive behaviors in autism (McDougle et al., 1996; Hollander et al., 2005).

Serotonin reuptake inhibitiors Clomipramine There are two types of serotonin reuptake inhibitors – the tricyclic antidepressant clomipramine and the so-called specific serotonin reuptake inhibitors or SSRIs. They all block serotonin reuptake by presynaptic serotonin transporters – the presumed basis of their efficacy in OCD and the reason they have been prescribed in ASDs. Clomipramine was first studied in 1993, when Gordon et al. reported on 24 subjects with autistic disorder, 12 of whom completed a 10-week, double-blind crossover study comparing clomipramine and placebo, and 12 who completed a similar comparison of clomipramine and desipramine. (Desipramine is a tricyclic antidepressant that does not block the serotonin transporter.) Clomipramine was found to be significantly superior to both placebo and desipramine on ratings of stereotypies, compulsive, ritualized behavior, and anger. Both tricyclic antidepressants were superior to placebo in reducing hyperactivity. These positive results were not, however, replicated in a double-blind, placebo, crossover study comparing clomipramine and haloperidol (Remington et al., 2001). In this Canadian study of 36 subjects, only 37.5% on clomipramine were able to complete the 7-week trial (compared to 69.7% on haloperidol) because of side effects and behavior problems. Two open-label studies of clomipramine in autism also identified significant side effects. Brodkin et al. (1997), in a 12-week, prospective, open-label study of 35 adults with PDDs, found that 55% of the 33 subjects who completed the study experienced a significant reduction in repetitive thoughts and behavior and also had some improvement in social relatedness, but 13 subjects had clinically significant adverse effects, including three who experienced seizures (one without a prior diagnosis of seizure disorder and two on

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anticonvulsants). Sanchez et al. (1996), in a 5-week open study of young autistic children, found serious untoward side effects and general worsening on clinical global assessment.

SSRIs The SSRIs used in the United States are fluoxetine, sertraline, paroxetine, fluvoxamine, citalopram, and escitalopram. Although all these drugs are powerful serotonin reuptake inhibitors, they differ somewhat in their minor pharmacologic actions, thus leading to differences across patients in clinical response and adverse effects. The first study of an SSRI in autism was an open trial of fluoxetine (Cook et al., 1992). Subjects included both children and adults, some with autistic disorder and some with intellectual disability without autism. Improvement was noted in 15 of 23 subjects with autism and 10 of 16 with intellectual disability. Doses ranged from 20 mg every other day to 80 mg per day. Nine patients experienced significant side effects, including restlessness, hyperactivity, agitation, and insomnia. Following Cook’s study, there have been several other open-label studies and case series of SSRIs in autism. DeLong et al. (2002) reported on a clinical series of young children with ASDs treated with fluoxetine for periods up to 5 years. Nearly 70% of their patients were judged to have had a good or excellent response, but no standardized instruments were used. Martin (2003) reported on an open-label pediatric study of fluvoxamine, in which only 3 of 18 subjects were rated as full responders. There were no significant positive effects of fluvoxamine on repetitive behaviors, as measured by the Children’s Yale–Brown Obsessive Compulsive Scale (CY-BOCS). McDougle et al. (1998) published an open-label study of sertraline in 42 adults with autism spectrum disorders. The medication was well tolerated and behavior ratings of repetitive behaviors, aggression, and social relatedness showed improvement over 12 weeks in the majority of patients with autistic disorder and PDD-NOS, but not in the 6 patients with Asperger’s syndrome. The only pediatric study of sertraline was by Steingard et al. (1997) – a case series of nine children who showed improvements in anxiety, irritability, and need for sameness. These benefits, however, failed to persist in three of the patients. Citalopram and escitalopram have also been the focus of open studies and case series. Couturier and Nicholson (2002) reported on 17 children who received citalopram in an open study; 10 were improved or much improved over a period of 7 months. Benefits were seen in anxiety and aggression, but not in repetitive behaviors. Four children experienced intolerable side effects of agitation, insomnia, and tics. Namerow (2003) published a case series of 15 children with PDDs treated with citalopram for 7 months. The medication was well tolerated and improvements were noted in anxiety and mood. Owley et al. (2005) reported on a 10week, open-label study of escitalopram in 28 children with PDDs. There was significant improvement in the ABC-C (Aberrant Behavior Checklist-Community version) irritability score, a measure that comprises behaviors such as tantrums and aggression. Their results also showed a wide range of doses at which subjects responded and at which side effects emerged. The results of these open studies of SSRIs in autism have been, at best, mixed. Seven randomized, double-blind, placebo-controlled studies have also yielded inconsistent results. The first double-blind, placebo-controlled study of an SSRI in autism was with adults and utilized fluvoxamine (McDougle et al., 1996). Thirty subjects completed a 12-week trial. Fluvoxamine was significantly superior to placebo in reducing repetitive thoughts and behavior, maladaptive behavior, and aggression. Of subjects on fluvoxamine, 53% were

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characterized as responders, compared with none on placebo. The medicine was well tolerated. Following this adult study, McDougle et al. (2000) conducted a randomized placebocontrolled fluvoxamine study in 34 children and adolescents with PDDs. Results were quite different. Only one of 18 fluvoxamine-treated subjects could be classified as a responder, and side effects of insomnia, hyperactivity, agitation, and aggression were common. A Japanese study (Sugie et al., 2005) reported a better response to fluvoxamine. Five of 18 subjects who completed this 12-week, placebo-controlled crossover study were found to be very much improved or much improved on a Clinical Global Impression-Improvement (CGI-I) scale while on fluvoxamine; another five were minimally improved. Hollander et al. (2005) reported on an 8-week, double-blind crossover study of fluoxetine in 39 children with autistic disorder and Asperger’s syndrome. Subjects were randomly assigned to fluoxetine or placebo for 8 weeks, after which there was a 4-week drug washout period, followed by an 8-week crossover phase. Thirty-four subjects completed both phases of the study, but statistical analyses were based on 39 subjects who completed the first phase. Fluoxetine was started at a low dose of 2.5 mg daily; medication was gradually titrated upward for 4 weeks, then held at the 4-week dose. The primary outcome variable was repetitive behaviors, as measured by the CY-BOCS. Low-dose fluoxetine was found to be superior to placebo on this measure. There were no differences between drug and placebo on a global measure of autistic severity. There were also no differences between drug and placebo on reported side effects. In contrast to Hollander’s findings with fluoxetine, a large, multi-center study of citalopram failed to show superiority over placebo (King et al., 2009). In this investigation, 149 children with autistic disorder, Asperger’s syndrome, and PDD-NOS were randomized to 12 weeks of citalopram or placebo. Doses of citalopram began at 2.5 mg per day and were titrated upward in 2.5 mg increments for 5 weeks (with some limits based on patients’ weight). At 12 weeks, no differences were found between citalopram and placebo in repetitive behaviors as measured by the CY-BOCS and the Repetitive Behavior Scale-Revised. There were no differences between the groups on CGI-I score, and the overall rate of adverse events was significantly higher in the citalopram group. Citalopram was associated with increased energy level, insomnia, hyperactivity, and impulsiveness. Stereotypies were more common with citalopram than with placebo. Thus, there have been two randomized, placebo-controlled trials of SSRIs that have shown positive effects in reducing repetitive and restrictive behaviors – one in adults (McDougle et al., 1996) and one in children (Hollander et al., 2005 ). There has also been one small, pediatric study that has shown minimal to moderate effectiveness in overall clinical symptomatology (Sugie et al., 2005). There have been two pediatric studies (McDougle et al., 2000; King et al. 2009) – including one major, multi-institutional collaborative investigation with a large number of subjects – that have failed to show a positive effect of SSRIs on repetitive behavior and have reported a significant burden of side effects.

Antipsychotics Antipsychotic medications are used in autism to treat the symptom domain of “irritability,” which is defined to include behaviors such as severe tantrums, physical aggression, and selfinjury. When employed for this purpose, antipsychotics may also reduce interfering

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repetitive behaviors. This effect has been demonstrated in a number of randomized controlled studies. In an early paper, Campbell et al. (1978) found haloperidol to be superior to placebo in reducing stereotypies. In a major study of children with autistic disorder and severe behavior problems, the Research Units on Pediatric Psychopharmacology Autism Network (RUPPAN) compared risperidone to placebo over 8 weeks of acute therapy, followed by a 4-month open extension and a 3-week randomized discontinuation phase. (See description below.) The primary outcome measure was the irritability subscale of the Aberrant Behavior Checklist (ABC; Aman et al., 1985). A number of secondary measures were also looked at, including repetitive behaviors, as measured by the stereotypy subscale of the ABC and the compulsions subscale of the CY-BOCS. During the 8-week initial treatment phase, risperidone was significantly superior to placebo on both measures of repetitive behavior (McCracken et al., 2002; McDougle et al., 2005). Shea et al. (2004) reported on an 8-week Canadian trial of risperidone that targeted disruptive behaviors in children with PDDs. This study also found a significant reduction in repetitive behaviors, as measured by the ABC stereotypy subscale. In a drug company-sponsored study of aripiprazole, more than 200 children and adolescents with autistic disorder and behaviors such as tantrums, aggression, and self-injurious behavior were randomized to drug or placebo for an 8-week period (Marcus et al., 2009). Aripiprazole was found to be significantly superior to placebo, at three dose strengths, on the ABC stereotypy subscale, and at the highest dose strength on the CY-BOCS. Thus, every randomized controlled study that has looked at the effect of antipsychotic medication on repetitive behaviors in autism has shown a positive result. However – as discussed later in this chapter – antipsychotic medications are associated with a number of adverse effects and long-term health risks. Their use should therefore be limited to treating repetitive behaviors that are accompanied by severe, disruptive behaviors, such as aggression, or have a significant, negative impact on social engagement, learning, and quality of life.

Anticonvulsants Other than serotonin reuptake blockers and antipsychotics, there are few medicines used in clinical practice to target interfering repetitive behaviors. Hollander et al. (2006a) carried out a small, randomized placebo-controlled study with divalproex sodium, to see if this anticonvulsant medication would have efficacy in treating repetitive behaviors. Thirteen subjects with ASDs (12 children and 1 adult) were treated with divalproex or placebo for 8 weeks. At the end of this period, significant group differences were detected on the CY-BOCS, with improvement in repetitive behaviors on medication, but not on placebo. The mechanism by which divalproex exerted this effect is unclear. In an earlier study, Belsito et al. (2001) looked at the anticonvulsant lamotrigine in a doubleblind, placebo-controlled study of 35 children with autistic disorder. In this trial, the dose of lamotrigine was titrated upward for the first 8 weeks, maintained for 4 weeks then tapered for 2 weeks. Subjects were followed for a 4-week, drug-free period. A number of outcome measures were used, including the ABC and the Vineland Adaptive Behavior Scales. Twenty-eight children completed the study. No significant difference was found between the two groups on any measure or subscale at 4, 8, 12, and 18 weeks – including the ABC stereotypy subscale. Topiramate, another anticonvulsant, has been used to treat tics – repetitive, stereotypic motor behavior – in Tourette’s disorder (Jankovic et al., 2010). Hardan et al. (2004) reported on a retrospective chart review of topiramate in 15 children with PDDs. Treatment response

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was assessed on the clinical global improvement scale and the Connors parent scale. Eight patients were judged to have a positive response to topiramate over a period of 10–40 weeks. Improvement was noted in conduct, hyperactivity, and inattention. There was, however, no measure of repetitive behavior. Individuals with autism are frequently treated with anticonvulsants for seizures and mood disorders. Clinically, it has often been observed that the addition of an anticonvulsant can reduce interfering repetitive behaviors. At present, however, the only study suggesting their efficacy for treating this core symptom of autism is the 2006 study by Hollander et al. (2006a), and this research has not been replicated.

Summary Since they were first shown to be effective in childhood OCD, serotonin reuptake inhibitors have been used clinically for repetitive behaviors in autism. Surveys of medication use have consistently shown that SSRIs are a commonly used medication for ASDs (Langworthy-Lam et al., 2002; Mandell et al., 2008; Esbensen et al., 2009; Rosenberg et al., 2010). Some of this use is for depression and anxiety, but they are also widely employed to reduce “OCD-like” symptoms. As reviewed above, the literature on their effectiveness for interfering repetitive behaviors is mixed at best, with a recent, large, double-blind study suggesting minimal efficacy and a high rate of adverse effects such as activation, behavioral disinhibition, and insomnia. In children with ASDs, the SSRIs would best be prescribed for their approved purposes. (The US FDA has approved fluoxetine for depression and OCD, fluvoxamine for OCD, and escitalopram for depression in pediatric populations.) If SSRIs are to be prescribed empirically for repetitive behaviors, parents need to be informed of the “trial-and-error” basis for this use and the limited research support. Doses should be low and increased gradually, with careful monitoring for emergence of behavioral side effects. It should be noted that in the positive study by Hollander et al. (2005), dosing of fluoxetine began at 2 mg daily and did not exceed 10 mg, on average. There is also some suggestion that SSRIs may be more effective in adults with autism than with children (Posey et al., 2006a). This would imply that studies of their use with adolescents are needed, to see if there are developmental factors that might confer greater efficacy in this age group. Although clearly effective for interfering repetitive behaviors, antipsychotics should generally be reserved for cases where the primary concern is so-called “irritability.” (See discussion below.) In such cases they may be effective in reducing both repetitive behaviors themselves and the anxiety and agitation that occurs when these behaviors are interrupted or when a child is redirected to more adaptive tasks. Other medications might also be tried for interfering repetitive behaviors on an individualized, empirical basis. Among these medicines are divalproex sodium, the one anticonvulsant with some research supporting its use, and the alpha-2 agonists clonidine and guanfacine (discussed below in medications for hyperactivity in autism).

Symptom domain of irritability (temper tantrums, aggression toward self and others) Severe disruptive behaviors, defined as frequent temper tantrums, property destruction, physical aggression towards others, and self-injury, occur in a significant minority of children with ASDs and can be especially challenging for parents and teachers. These behaviors are frequently grouped under the term “irritability” because this word is the name that was given to a commonly used measure of these behaviors – the irritability subscale of the ABC.

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It must always be kept in mind that irritability and aggression in children with ASDs can be caused or aggravated by pain and physical distress, and medical conditions or pain syndromes always need to be investigated and treated. Disruptive behaviors can also result from co-occurring psychiatric conditions such as depression and anxiety. When this occurs, treatment needs to be chosen based on the comorbid psychiatric diagnosis. The most effective treatment approach to severe irritability and aggression in autism is behavioral intervention. (See Chapter 10.) Psychotropic medications can, however, be effective adjuncts to behavioral treatment and can reduce the frequency and intensity of severe behaviors and support appropriate functioning in social, academic, and vocational settings. Medicines for irritability in autism have been explored largely because of their effectiveness in other psychiatric disorders with somewhat similar symptom domains. As of this writing, two medications have FDA approval in the US for treatment of irritability in autistic disorder – the antipsychotics risperidone and aripiprazole.

Antipsychotics The first antipsychotic medication extensively studied in autism was haloperidol, which was the subject of several randomized controlled studies by Campbell and colleagues in the 1980s (Campbell et al., 1978; Anderson et al., 1984, 1989; Perry et al., 1989). Haloperidol was consistently shown to be effective in reducing severe disruptive behaviors, but its use was frequently accompanied by severe motor side effects – acute dystonia and dyskinesias. There have also been a number of small studies of other first-generation antipsychotics, including chlorpromazine, trifluoperazine, thiothixene, trifluperidol, fluphenazine, and molindone in subjects with autism (Posey et al., 2008). As with haloperidol, the risk of motor side effects has limited their use in autism.

Risperidone When the second-generation, atypical antipsychotics were introduced in the 1990s, they began to be used by clinicians to address severely disruptive behavior in children with ASDs. The first major study of an atypical antipsychotic in autism was done by an NIMH-sponsored consortium, RUPPAN – a collaboration of six academic medical centers. The initial report from this group (McCracken et al., 2002) presented the results of a randomized, 8-week, double-blind trial of risperidone compared with placebo for the treatment of autistic disorder accompanied by severe tantrums, aggression, or self-injurious behavior. One hundred and one subjects, 5–17 years of age, were studied; all had autistic disorder. Primary outcome measures were the CGI-I score and the irritability subscale of the ABC. At the end of 8 weeks, 76% of children on risperidone were rated as much improved or very much improved, compared to 12% in the placebo group. Of subjects on risperidone, 69% had a drop of at least 25% in their ABC irritability score, vs. 12% on placebo. Scores on the hyperactivity and stereotypy subscales of the ABC also showed improvement in the risperidone group. Adverse events noted in this study were weight gain (an average of 2.7 kg), increased appetite, fatigue, drowsiness, drooling, dizziness, constipation, tremor, and tachycardia. Three patients in the risperidone group withdrew because of lack of effectiveness, compared to 18 withdrawals in the placebo group. The final mean dose of risperidone was 1.8 mg. McDougle et al. (2005) used data gathered during the acute phase of the RUPPAN study to assess the effectiveness of risperidone on core symptoms of autism, such as social and

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communication impairment and repetitive behaviors. By week eight of the acute treatment phase, there was a significant reduction of repetitive behaviors in the treatment group, but risperidone was not superior to placebo on measures of qualitative impairment in social interaction and communication. The initial RUPPAN risperidone study was followed by a 4-month open treatment and randomized discontinuation study (Research Units on Pediatric Psychopharmacology Autism Network, 2005a). In this phase, 63 children who were shown to be risperidone responders over 8 weeks were openly continued on the medication for 4 months, then randomized to two groups over 8 weeks in a double-blind fashion: one group who stayed on their same dose of risperidone and one group who had placebo gradually substituted for the medication. The mean risperidone dose was 1.96 mg/day at entry and remained stable over 16 weeks of open treatment. During this interval there was a small and clinically insignificant change in subjects’ scores on the ABC irritability subscale. Six subjects dropped out during this phase: five for loss of efficacy and one for adverse medication effects. The subjects gained an average of 5.1 kg. Thirty-two subjects participated in the placebo-substitution randomization phase. The relapse rates were 62.5% for gradual placebo substitution and 12.5% for continued risperidone; this difference was statistically significant. Thus, the effectiveness of risperidone demonstrated during the 8-week, acute phase study was maintained for a total of 6–8 months, and severe behavior symptoms were quick to re-appear upon discontinuation of the medication. Another large study of risperidone for disruptive behavior in autism was undertaken by a Canadian consortium. Shea et al. (2004) reported on a double-blind, placebo-controlled study in 79 children, age 5–12 years, with various ASDs. The primary outcome measure was the ABC irritability subscale. Subjects in the risperidone group showed a 64% improvement over baseline on this measure, compared with a 31% improvement in patients taking placebo. Risperidone was also associated with a significant decrease in scores on other ABC subscales, reflecting some core symptoms of ASD, such as inappropriate speech and lethargy/social withdrawal, and with decreased hyperactivity on two separate measures. Somnolence was the most common side effect in children on risperidone. The mean weight gain in the risperidone group was 2.7 kg, while in the placebo group it was 1.0 kg. Extrapyramidal side effects were reported in 11 patients on risperidone and in 5 on placebo; tremor and hypokinesia were the most common extrapyramidal events. In a later analysis of data from this Canadian study, Pandina et al. (2007) detailed the extrapyramidal side effects from risperidone: dyskinesia, hyperkinesia, hypokinesia, involuntary muscle contractions (n = 1 for each), and tremor (n = 2).

Aripiprazole Aripiprazole has also been the subject of several open-label and controlled studies. ValicentiMcDermott and Demb (2006) published a retrospective chart review of 32 patients with developmental disabilities, including 24 with ASDs. Aripiprazole was prescribed at a mean dosage of 10.55 mg/day for an average length of treatment and follow-up of 6 months. Eighteen children (56%) were considered responders; however, the rate of response among the children diagnosed with autistic disorder or PDD-NOS was lower (37%). Masi et al. (2009) reported on a retrospective, naturalistic study of aripiprazole in PDDs. Patients were followed for 4–12 months; the mean final dosage of aripiprazole was 8.1 mg/day. At the endpoint, two-thirds of patients were rated as much improved or very much improved, based

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on a CGI-I score and two standardized measures of autism symptom severity. No significant changes in appetite or weight were noted, except in one patient, who experienced a weight gain of 9 kg. After an earlier report that showed a positive response to aripiprazole in five patients (Stigler et al., 2004), Stigler et al. (2009) conducted a 14-week, prospective, open-label study of 25 patients (age 5–17 years) with PDD-NOS or Asperger’s disorder. The mean final aripiprazole dosage was 7.8 mg/day. Twenty-two (88%) patients were responders, with reduction of interfering symptoms of irritability (aggression, self-injury, and tantrums). Aripiprazole was well tolerated with mild extrapyramidal symptoms (EPS) reported in nine subjects. No subject withdrew from the study because of a drug-related adverse event. These open studies were followed by two large, multi-center controlled trials – conducted at 37 sites in the United States and supported by Bristol-Myers Squibb. The first of these studies was a fixed-dose, double-blind, placebo-controlled study of 218 children and adolescents with autistic disorder and irritability, agitation, self-injurious behavior, or a combination of these symptoms (Marcus et al., 2009). Patients were randomized to receive aripiprazole 5, 10, or 15 mg per day or placebo for 8 weeks. The primary efficacy measure was the mean change from baseline to endpoint on the caregiver-rated ABC irritability score and a CGI-I score. All aripiprazole dose groups demonstrated significantly greater improvements than the placebo group. The most common cause for discontinuation was sedation (in seven subjects). In addition, there were two serious adverse events: pre-syncope (in a patient on 5 mg/day of aripiprazole) and increased aggression (in a patient on 10 mg/day). Mean weight gain was moderate: 1.3 kg on 5 and 10 mg/day, and 1.5 kg on 15 mg/day. Common adverse effects included sedation, fatigue, increased appetite, vomiting, drooling, and tremor. There were no statistically significant differences in efficacy among the three aripiprazole groups. The second multi-center controlled study of aripiprazole included 98 children and adolescents with autistic disorder and significant symptoms of maladaptive behavior (Owen et al., 2009). Subjects were randomized to flexibly dosed aripiprazole or placebo. Outcome measures were the same as in the Marcus et al. study. A significant improvement in ABC irritability scores was found in patients taking aripiprazole compared to those on placebo; however, clinically significant residual symptoms were noted to persist in some patients after 8 weeks. The mean dose at the end of the study was 8.6 mg/day. Of subjects on aripiprazole, 15% experienced EPS. Mean weight gain was 2.0 kg on aripiprazole and 0.8 kg on placebo. The 8-week, randomized controlled trials of risperidone and aripiprazole were the basis for FDA approval for their use in treating irritability and aggression in autistic disorder. In addition, the RUPPAN studies provided evidence for persistent efficacy of risperidone over 6 months. Significant problems with both medications are weight gain and EPS. Clinical decisions regarding their use need to balance their significant risks against demonstrated benefits in reducing severe self injury and aggression. If they are used, patients need to be carefully monitored for symptom improvement and adverse events, and the goals of treatment should be reassessed at regular intervals.

Other antipsychotics In addition to risperidone and aripiprazole, several other second-generation antipsychotic medications have been evaluated for their efficacy in ASDs, most in small, open-label studies.

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Four small trials of quetiapine have been reported. Martin et al. (1999) found no statistically significant improvement in six children with autistic disorder treated with quetiapine in a 16week open-label trial. Findling et al. (2004) reported that only two of nine children with autistic disorder responded to this medication, at doses of 100–450 mg. In a retrospective analysis of 20 patients with PDDs treated with quetiapine over an average of 60 weeks, Corson et al. (2004) judged 40% to be responders. Hardan et al. (2005) reported a slightly better response in ten consecutive child and adolescent patients with PDDs who were treated with quetiapine (at a mean dose of 477 mg, for a mean duration of 22 weeks). Six were judged to be responders – but concomitant psychotropic medications, including other antipsychotics, were permitted. Two studies have evaluated ziprasidone in ASDs. McDougle et al. (2002) reported on a series of 12 patients with autism or PDD-NOS treated with ziprasidone, at a mean daily dose of 60 mg for at least 6 weeks. Six of the 12 patients were considered responders based on clinical global impression. Transient sedation was the most common side effect and no cardiovascular side effects, including chest pain, tachycardia, palpitations, dizziness, or syncope, were observed. Malone et al. (2007) conducted a 6-week, open-label study of ziprasidone in 12 adolescents with autism. Nine patients were treatment responders. Ziprasidone was weight-neutral, and the QTc on EKGs increased by a mean of 15 ms. Two subjects had acute dystonic reactions. There have also been several open-label studies of olanzapine (Potenza et al., 1999; Malone et al., 2001; Kemner et al., 2002). All found significant weight gain: an average in every study of more than 4.1 kg over treatment durations that ranged from 6 to 12 weeks. Malone et al. (2001) compared olanzapine with haloperidol in a parallel group design and found significant improvement in anger/uncooperativeness and hyperactivity in the olanzapine group, which was not seen in the haloperidol group. Hollander et al. (2006b) completed an 8-week, randomized, placebocontrolled pilot study in 11 patients with diagnoses of autistic disorder, Asperger’s syndrome, and PDD-NOS. Of the group on olanzapine, 50% (versus 20% of those on placebo) were considered to be responders, based on CGI-I score. Olanzapine was associated with significant weight gain. Other side effects, including increased appetite and sedation, were more common in the treatment group. Extrapyramidal side effects did not occur. These small studies of quetiapine, ziprasidone, and olanzapine are preliminary at best. They suggest some potential therapeutic benefit, particularly for ziprasidone. At present, use of these three second-generation antipsychotic medications should be restricted to cases of significant irritability and aggression in which risperidone or aripiprazole were ineffective or poorly tolerated.

Anticonvulsants Anticonvulsants have already been discussed in the context of the symptom domain of repetitive and restricted behaviors. Several anticonvulsants have also been investigated for their effects on irritability and aggression – both as primary and adjunctive medications. Two research reports have focused on valproate. Hellings et al. (2005) studied 30 children and adolescents in a double-blind, placebo-controlled study of liquid valproic acid (VPA). Mean VPA trough blood levels were 75.5 μg/ml at week 4 and 77.8 μg/ml at week 8. No significant differences were found between drug and placebo on the primary outcome measure, ABC irritability score. Two subjects receiving VPA developed increased serum ammonia levels, one with an associated parent report of slurred speech and mild cognitive slowing. Hollander et al. (2010) conducted a 12-week, randomized, double-blind, placebo-controlled study to examine the effect of divalproex sodium on irritability/aggression in 27 subjects (age

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5–17 years) with various ASDs. Primary outcome measures were the ABC irritability subscale and CGI-I, which focused on irritability. Of divalproex subjects, 62.5% subjects were responders, vs. 9% of control subjects. Subjects with VPA blood levels between 87 and 110 μg/ml had a 100% response rate on the CGI, whereas subjects with levels < 87 μg/ml had a 60% response rate and subjects with levels >110 μg/ml had a response rate of 33%. No serious adverse events, altered mental status, abnormalities in blood pressure and heart rate, elevations of LFTs, suppression of bloodlines, or pancreatitis were noted in any subjects. The anticonvulsants lamotrigine and levetiracetam have also been investigated in autism. As discussed in a previous section, one double-blind, placebo-controlled study with lamotrigine showed no positive effects (Belsito et al., 2001). A 10-week randomized controlled study of levetiracetam also showed no benefit over placebo on any of the ABC subscales or on measures of hyperactivity and repetitive behavior (Wasserman et al., 2006). In a randomized, double-blind study, Rezaei et al. (2010) compared a combination of topiramate and risperidone to combined placebo and risperidone in 40 children with autistic disorder. All drug doses were rapidly titrated upward at the start of the study, with final dose based on weight. The medications were then continued for a total of 8 weeks. The primary outcome measure was the ABC Community Rating Scale irritability score. Use of topiramate as an adjuvant to risperidone resulted in a significantly greater reduction in ABC-C irritability score, and also significantly benefited stereotypic behavior and hyperactivity, when compared with the combination of risperidone and placebo. Many children with ASDs have co-occurring clinical seizures and are prescribed anticonvulsants. It is possible that these individuals might obtain a secondary behavioral benefit from these medicines, but this is unclear at present. There is also a suggestion – but no direct evidence – that early treatment of seizures with anticonvulsants may ameliorate the progression of autistic symptoms, but this, too, is far from proven. (See the section on epilepsy in Chapter 6.) For individuals with ASDs and no clinical seizures, the primary use of anticonvulsants is to treat co-occurring mood disorders. Their use for the symptom domain of irritability, aggression, and self-injury – when these symptoms are not attributable to a comorbid mood disorder – is not strongly supported by current research. There has been one randomized, controlled study that suggests that divalproex sodium may be useful for this symptom domain and one showing that topiramate has positive adjuvant effects when added to risperidone. Further controlled studies are needed.

Summary At this point the only medications that have been demonstrated to be clearly effective for the symptom domain of irritability are the two antipsychotics that have been approved for this purpose in the US, risperidone and aripiprazole. Before using these medicines, a careful risk/ benefit assessment must be undertaken and the child carefully monitored for side effects. If these medicines are ineffective or cannot be tolerated, there is some evidence to support the use of alternative antipsychotics or anticonvulsants. The latter may also be effective in treating comorbid mood disorders.

Symptom domain of inattention, hyperactivity, and impulsivity Symptoms that are usually associated with Attention Deficit Hyperactivity Disorder (ADHD) can occur in autism. Murray (2010) concluded that about 50% of children with

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ASDs exhibit significant comorbid ADHD symptoms; these children have more severe social impairments, worse adaptive functioning, more difficulty with executive control, and poorer outcomes than autistic children without these symptoms. At present, a separate diagnosis of ADHD is not made in PDDs, but the neurodevelopmental work group for DSM-5 has been considering possible modification of ADHD criteria to allow for comorbid diagnoses of both ASD and ADHD. Medications for this symptom domain are usually selected based on their efficacy in ADHD without autism. Their purpose is to reduce hyperactivity, impulsivity, and inattention and improve functionality in social, academic, and vocational settings. A few of these medications have been specifically studied in ASDs.

Stimulants Of the various psychostimulants for ADHD, only immediate-release methylphenidate has thus far been studied in autism. In 1995, Quintana et al. published a study with ten subjects on methylphenidate who showed a statistically significant improvement over placebo in hyperactivity, as measured by the ABC hyperactivity subscale. In this study, subjects were monitored for 6 weeks: 2 weeks baseline, 2 weeks of methylphenidate or placebo at 10 mg twice a day, and 2 weeks of methylphenidate or placebo at 20 mg twice a day. Significant improvement in ABC irritability factor was also noted. Side effects, except for weight loss, were minimal. In 2000, Handen et al. published a double-blind, placebo-controlled, crossover study of methylphenidate (0.3 and 0.6 mg/kg per dose) in 13 children with autism and symptoms of ADHD. Eight subjects responded positively, based upon a minimum 50% decrease on the Conners Hyperactivity Index. Ratings of stereotypy and inappropriate speech, which are often associated with autistic core features, also decreased. Reported adverse side effects were social withdrawal and irritability in some children, especially at the 0.6 mg/kg dose. The Research Units on Pediatric Psychopharmacology Autism Network (2005b) conducted a large study of methylphenidate to evaluate its efficacy and tolerability in 72 children (age 5–14 years) with autisic disorder, Asperger’s disorder or PDD-NOS and interfering symptoms of ADHD. The study began with a 1-week, test-dose phase to assess tolerability. The next phase involved a randomized, blinded crossover trial of one week each of placebo and three doses of methylphenidate (0.125, 0.25, or 0.5 mg/kg per dose), administered three times daily. The study concluded with an 8-week, open-label continuation for responders. Children were considered responders if they were rated as much improved or very much improved on the CGI-I and also demonstrated a 30% reduction in either their parent- or teacher-rated ABC hyperactivity subscale score or a 25% reduction on both ratings. Other scales and measures were used to perform secondary analyses of efficacy and tolerability. A total of 35 (49%) of the 72 children responded best to one of the three doses of methylphenidate. Parent ratings indicated that the best treatment response was seen with the 0.25 mg/ kg dose, while the teacher ratings indicated the best outcome was with the 0.50 mg/kg dose. The effect sizes ranged from 0.20 to 0.54, suggesting small to medium responses. Of 35 participants, 31 completed the open-label phase of the study, with benefits sustained for the majority. Hyperactivity/impulsivity symptoms improved more than inattention, and no exacerbations of stereotypies or other repetitive behaviors were observed. Additionally, positive effects of methylphenidate were noted in social communication skills and selfregulation, including initiating joint attention. Side effects noted were mood lability, worsening of hyperactivity, tics, decreased appetite, insomnia, and aggression. There was an 18%

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rate of discontinuation owing to adverse events, mostly irritable mood. The authors concluded that methylphenidate was often efficacious in treating hyperactivity associated with ASDs, but the response was less than that seen in typically developing children with attention-deficit/hyperactivity disorder, and adverse effects were more frequent. In a follow-up to the RUPPAN methylphenidate study, Posey et al. (2007) looked at several different outcome measures in 66 subjects. Positive effects at the 0.25 and 0.5 mg/kg doses were noted on hyperactivity when measured by another instrument, the Swanson, Nolan, and Pelham Questionnaire (SNAP). There were no significant effects on oppositional defiant disorder or stereotyped and repetitive behavior. This study did not report any exacerbation of stereotypies or other repetitive behaviors, except in two subjects who were unable to tolerate methylphenidate during the test-dose phase. There is thus some clear evidence that methylphenidate can be helpful in reducing interfering ADHD symptoms in children with ASDs. This medication is somewhat less effective in ASDs than in ADHD, and a significant percentage of autistic children cannot tolerate methylphenidate because of side effects. It should be noted that the doses used in these ASD studies were lower than the peak doses that have been used for ADHD. Generalization of these findings to time-released methylphenidate and other stimulants would require more study. Any child with autism who is prescribed stimulant medication needs to be carefully monitored for mood symptoms and worsening of stereotypies.

Nonstimulant medications Nonstimulant medications that have been approved in the US to treat ADHD are atomoxetine and the alpha-2 adrenergic agonists, clonidine and guanfacine. Studies involving use of these medications in ASDs are few in number and have tended to be small and not controlled.

Atomoxetine Three open-label and one randomized controlled trial have evaluated the use of atomoxetine in children with autism and symptoms of ADHD. Troost et al. (2006) reported on a 10-week open study using atomoxetine (at a mean dose of 1.2 mg/kg/day) in 12 children with ASD and ADHD-symptoms. Significant improvement was noted on three measures of hyperactivity, but not the ABC hyperactivity score. No change was found in any of the other ABC subscales. Five patients (42%) discontinued treatment because of side effects. The most frequent side effects were gastrointestinal symptoms, irritability, sleep problems, and fatigue. Two other small, open-label studies of atomoxetine in children with ASD also reported modest positive effects on hyperactivity (Posey et al., 2006b; Zeiner et al., 2011). In 2006, Arnold and colleagues published a double-blind, placebo-controlled crossover trial of atomoxetine in 16 children (age 5–15 years) with ASDs, using a crossover design in which subjects were randomized to clinically titrated atomoxetine and placebo for 6 weeks (3-week titration), followed by a 1-week washout period. The primary outcome measures were the ABC Hyperactivity subscale, the CGI-I, CGI-Severity, and the SNAP hyperactivity subscale. The mean final dose was 44.2 mg/day for atomoxetine and 48.0 mg/day for placebo. Nine subjects on atomoxetine (57%) and four on placebo (25%) were designated responders. Of the nine responders to atomoxetine, two also responded to placebo, thus the overall response rate was 43%. Adverse events reported were upper gastrointestinal symptoms (most common), appetite suppression (75% on atomoxetine vs. 50% on placebo), and irritability/

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mood swings (equally common on both atomoxetine and placebo). One participant was rehospitalized for recurrent violence while on atomoxetine. An important feature of this study was that children were permitted to take concomitant medications (including four who were taking atypical antipsychotics). These small studies suggest that atomoxetine might possibly be a useful alternative to methylphenidate to treat hyperactivity and impulsivity associated with autism. The overall side effects profile appears to be mild, including mostly tolerable gastrointestinal effects. However, this research is preliminary and no large, placebo-controlled, parallel design trials have yet been published.

Alpha-2 adrenergic agonists Clonidine and guanafacine have been used to treat hyperactivity and impulsiveness and, in time-released formulations, have FDA approval for use in ADHD. Two small, randomized, placebo-controlled studies have been reported with clonidine, one controlled study and two open-label studies with guanafacine. Fankhauser et al. (1992) found that transdermal clonidine treatment showed a significant superiority over placebo on three subscales of the Ritvo-Freeman Real Life Rating Scale (social relationship to people, affectual responses, and sensory responses) in nine autistic children and adults. There were significant adverse effects of sedation and fatigue during the first 2 weeks of clonidine treatment. In another 1992 small, double-blind, placebo-controlled, crossover study, Jaselskis et al. reported that teacher ratings on the ABC irritability, stereotypy, hyperactivity, and inappropriate speech factors were lower during treatment with clonidine than during treatment with placebo. Parent Conners questionnaire ratings were noted to be significantly improved during clonidine treatment, but clinician ratings of videotaped sessions were not. The clonidine dose had to be lowered in three patients because of hypotension. Other side effects noted were drowsiness and decreased activity. In 2004, Posey et al. published a retrospective review of 80 children and adolescents with PDDs treated with open-label guanfacine in order to gather preliminary data on effectiveness and safety. The mean daily dose was 2.6 mg and the mean duration of treatment was 334 days. Treatment was judged to be effective in 19 of 80 subjects. Guanfacine was well tolerated in the study. Even though transient sedation was frequent, it did not lead to drug discontinuation in any case. Other side effects included irritability, constipation, headache, and nocturnal enuresis. There were no notable changes in heart rate and blood pressure. As part of the RUPPAN hyperactivity study, Scahill et al. (2006) conducted a prospective, open-label trial of guanfacine in 23 boys and 2 girls who failed to respond to methylphenidate. After 8 weeks of treatment, significant improvements were noted in parent- and teacher-rated ABC hyperactivity scores. Twelve children (48%) were rated as much improved or very much improved. Common adverse effects included irritability, sedation, sleep disturbance (insomnia or midsleep awakening), constipation, decreased appetite, and perceptual disturbance. Guanfacine was discontinued secondary to worsening of irritability in three subjects. There were no significant changes reported in pulse, blood pressure, or electrocardiogram. In 2008, Handen et al. reported on a 6-week, double-blind, placebo-controlled, crossover trial of guanfacine (maximum dose: 3 mg/day) in 11 children (age 5–9 years) with developmental disabilities and symptoms of inattention/overactivity (seven with ASDs). Primary outcome measures used were the ABC and clinical global impression. Five of the 11 subjects

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(45%) were judged to be guanfacine responders based on a 50% difference in the ABC hyperactivity scores between the placebo and guanfacine conditions. Three of the 11 subjects were unable to tolerate the maximum target dose of 3 mg/day. In this study, the most common side effects were drowsiness/lethargy and irritability. Patients in this study could be on concomitant medication. It is of some note that clonidine has been used to treat ADHD in children for more than 20 years, but has always had drawbacks of sedation, a requirement for frequent dosing, and associated concerns about possible rebound hypertension. Guanfacine is an alternative α-2 agonist that appears to be less sedating, with a longer plasma half-life than clonidine. Two small studies conducted in the 1990s concluded that clonidine was modestly effective in reducing hyperarousal, hyperactivity, and irritability in ASD. In more recent studies, guanfacine has led to improvement in hyperactivity in ASDs. Larger controlled trials are needed, particularly with the newer time-released preparations of these medicines. The α-2 agonists may be an alternative to psychostimulants among children with ASD, with careful monitoring for side effects like sedation and hypotension.

Antipsychotics As mentioned in the previous section, antipsychotic medications are effective in reducing hyperactivity in autism. However, because of their side effect profile, their use should be restricted to cases in which severe irritability, aggression, and self-injury are also present.

Summary When hyperactivity and inattention are the symptom domain of primary concern, empirical use of stimulant medicines, atomoxetine, and α-2 agonists can be considered. None of these medicines has specific approval in the US for ASDs, but all are indicated for ADHD. As has been discussed, response to methylphenidate is less robust in ASDs than in ADHD and tolerance may be poorer. Stimulants should therefore be prescribed at low doses, with slow upward titration and careful monitoring for mood reactions, increased stereotypies and tics. Clonidine, guanfacine, and atomoxetine are reasonable alternatives for empirical trial in children who cannot tolerate or do not benefit from stimulant medications.

Sleep problems Sleep problems – primarily insomnia – are commonly reported in ASD children, with rates ranging from 40 to 80% (Johnson et al., 2009). Sleep abnormalities include longer sleep-onset latency, frequent night wakings, and reduced sleep duration. When present, these sleep problems cause concerns about nighttime safety and may aggravate disruptive daytime behavior, irritability, and certain medical conditions. Treatment of sleep disorders in children with ASDs should incorporate approaches similar to those used with other children, including sleep hygiene measures, sleep scheduling, and other behavior interventions. If pharmacotherapy is considered, guidelines for treating insomnia in typically developing children should be followed (Owens and Moturi, 2009). A thorough sleep history should be obtained and sleep apnea and dyssomnias ruled out. (See discussion in Chapter 7.) Specific studies of medications to treat insomnia in ASDs are few. Among these are three reports on the use of melatonin and one on clonidine.

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Melatonin is an endogenous neurohormone secreted by the pineal gland in response to decreasing levels of light. Melatonin secretion increases rapidly after nightfall, peaks in the middle of the night then decreases towards dawn. Exogenous melatonin can be taken orally, and preparations are available over the counter, but vary in quality and purity. Wasdell et al. (2008) conducted a randomized double-blind, placebo-controlled crossover trial of controlledrelease melatonin (at a fixed dose of 5 mg) in 51 children with neurodevelopmental disorders, including ASDs. This trial was followed by a 3-month open-label study. Target symptoms were delayed sleep phase and impaired sleep maintenance. Fifty patients completed the crossover trial, and 47 completed the open-label phase in this study. Total nighttime sleep and sleep latency improved by approximately 30 minutes, and significant improvement was observed in clinician and parent ratings. Treatment was effective in reducing family stress. Wirojanan et al. (2009) reported on a 4-week, randomized, double-blind crossover study of 12 children with ASD and Fragile X syndrome, using melatonin (3 mg) or placebo, given for 2week periods. Sleep duration was longer on melatonin than placebo by 21 minutes, mean sleeponset latency was shorter by 28 minutes, and mean sleep-onset time was earlier by 42 minutes. All these differences were statistically significant. In a much longer crossover trial with 22 ASD children, Wright et al. (2011) blindly administered 3 months of melatonin and 3 months of placebo. Melatonin significantly improved sleep latency (by an average of 47 min) and total sleep (by an average of 52 min) compared to placebo, but had no effect on number of night wakings. The side-effect profile was benign and not significantly different in the two phases. The use of clonidine in ASDs is reviewed in the previous chapter section. In an open study that also considered symptoms of hyperactivity, inattention, mood disorder, and aggressive behavior, Ming et al. (2008) found that clonidine was effective in reducing sleep initiation latency and night waking. Adverse effects were few. The medication was stopped in one child after a single dose because of significant skin pallor. One child displayed increased irritable mood. The available research thus provides little guidance for the physician who wishes to prescribe medication to help manage insomnia in children with ASDs. Melatonin has the best empirical evidence supporting its use, and appears to have minimal side effects. Hollway and Aman (2011) recently reviewed some of the other treatment options, with caveats. As they pointed out, diphenhydramine is commonly used in children with sleep problems, but there is no empirical evidence showing its effectiveness; clonidine has also not been adequately researched, and there is some evidence it may decrease REM sleep; benzodiazepines can cause behavioral disinhibition and have an associated risk of dependency; zolpidem has had mixed results in pediatric insomnia; zaleplon, ramelteon, trazodone, and mirtazapine – commonly used hypnotics in adults – have not yet been shown to be effective and safe in controlled studies in pediatric populations. Pharmacologic treatment of insomnia in children with ASDs, when undertaken, must therefore be an individualized, empirical process – until more randomized, controlled studies have been completed.

Medication and nutritional treatments from complementary and alternative medicine In contrast to the pharmacologic interventions discussed previously in this chapter – which target specific symptoms or symptom domains – most complementary and alternative treatments have a broader purpose: to treat autism itself. The anticipated outcome of these treatments is improvement in core symptoms. Complementary and alternative medicine (CAM) approaches to autism can be described as those that are outside the realm of, or used

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instead of, traditional medical practice; they are numerous and varied. In the US, most of these treatments do not require a doctor’s prescription. Many CAM approaches are directly connected to particular hypotheses about the nature and cause of autism. Most of these hypotheses and their associated therapies have meager empirical support, but are endorsed by parents and advocates who claim effectiveness based on personal experience. Rates of CAM use in children with ASDs are in the range of 30–90% (Hyman and Levy, 2011), with as many as 50–70% of autistic children receiving biologically based CAM (Akins et al., 2010). This section will review some of the pharmacologic and nutritional treatments from complementary and alternative medicine: those that derive from etiologic hypotheses and those that do not. The evidence supporting these treatments will be presented. Where appropriate, safety cautions will be mentioned. Physicians who treat children with ASDs need to inquire about CAM use and attempt to understand parents’ purposes and goals in seeking these treatments. They also need to help parents to evaluate the information they obtain on CAM therapies and to make the best and safest decisions for their children.

Treatments targeting gastrointestinal functioning Many CAM treatments are directed at gastrointestinal functioning. Underlying these approaches is a hypothesis that problems with gastrointestinal motility or absorption and/ or immune-mediated GI disease cause the neurodevelopmental features of ASDs. Controversies around the co-occurrence of gastrointestinal disorders and ASDs have been reviewed in Chapter 7, and research on immune dysfunction and autism has been discussed in Chapter 6. While this “multi-systemic” approach to autism continues to stimulate research, there is at present no empirical confirmation for the suggestion that GI disease or dysfunction causes the core symptoms of autism. As has already been mentioned, children with ASDs and GI symptoms should have the same diagnostic assessment and treatment as children without ASDs.

Probiotics Probiotics are non-pathogenic bacteria of genus Lactobacillus or Bifidobacterium. They are administered with the intent of altering intestinal flora and thereby affecting products of digestion that could otherwise enter the circulation, activate immune inflammatory cells, and impact brain function – the so-called “leaky gut hypothesis.” According to this hypothesis, the intestinal mucosa in children with autism is abnormally permeable. Digestion products of natural foods such as cow’s milk and bread are able to enter the blood through the leaky mucosa; these products include peptides that induce an antigenic response and exogenous opioids (White, 2003). It has been further theorized that, once in the brain, these exogenous opioids could cause the core social deficits of autism (Sahley and Panksepp, 1987). The leaky gut hypothesis is highly speculative, and there are no studies supporting the use of probiotics. Known side effects include flatulence, constipation, skin rash, and itching (Zimmer and Molloy, 2007).

Digestive enzymes The use of digestive enzymes is also based on the leaky gut hypothesis. Empirical support for this treatment is lacking, except for a 2002 study by Brudnak et al. that had significantly flawed methodology (Hyman and Levy, 2011).

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Gluten- and casein-free diet Almost one-third of children with autism are treated with special diets at some time in their lives (Levy et al., 2003a). The gluten-free, casein-free (GFCF) diet has been especially popular and enduring. Its use is based on the leaky gut hypothesis. Two randomized controlled trials of the GFCF diet have yielded conflicting results. Knivsberg et al. (2002) found improvement in autistic behaviors and motor skills in the treated group; but Elder et al. (2006) found no differences between those on diet and a control group on clinician observational measures of autistic symptoms and language. No side effects have been reported from using these diets. In the absence of confirmed gluten enteropathy or lactose intolerance, there is no medically sound reason to prescribe these dietary restrictions. They are, however, unlikely to be harmful. Parents who elect to use this treatment should have consultation from a nutritionist to ensure adequate intake of vitamin D and calcium in the absence of dairy products.

Secretin Secretin is a polypeptide hormone produced in the duodenum that stimulates pancreatic secretions. In an exogenous formulation, it can be given by gastroenterologists during diagnostic endoscopy to assess pancreatic functioning. It has US FDA approval for this purpose. In 1998, Horvath et al. published a report on three children with symptoms of autism who were given intravenous secretin during endoscopy. All three children showed subsequent improvements in gastrointestinal symptoms, eye contact, alertness, and language – changes that were attributed to the secretin infusion. This report led to use of secretin as a therapeutic agent by physicians who were willing to prescribe it and to demands on the part of parents to have it given to their autistic children. Its use was further rationalized by the presence of secretin receptors in the brains of rats and pigs (Charlton et al., 1981), although the mechanism of secretin’s action within the brain was unknown. Because of claims that were made for secretin as a treatment for the core symptoms of autism, a number of randomized, placebo-controlled studies were undertaken to assess its efficacy. Single doses of intravenous porcine secretin (Owley et al., 2001; Corbett et al., 2001; Kern et al., 2002) and synthetic secretin (Molloy et al., 2002; Levy et al.,2003b; Sandler et al., 1999) were used, as were repeated doses (Roberts et al., 2001; Sponheim et al., 2002). Outcome measures addressed communication (Levy et al., 2003a, b; Molloy et al., 2002); core autistic features (Roberts et al., 2001; Sandler et al., 1999); behavior (Owley et al., 2001; Sponheim et al., 2002) and visual–spatial skills (Molloy et al., 2002; Owley et al., 2001). According to a Cochrane database review of these studies, no significant differences were found between secretin and control groups on any of these measures (Williams et al., 2005). One can conclude from this extensive research that there is no evidence that secretin is effective in the treatment of children with ASDs.

Antioxidant treatments There is a research hypothesis that, in some cases of autism, genetically vulnerable individuals have a decreased capacity for intracellular methylation and for antioxidant activity in the presence of oxidative stress. The biochemical pathways involved and the evidence supporting this hypothesis were discussed in Chapter 6. CAM practitioners often advocate administering “antioxidant” dietary supplements to autistic children. The compounds recommended are usually those that play a role in the folate and methionine cycles and in the production of

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glutathione, which is central to maintaining cellular oxidation/reduction balance: e.g. vitamin B12, folate, and glutathione itself. (See Figure 6.3 in Chapter 6.) Two studies have been reported with vitamin B12, in the form of injectable methylcobalamin or methyl B12. In an open-label trial, James et al. (2009) showed that 3 months of treatment with methylcobalamin (twice a week) combined with oral folinic acid (twice daily) raised levels of glutathione in autistic children, but not in controls (whose levels were higher pre-treatment). There was no measure of clinical outcome. Bertoglio et al. (2010) reported on a 12-week, double-blind, crossover study with injectable methyl B12, followed by a 6-month extension study. No significant effects were seen either on behavioral measures or glutathione status. The only studies on folate have been the James et al. (2009) study combining folinic acid and methyl B12, and a single-subject intervention by Moretti et al. (2005) in a child with autism and cerebral folate deficiency. No studies have been reported on glutathione in autism, despite its being frequently recommended by CAM practitioners. Another antioxidant is vitamin C, which is also frequently recommended. Hyman and Levy (2011) cited two studies of vitamin C in autism, one of which (Dolske et al., 1993) had moderately acceptable methodology and showed some positive effects on sensory and motor symptoms.

Immune therapy Hypotheses regarding an immune-mediated etiology for autism and research evidence in support of these hypotheses were reviewed in Chapter 6 – including evidence of decreased peripheral immune system molecules and presence of auto-antibodies to brain tissue in some children with autism. A number of CAM therapies purport to address immune functioning, including antibiotic, antiviral, and antifungal treatments (Hyman and Levy, 2011) and DMG (see discussion below). Because intravenous immunoglobulin treatment (IVIG) may compensate for a presumed antibody deficiency, as well as block endogenous auto-antibodies, and because it has therapeutic benefit in some autoimmune neurologic disorders, this treatment has been tried in autism. Hyman and Levy (2011) cited three open-label studies of IVIG treatment in children with autism. Two of the studies had serious methodological problems. The third showed no benefit of IVIG on two measures of autism symptom severity over a period of 6 months (DelGiudice-Asch et al., 1999). Niederhofer et al. (2003) reported on a double-blind, placebo-controlled, crossover study of IVIG in 12 boys with autistic disorder. IVIG treatment resulted in significantly better scores on four subscales of the ABC at 6 and 13 weeks. Handen et al. (2009) conducted a randomized, double-blind, placebocontrolled study of oral human immunoglobulin in 125 children with autism, targeting both gastrointestinal symptoms and behavior problems. No differences were found between the control and treatment groups. At this point, there is no evidence of effectiveness in ASDs of either CAM treatments to address immune dysfunction or of IVIG. Furthermore, as Levy and Hyman (2005) noted, IVIG is in short supply and should be used only for treatment of diseases in which it has demonstrated benefit. It also carries some risk of blood-borne infection, thromboembolic events, and aseptic meningitis. A Canadian expert panel recommended against use of IVIG as a treatment for autism (Feasby et al., 2007).

Chelation The notion that autism could be linked to mercury toxicity is discussed in Chapter 2, with reference to concerns expressed about the preservative thimerosal, which was formerly used

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in pediatric vaccines. It has also been suggested that children with ASDs are unable to excrete heavy metals and thus suffer from an increased body burden of these toxins, which are said to be responsible for their autistic symptoms. CAM practioners frequently test hair, blood, and urine samples for the presence of heavy metals, sometimes provoking their urinary excretion with a test dose of a chelating agent. If these tests are interpreted as being positive, chelating agents such as ethylene diamine tetraacetic acid (EDTA), dimercaptosuccinic acid (DMSA), and thiamine tetrahydrofurfuryl disulfide (TTFD) are sometimes prescribed. Adverse effects of hepatotoxicity and anaphylaxis have been reported with chelating agents, as have deaths from hypocalcemia (Centers for Disease Control and Prevention, 2006). There is no evidence in peer-reviewed literature supporting chelation therapy; the few open-label studies that have been published have had methodological problems and have not shown benefit (Hyman and Levy, 2011). The risk–benefit calculation with regard to chelation should be straightforward: it is an unsafe treatment with no evidence of efficacy.

Miscellaneous dietary supplements Dietary supplements used in ASDs include a broad range of vitamins, proteins/amino acids, and fatty acids. In addition to the vitamins chosen for their purported effect as antioxidants, the combination of vitamin B6 and magnesium has been a popular CAM treatment. Vitamin B6 is a cofactor in the synthesis of several neurotransmitters. It is used in high doses in autism, and magnesium is added to ameliorate possible side effects from these large doses. This treatment was first introduced in the 1960s. Since that time, many open-label and placebo-controlled studies have been published – some showing a decrease in autistic symptoms and other improvements in behavior. A Cochrane review by Nye and Brice (2005), however, found that only three of these studies were randomized, placebo-controlled investigations. The reviewers concluded that, based on these studies, there was insufficient evidence to make a recommendation with regard to the use of vitamin B6/magnesium as a treatment for autism. Amino acids and peptides studied in autism have included dimethylglycine (DMG) and carnosine. DMG is a derivative of the amino acid glycine. It is promoted by manufacturers as a substance to enhance exercise endurance and immune function. It may serve as a methyl donor. There have been two controlled studies of DMG in autism – one negative (Bolman and Richmond, 1999) and one positive (Kern et al., 2001). Carnosine, an amino acid dipeptide, is reported to have neuroprotective effects and antioxidant properties and to affect GABA receptors (Akins et al., 2010). Chez et al. (2002) reported improvement in parentrated symptoms of autism and in receptive language in an 8-week, placebo-controlled, double-blind study of carnosine. This research has not been replicated. Another nutritional supplement used in autism is carnitine, a compound that transports fatty acids into mitochondria. As discussed in the section of Chapter 6 on mitochondrial dysfunction, low carnitine levels have been reported in a series of children with autism (Filipek et al., 2004). Carnitine supplementation has been used in other neurologic conditions, but not specifically studied in autism. Other amino acids that are sometimes used for ASDs are tryptophan and tyrosine (Zimmer and Molloy, 2007). Omega-3 fatty acids have been investigated as therapeutic agents in a number of child psychiatric disorders, having some efficacy in depression (Rey et al., 2008). Amminger et al. (2007) reported on a 6-week, randomized, double-blind, controlled study in 13 children with autism and severe behavior problems, noting some improvements in stereotypies and

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hyperactivity and no adverse effects. There were, however, no statistically significant differences in outcome between the treatment and placebo groups in this small, relatively short study. The dose used was 1.5 g/day, with nearly equal amounts of the fatty acids EPA and DHA. In an open-label Israeli study, Meiri (2009) found improvement on an autism symptom checklist in eight of nine subjects who took 1 g daily of omega-3 fatty acids for 12 weeks. Bent et al. (2011), however, found no positive effect on hyperactivity in a 12-week, randomized, controlled trial in 27 children with ASDs who took 1.3 g per day of omega-3 fatty acids. These studies, plus a handful of earlier, open-label investigations (Bent et al., 2009), suggest that omega-3 fatty acids may have positive effects on specific symptoms in ASDs, but this remains to be confirmed.

Summary Although largely unsupported in peer-reviewed, controlled research, many of the treatments with the dietary supplements discussed in this chapter section are likely safe, if not taken in excess. Akins et al. (2010) specifically mentioned vitamin B6/magnesium, DMG, carnosine, carnitine, omega-3 fatty acids, methylcobalamin, folate, and glutathione as likely safe supplements. Professionals who work with children with ASDs need to maintain an open dialogue with parents about CAM treatments. This is especially true of physicians, who can help parents to understand that these treatments affect the health of their children and may interact with medications prescribed by the physician. Ideally, parents will seek the advice of their child’s doctor in making decisions about whether to try CAM treatments. Akins et al. (2010) have suggested that physicians should “tolerate” their patients taking nutritional supplements and eating special diets that are not known to be harmful. At the same time, doctors can provide information on the scientific support (or lack of it) for specific treatments and encourage parents to use objective outcome measures to assess treatment effectiveness. Dangerous treatments can be firmly warned against. In addition, physicians may want to provide guidance to parents on how they can independently evaluate the claims of CAM practioners and internet boosters of CAM therapies. Coplan (2010) has offered specific suggestions to help parents distinguish “sense” from “nonsense” in such claims.

Summary and conclusion This chapter has reviewed research on pharmacotherapy in autism, including some treatments with medications and dietary supplements that derive from complementary and alternative medicine. As should be clear from the foregoing discussion, the best research on treatment efficacy is with psychotropic medications, but even this research is limited in terms of the number of sufficiently large, randomized, placebo-controlled studies that show positive medication effects. Where medicines have been found to have positive effects, these have been on specific symptoms and symptom domains, not on core features of ASDs. It is with this purpose that they should be prescribed and their effectiveness systematically assessed in individual cases. Huffman et al. (2011) recently completed a comprehensive review of 115 English-language articles on the mangagement of symptoms in children with ASDs with pharmacologic and CAM treatments. Medications judged to be effective, based on the quality and strength of the research evidence, were risperidone (for maladaptive behavior, hyperactivity, and irritability) and methylphenidate (for inattention and hyperactivity). Secretin was clearly shown to be ineffective for core symptoms, self-stimulatory behaviour, and gastrointestinal problems. No CAM treatments were determined to have more than marginal effectiveness.

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Pharmacotherapy can be helpful to many children with ASDs, but medications must be selected and used with care. The primary goal of pharmacotherapy is to support a child’s engagement in the behaviorally based interventions, special education, and habilitative therapies that target the core symptoms of autism.

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Malone, R. P., Delaney, M. A., Hyman, S. B., et al. (2007). Ziprasidone in adolescents with autism: an open-label pilot study. J Child Adolesc Psychopharmacol, 17(6), 779–90. Marcus, R. N., Owen, R., Kamen, L., et al. (2009). A placebo-controlled, fixed-dose study of aripiprazole in children and adolescents with irritability associated with autistic disorder. J Am Acad Child Adolesc Psychiatry, 48(11), 1110–9. Martin, A., Koenig, K., Scahill, L., et al. (1999). Open-label quetiapine in the treatment of children and adolescents with autistic disorder. J Child Adolesc Psychopharmacol, 9(2), 99–107. Martin, A., Koenig, K., Anderson, G. M., et al. (2003). Low-dose fluvoxamine treatment of children and adolescents with pervasive developmental disorders: a prospective, open-label study. J Autism Dev Disord, 33(1), 77–85. Masi, G., Cosenza, A., Millepiedi, S., et al. (2009). Aripiprazole monotherapy in children and young adolescents with pervasive developmental disorders: a retrospective study. CNS Drugs, 23(6), 511–21. McCracken, J. T., McGough, J., Shah, B., et al. (2002). Risperidone in children with autism and serious behavioral problems. N Engl J Med, 347(5), 314–21. McDougle, C. J., Brodkin, E. S., Naylor, S. T., et al. (1998). Sertraline in adults with pervasive developmental disorders: a prospective open-label investigation. J Clin Psychopharmacol, 18(1), 62–6. McDougle, C. J., Kem, D. L. and Posey, D. J. (2002). Case series: use of ziprasidone for maladaptive symptoms in youths with autism. J Am Acad Child Adolesc Psychiatry, 41(8), 921–7. McDougle, C. J., Kresch, L. E. and Posey, D. J. (2000). Repetitive thoughts and behavior in pervasive developmental disorders: treatment with serotonin reuptake inhibitors. J Autism Dev Disord, 30(5), 427–35. McDougle, C. J., Naylor, S. T., Cohen, D. J., et al. (1996). A double-blind, placebo-controlled study of fluvoxamine in adults with autistic disorder. Arch Gen Psychiatry, 53(11), 1001–08. McDougle, C. J., Scahill, L., Aman, M. G., et al. (2005). Risperidone for the core symptom domains of autism: results from the study by the Autism Network of the Research Units on

Pediatric Psychopharmacology. Am J Psychiatry, 162(6), 1142–8. Meiri, G., Bichovsky, Y. and Belmaker, R. H. (2009). Omega 3 fatty acid treatment in autism. J Child Adolesc Psychopharmacol, 19, 449–51. Ming, X., Gordon, E., Kang, N., et al. (2008). Use of clonidine in children with autism spectrum disorders. Brain Dev, 30(7), 454–60. Molloy, C. A., Manning-Courtney, P., Swayne, S., et al. (2002). Lack of benefit of intravenous synthetic human secretin in the treatment of autism. J Autism Dev Disord, 32, 545–51. Moretti, P., Sahoo, T., Hyland, K., et al. (2005). Cerebral folate deficiency with developmental delay, autism, and response to folinic acid. Neurology, 64(6), 1088–90. Murray, M. J. (2010). Attention-deficit/ hyperactivity disorder in the context of autism spectrum disorders. Current Psychiatry Rep, 12(5), 382–8. Namerow, L. B., Thomas, P., Bostic, J. Q., et al. (2003). Use of citalopram in pervasive developmental disorders. J Dev Behav Pediatr, 24, 104–08. Niederhofer, H., Staffen, W. and Mair, A. (2003). Immunoglobulins as an alternative strategy of psychopharmacological treatment of children with autistic disorder. Neuropsychopharmacology, 28, 1014–5. Nye, C. and Brice, A. (2005). Combined vitamin B6–magnesium in autism spectrum disorder. Cochrane Database Syst Rev, 19(4), CD003497. Owen, R., Sikich, L., Marcus, R. N., et al. (2009). Aripiprazole in the treatment of irritability in children and adolescents with autistic disorder. Pediatrics, 124(6), 1533–40. Owens, J. A. and Moturi, S. (2009). Pharmacologic treatment of pediatric insomnia. Child Adolesc Psychiatr Clin N Am, 18(4), 1001–16. Owley, T., McMahon, W., Cook, E. H., et al. (2001). Multisite, double-blind, placebo-controlled trial of porcine secretin in autism. J Am Acad Child Adolesc Psychiatry, 40(11), 1293–9. Owley, T., Walton, L., Salt, J., et al. (2005). An open-label trial of escitalopram in pervasive developmental disorders. J Am Acad Child Adolesc Psychiatry, 44(4), 343–8. Pandina, G. J., Bossie, C. A., Youssef, E., et al. (2007). Risperidone improves behavioral

Chapter 11: Medication and nutritional treatments

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Section 3

Assessing and Treating Adults with Autism Spectrum Disorders

Chapter

Diagnosis and assessment of adults with ASDs

12

Susan V. McLeer and Donna N. McNelis

Introduction Most early studies of Autism Spectrum Disorders (ASDs) in developed countries had prevalence estimates that were just short of 60 per 10 000 (Baird et al., 2000; Bertrand et al., 2001; Chakrabarti and Fombonne, 2005), with the prevalence of Autistic Disorder (AD) between 10 and 20 per 10 000. However, a study of 55 000 British 8- and 9-year old children suggested an ASD prevalence that may be as high as 110 per 10 000 (Baird et al., 2006). More recently, the US Centers for Disease Control and Prevention indicated that ASDs affect 1 in 88 children, including 1 in 54 boys (Autism and Developmental Disabilities Monitoring Network, 2012). In Canada, the prevalence has been reported to be 1 in 165 (Fombonne, 2003) and in the UK 1 in 110 (Wing and Potter, 2002). Adequate explanations for this increase in prevalence have not been delineated to date, but may involve an actual increase in ASD cases as well as an apparent increase secondary to improved diagnostic instruments and earlier detection. What is clear is that children with ASDs become adults with ASDs. As they age, their symptoms and disabilities do not go away, but rather new environmental challenges await them. The young adult’s ability to meet these new challenges can be enhanced through careful individualized assessment of adult developmental needs and functional strengths, coupled with delineation of a habilitation plan that seeks to maximize the individual’s strengths (as opposed to a limited clinical focus solely on a person’s disabilities). The best predictors for functional outcome in children with ASDs are age when diagnosed, cognitive status, and age when language is acquired (McGovern and Sigman, 2005; Turner et al., 2006). However, given that most developed counties have had a proliferation of early detection and intervention programs for children and adolescents with ASDs, it may well be that positive correlations will be found between access and utilization of such programs and long-term prognosis. However, to date, few systematic studies evaluating the long-term impact of these specialized programs have been completed. In the US, federal and state funding for program development, diagnosis, and intervention, as well as improved health insurance coverage for ASDs, has increased significantly over the last decade. Early access to “state-of-the-art” interventions, while not curative, is predicted to change the trajectory for children with autism and to provide skill sets that may facilitate the transition through childhood and adolescence into adulthood. Unfortunately, funding for program support changes when an individual with an ASD turns 21 years old. As an adult, in most jurisdictions, a person can no longer access the same programs that have been so critical for development during childhood. In addition, in most states, the public funding and private The Autism Spectrum, ed. Mark E. Reber. Published by Cambridge University Press. © Cambridge University Press 2012. 285

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insurance necessary for support of medical expenses incurred secondary to having an ASD are not available to adults. Hence, the aging of children and youth with ASDs becomes a matter of enormous importance from a public health perspective. From the perspective of individuals with ASDs and their families, adulthood threatens the loss of access to essential resources and poses the risk of tragic consequence as individuals with ASDs struggle to feel valued and achieve a modicum of adult independence. An early study on the trajectory of development in adolescents and adults with autistic disorder indicated that 60–75% of people will have a poor outcome (Seltzer et al., 2004). Howlin et al. (2004) reported a slightly better outcome, with 22% of subjects reporting very good to good outcomes, 19% fair and 58% poor to very poor outcomes. These are dire predictions. With increased advocacy, deployment of new resources and funding streams, poor outcome should no longer be acceptable.

What are the future concerns of parents caring for a child with an ASD? The Easter Seals’ study “Living with Autism” (Easter Seals, 2008) surveyed over 2500 parents of children with autism and typically developing children in the US. Parental concerns about their children’s future regarding daily life, relationships, independence, education, housing, employment, finances, and health care were compared. Nearly 80% of parents of children with ASDs indicated that they were extremely or very concerned about their child’s independence as an adult. Only 14% believed that their children would be able to make life decisions, and only 17% believed that their children would make friends. Almost all reported that they were “financially drowning,” and expressed concerns about their children’s financial future. As most children with ASDs are entitled to receive special education services through the public school system, most parents expressed considerable concern about what would happen when the child reached the age of 21 years, a phenomenon referred to as “aging out.” The loss of access to professional staff and services was of great concern. An equally pervasive worry expressed by parents was, “What is going to happen to my child when I’m no longer around or able to care for him?” This chapter will review diagnostic considerations and methodologies that can be utilized for case identification and tools that are available for diagnosing ASDs in adulthood. Functional assessment methods and tools that can facilitate treatment planning will be reviewed in the next chapter. Functional assessment will be considered within the matrix of the different stages and tasks of adult development since life challenges change throughout the life span and these changes need to be considered in developing a plan for maximizing a person’s quality of life, functional abilities, and safety. Additionally, treatment and service planning will be considered from a broad perspective – not limited merely to medical interventions – but addressing a range of interventions and the organization of resources for improving the quality of life of adults with autism.

Diagnostic considerations Diagnostic criteria Autism spectrum disorders constitute a spectrum of developmental disabilities that are characterized by atypical development in socialization, communication, and behavior. Symptoms and signs of most ASDs are present before the age of 3 years and there may be coexisting disorders of function in the domains of sensory processing, cognition, learning,

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and attentiveness. ASD is a term that is not used as a specific diagnostic category at this time, but, in actuality, refers to the spectrum encompassed by five specific disorders as classified by the American Psychiatric Association (2000): Autistic disorder (AD), Rett’s disorder, childhood disintegrative disorder, Asperger’s disorder, and pervasive developmental disorder not otherwise specified (including atypical autism). The characteristics and vicissitudes of these disorders have all been discussed in earlier sections of this book and will not be reviewed here. It is important to note, however, that at the time of this writing, the APA has drafted and is in the process of reviewing a new 5th edition of the Diagnostic and Statistical Manual. The proposed 5th edition contains significant changes in the diagnostic nomenclature. All five disorders currently listed in the 4th edition will be reclassified as one, Autism Spectrum Disorder, which is characterized by (a) deficits in social communication and interaction, and (b) restricted repetitive patterns of behavior, interests, and activities, both being present in early childhood. It is noted that some of these symptoms may not become fully manifest until social demands exceed the individuals’ limited capacities (APA, 2010). The rationale for the proposed changes is that a single spectrum disorder may be a better reflection of the state of knowledge about the pathology and clinical presentation of these disorders. An additional consideration for families, not mentioned by the APA, is that by eliminating the current division into five diagnostic categories and presenting criteria along a quantitative spectrum, families with children, including adult children, with autism may be better able to access funding streams and resources that will facilitate the transitioning of the adult from adolescence into adulthood and through the various phases of adult life.

Case identification and screening Given that most diagnoses will have been established during childhood, the task from a public health perspective is one of identifying persons in need of service who have “aged out,” i.e. turned 21, and are no longer provided services by the public school system. Systems are needed for tracking such individuals to determine their specific needs in meeting the challenges of adulthood. In addition to those who have been supported and provided services through a variety of entitlements for children and youth with ASDs, there may be some higher-functioning individuals on the autism spectrum who have not been identified during childhood, but will be when they fail to cope in the face of the developmental challenges of adulthood. To this end, two screening instruments have been designed to detect autism and/ or autistic traits in the adult population with normal intelligence: the Autism-Spectrum Quotient (AQ) (Baron-Cohen et al., 2001) and the Autism-Spectrum Disorders in Adults Screening Questionnaire (ASDASQ) (Nylander and Gillberg, 2001).

Screening methodology Autism-spectrum quotient The AQ is a brief, self-administered, 50-item questionnaire designed specifically for adults that establishes the degree to which an adult of normal intelligence might have features that are part of the core autism phenotype. This means that the instrument screens for symptoms, but is not diagnostic. Assessments include five different areas: social skill, attention switching, attention to detail, communication, and imagination. Questions require answers along a four-point Likert scale: “definitely agree”, “slightly agree”, “slightly disagree,” or “definitely disagree.” Eighty percent of adults with Asperger’s syndrome (AS) score above a cut-off of

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32, with only 2% of control adults obtaining a score above 32. The AQ has good test–retest and interrater reliability. Cronbach’s alpha coefficient varies from 0.63 to 0.78 (Baron-Cohen et al., 2001; Kurita et al., 2005). The discriminant power of the AQ, as indicated by area under the ROC curve, was 0.78 with a standard error of 0.06 and a 95% confidence interval of 0.7– 0.9. This determination is indicative of the probability that a randomly selected “truepositive” individual will score higher on the test than a randomly selected “true-negative” individual. Furthermore, using a cut-off of 26, there was a sensitivity of 0.95 and a specificity of 0.52 with a positive predictive value of 0.84 and a negative predictive value of 0.78 (Woodbury-Smith et al., 2005). The AQ can also be used to identify ASD traits in family members without an ASD diagnosis (Bishop et al., 2004). A study in the Netherlands confirmed that the instrument has high criterion validity as well as reliability and internal consistency in assessing ASD symptoms (Hoekstra et al., 2008). The AQ has met rigorous psychometric criteria, is brief and easy to administer. Hence, it is a good instrument for screening adults with normal intelligence for symptoms of ASDs.

Autism-Spectrum Disorders in Adults Screening Questionnaire This instrument consists of a list of nine questions with “yes” or “no” answers about symptoms and functional limitations found in people with autism spectrum disorders. Psychometric properties were delineated in the Dutch-language fact sheet accessed online (ADDASQ Fact Sheet, September 10, 2010). The internal consistency of items, utilizing Cronbach’s alpha coefficient, is good (0.85). Test–retest reliability was determined for only 38 people after a period of 11–13 months and was found to be only weak to moderate (0.22– 0.65). The interrater reliability was also determined to be weak to moderate (0.36–0.42). The instrument does not have adequately established psychometric data so that, in addition to the weak-to-moderate reliabilities established, the validity of the instrument has not been established and standardization has not been done (Chang et al., 2003; Brereton and Tonge, 2002). Therefore, in spite of the ease and rapidity of administration, this screening instrument cannot be recommended until the authors have better established validity. Screening instruments always carry a risk of classifying someone as having a disorder when in fact they do not. Therefore, once potential cases have been identified through screening, a more definitive approach to diagnosis must be undertaken. Diagnostic systems tend to be dichotomous in nature. If one meets the diagnostic criteria, then one has an ASD; if not, one does not. Diagnosis is important in classifying individuals with similar symptoms and projected outcomes. It informs the direction for assessment and treatment while also providing a label that will access a variety of funding streams as well as provide reimbursement for services rendered through third-party insurers. What a dichotomous diagnosis does not do is facilitate the planning of individualized interventions designed to maximize a person’s developmental trajectory from childhood, through adolescence, and into adulthood. In addition, when a diagnosis varies along quantitative dimensions as it does with disorders on the autism spectrum, one needs to take into consideration both the vulnerabilities and strengths and assets of an individual.

Diagnostic methodology Diagnosis of an ASD is usually made on the basis of a clinical interview using the diagnostic criteria of the Diagnostic and Statistical Manual (DSM) (American Psychiatric Association, 2000). However, it is important to be aware that there are also standardized instruments

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available for assessment. The psychometric properties of the two standardized instruments noted below have been well established. However, the main problem for a busy clinician is that both instruments require formal training and take considerable time to administer.

Autism Diagnostic Observation Schedule The ADOS (Lord et al., 2000) is a semi-structured interactive assessment for use by a welltrained clinician when interviewing the identified patient. The ADOS has four modules, of which one is selected for use based on the age and expressive language level of the person being assessed. Module 1 is designed for young, nonverbal children and module 4 is for use with verbally fluent adolescents and adults. Scores on key items from the communication and social interaction domains are used to develop a diagnostic algorithm with cut-off scores indicating whether criteria for autistic disorder on another ASD are met. It usually takes 30– 45 minutes to administer each module. However, in order to be certified, clinicians must complete a day-long training and be determined to have an interrater reliability of 80% to use this instrument. In addition to training requirements and the time necessary for administration, there are other limitations as well. The ADOS does not use scores relating to restricted or repetitive behaviors in the diagnostic algorithm, so other instruments are required to provide a full diagnostic picture. This may become particularly problematic when the fifth edition of the DSM is finalized.

Autism Diagnostic Interview-Revised The ADI-R (Lord et al., 1994) is considered the gold standard for diagnostic instruments used in research settings. It also is a semi-structured clinical interview, but it is used in interviewing caregivers of children or adults in need of assessment. Again, there is need for clinician training prior to use of the instrument. The training is longer than with the ADOS and requires 2.5 days of training at the University of Michigan’s Autism and Communication Disorders Center. An interrater reliability of 90% is required and is usually established by post-training videotapes sent to the Center for ratings. The ADI-R has 93 items focusing on three domains: (a) quality of social interaction, (b) communication and language, and (c) repetitive, restricted, or stereotypical behaviors. There are other questions that facilitate treatment planning, including questions about self-injurious behaviors and hyperactivity. The clinician asks the questions and then rates the items on a three-point scale based on response. Scores are generated in each of the three major content areas and there is a specified cut-off score for each section. Autism is diagnosed when scores exceed the cut-off in all three major domains. Interrater and test–retest reliability, as well as internal validity, have all been demonstrated (Mazefsky and Oswald, 2006). The use of this diagnostic instrument is usually limited to the research setting because it takes between 2.5 and 3 hours to administer. The combined use of the ADOS and the ADI-R increases the accuracy and reliability of an ASD diagnosis (Levy et al., 2009). However, the required training and time for administration pose significant problems for clinicians outside of research settings. There are two other diagnostic instruments that are also available: the Diagnostic Interview for Social and Communication Disorders (DISCO) (Wing et al., 2002) and the Developmental, Dimensional, and Diagnostic Interview (3di) (Skuse, 2004). These assess a broader diagnostic spectrum; however, they also require training and take several hours for administration. Assessment is not over once the diagnosis of an ASD has been made. There is then a need to consider what the developmental challenges are for the individual adult with an ASD and, within a developmental framework, to assess the person’s ability to function in key domains.

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Developmental considerations “Adulthood” is not a static condition, but in and of itself consists of a variety of developmental stages, each posing different transitions and challenges for the person with an autism spectrum disorder. These developmental stages of adulthood must be considered in choosing assessment techniques and identifying interventions and support systems that will maximize adult functioning. Unfortunately, there are no published studies of adult development in individuals with ASDs. Therefore, one needs to be creative in utilizing current knowledge and studies and to infer how developmental concepts can be applied and used in assessing adults with ASDs. In the foreword to the first edition of Childhood and Society (1950), Erik Erikson states, “It is human to have a long childhood; it is civilized to have an ever longer childhood. Long childhood makes a technical and mental virtuoso out of man, but it also leaves a lifelong residue of emotional immaturity in him. While tribes and nations, in many intuitive ways, use child training to the end of gaining their particular form of mature human identity, their unique version of integrity, they are, and remain, beset by the irrational fears which stem from the very state of childhood which they exploited in their specific ways.” (Italics added are those of the authors.) And so, in extrapolating what is known about human development, it is important to consider what would be the form of mature human identity and integrity in each individual adult with an ASD and consider how vulnerabilities might be “exploited” in specific ways, to draw on his/her uniqueness and, in almost a Zen mode, utilize what might be classified or conceptualized as a weakness or disability and turn it into a strength that can be tapped for improved adult functioning. To clarify this conception, one can consider the potential of an 18-year-old man with Asperger’s syndrome who is very demanding about routines being followed. He has a tendency to become intensely focused on certain subjects and displays an interest in parts of objects instead of whole objects. His lack of flexibility and his fixation on certain subjects and parts of objects resulted in behavior that was quite problematic when he was younger. Changes in routine or efforts to divert his attention used to result in angry outbursts, if not major temper tantrums, but his focus on and need for a predictable environment can actually be reframed as an asset in the workplace, making him more likely to be a dependable employee who shows up for work on time each day. His intense focus on particular subjects and parts of objects can be reframed as well, and he might well become a highly detailed worker who completes tasks accurately (Quill, 2000). The developmental process has been defined as the emergence of mental structure, functions, and resulting behavior secondary to the interaction among body, mind, and environment over the course of the lifespan. It is never exclusively the result of any one of the three variables (Colarusso, 2009). While this is a rather contemporary definition, the same concept of human development as an interactive process that continues from birth to death has been around intuitively for centuries. William Shakespeare wrote in 1600: All the world’s a stage, And all the men and women merely players. They have their exits and their entrances, And one man in his time plays many parts, His acts being seven age.

Shakespeare delineated the characteristics of the seven ages of man from birth to oblivion (The Arden Shakespeare, 1996). Even in this literary allusion to development, the interaction of mind, body, and environment was implied.

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There have been multiple developmental theorists who have written about development across the life cycle, but none have addressed it from the perspective of an individual with a disability such as autism. However, in exploring developmental concepts it is important to consider the basic definition of the interaction among body, mind, and environment – the sum total of which is unique – when one applies these concepts to individuals with developmental disabilities. One of the earliest developmental theorists to provide an integrated psychosocial model for development across the life span was Erik Erikson, who delineated eight ages of man (1963). Erikson viewed development as a dynamic process resulting in changes that were necessary to transcend a variety of conflicts and states of disequilibrium encountered as one ages. In Erikson’s description of the eight ages of man, the transition between adolescence and young adulthood was conceptualized as the need to resolve the conflict designated identity versus role confusion during adolescence and move on to work through the basic concept of intimacy versus isolation, isolation being an undesired state that one could well imagine as a major risk for the adult with an ASD. Middle adulthood was conceptualized by Erikson as the conflict generativity versus stagnation, while the older adult has the task of resolving the conflict integrity versus despair, implying that “in the final consolidation, death loses its sting” (Erikson, 1963, p. 269). The face validity of these ages of man for adults with disabilities appears substantial. More contemporary developmental theorists have used other paradigms and conducted different studies in order to delineate the essential tasks faced developmentally from birth to death. Levinson et al. (1978) proposed that the life cycle has distinct, identifiable eras of approximately 20 years duration. Within these eras, he suggests that there are alternating periods of 6–7 years of stability followed by 4–5-year intervals of transition, with each transition having its own tasks which must be mastered. The schema is complex and will not be discussed in detail here. Rather, in order to present a developmental framework that will not be unduly cumbersome but useful in thinking about planning for transitions from childhood and adolescence through the different developmental phases of adulthood, a more simple and straightforward system will be outlined, consisting of three basic phases of adult development. Each developmental phase will be described with regard to tasks that have been outlined based on non-developmentally disabled individuals and adaptations considered for those with an ASD. The critical questions are: “How do these developmental tasks translate for young adults with autism?” “Are there predictable times in the life cycle when adults with an ASD will need additional supports in order to manage the expected changes and transitions involved in life’s challenges?” Obviously, much depends on the severity of the individual’s disabilities and the mixture of strengths and vulnerabilities of both the adult child and the adult child’s family.

Young adulthood (20–40 years of age) During young adulthood, there are continued activities that are directed toward increasing an independent sense of self through the development of new or expanded relationships with peers, a realignment of relationships with parents, the development of a work ethic with career-directed training, and the establishment of new ways of relaxing and “playing,” all of which enhance a sense of new independence, evolving adult sexuality, and continued maturity. The establishment of intimate relationships that provide opportunities for support, understanding and expression of sexuality, growth in peer relationships, increased economic

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independence and, for some, the development of a new nuclear family, are all quests that predominate this period of development. Most developmental tasks that have been delineated for young adults who do not have developmental disabilities are applicable to a person with autism. Caregivers, both parents and those associated with a variety of social and medical services, will need to work together to help the young person meet the challenges involved. Without question, very few adults with an ASD will attain full independence. Most will continue to need some degree of support and supplemental services for the rest of their lives; however, this does not mean that the young person cannot move forward toward the development of a young adult’s sense of self. Consequently, any treatment plan needs to take the individual’s stage of adult development into consideration. The following paragraphs outline some general goals and tasks for caregivers and parents of young adults with ASDs in the context of the developmental challenges of this stage of life. Determine how to access living accommodations that will maximize the young adult’s sense of self. Late adolescence and early adulthood is a time when parents need to assess what kind of an environment would be most beneficial for the emerging adult with autism and think through how such an environment can be maintained when parents are no longer able to provide a home either because of physical or financial limitations or death. While some adults with ASDs will be able to live independently, the vast majority will require support for a variety of life circumstances that will be encountered, e.g. finances, dealing with government and service agencies necessary to afford and/or access services. For individuals who are more impaired, parents will need to confront the fact of their own aging and that they will not be able to continue to care for their disabled adult child for the duration of his/her life. Realistic planning about transitioning to an appropriate home environment, apart from the child’s parents, becomes paramount. There are a variety of living arrangements that can be considered, including independent living, supported living, supervised group living, adult foster care, in-home services with respite care for caretakers. For those with more substantial disabilities and inadequate strengths for living in the community, parents need to give thought to starting a transition to a facility with significant support for adults with autism. For those young people with more resources and strengths, the options are more expansive and may range from living independently in an apartment to a dormitory room at college or a group residence with other young adults with ASDs. The group residence may be set up with a variety of services that are available to the young adults living there. The home may be owned through a trust set up for the young adults, complete with planning for payment of personal support. Conversely, the house may be a facility provided through a social service agency or the community mental health system in the county or state of residence. When feasible, living with peers in a home separate from the parents’ home will facilitate the young adult’s developing a sense of self. The availability of financial resources is a major variable for successful planning. Develop a financial plan so that the young adult with an ASD has the opportunity to learn how to manage money and other resources. In the US, while many entitlements for education and training are lost when the young person with autism reaches the age of 21 years, there are new sources of money that become available. Below the age of 18 years, in order to be eligible for Supplemental Social Security Income (SSI), the family income and resources must be very limited. However, once the adult child reaches the age of 18 years, eligibility for SSI is no longer dependent on family income and resources, only those of the child. As of 2010, the adult child with a disability must not have an income greater than $2000/year for eligibility.

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However, the family may establish a Supplemental Needs Trust for the adult child that will not be considered in determination of SSI eligibility. In addition, if parents have died or retired, the adult child may be eligible as well for Social Security Disability Income (SSDI). At the time of the young person’s 18th birthday, the other change that is significant is that parents are legally no longer considered to be the young person’s guardians. Therefore, if continued guardianship is required, as it is in most instances, the parents will need to petition the court to grant them guardianship status over the adult child with an ASD. If parents are not available, each state has a legal mechanism for appointing a guardian for the individual. Finally, it is important for parents to develop a financial plan that will include a way for the young adult to have some disposable money that can be self-managed and, possibly, a mechanism that allows the young adult to contribute to the cost of housing. Young adulthood is a time for the family to engage in long-term planning, targeting the establishment of a financial plan that will facilitate housing and care when the parents are no longer able to provide for their adult child with an ASD. It is important to understand what monies are accessible both from the family and through public entitlements. It is crucial for parents to stay on top of new developments at the federal and state level that increase the availability of financial resources for adults with autism. In the US, Easter Seals and the Kaiser Commission provide websites that monitor resource availability for adults with ASDs, including state-by-state coverage. Parents need to be knowledgeable about resources that can be leveraged and stay politically tuned in to developments that may provide for more resources for the adult with autism. There are changes in resources at the state level practically on a daily basis. Involvement with family support groups, advocacy groups, and websites facilitates staying informed about new opportunities for accessing resources. Additionally, through working with advocacy groups, parents can have a substantial impact on public policy and resource availability, thereby improving the future for their adult child while enhancing their own sense of effectiveness. Determine how to provide sufficient privacy that the adult with an ASD can enjoy his/her sexuality. Hopefully, by the time the young person is 18 years old, sexuality, including birth control and masturbation, will have been discussed. The adolescent and young adult with autism is a sexual person and he/she will almost always have explored his/her sexuality prior to reaching the age of 18 years. It is necessary to have concrete discussions about the risks of sexual predators and to give the young person some tools to use in determining how to behave when encountering strangers. Additionally, during late adolescence and early adulthood, it is important to insure that the young person understands safe sex and the realities of having children. In addition to genetic counseling, there needs to be a frank discussion about what is necessary in parenting a child. Discussing how having a baby would affect the young person’s lifestyle, likes and dislikes, rigidities, and difficulties in relationships is critical. Such discussions need to be direct and concrete because many adults with ASDs may find that child-rearing is a task that is overwhelming and is best avoided in order to have a maximally productive life. Genetic counseling may be indicated for those young adults who are higherfunctioning in order for them to determine the risks in having biological children versus adopted children. Identify strengths and interests in order to determine whether or not more education and training is needed to prepare for entering the work force. Work, in most societies, is much more than simply a paycheck. One’s self-image is enhanced by the ability to work productively. Additionally, the work environment provides an “other than home” social matrix for all people, with and without disabilities. Completing a rigorous assessment of strengths and

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abilities that can be drawn upon for planning additional education or vocational training is critical during the end of adolescence and beginning of early adulthood. Some adults who are higher-functioning will enter mainstream jobs; others will need more supported positions with job coaches and managers who are skilled in working with and encouraging young adults with ASDs. With the strengths and vulnerabilities associated with an ASD, training and coaching of the young person in the work or educational environment is important, as most adults with ASDs have difficulties with abstraction and generalization from an educational setting into the workplace. Coaching may also be necessary to facilitate the appropriate modes of communicating and socially interacting. Evaluate the “home” environment, the educational/training setting and/or workplace as places where the young adult with an ASD can interact with people who are not parents. It is important for the young person to have access to non-parental adults who can help with tasks that are overwhelming and provide encouragement and support. Non-parental adults are critically important to the development and growth of both those with and without developmental disabilities. Additionally, it is important for the young adult with autism to have access to peers who are struggling with similar developmental challenges. Many higherfunctioning adults with ASDs have found that they are able to reconnect with people from their earlier school years and community using the resources of the internet, e.g. FaceBook and My Space. Again, if these modalities are used, it is important to again re-emphasize how to identify potential sexual predators and know how to deal with them to insure personal safety. Build into any plan a process whereby the young adult with an ASD can have fun, i.e. play. The old saying, “all work and no play makes Jack a dull boy” is an adage that holds for all people, with and without ASDs. All work and no play actually make people cranky and unhappy so having time for doing things that are fun is critical. Determining earlier interests and activities that were enjoyable for the young person and facilitating continued enjoyment of these among other activities is important in order for the person to develop a fully rounded adult life. Introducing the young adult to community resources, e.g. museums, concerts, etc., is also helpful for the transition to adult forms of play and use of leisure time. All of these steps will facilitate a young adult’s developing a sense of self as an individual who is no longer a child, but is aging into adult life like everyone else. With adequate planning, the risk of isolation is avoided and the young person develops a consciousness of self as separate from parents. For those who will undertake more education and training, there may be access to jobs that provide the additional growth experience of interacting with other older and more experienced adults as well as peers.

Middle adulthood (40–60 years of age) During middle adulthood, people have increased encounters with death and loss. This holds true for people with and without developmental disabilities. Consequently, during this life phase, there is a realignment of one’s sense of time, individual vulnerability, and mortality. Chronic illness may appear for the first time and a person must make peace with his or her aging body. If a person has had children, or even has had a close relationship with the children of close friends, there is a need to adjust to the adult child’s growth and everevolving competence and independence. It is a time when younger friends may need to be relied upon for support and help as the person in middle adulthood ages. Parents, and in the case of people with significant developmental disabilities extended caretakers age and

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develop significant needs requiring understanding, if not support, from the middle-aged child. Conversely, aging parents who have children with developmental disabilities must come to terms with the need to find other caretakers when the middle-aged adult child’s needs outstrip the aging parents’ ability to provide support and care. This shift in who provides support for a person with an ASD requires considerable emotional readjustment. Middle adulthood is a time for mentorship of others and, if one has had grandchildren, a time to enjoy and mentor the grandchildren. Peers’ friendships become increasingly important sources of support and new forms of leisure activities must be found which are not as taxing on an aging body. All of these changes contribute to a new, evolving sense of self as a middle-aged adult and these processes are clearly operative among people with ASDs. Manage loss. For the adult with ASD, there is no protection from the pain of loss. Handling the difficulties associated with loss starts in young adulthood with separation from parents. Such separation may have involved living apart from the parents, but even if that task was not undertaken as a young adult, the tasks of going on for vocational training, education, and entering the workplace result in the inevitable process of becoming an adult and leaving childhood behind, providing the dual experience of growth and loss. During middle adulthood, older adults and peers who are important in the person’s life will move away; some may die. For those in more supported settings, important and cherished care providers will change jobs and leave. Being attuned to the importance of such losses is important and one needs to encourage the middle-aged adult with autism not to withdraw, but to try again to develop a relationship with someone else for friendship and support. Such losses are particularly difficult for adults with ASDs because adaptation to significant change is difficult. These are times when the individual may develop some significant behavioral or emotional problems associated with the loss. These need to be recognized and appropriate support and treatment provided to help the person through this particularly difficult time in life. Address unfinished business regarding living circumstances. If the parents have not yet developed a plan for the adult child to live apart from them and have a life that is shared with other adults and peers, then they may well need counseling and support to help facilitate planning for such a transition. The parents themselves may be having trouble adjusting to their own aging and mortality. When parents have a middle-aged adult child, realistically, time is running out. Now is the time for any planning that has not yet been done before to insure continued care after parental death. Begin process of accepting an aging body. People with ASDs may well have coexisting medical problems, including psychiatric disorders that need attention. Hence, in earlier adulthood there is a need for connection with medical care providers. However, by midadulthood, as for those without autism, chronic illnesses will start to become manifest. Regular access to primary care providers who can both provide and coordinate services is important. For middle-aged adults who are physically active, it is important to help the person recognize that the physical abilities of youth are waning and new, less vigorous methods of play are needed. In middle adulthood, most people no longer physically rebound as well as they did in their younger years. Injuries can more easily result from rigorous activities. In fact, some activities can even lead to premature death because the abilities and strengths of youth may no longer be accessible when confronted with emergencies and surprising changes, e.g. sudden change of weather while hiking, capsizing of a boat. Begin process of accepting time limitations and ultimate death. This is one of the things that all people must adjust to during middle adulthood. How this is managed and the timing

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of this process in adults with ASDs is not something that has been studied to date. Hence, little can be said at this time about this process. Change parental role with children and become a grandparent. Those who study and write about adult development may generalize too much about the role of being a parent. Not all people become parents and have children; not all people become grandparents. Among adults with autism, few will have children and grandchildren. However, this does not mean that the middle-aged adult with an ASD cannot provide some types of support for a younger person. Provide mentorship for others. For people with higher-functioning autism, being able to demonstrate to others how one has managed in a particular life setting, at work or in the community, can be a valuable experience for both the younger person as well as for the middle-aged adult. Being a role model for those who are younger and have autism, and being able to support a younger person through many experiences that the middle-aged adult has successfully encountered is an experience that can be rewarding and provide an adult sense of being “older and wiser.” Those who are higher-functioning may have positions in the mainstream work place where they can be helpful to other workers, e.g. helping staff with problems with information technology, adherence to regulations in the work place. Adjust to the reversal of roles with parents. While the adult with an ASD is not likely to provide for the care of aging parents, the task becomes one of growing less reliant on the aging parent and more content with “other than parent” caregivers. For the aging parents, who have spent a significant amount of time worrying about “What will happen when we are no longer here?”, recognizing that their adult child with autism is able to live, work, and play without them becomes a form of support and reassurance in and of itself. Develop peer relationships and forms of play that are appropriate for middle adulthood. These are all tasks that can be easily incorporated into the life of a middle-aged adult person with an ASD. Helping the person adjust to the limitations of an aging body is important in this process, including having support personnel encourage “play” activities that are ageappropriate. Most people with ASDs will live in group settings with peers who are dealing with the same age-related conditions. It is a time to enjoy feelings of pride about what has been accomplished, being able to live as an adult in the community, with peers, and with a job.

Late adulthood (60 years of age and older) Some of the developmental tasks of late adulthood apply to adults with ASDs, while others may not. Some of the challenges of this period of life will require additional assistance so that the tasks can be successfully managed. The need for additional support is not something that is restricted to those with autism; rather, late adulthood is a time of substantial challenge for all, requiring considerable strength coupled with support from significant people in one’s life. As fewer adults with ASDs will have had children and grandchildren, such relatives may not be available for support and companionship. Many people in the general population find that reliance on non-family for support can be immensely stressful. Interestingly, those with ASDs will have already, at many points in their life, dealt with the need for other adults to help them and provide supportive services. Therefore, one of the big challenges for people without autism may well have been successfully traversed earlier in life in those with an ASD. Maintain body image and physical integrity, in the face of age-related decline. As with middle adulthood, this is a time when behavioral and mental health care needs to be closely

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coordinated with primary care. Chronic illness, musculoskeletal problems, and the potential of developing a dementia are all risks during old age. Healthy eating coupled with exercise can enhance the quality of life of everyone, including those with ASDs. Given the difficulties that adults with autism have in managing change, the need for hospitalization for medical illness or surgeries will be particularly stressful and efforts should be made to make the environment more familiar and predictable. If the older adult with an ASD is hospitalized, having caretakers and/or peers whom the person knows well to visit and even stay overnight will help considerably. Anxiety may be very high and using the services of a psychiatric consultation team to manage anxiety may be useful. Most important is to try to prevent hospitalization through the provision of optimal medical care. This is a period of life when additional neuropsychological testing may be necessary, should there be evidence of cognitive decline. Early identification of the development of a dementia is important in order to provide the additional support, medication, and services required. Conduct a life review. Having primary support persons who can provide praise and encouragement for the successes of the older adult with autism is helpful at this stage of life. Providing continuing access to activities that have been enjoyable in the past for the individual is helpful; activities not in keeping with individual preferences are not helpful at this point. Maintain sexual interests and activities. Many myths prevail regarding the role of sexuality in older adults. Sex is of continued interest throughout life, and privacy needs to be maintained and respected in order for people to express and continue to enjoy their sexuality. For people on psychotropic medication, it is important to inquire whether or not their ability to function sexually has changed, as many medications that are used to treat depression and anxiety interfere with sexual functioning. Deal with losses and death of significant loved ones. Dealing with loss is clearly one of the most difficult challenges during the last phase of life. The losses are considerable, and may include loss of independent living, friends, older supportive adults, parents, and living space. With change being so difficult for adults with ASDs, this is a time of considerable stress and it is important to provide some evidence of continuity between the person’s life in the present and life in the past. Caregiving personnel should be assigned so that there is continuity and not a lot of changes, as it is hard for the older person with an ASD to be comfortable with new people. Continued access to friends and family is paramount. Accept the implications of retirement. For working adults with ASDs, there is little to no data available regarding the transition into retirement. It is conceivable that as long as the day’s schedule and plan are predictable, the transition might be less troublesome than for the general population. However, the need to have some modicum of independence and the opportunity to contribute to daily activities can only have a salutary effect in supporting how the individual sees himself as a valued older adult citizen. Access to a variety of community resources for leisure activities and interests is invaluable for people with and without ASDs at this stage of life. Divest oneself of attachment to possessions. This, while listed as a developmental task, may not be an appropriate developmental task for an older adult with an ASD who may have limited possessions. In fact, possessions that have been with the person throughout life may be very important in providing a sense of grounding and stability during this final transition period. Surrounding the person with things that are loved is helpful and at the end of life, hospice care might be the best choice so that minimal change of environment, places, people, and things can be achieved.

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Functional assessment Functional assessment is a critical step prior to developing a comprehensive treatment and support plan. Because a plan should be person-centered and strength-based, when performing an assessment one needs to spend time with the individual with autism, as well as with other important support persons within the family and community. The treatment plan will then be able to address not only the difficulties and challenges faced by the person with an ASD, but also what personal characteristics, attributes, family and community-based resources constitute significant strengths. The goals of a personcentered, strength-based treatment plan include optimizing the individual’s quality of life, sense of self-respect, and function. This approach involves a paradigm change from the traditional medical assessment wherein a health care provider determines what the basic problems are that present challenges to the designated patient and then prescribes a treatment plan that the patient reviews and agrees to. In a person-centered treatment, the individual is integral to the planning process, having a major role in determining both short- and long-term life goals. While it is essential to identify problems, the individual and the health care provider must also collaborate to delineate strengths and assets that can be used in achieving both short- and long-term treatment goals. Assets may be characteristics, knowledge, interests, and skills possessed by the individual with autism or resources provided by family, friends, and the greater community, including community institutions that are readily available, e.g. museums and accessible transportation systems. The essence of the process becomes one of seeing the designated patient as more than a person with a specified problem, illness or disability. The person has a life that is complex and involves the experience of living with family members and friends within a community. The person also has interests and areas of expertise – assets that fuel one’s selfregard and sense of hope for the future. Any functional assessment of an adult with an ASD requires that the developmental stages of adult life be recognized and integrated into the treatment plan. This chapter section will enumerate the standardized tools that can be used for systematically identifying individual strengths and vulnerabilities in specific functional domains, recognizing that each stage of life poses new challenges requiring both supportive services and opportunities for continued growth and success. The functional domains of interest in ASD are presented in Table 12.1.

Table 12.1 Functional domains of interest in ASDs

Functional domains of interest Cognitive ability Linguistic and communication skills Educational achievement and aptitude Vocational skills and aptitude Social skills Health care needs

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Cognitive abilities People with ASDs display a broad range of cognitive abilities, from being highly intelligent and skilled in specific areas of interest to having varying degrees of intellectual disability/ mental retardation that are further compromised by the social and communication impairments associated with autism. Knowing what a person’s cognitive abilities and potentials are significantly impacts planning for the future. These skills and abilities determine, in part, where a person might be able to live and the level of independence that might be achieved. Educational and vocational planning is linked tightly to an individual’s cognitive resources. An assessment of cognitive abilities may have been done, in part, during childhood. However, even among those diagnosed with ASDs, the likelihood is that neuropsychological functioning has not been adequately assessed. Schools are frequently underfunded and testing is often limited to intelligence, achievement, and speech and language assessment. Neuropsychological testing is costly (US$800–$2500) and is apt not to be part of the package offered through federal and state entitlement programs. Nonetheless, such testing is worth the capital investment in planning for the young adult’s future. Cognitive ability reflects more than a person’s intelligence quotient (IQ) and educational achievement. It is better understood along the domains of information processing, which includes three components: input, processing, and output. Input involves visual, auditory, and tactile perceptions as well as vestibular and kinesthetic perceptions, including spatial orientation. Processing abilities include the functioning of attentional and memory systems (short-term encoding and storage, active working memory, and long-term memory storage), as well as executive functioning. Executive functioning involves how information is organized, sequenced and prioritized; these combine to affect the processes involved in abstraction, judgment, and planning. Output includes the physical, motor response to information, affecting motor skills and coordination, speech and expressive language, and other complex behaviors. Most individuals, those with ASDs included, do not have even functioning across all domains, but rather have areas of strength as well as weaknesses and vulnerabilities. Information on these strengths and weaknesses is critical in determining a person’s need for social support, further education, and vocational training. In order to assess these areas of function, one must assess intelligence. The most widely used instrument is the Wechsler Adult Intelligence Scale-III (WAIS-III) (Wechsler, 1997). For those people who are not able to speak, the Peabody Picture Vocabulary Test, 3rd Edition (PPVT-III) (Dunn and Dunn, 1997) is the leading measure of receptive vocabulary for standard English. It takes only 10–15 minutes to administer and has strong psychometric data. The PPVT-III has norms through the age of 90 years. Another instrument that has been used as well for determining nonverbal intelligence is the Test of Non-Verbal Intelligence, 4th Edition (TONI-4) (Brown et al., 2009), which can be used up to the age of 89 years. Neuropsychological assessment begins with intelligence testing, but moves beyond it to address other components of information processing. Choosing the appropriate neuropsychologist is a difficult task, because adult neuropsychologists have historically been concerned with assessing dementia or acquired brain damage. Child and adolescent neuropsychologists are more apt to be focused on identifying developmental learning disabilities and examining information processing as it relates to educational and/or vocational functioning. Such assessment is of greater use in determining cognitive abilities and challenges in adults with ASDs. Hopefully, with the recognition of need, there will be more adult neuropsychologists who develop the skills necessary for such an evaluation. Achievement testing

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may also be of use to determine established skills in reading, mathematics, and other standard areas of school achievement. Detailed descriptions of both neuropsychological instruments as well as the WAIS-III and standardized achievement tests are available in most standard texts (Swanda and Haaland, 2009). The cognitive assessment of individuals with ASDs needs to be attended to during late adolescence and early adulthood and then potentially again, as needed, in middle or late adulthood in order to have the necessary information for developing a person-centered treatment plan. In late adolescence and early adulthood, this information will be used to determine the degree of independent living that can be achieved and what kinds of supports will be needed to live in the community. These data will also be invaluable in determining a plan for continued education or vocational planning. In middle and late adulthood, individuals with ASDs face the same problems as the general population with development of chronic illnesses, particularly those involving the cardiovascular system. They may also undergo surgeries (coupled with anesthesia) that can compromise neuropsychological functioning and – in later life – experience dementia. For those with coexisting psychiatric illness who are treated with atypical neuroleptics, there is an increased risk of metabolic syndrome characterized by weight gain, pathological lipid profile and an increased risk of diabetes, cardiovascular disease, and stroke. Any conditions that impair cognitive functioning may suggest a need for further neuropsychological assessment.

Linguistic and communication assessment In people with ASDs, linguistic and communication deficits vary in severity, number, and affected domain. Some people with ASDs never develop speech at all. In those who have developed speech by adulthood, it is likely that they would have received intensive speech and language therapy during childhood and adolescence. Young adults with autism who are planning to move into housing in the community and to obtain either more education, vocational training, or a job with training on site, will need to use a means of communicating that is understandable, socially acceptable, and reliable. It is important that the person be able to understand others and be understood. Verbal communication is clearly interconnected with social skills and behaviors, so it is important to access records of past speech and language assessment and treatment, to determine what has been done and what progress the person has made in this area. Linking speech and language intervention services with those targeting social skills and problematic behaviors is critical if the young person is to transition effectively into the community and work place. The ADI-R is useful for categorically assessing language and communication as well as reciprocal social interactions. Frequently, if problems in social interactions are addressed, many areas of communication difficulty will improve as well. The Vineland II (Sparrow et al., 2005) is also used for assessment of this domain. (See description below.) The Comprehensive Assessment of Spoken Language (CASL) (Carrow-Woolfolk, 1999) is another tool that may be used. It has strong psychometrics, but has norms established only through 21 years of age. The CASL is used for detecting delayed language, oral language disorders, dyslexia, and aphasia.

Social skills Both communication and social skills are essential in order for a person to participate in higher education or vocational training and be productive in the workplace. Social skills are

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defined as specific behaviors that result in positive social interactions, including both verbal and nonverbal behaviors that are necessary for social interaction (Elliott and Gresham, 1987; Gresham, 1986). Behaviors that culminate in effective social interaction include, but are not limited to, smiling, making eye contact, asking and responding to questions, and giving and acknowledging compliments (Beidel et al., 2000). In assessing social skills, it is important first to determine whether there are any disruptive or self-injurious behaviors that need treatment. One can then choose one or more of the standardized assessment tools that examine communication and social skills, together with educational, vocational, and adaptive functioning. These tests will be discussed in the following section.

Education and vocational assessment Test instruments that form a foundation for assessing educational and vocational aptitude are the following. Vineland Adaptive Behavior Scales-II (Sparrow et al., 1984, 2005). The Vineland Scales are designed to assess the adaptive behavior of individuals from birth to 90 years of age. The scales are useful in determining the extent to which handicaps affect daily functioning. Domains assessed include communication, daily living skills, and socialization, with expanded coverage of adult adaptive behaviors. The Vineland Scales have been extensively used clinically and have well-established psychometric properties, including reliability and validity. This semi-structured interview takes from 20 to 60 minutes to administer to the adult with an ASD and the caretaker/parent. Adolescent and Adult Psychoeducational Profile (Mesibov et al., 1988). This tool is administered by a clinician and involves direct assessment and caregiver interviews. There are six areas that are assessed: vocational skills, independent functioning, leisure skills, vocational behavior, functional communication, and interpersonal behavior. Skills in each area are rated on a three-point scale, indicating passing, emerging or failing. Adequate reliability and validity have been established for this instrument (Mesibov et al., 1988).

Table 12.2 Vineland Adaptive Behavior Scales-II

Domains and index

Subdomains

Communication

Receptive Expressive Written

Daily living skills

Personal Domestic Community

Socialization

Interpersonal relationships Play and leisure time Coping skills

Maladaptive behavior index

Internalizing Externalizing Other

Vineland Adaptive Behavior Scales, Second Edition (Vineland-II). Copyright © 2006 NCS Pearson, Inc. Reproduced with permission. All rights reserved. “Vineland” is a trademark, in the US and/or other countries, of Pearson Education, Inc. or its affiliates(s).

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Scales of Independent Behaviors-Revised (SIB-R) (Bruininks et al., 1996). The SIB-R has norm-referenced assessments in 14 areas of adaptive behaviors and 8 areas of problematic behaviors. Psychometric validation of the instrument has been expanded over the previous edition. The SIB-R now has more use in geriatric populations as well as young and middleaged adults (Tan et al., 2010). There is a support scale which indicates the level of support needed. Environmental Rating Scale (ERS) (Van Bourgondien et al., 1998). The ERS is used to assess environmental adaptation. It has 32 items and five subscales: communication, structure, social and leisure skill development, developmental assessment and planning, behavior management. Semi-structured interviews are conducted with the caregiver. Psychometric properties have been adequately established (Van Bourgondien et al., 1998).

Health care needs A thorough evaluation of coexisting medical illness and psychiatric disorders needs to be conducted. A careful delineation of any medication taken needs to be completed with particular note made of psychotropic medication that may have been prescribed. With psychotropic medication, one needs to have a psychiatrist evaluate any ongoing clinical need for pharmacotherapy, because many psychotropic medications have significant sideeffect profiles that impact health parameters such as lipid profile, weight gain, cardiovascular status, and increased risk for the development of diabetes. If there is continued need for medication, then adults with ASDs must be carefully monitored for any side effects, drug interaction, and/or untoward effects of medication. The details of the medical evaluation and development of the treatment plan will be presented in the next chapter.

Summary This chapter has outlined adult developmental stages and tasks that provide challenges for the adult with an ASD. In addition, tools for assessing domains of function have been identified and discussed in relationship to these developmental stages and tasks. In the next chapter, use of these assessment tools for treatment and service planning will be further elaborated, showing how they can be incorporated into individualized needs assessments and used to generate data and develop a plan for home living, community living, lifelong learning, employment, and alternatives to work. Issues of personal satisfaction, health, mental health, and safety will also be addressed.

References American Psychiatric Association. (2000). Diagnostic and Statistical Manual of Mental Disorders (4th ed.). Washington, DC: American Psychiatric Association. American Psychiatric Association. (2010). DSM-5: The Future of Psychiatric Diagnosis. Washington, DC. Available at: http:// www.dsm5.org (accessed 17 August 2011). ASDASQ Factsheet. (2010). Psychometric Properties of the ASDASQ. Available at: https:// www.nifpnet.nl/LinkClick.aspx?

fileticket=z1Rj2jANmQ4%3D&tabid=328 (accessed 10 September 2010). Autism and Developmental Disabilities Monitoring Network (2012). Prevalence of autism spectrum disorders – autism and developmental disabilities monitoring network, 14 sites, United States, 2008. MMWR Surveill Summ, 61 (3), 1–19. Baird, G., Charman T., Baron-Cohen S., et al. (2000). A screening instrument for autism at 18 months of age: a 6-year follow-up study.

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J Am Acad Child Adolesc Psychiatry, 29, 694–702. Baird, G., Simonoff, E., Pickles, A., et al. (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, 368, 210–5. Baron-Cohen, S., Wheelwright, S., Skinner, R., et al. (2001). The autism-spectrum quotient (AQ): evidence from Asperger syndrome/ high-functioning autism, males and females, scientists and mathematicians. J Autism Dev Disord, 31, 5–17. Beidel, D. C., Turner, S. M. and Morris, T. L. (2000). Behavioral treatment of childhood social phobia. J Consult Clin Psychol, 68, 1072–80. Bertrand, J., Mars, A., Boyle, C., et al. (2001). Prevalence of autism in a United States population: the Brick Township, New Jersey, investigation. Pediatrics, 108, 155–61. Bishop, D. V., Maybery, M., Maley, A., et al. (2004). Using self-report to identify the broad phenotype in parents of children with autistic spectrum disorders: a study using the Autism-Spectrum Quotient. J Child Psychol Psychiatry, 45(8), 1431–6. Brereton, A. V. and Tonge, B. J. (2002). Autism and related disorders in adults. Curr Opin Psychiatry, 15, 483–7. Brown, L., Sherbenou, R. J. and Johnsen, S. K. (2009). Test of Nonverbal Intelligence (4th ed.) (TONI-4). Austin, TX: PRO-ED. Bruininks, R. H., Woodcock, R. W., Weatherman, R. F., et al. (1996). Scales of Independent Behaviors-Revised (SIB-R). Chicago, IL: Riverside Publishing. Carrow-Woolfolk, E. (1999). Comprehensive Assessment of Spoken Language (CASL). Circle Pines, MN: American Guidance Service. Chakrabarti, S. and Fombonne, E. (2005). Pervasive developmental disorders in preschool children: confirmation of high prevalence. Am J Psychiatry, 162, 1133–41. Chang, H. L., Juang, Y. Y., Wang, W. T., et al. (2003). Screening for autism spectrum disorder in adult psychiatric outpatients in a clinic in Taiwan. Gen Hosp Psychiatry, 25, 284–8. Colarusso, C. A. (2009). Adulthood. In B. J. Sadock, V. A. Sadock and P. Ruiz (Eds.),

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Kaplan & Sadock’s Comprehensive Textbook of Psychiatry (9th ed., pp. 3909–31). Philadelphia, PA: Lippincott Williams & Wilkins. Dunn, L. M. and Dunn, L. M. (1997). Peabody Picture Vocabulary Test (3rd ed.) (PPVT-III). Circle Pines, MN: American Guidance. Easter Seals. (2008). Easter Seals’ Living with Autism Study. Available at: http://www. easterseals.com/site/DocServer/ Study_FINAL_Harris_ 12.4.08_Compressed. pdf?docID=83143 (accessed 8 August 2011). Elliott, S. N. and Gresham, F. M. (1987). Children’s social skills: assessment and classification practices. J Counsel Dev, 66, 96–9. Erikson, E. (1950). Childhood and Society (1st ed.). New York, NY: WW Norton. Erikson, E. (1963). Childhood and Society (2nd ed.). New York, NY: WW Norton. Fombonne, E. (2003). Epidemiological surveys of autism and other pervasive developmental disorders: an update. J Autism Dev Disord, 33, 365–82. Gresham, F. M. (1986). Conceptual and definitional issues in the assessment of children’s social skills: implications for classification and training. J Clin Child Psychol, 15, 3–15. Hoekstra, R. A., Bartels, M., Cath, D. C., et al. (2008). Factor structure, reliability and criterion validity of the Autism-Spectrum Quotient (AQ): a study in Dutch populations and patient groups. J Autism Dev Disord, 38 (8), 1555–66. Howlin, P., Goode, S. and Hutton, J. (2004). Adult outcome for children with autism. J Child Psychol Psychiatry, 45(2), 212–29. Kurita, H., Koyama, T. and Osada, H. (2005). Autism-Spectrum Quotient-Japanese version and its short forms for screening normally intelligent persons with pervasive developmental disorders. Psychiatry Clin Neurosci, 59, 490–6. Levinson, D. J., Darrow, C. N., Klein, E. B., et al. (1978). The Seasons of a Man’s Life. New York, NY: Knopf. Levy, S. E., Mandell, D. S. and Schultz, R. T. (2009). Autism. Lancet, 374, 1627–38. Lord, C., Rutter, M. and Le Couteur, A. (1994). Autism Diagnostic Interview-Revised: a revised version of a diagnostic interview for caregivers of individuals with possible

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pervasive developmental disorders. J Autism Dev Disord, 24(5), 659–85. Lord, C., Risi, S., Lambrecht, L., et al. (2000). The autism diagnostic observation schedule-generic: a standard measure of social and communication deficits associated with the spectrum of autism. J Autism Dev Disord, 30(3), 205–23. Mazefsky, C. A. and 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), 533–49. McGovern, C. W. and Sigman, M. (2005). Continuity and change from early childhood to adolescence in autism. J Child Psychol Psychiatry, 46, 401–08. Mesibov, G., Schopler, E., Schaffer, B., et al. (1988). Adolescent and Adult Psychoeducational Profile (AAPEP). Individualized Assessment and Treatment for Autistic and Developmentally Disabled Children (Vol. IV). Austin, TX: Pro-Ed. Nylander, L. and Gillberg, C. (2001). Screening for autism spectrum disorders in adult psychiatric out-patients: a preliminary report. Acta Psychiatr Scand, 103(6), 428–34. Quill, K. A. (2000). Do–Watch–Listen–Say: Social and Communication Intervention for Children with Autism. Baltimore, MD: Paul H. Brookes. Seltzer, M. M., Shattuck, P., Abbeduto, L., et al. (2004). Trajectory of development in adolescents and adults with autism. Ment Retard Dev Disabil Res Rev, 10, 234–47. Shakespeare, W. (1996). As You Like It, The Arden Shakespeare, ed. A. Latham. London: Routledge. Skuse, D., Warrington, R., Bishop, D., et al. (2004). The developmental, dimensional and diagnostic interview (3di): a novel computerized assessment for autism spectrum disorders. J Am Acad Child Adolesc Psychiatry, 43(5), 548–58. Sparrow, S. S., Cicchetti, D. V. and Balla, D. A. (2005). Vineland Adaptive Behavior Scales

(Vineland II) (2nd ed.). Minneapolis, MN: Pearson Assessments. Sparrow, S. S., Balla, D. A. and Cicchetti, D. V. (1984). Vineland Adaptive Behavior Scales. Circle Pines, MN: American Guidance Service. Swanda, R. M. and Haaland, K. Y. (2009). Clinical neuropsychology and intellectual assessment of adults. In B. J. Sadock, V. A. Sadock and P. Ruiz (Eds.), Kaplan & Sadock’s Comprehensive Textbook of Psychiatry (9th ed., pp. 935–50). Philadelphia, PA: Lippincott Williams & Wilkins. Tan, J. E., Hultsch, D. F., Hunter, M. A., et al. (2010). Psychometric investigation of the modified scales of independent behavior – revised in an elderly sample. Clin Gerontol, 33, 69–83. Turner, L. M., Stone, W. L., Pozdol, S. L., et al. (2006). Follow-up of children with autism spectrum disorders from age 2 to age 9. Autism, 10, 243–65. Van Bourgondien, M. E., Reichle, N. C., Campbell, D. G., et al. (1998). The Environment Rating Scale (ERS): a measure of the quality of the residential environment for adults with autism. Res Dev Disabil, 19, 381–94. Wechsler, D. (1997). Wechsler Adult Intelligence Scale-3rd ed. San Antonio, TX: The Psychological Corporation. Wing, L., Leekam, S. R., Libby, S. J., et al. (2002). The Diagnostic Interview for Social and Communication Disorders: background, inter-rater reliability and clinical use. J Child Psychol Psychiatry, 43(3), 307–25. Wing, L. and Potter, D. (2002). The epidemiology of autistic spectrum disorders: is the prevalence rising? Ment Retard Dev Disabil Res Rev, 8, 151–61. Woodbury-Smith, M. R., Robinson, J., Wheelwright, S., et al. (2005). Screening adults for Asperger Syndrome using the AQ: a preliminary study of its diagnostic validity in clinical practice. J Autism Dev Disord, 35(3), 331–5.

Section 3 Chapter

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Assessing and Treating Adults with ASDs

Service and treatment planning for adults with ASDs Donna N. McNelis and Susan V. McLeer

If you have met one person with autism, you’ve met one person with autism. The heterogeneity of autism is evident in the broad range of severity, abilities, challenges, and extraordinary talents. Stephen M. Shore, EdD (2010)

Introduction As stated in the previous chapter, children with Autism Spectrum Disorders (ASDs) mature into adulthood. In the US, with the passage of the Individuals with Disabilities Act (IDEA) in 1975 and consolidation of several amendments in 1997, we now have a generation of individuals with autism who have participated in intensive early intervention services and special education in both specialized and integrated settings. Although IDEA legislation changed the lives of children, it did not substantially assist with the transition of services from the special education environment to the world of mainstream education, employment, and relationships. Given the three cardinal features of ASDs – impaired social interactions, impaired communication, and restricted or repetitive range of interests – the focus of this chapter will be on how these domains of impairment might best be managed in the broader ecological context of adult life. The previous chapter emphasized diagnostic considerations and a detailed assessment of strengths and vulnerabilities within an adult developmental framework. This chapter will discuss application of a comprehensive assessment to a service and treatment planning process as the final common denominator for maximizing quality of life for adults with ASDs. Much of what will be discussed here is theory-driven, since few studies have been done to determine whether there is a significant impact of early and specific intervention on adult functioning. A synthesis of what may be considered “best practices” will be offered, but rigorous, empirical studies on effectiveness and outcome are still needed. What is clear in reviewing the current literature is that among those who received intensive early intervention during childhood and adolescence, there remains a substantial problem facilitating the transition into adulthood. Skills learned in the classroom do not readily generalize or necessarily enhance other skills that are needed as an adult (Wehman et al., 2009). Many service delivery programs for adults with autism are in the public sector and rely on federal and state funding. These programs are highly regulated and require particular models of assessment and care. These models change over time, but it is important for providers to The Autism Spectrum, ed. Mark E. Reber. Published by Cambridge University Press. © Cambridge University Press 2012. 305

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be aware of them and to be conversant in the concepts used in the developmental disabilities field. One concept that is at the forefront is “strengths-based” assessments; another is “person-centered” care. In the previous chapter, both concepts were embraced without further definition, but in this chapter, there will be a clear delineation of what is meant by these approaches. Recommendations for needs assessment and service planning will follow these models.

Strengths-based assessment Within the last decade, strengths-based assessment has garnered considerable support among providers of services to people with intellectual and developmental disabilities and among providers of special education, mental health services, services to the elderly, family services, and other social services (Dunst et al., 1994; Nelson and Pearson, 1991). Strengthsbased assessment has been defined as “the measurement of those emotional and behavioral skills, competencies, and characteristics that create a sense of personal accomplishment; contribute to satisfying relationships with family members, peers, and adults; enhance one’s ability to deal with adversity and stress; and promote one’s personal, social, and academic development” (Epstein and Sharma, 1998). A strengths perspective attempts to understand people in terms of their unique styles which might otherwise be overlooked or disregarded. The following case example demonstrates a strengths-based perspective. Edmund is a 35-year-old young man with ASD who resides in a three-person group home setting. Edmund has been fascinated by circles since early childhood. Most particularly, his interests focus on tires and he has an unusual capacity to identify specific brands of auto tires. His interests were particularly distressing to direct care staff who would accompany him on shopping trips or other outings, since Edmund’s walk through any parking lot took a great deal of time. Additionally, whenever his ritual of naming each tire by brand and model number was interrupted, hurried or curtailed, he would become very angry. In reviewing his service plan, using a non-strengths-based perspective, one might decide to limit Edmund’s trips through parking lots since they were likely to produce anxiety and agitation. From a strength-based perspective, Edmund’s uncanny tire recognition capacity would be recognized as extremely useful for employment in a tire shop or some other setting where tire brands and model numbers are used for inventory purposes.

A strengths-based perspective is very different from a disease model where deficits are codified and diagnoses guide treatment. A strengths-perspective amplifies other individual characteristics and uses a systems perspective of the whole being greater than the sum of its parts. Diagnoses remain important in a strengths-based perspective as part of a personcentered needs assessment, but they are merely one aspect of a much more complex equation. In addition to the strengths of a person with an ASD, the identification of community and environmental strengths is an essential part of a strengths-based assessment process (Rapp and Goscha, 2006). With the media’s increased public focus on autism, there has been a subsequent increase in the public’s acceptance and appreciation of people with ASDs, resulting in an increased recognition of the unique assets and capacities that individuals with autism bring with them into the work place. When conducting a strengths-based assessment, the level of function and other concurrent diagnoses of the individual must be considered. Adults with ASDs who also have concurrent intellectual and developmental disabilities have different strengths than adults with high-functioning autism who may be intellectually gifted. As early as the 1940s, the

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Viennese pediatrician Hans Asperger discovered that most individuals with features of what we now call autism spectrum disorders, as well as those with learning difficulties, were at increased risk for unfavorable long-term outcomes. However, he also noted that there were many individuals with significant disabilities who went on to excel later in life. He conceptualized their strength as “a compensatory ability to counterbalance their deficiencies . . . with unswerving determination, narrowness and single-mindedness . . . which lead to outstanding achievements in their chosen areas” (Frith, 1991). Hence, the concept of using strengths to compensate for circumscribed areas of disability was launched.

Person-centered care – integrating sciences and values For more than a decade and a half, person-centered models have been described in the literature. These include “Personal Futures Planning” (Mount, 1992), “Essential Lifestyle Planning” (Smull and Burke, 1992) and “Planning Alternative Tomorrows with Hope” (PATH) (Pearpoint et al., 1993). All three approaches have been well documented and the techniques are manual-driven. Furthermore, they have been broadly used throughout North America and the UK. Each requires that staff receive several days of manual-driven training in the use of these specific planning methodologies. Table 13.1 illustrates differences in person-centered planning as compared to traditional clinical treatment planning. In order to facilitate the use of a person-centered process, researchers and clinicians at the University of Maine developed a useful checklist (Figure 13.1) to ascertain the completeness of the process (Center for Community Inclusion, 1995). Additionally, the detailed assessment data described in the previous chapter need to be included and integrated in a person-centered plan. This information is used to enrich the comprehensive needs assessment, capturing specific adult developmentally driven preferences, strengths, and vulnerabilities. Planning is then comprehensively based on the person’s

Table 13.1 Comparisons between Traditional Clinical Treatment Planning and Person-Centered Planning

Traditional clinical treatment planning

Person-centered planning

Identify deficits, disorders and problems

Focus on capacities, aspirations and strengths

Provide clinical services in specialized centers to resolve identified problems

Spend time getting to know the person in typical community settings

Assemble professional interdisciplinary team to make decisions

Help the person, family and friends take the lead in making decisions

See problems primarily in the person

Consider problems primarily in the social ecology

Protect confidentiality by sharing information with staff who have a formal relationship with the team

Ask the individual who he or she wants to be involved in providing support and services

Monitor changes in target behaviors or symptom reduction with objective, precise, quantitative measurements

Evaluate outcomes by analyzing changes in quality of life and other subjective, qualitative signs

Keep evaluation of outcomes as independent as possible in order to minimize the placebo effect

Adopt an action plan perspective using information interactively to improve quality of supports and services

Adapted from Amado and McBride (2001).

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Figure 13.1 Checklist for person-centered planning.

current educational, vocational, and living skills, as well as on goals for future skills, in a manner that is consistent with a person’s life intentions. Individuals with ASDs often require a multitude of resources during their lifetime (Aman, 2005). LeBlanc et al. (1997) suggested using a life-span approach in constructing a person-centered plan that addresses education and services in each person’s complex network of human relationships and environments. Most importantly, it was suggested that: the plan should travel with individuals and their families across all of life’s changes ensuring that service providers are always aiming toward quality-of-life and life span goals that are constantly defined and redefined by ever-changing individual abilities and choices and family lifestyles. (LeBlanc et al., 1997)

This is a sensible approach because it builds upon prior achievement and successes in earlier stages of life. For example, the goals for young adults with ASDs, as for other young adults, include the necessity of employment in order to be a contributing member of society. The following case example illustrates this point.

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Betty is a 32-year-old woman with ASD. She lives in a house with three other women and she began a job 2 years ago where she assisted with clerical tasks in an office. Betty has built some very meaningful relationships with a number of her co-workers. So much so that she is basically the only one of our group who has a birthday party complete with candles and presents. On days when she is not at work, people ask where she is. When Betty first started, she needed staff to prompt her to say hello to others or thank you. Now, she initiates conversations with her friends and co-workers. She will also call someone at work to let them know if she is not coming in. It is interesting that to celebrate her first-year anniversary of employment, she wanted to go out to lunch and was encouraged to invite a friend. For her second year of employment, she invited three friends on her own and then told the office staff that the party has grown. Betty says “my eyes are wonderful”. This is her way of saying “I am happy”.

Person-centered approaches vs. traditional clinical planning Because it takes approximately 15 years for advances in theory and research to reach community providers of services, both strengths-based assessment and person-centered care are relatively new paradigms, paradigms that integrate the extensive needs assessment, including biopsychosocial underpinnings, with a person-centered value system. Both approaches represent a synthesis of science and values. With all of the advances in discerning neurodevelopment and the neurobiological understanding of behavior, one must underscore the importance of this integrated approach. Using only a traditional clinical planning approach undercuts the uniqueness and strengths of the individual and does not honor the aspect of choice and self-determination. Using only a person-centered planning approach does not honor the additive scientific advances that are continuously improving the care that can be provided for people with ASDs. Both are required for truly effective and comprehensive care.

Comprehensive needs assessment There are multiple critical components of a strengths-based and person-centered needs assessment and many human service providers have developed or adapted tools to fit these specific needs. The success of a person-centered plan is directly related to the thoroughness of the needs assessment process, the integration of an enormous amount of information and the involvement of the person and their support network. (See Figure 13.2.) Due to the heterogeneity of presentation of ASDs, some higher-functioning adults with an ASD might not have had previous involvement with provider agencies, treatment personnel, health care or vocational systems. The outline below therefore specifies data that need to be collected from either previous records or from the person with autism and his family. The domains of interest for subsequent treatment planning are illustrated in Table 13.2.

I. Health assessment General (non-psychiatric) medical conditions The literature is clear in indicating that people with ASDs are at elevated risk for a wide range of learning disabilities, with nearly 75% of people with autistic disorder being reported to

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Figure 13.2 Network of providers and services for ASDs. Adapted from McGonigle (2008), with permission of the author. Table 13.2 Comprehensive Needs Assessment

Domains of interest

Subcategories

I. Health assessment

Non-psychiatric medical issues Psychiatric medical issues

II. Support assessment

Home living activities Community living activities Lifelong learning activities Employment skills Alternatives to work Finance and money management

III. Personal and social–emotional adjustment

Relationship issues Behavioral supports

IV. Environmental assessment

Family environment Housing environment Work environment Neighborhood environment Greater community environment

V. Safety and need for protection assessment

Advocacy and self-protection Extent of need for supervision

VI. Quality of life assessment

Personal satisfaction in domain noted above

Reprinted from Berry et al. (2008), with permission of the authors.

have intellectual disability (formerly referred to as mental retardation) (Aman, 2005). However, some of those classified with intellectual disability may actually have higher intellectual potential than indicated by the assessment instruments currently in use. A variety of seizure disorders are also more likely to occur in some adults with ASDs, with the

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frequency of occurrence ranging from 25% to 30% (Fombonne, 2003; Aman, 2005). Onset can be as late as adolescence or adulthood and risk appears heightened in those who function at a lower level. The late onset of a seizure disorder can be diagnostically confusing and may appear like a new behavioral problem. The importance of establishing a correct diagnosis cannot be overemphasized. Fragile X and tuberous sclerosis also occur in persons with autism at higher rates than one would expect on a chance basis, with the risk for tuberous sclerosis being about 100-times higher than in the general population (Volkmar et al., 1999; Rutter et al., 1994). Those with tuberous sclerosis frequently have co-occurring intellectual disability and seizure disorders (Smalley et al., 1992). Gastrointestinal disorders have also been alleged to be found more frequently in those with ASDs. However, a major metaanalysis of the research literature has established that none of the studies are adequate to validate the claim of increased gastrointestinal pathology in this population (Erikson et al., 2005). Another large study conducted at the Mayo Clinic found that there was no increase in gastrointestinal pathology, but that constipation and food selectivity might well be secondary to an inadequate intake of water and fiber, caused by the individual’s behavioral need for restricted patterns of food intake (Ibrahim et al., 2009). Like people in the general population, adults with ASDs may acquire both acute and chronic illnesses requiring medical care. Because many people with autism may have difficulty articulating their health needs, it is important that all adults with ASDs are linked with a primary care provider who will look carefully for coexisting illnesses and provide state-of-the-art care in order to maximize the person’s well-being and health.

Psychiatric disorders and behavioral symptoms Among adults with intellectual disabilities, the greatest predictor of behavioral problems and use of psychotropic medication is a coexisting ASD (Cowley et al., 2005; Tsakanikos et al., 2007). On the other hand, studies using rigorous clinical and population-based samples have indicated that the presence of an ASD in adults does not increase the likelihood of a comorbid psychiatric disorder (Melville et al., 2008; Tsakanikos et al., 2006). Treatable psychiatric disorders may therefore be expected to occur in adults with ASDs at the same frequency as in other individuals of comparable age and intellectual functioning (Kannabiran and McCarthy, 2009; Underwood et al., 2010). (See Figure 13.3.) There was considerable confusion in the early literature about the relationship between schizophrenia and autism. Bleuler (1911) first used the term autism when describing detachment from reality and describing an inner life in referencing schizophrenia; unfortunately, although he had profound clinical intuition in identifying characteristics of schizophrenia, the conceptual resources at his disposal in the early part of the twentieth century did not effectively contribute to precision in defining and diagnosing autism (Parnas et al., 2002). Subsequently, Kanner’s early observation that autism was a precursor to schizophrenia (Volkmar et al., 1999) has been disproven by multiple large-scale studies (Chung et al., 1990; Wolff and McGuire, 1995). It is now clear that the prevalence of schizophrenia among people with ASDs does not differ from that in the general population. Affective disorders and anxiety disorders are prevalent in adults with ASDs and may increase with age (Howlin, 2004). As early as 1970, Rutter noted depression in young adults, and subsequent reviews have noted high degrees of affective disorders in persons with autism, as well as in their families (Rutter, 1970; Lainhart and Folstein, 1994; Bolton et al., 1998). In fact, it may well be that higher-functioning adults with ASDs may be at particular

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Figure 13.3 Co-occurring conditions in ASDs. Reprinted from Ghaziuddin (2005), with permission of the publisher.

risk for coexisting depression and anxiety, especially as they move throughout the adult life span. It has been postulated that higher-functioning adults with ASDs are viewed as “well” because they have such good outcomes when compared to those people with lower functioning. Consequently, their internal pain, e.g. symptoms of anxiety and depression, may not be detected. Second, as so many higher-functioning adults with ASDs have such perfectionist strivings as well as a profound need to “fit in” with others, they are highly likely to experience great internal stress (Szatmari et al., 1989). Among the anxiety disorders, obsessive– compulsive disorder is particularly difficult to distinguish from ritualistic and stereotypic behaviors that are characteristic of autism. Apart from having a separately diagnosed psychiatric disorder, people with autism, particularly those with intellectual disabilities who are lower-functioning, may display extremely problematic behaviors, including hyperactivity, aggression, tantrums, and selfinjurious behaviors (Findling, 2005; McCarthy et al., 2010). These behaviors are often treated with a variety of psychotropic medications, many of which have problematic sideeffect profiles and drug–drug interactions. Controlled studies on the effectiveness of medications targeting problematic behaviors have been few in number among children and adolescents with ASDs and even more limited among adults. Findling (2005) has reviewed the controlled studies in the literature and examined the evidence base for effectiveness. He notes that psychostimulants have been used for hyperactivity,

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impulsivity, and inattentiveness and found to be moderately effective, but with side effects of increased stereotypies, irritability, increased dysphoria, social withdrawal, and crying. Alpha-2 agonists, while not rigorously studied, have shown some effectiveness on a caseby-case basis. Serotonin reuptake inhibitors (SSRIs) have frequently been prescribed to reduce stereotypies, repetitive behaviors, and over-adherence to routine. However, a multi-site, placebo-controlled study of citalopram in 149 subjects with ASDs (ages 5–17 years) demonstrated no benefit over placebo. Moreover, citalopram was more likely to be associated with adverse effects, including increased energy level, impulsiveness, decreased concentration, hyperactivity, stereotypy, diarrhea, insomnia, and dry, itchy skin (King et al., 2009). There is no reason to believe that other SSRIs would have different effects from citalopram. No large-scale, definitive studies have been done to evaluate the effect of SSRIs in adults with ASDs. Mood stabilizers and anxiolytics have been inadequately studied to date. The most commonly used medications for behavioral problems have been the typical and the atypical antipsychotic drugs. The typical antipsychotics have demonstrated effectiveness on symptoms of irritability, aggression, hyperactivity, and tantrums; however, tardive dyskinesia and other involuntary movements as well as other extrapyramidal side effects, some irreversible, constitute significant risks. The atypical antipsychotics have a less troublesome side-effect profile for neurological dysfunction, but carry an increased risk for weight gain and diabetes, as well as a lipid profile that poses risk of early cardiovascular complications such as stroke or coronary insufficiency. However, these medications are easier to tolerate and may be very helpful with extremely problematic behaviors. Whenever a person is on an atypical antipsychotic medication, it is important to monitor weight, abdominal girth, lipids, and glucose clearance. Linkages to primary care providers are essential. Esbensen et al. (2009) examined the prevalence of medication use during adolescence and adulthood in people with ASDs. At the onset of the study (1998–2000), they found that 77% of adults were taking at least one prescribed medicine; 60% were on at least one psychotropic drug. At the second survey time (2004–2005), 88% were on at least one medication while 70% were on at least one psychotropic drug. Antidepressants (SSRIs) were the most frequently prescribed, followed by atypical and then typical antipsychotic medication. Given how many medications are prescribed for adults with ASDs and the high prevalence of psychotropic drug use, it is critically important that primary care providers be aware of any psychotropic medications being prescribed. Those who are on psychotropic medications because of co-occurring psychiatric disorders or problematic behaviors are highly apt to have drug– drug interactions, because these medications are metabolized through the same cytochrome P450 pathways that metabolize drugs prescribed for other medical illnesses. Given the complexities of health care for adults with ASDs, developing a complete medical record that reflects “the whole picture” is important if treatment pitfalls are to be avoided. Such an outline is offered below: 1. Lifetime Medical History Assessment: a. Birth and Childhood Development: course of pregnancy, birth, childhood illness/ injuries. List specific treatments. b. Social and Family History: describe any current or chronic family history of autism, mental retardation or mental illness. Discuss past living situations, relationships, and significant medical histories of parents and siblings.

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c. Adulthood: Past, Present, and Chronic Conditions: note any illnesses or injuries. Describe any specialty treatments. Discuss current status of systems. d. Lifetime Mental Health Issues: provide a brief narrative of the individual’s past and present mental conditions and treatment. Specify and describe each clinically significant psychiatric diagnosis. A trauma assessment must be part of this assessment and include investigation of physical, psychological or sexual abuse. e. Lifetime Hospitalizations and Surgical Procedures: note any surgical, psychiatric, and/or medical inpatient hospitalizations. Note any outpatient services and emergency room visits. Specify hospital, date of onset, duration, and reason for treatment. f. Allergies/Precautions: include allergies to drugs and foods, contraindications to and precautions for specific medications and/or conditions. g. Dietary Concerns: favorites, consistency, dysphagia. h. Medications – Past: list any known past medications including purpose, dosage, and comments. i. Medications – Current: list current medications including purpose, dosage, and comments. j. Most Recent Medical Examinations (complete where applicable). 2. History of Disability Evaluations: a. Documentation of disability: include medical and physical limitations, as indicated by a physician and/or psychologist/neuropsychologist. b. Need for adaptive equipment. c. Refusal of medical, dental or other treatments: describe instances where refusals of medical, dental, or other medical examinations and/or treatments have occurred. d. Ability to self-administer medication and use adaptive equipment. e. Ability for self-care, including activities of daily living and maintenance of physical and dental hygiene; also indicate amount of assistance needed for competent home care and any current problems or concerns. Table 13.3 Most recent medical examination

SPECIALTY: Primary care Dental Neurologic Ophthalmologic Audiologic Gynecologic Podiatry Psychiatry Other Other

DOCTOR:

ADDRESS:

DATE SEEN:

SUMMARY:

RETURN DATE:

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II. Support assessment As noted in Table 13.2, there are six domains that need to be assessed to determine need for support. These six areas can be scored on a scale ranging from no support needed to gestural prompting, verbal prompting, partial physical assistance or dependence on others. These assessments should focus on the strengths of the individual and concentrate on the supports needed to achieve maximum independence. The previous chapter discussed the Scales of Independent Behaviors-Revised (SIB-R) (Bruininks et al., 1996). There is also a planning tool that has been developed, the Checklist of Adaptive Living Skills (CALS) (Morreau and Bruininks, 1991), which has been validated in reference to the SIB-R. The CALS is a criterion-referenced tool for planning and identifying individual needs, then linking those needs to a specifically designed curriculum, The Adaptive Living Skills Curriculum (ALSC) (Bruininks et al., 1991). The ALSC is designed to teach specific skills needed for everyday living, including personal care, home living, school, work and leisure, and community participation. This planning tool can be used to assess function in the areas noted below and for developing a service plan for support and improvement of living skills. 1. Home Living Activities: matters affecting personal care and health, such as washing, bathing, shaving, menstrual self-care, and medication usage need to be assessed. Also activities of daily living and self-help such as eating, meal preparation, travelling, and use of communication devices, including but not limited to telephones and alarms for assistance. Physical ability to move about and used nonverbal means of communication need to be included in this part of the assessment. 2. Community Living Activities: the assessment includes the adult’s ability to socialize with peers and supportive adults. It includes the individual’s use of leisure time and specific areas of interest and enjoyment. Current and potential levels of interactions at home and in the community (e.g. use of community institutions such as grocery stores, churches, movies, gym) are all important to determine, as is the person’s ability to access community resources through use of public transportation systems. 3. Lifelong Learning Activities: this assessment should make use of some of the formal assessment outlined in the preceding chapter. Any history obtained from the adult with autism and/or family should be used to assess the person’s ability to utilize technology for learning and to determine the individual’s level of participation in educational planning and activities. 4. Employment Skills: it is essential to have a professional team assess vocational skills and aptitude. Some of the techniques discussed in the preceding chapter are of use in this regard. In addition, the adult’s social and communications skills need to be assessed to determine his/her potential ability to function in the workplace. At this juncture, one should discuss the person’s interests and work preferences and collaborate on a plan that matches preferences and skills. The assessment of individual strengths and needs will be of great use in determining what sites would be appropriate for work, e.g. mainstream job, supervised setting for disabled adults, or sheltered workshop. In addition, if further job training is necessary, one needs to consider whether it would be better to provide training on site, in the workplace. The ability of many people with ASDs to generalize is often limited, so training in the workplace results in a better outcome with better job performance.

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5. Alternatives to Work: given that some people with autism are not able to sustain a mainstream job, or even a sheltered job, one needs to consider other alternatives such as volunteering for shorter periods of time in the community, joining an interest group (such as a railroad club), attending cultural events and performances, or participating in friendship circles. As adults with ASDs move through middle adulthood to late adulthood, higher-functioning persons will need considerable support as they approach retirement. It is important to facilitate the development of life plans for using time in an enjoyable and productive manner after retirement. 6. Finance and Money Management: an overarching goal in this assessment is to determine the person’s ability to manage money and resources. One needs to define the need for support in budgeting and banking activities and in making decisions about the use of money and resources. Most people with ASDs will need some assistance during critical times of life in order to access a variety of potential resources, e.g. Medicaid, Supplemental Social Security, state and federal grants, and waivers that fund care and treatment services. It is important to know under what conditions supports are required and to identify reliable and trustworthy people who can provide necessary aid in managing finances and other resources.

III. Personal and social–emotional adjustment In considering personal and social–emotional adjustment, it is necessary to assess the person’s ability to relate and manage relational issues in accord with adult developmental stages. Additionally, a formal assessment of the person’s need for behavioral supports is essential. 1. Relational Issues: here one wants to determine the number and quality of friendships and how friends are accessed. Is technology used to connect with friends – e.g. via Facebook or My Space – or can technology become a resource for further interaction? One needs to determine how sexuality is experienced and enjoyed as well as a person’s need for further education regarding safe sex and birth control. Social and communication strengths as well as problems that may interfere with relationships need to be identified; some of the assessment instruments noted in the preceding chapter may be of use here. 2. Behavioral Supports: the following areas must be assessed and rated on a scale of 0 to 5 (with 0 indicating that no assistance is necessary to maintain safety and 5 representing total dependence on others for that purpose): (a) self-injurious behaviors, (b) dangerous behaviors toward others, (c) behaviors which compromise safety and risk injury or victimization, (d) socially inappropriate behaviors, such as tantrums and unwarranted sexual intrusions on others.

IV. Environmental assessment In looking at the environment, one must again consider the heterogeneity of ASDs. On one end of the spectrum, adults will find themselves living independently, educated or vocationally trained, married, and employed. Individuals who depend on support personnel will have a very different environmental landscape. The Reflecting on Social Roles Inventory (O’Brien, 2006) can be used for this assessment. This is a rating scale that assesses both the home and neighborhood and examines details such as whether the adult with autism plays an active (or partial) role in the work of the household. Additionally, the involvement of the person in decision-making within the home

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with regard to issues such as the selection of furnishings is assessed. The safety of the neighborhood is evaluated. Domains considered are: family and friends, work, learning, spiritual/religious, community association, sports/fitness, and creativity. Data collected with this instrument can be readily used in developing a strengths-based, person-centered plan.

V. Safety and need for protection assessment There are two areas that need further assessment in order to insure continued safety. These include a person’s ability to advocate for him/herself and, conversely, the need for supports and supervision in specific settings in order to prevent untoward outcomes that might be dangerous to the person or others. 1. Advocacy and Self Protection: the individual’s abilities to self-advocate, make good (i.e. not risky) decisions across a variety of settings and avoid victimization must be assessed. 2. Extent of need for supervision in order to be safe: this assessment should involve the adult with autism as well as others who have been involved in his/her previous care. It is essential to determine the person’s abilities to recognize danger, comply with standard fire, health, and safety evacuation plans, use appliances safely, and access emergency services. In addition, the person’s judgment in interacting with strangers needs to be assessed. If there are behaviors that endanger others (e.g. preoccupation with setting fires), these need to be identified and clearly described. From this assessment, a plan will evolve regarding the need for support and/or supervision in various settings. Where safety is concerned, the plan must not be one that is negotiated, but established on the basis of risks of harm to self and others.

VI. Quality of life assessment Throughout the process of planning and treatment, quality of life needs to be attended to and monitored. Robertson (2010) has suggested that quality of life is enhanced through a “neurodiversity” perspective that underscores a person’s strengths as opposed to deficits. Schalock (2000, 2004) has identified eight core domains and variables, which he calls “indicators,” that contribute to a person’s quality of life. These are presented in Table 13.4. To date, there does not appear to be a psychometrically standardized instrument for assessing quality of life in adult autism. However, the domains and variables noted in Table 13.4 are useful, as are individual satisfaction surveys. A useful approach to this type of inquiry is to use a five-point rating scale, ranging from extremely satisfied to not at all satisfied. An example of an individual satisfaction survey is provided in Table 13.5.

Strengths-based, person-centered treatment and service plan Following the comprehensive needs assessment, a treatment plan must be developed, with the adult with autism playing a major role in identifying goals that he/she would like to achieve. The plan should include immediate and long-term goals for home living arrangements, community living arrangements, continued education and vocational training or placement, financial and resource money management. In each of these areas, the need for support from others will need

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Table 13.4 Quality of life variables

Core domains

Indicators

Self-determination

Autonomy, choices, decisions, personal control, self-direction, personal goals and values

Social inclusion

Acceptance, status, supports, work environment, community activities, roles, volunteer activities, residential environment

Material well-being

Ownership, financial security, food, employment, possessions, socioeconomic status, shelter

Personal development

Education, skills, fulfillment, personal competence, purposeful activity, advancement

Emotional wellbeing

Spirituality, happiness, safety, freedom from stress, self-concept, contentment

Interpersonal relations

Intimacy, affection, family, interactions, friendships, support

Rights

Privacy, voting, access, due process, ownership, civic responsibilities

Physical well-being

Health, nutrition, recreation, mobility, health care, health insurance, leisure, activities of daily living

Reprinted from Schalock (2000), with permission of the publisher.

Table 13.5 Individual satisfaction survey

Question

Extremely

Very much

1. I am satisfied with people who work with me 2. I am given choices for activities I participate in 3. I am satisfied with my doctor 4. I feel my privacy is respected 5. I like where I live 6. I am satisfied with my use of money 7. I am doing what I want to do 8. I make my own decisions 10. The staff help me 11. I am treated with respect 12. I would recommend the staff to others 13. Please add additional comments and suggestions Reprinted from Berry et al. (2008), with permission of the authors.

Moderately

Slightly

Not at all

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to be considered. Other parts of the plan that must also be addressed include accessing friends and developing leisure activities. Health care will need to be specified, including ongoing primary care, specialty care when required and further assessment and treatment for coexisting psychiatric disorders. The goals should be person-driven, made with the input of the adult with autism and not imposed by the treatment team. Clearly, there needs to be a realistic assessment of goals based on the completed strengths assessment. Finally, the developmental stage of adult life should be considered when identifying life goals and establishing a treatment plan.

Management of problematic behaviors As has been mentioned earlier in the chapter, adults with ASDs can have problematic behaviors that need to be addressed, including self-injurious behaviors, behaviors that are dangerous to self and others, and inappropriate behaviors that interfere with functioning with other people, at home and in the workplace. The first consideration in addressing such behavior is the behavioral assessment and development of a behavioral or cognitivebehavioral therapeutic plan. If behavioral or cognitive-behavioral interventions are not sufficient, psychopharmacological management may also be indicated.

Behavioral assessment and treatment Applied behavior analysis (ABA) is the predominant behavioral approach that has been used for the past several decades. It was determined to be the treatment of choice for persons with autism in the early 1980s (DeMyer et al., 1981). The intent is to promote pro-social adaptive functioning while examining the context in which the behavior occurs. Chapter 10 gives a complete description of ABA and the methodology for functional behavior analysis. As Foxx cites, “the treatment of individuals with autism has been rife with non-scientifically based practices, whereas applied behavior analysis has extensive evidence of effectiveness with ASD” (Foxx, 2008). It is a reasonable assumption that the ABA approach is known to the majority of adults with ASDs because they participated in special education and other human service programs as children. ABA works for behaviorally mediated problems. Biological or medical problems that may be inducing the behavior of concern must be considered and treated first, e.g. gastrointestinal problems, allergies, dental pain, ear or sinus infections, back pain, seizures, and headaches. If an adult has limited expressive language and an acute toothache, a behavior plan is not the way to proceed until the dental pain has been addressed. When physical pain is suspected, the immediate family member or caregiver who knows the person best is a crucial resource in interpreting the meaning of an unexpected challenging behavior. Second, coexisting psychiatric disorders should be considered as contributing or causing problematic behaviors and, if diagnosed, treated in accord with evidence-based practice. Third, consistency of approach among care providers and support personnel is necessary for ABA to be effective. This is an area where eclecticism is detrimental to positive behavioral outcomes (Foxx, 2008). Finally, skill building is an essential strategy to teach new behaviors, and it is essential to match reinforcers for behavioral change with the developmental and functional level and interests of the adult with an ASD.

Psychopharmacological assessment and treatment In considering psychopharmacological interventions, the person needs to be evaluated for coexisting psychiatric disorders and treated in accord with practice guidelines for specific

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disorders. In addition, as individuals mature through adolescence and into adulthood, becoming stronger, behaviors that were present earlier in life may not be as easy to manage without undue risk of harm (Aman, 2005). Medication surveys indicate a strong relationship between age and use of psychotropic medications (Esbensen et al., 2009). The meaning of this finding is not at all clear and may reflect a cohort change in prescribing practices. Additionally, as psychosocial interventions are more difficult to provide, an increased use of pharmacological agents could be indicative of time pressures and perceived ease of intervention, which are not good rationales for use of medication. On the other hand, there are times when medication is indicated in order for the person to achieve goals that he/she or his/her family have previously identified as being important. It should also be noted that once problematic behaviors have been managed with medication, the individual may then be better able to participate in a behavioral treatment. Complementary and alternative medicines: clinicians must be flexible in their opinions with regard to complementary and alternative medicines (CAM) (Aman, 2005; Levy and Hyman, 2008). Families need scientific advice from treatment personnel, but their use of CAM practices needs to be understood. CAM practices in the general population are aimed at promotion of health. With further evidence-based study, some of these practices may be found to be useful in persons with ASDs.

Treatment and service teams Staff, treatment personnel, and others need to remember that they are working with adults who are more than their problematic behaviors. These individuals, moreover, are people with autism spectrum disorders, not “autistic adults.” They share developmentally based life goals with all other adults. They have rights of citizenry, decision-making, and choice, independent of their verbal or functional abilities. They deserve to be assessed with an emphasis on strengths and abilities, and should play leadership roles in determining their life goals and service plan. Multi-disciplinary treatment teams function as “boundary spanners” in working with adults and their families. To this end and because the multi-disciplinary team may be comprised of professionals representing the many disciplines of neurology, nursing, occupational therapy, physical therapy, psychiatry, psychology, social work, speech and language, vocational training and placement, as well as others, there must be a facilitator who functions as the team coordinator. The facilitator must work with the person with an ASD so that they function as co-leaders in developing the strength-based, person-centered treatment and service plan, a plan that is anchored to the adult stages of development (see Figure 13.2). The care coordinator must be able to synthesize and integrate the enormous amount of information available from the comprehensive assessment and work with the co-leader of the team – the adult with autism. Family and friends are members of the team and are critical in providing supports, particularly to an adult with an ASD who has very compromised functioning. The role of the facilitator is important in that he or she must encourage input from all team members and keep the focus on planning. Additionally, the facilitator needs to encourage openness and diversity in opinions and be able to manage conflicts among the group. Team members from various disciplines may have differing perspectives, but all views should be accounted for when collaborating. Team members bring specific strengths to the multi-disciplinary team and the facilitator is responsible for coordinating all efforts. The facilitator of the team should also be the one who is knowledgeable about state and regional legislative, educational, vocational, pharmacological, and health developments that

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are ever-changing (Aman, 2005). However, it is important to realize that most families and advocates have developed elaborate systems for staying informed about the latest scientific advances and newly released resources for accessing services for people with ASDs. Hence, there needs to be a tremendous amount of respect and regard for families and advocates in this process. They can be extremely helpful in identifying new approaches and new resources for supporting care. The facilitator must partner with the families in order to have the most current information. The need for consistent and evidence-based training for staff who work with persons with ASDs is essential. Cross-training of team members should be an expectation and even specialized consultants, who may have minimal contact with the individual, must know and appreciate the comprehensive treatment and service goals.

Utilization of technology The United States’ shortage of human service personnel, including direct support professionals, has been a problem at the federal, state, and service provider levels for many years (Hoge et al., 2005; IOM, 2003; Smull, 1989). Additionally, the workforce is exceedingly illequipped and under-resourced to provide lifetime services and support for individuals with ASDs (Shore, 2010). There are emerging technologies that may be useful in assisting in the provision of services and supports within community settings. Assistive technology (AT) and environmental interventions have been used by people with disabilities to enhance function and as tools to assist with living within community settings (Hammel et al., 2002). Until recently, AT connoted augmentative and alternative communication mechanisms (Braddock et al., 2004), but there are additional innovative technologies that can greatly assist with personal supports. Pearl, a robotic nurse, developed by a team from Carnegie Mellon and University of Pittsburgh, was developed to assist the homebound elderly to remind them to take their medication. As noted, human workers can become frustrated by saying “please take your medicine” 10–20 times or escorting an elderly person who is slowly traveling with a walker, but a robot can say “please take your medicine” 200 times without flinching, or instantly adapt to one’s speed of walking (Stresing, 2003). Personal support technologies (PST) have great ability to increase independence, productivity, and quality of life for persons with ASDs. For example, families or staff can have a programmed personal digital assistant (PDA) to assist individuals with job or daily living tasks such as grooming, and the PDA can interface with wireless protocols to track and monitor activities and give necessary prompts to complete work or personal tasks (Furniss et al., 2001). Computer-assisted technology, although promising in the research arena, has been impractical in real life for adults with ASDs. Studies indicate that access to computers and the internet is negligible for persons with disabilities. However, some researchers posit that with advances in computer capability and decreasing costs, increasing numbers of people with disabilities will have access to such technology (Hasselbring, 2001). Assisted Care Systems Technologies are designed to assist caregivers and can range from simple monitoring devices to complex assisted care systems (ACS) integrated into the actual structure of a residence (Braddock et al., 2004). Smart homes (Pentland, 1996) combine tracking technology and environmental control to provide robust prompting, including environmental cuing. Smart rooms or smart homes can provide the following: ceilingmounted lifts, bed weight and motion sensors, automatic faucets, keyless entry with RFID (radio frequency identification) badges, automated door openers, door sensors to alert staff,

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specialized joysticks/keyboards/mice to allow individuals to control their environment for entertainment and heating/cooling. Additionally, smart homes can assist staff with a webbased medication prompting system, building sensors (including motion, temperature, door/ window) to alert staff to changes within the home, devices to monitor balance and mobility, location badges with RFID for individuals and staff, and web-based information exchange portals for persons with ASDs, staff, managers, and family members. Recent software development in the area of pattern-recognition can alert staff to impending risks or adverse events, including social isolation and abnormal behavior (Pentland, 1996; Elite Care, 2010). Despite exciting technological advances, there are practical issues that must be addressed. Braddock et al. (2004) assert that barriers such as commercialization and regulatory burdens by the FDA and limited private insurance and Medicaid/Medicare funding structures must be overcome. Additionally, the continual obsolescence of technology is an additional limiting factor. Nonetheless, there is great promise if advocates, legislators, researchers, and practitioners look for creative approaches to assist with meeting some of the needs of persons with ASDs. Technology can be a part of that solution, partnered with a family and staff who share a person-centered conception of life in the community.

Vision for the future The Advancing Futures for Adults with Autism (Shore, 2010) organized a national “town hall meeting,” made up of affected families and individuals, state and private program directors, university researchers and professors, public policy authorities, and specialists from both the public and private sectors. The three main areas of focus were housing, employment, and community integration. The participants identified gaps in current services and strategies for filling them. The group discussed how to effectively address preparedness for meeting these needs within the next decades. The participants offered several strategies in their executive report: Five-year vision: 

Adults with autism will have access to the building blocks for fulfilling productive and independent lives which include housing, employment, and community life. 

Change existing systems to ensure funding streams which follow the person and can be used in a variety of ways to meet the person’s unique and evolving needs.  Increase the availability of qualified and motivated personnel who support adults with autism.  Ensure that adults with autism have access to the supports they need to develop the life skills necessary to live safe, independent, and productive lives. Additionally, they developed specific housing, employment, and community living strategies: Housing 

Adults with autism will have an increasing number of housing choices as the necessary underpinnings – financial, educational, and political – are put into place. 

Engage leaders and institutions that direct capital and influence housing policy by presenting a clear, compelling picture of the substantial demand for housing options for adults with ASDs.  Increase collaboration and coordination among housing and service agencies at the local, state, and federal level.

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Motivate the overall real estate community (including government agencies, developers, and others) to create housing options that are transit-oriented and accessible to employment and recreation, and increase opportunities for independence and integration.  Direct support toward residential service models that are person-centered and actively seek to meet the needs and interests of adults with ASDs.  Expand both public and private funding for residential services. Employment 

Adults with autism will have a measurable increase in job opportunities paired with ongoing training and support that enables them to be successful in the workplace.      

Engage employers by presenting a clear and compelling picture of employing adults with autism. Increase and expand the number of successful programs that match adults with ASDs with meaningful jobs. Ensure ongoing accessibility to employment supports (including both technical and social dimensions) for adults with ASDs. Encourage employers to develop model programs that adapt the work environment to help adults be productive and successful employees. Create and expand career development and vocational skills training programs while individuals are still in the educational system. Create meaningful alternatives to traditional employment, such as volunteering, entrepreneurial and self-ownership opportunities.

Community living 

Adults with autism will have the opportunity to be valued, contributing members of their communities based on their unique strengths, differences, and challenges. 

Create a comprehensive public awareness campaign that enables the general public to better understand, engage, and support adults with ASDs and their families.  Educate local recreation organizations as well as the community about the positive benefits of including adults with ASDs in their programs.  Educate first responders about the challenging behavior that might arise in dangerous situations involving adults with ASDs so they are prepared to handle these occasions in the most effective manner.  Assist adults with ASDs to access public and private transportation, making it possible for them to live, work, and recreate where they choose, including training for transportation service providers enabling them to be more responsive. It is noteworthy that the above recommendations from Advancing Futures for Adults with Autism were presented in a US Congressional briefing in July 2010. The intent was to launch a national agenda that will address the looming crisis in the lives of adults with ASD. It was proposed that the restricted menu of services that currently is offered to people with ASDs will be a temporary state of affairs, as successful lawsuits build upon each other to establish good case law that lessens discrimination and provides mechanisms for flexible funding for real, accessible individualized services. To this end, a number of advocacy networks have

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been formed which provide significant support for people with ASDs. An international example is the Autistic Self Advocacy Network (ASAN) which focuses on expanding the provision of services and support resources for people with autism. US national organizations have resources directed to similar points of focus and provide resources for lobbying at the federal and state levels for increased funding for services that enhance the quality of life for people with ASDs.

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Index 15q11–13 genetic disorders essential autism 135–6 syndromic autism 128–9, 131, 134, 135 22q11 deletion syndrome (22q11DS) 136–7 5-HTTLPR 153 Aberrant Behavior Checklist (ABC) 196 accommodation See living accommodation achievement testing 299 acquired epileptiform aphasia See Landau–Kleffner syndrome (LKS) Adaptive Behavior and Assessment System (ABAS-II) 190 Adaptive Living Skills Curriculum (ALSC) 315 adults (with ASDs) 286 diagnosis and assessment See diagnosis and assessment (adults) parental concerns 286 resource/program access issues 285–6 treatment/service planning See treatment/service planning (adults) advocacy 317, 323–4 affective disorders 311–12 age at diagnosis 39, 42 parental age as risk factor 48, 49 aggression, medication treatments 263–8, 312 aging 295, 296–7 alcohol, maternal intake 50 aloneness, extreme autistic 3 alpha-2 agonists 263, 271–2, See also specific drugs

American Academy of Pediatrics, surveillance/ screening recommendations 181 American Occupational Therapy Association (AOTA) guidelines 222 American Speech–Language– Hearing Association (ASHA) guidelines 221–2 amino acids, dietary supplementation 277 Analysis of Verbal Behavior (AVB) See Verbal Behavior Training analytic studies, epidemiology of autism 35, 41 androgens, testosterone levels 167, 168–9 Angelman syndrome 129–33 angiomyolipoma (AML), renal 121 anticonvulsant medications irritability 267–8 restricted and repetitive behaviors (RRBs) 262–3 antidepressant medications See serotonin reuptake inhibitors; specific drugs antioxidant treatments 275–6 antipsychotic medications 262 See also specific drugs hyperactivity 272 irritability 264–7 metabolic syndrome 300 restricted and repetitive behaviors (RRBs) 261–2, 263 anxiety disorders 117, 311–12 Apgar score, as risk factor 48, 49 Applied Behavioral Analysis (ABA) See also discrete trial training (DTT) adults 319 children 206, 213, 228

Applied Verbal Behavior program 209, 213, 228 Arc 131 arginine-vasopressin (AVP) 167, 168 aripiprazole 262, 265–6, 268 Asperger, Hans 9, 59 Asperger’s syndrome (AS) 14 clinical utility of diagnosis 14–15 diagnosis/classification 9–15, 26, 204–5 high-functioning autism (HFA) differentiation 12–14 prevalence 37 validity as unique diagnosis 11–14 assessment 186 adults See comprehensive needs assessment, adults; diagnosis and assessment (adults) children See comprehensive diagnostic assessment (children); habilitative treatments (children); evaluation process assisted care systems technologies 321–2 assistive technology (AT) 233–5, 321–2 astroglia 160, 163 atomoxetine 268–71 attention deficit hyperactivity disorder (ADHD) 117 comorbidity 136, 195 fragile X syndrome (FXS) symptoms 117 medication treatments for symptoms 268–72, 312–13 attention, joint See joint attention attitude, autistic 59 atypical autism 2, 6, 7, 16

327

328

Index

atypical pervasive developmental disorder 15 auditory evoked responses 123 augmentative and alternative communication (AAC) 231–3 Autism Diagnostic InterviewRevised (ADI-R) 25–6, 187, 289, 300 Autism Diagnostic Observation Schedule (ADOS) 187–8, 289 autism spectrum disorder, DSM-5 single diagnosis proposal 26–7 autism spectrum disorders (ASDs) 1 dimensional approach to diagnosis/classification 23–6 prevalence 36, 37–8, 285 terminology 1, 2 Autism-Spectrum Disorders in Adults Screening Questionnaire (ASDASQ) 287, 288 Autism-Spectrum Quotient (AQ) 287–8 Autistic Diagnostic Observation Schedule (ADOS) 26 autistic disorder (AD) 16 childhood disintegrative disorder (CDD) differentiation 19 clinical characteristics overview 60–2 diagnosis/classification 3–9, 204–5 early developmental deficits 65–71 functional neuroimaging 98–102 intellectual disability prevalence 309 later developmental deficits 71–4 neural microstructure 90–4 pervasive developmental disorder not otherwise specified (PDD-NOS) differentiation 16 prevalence 36–7, 285 autistic psychopathy 59 Autistic Self Advocacy Network (ASAN) 324

autoimmunity 51, 160–2 awareness, public/professional 39–40, 323

broad autistic spectrum, prevalence 36 Broca’s area, damage to 88

babbling 65, 68 behavior See also specific behavior types adaptive behavior assessment 190 adult health assessment 311–14 Angelman syndrome 130 fragile X syndrome (FXS) 117 gastrointestinal (GI) symptom presentations 193 Prader–Willi syndrome (PWS) 133, 134–5 psychiatric disorder/ASD symptom conceptualization 195 restricted/repetitive See restricted and repetitive behaviors (RRBs) Rett syndrome (RS) 127 target behavior definition 249 tuberous sclerosis complex (TSC) 121–2 behavioral treatments (adults) 319 behavioral treatments (children) 240 approaches to 239–40 behavior problems treatment 249–50 conclusions 253–4 diagnostic practice implications 240 research challenges 240, 251–3 See also discrete trial training (DTT); Pivotal Response Trainings; Verbal Behavior Training birth weight, low 49 birth, perinatal risk factors 47–8, 52, 155 Bleuler, Eugen 59 brain volume 95 macrocephaly and 94–5, 102 regional variations in 95–6 broad autism phenotype, terminology 3 genetic model 145

C4B null allele 160 candidate gene association studies 150–49 Carbone 247 carnitine 164, 277 carnosine 277 case-control studies, epidemiology research use 35 caudate nucleus, abnormalities in 96 cellular adhesion molecules (CAMs) 154–4 cerebellar abnormalities 90–1, 93, 96 CGG (cytosine, guanine, guanine) repeats 114–15 Checklist for Autism in Toddlers (CHAT) 180, 182 modified (M-CHAT) 180, 182–3 Checklist of Adaptive Living Skills (CALS) 315 chelation 276–7 chemokines 162–3 Child Symptom Inventory-4 196 Childhood Autism Rating Scale (CARS/CARS-2) 182, 185–6 childhood disintegrative disorder (CDD) See also dementia infantilis autistic disorder (AD) differentiation 19 diagnosis/classification 17–19, 26 Children’s Yale–Brown Obsessive Compulsive Scale – Pervasive Developmental Disorders (CYBOCS-PDD) 196 Children’s Sleep Habits Questionnaire (CSHQ) 194–5 chlorinated solvents, prenatal exposure 50 chromosomal banding 148–9 citalopram 260, 261, 313 classification systems See diagnostic and classification systems

Index

clinical course 13 Asperger’s syndrome (AS) and high-functioning autism (HFA) comparison 13 regressive autism 22, 74 clomipramine 259–60 clonidine 263, 271, 272, 273 CNTNAP2 gene 147, 150, 151, 154 cognitive assessment 13 adults 299–300 Asperger’s syndrome (AS) diagnostic validation 13 children 189–90 unreliability of early IQ scores 252 cohort studies, epidemiology research use 35 common disease–common variant hypothesis 146 communication 4 dyadic 63 lack of communication intent 219–20 non-verbal See gestures; nonverbal communication speech and language See speech and language co-morbidities 295 adult assessment 302, 309–14 aging process 295, 296–7 child assessment 192–6 co-occurring conditions in ASDs 312 comparative genomic hybridization (CGH) array 148 complementary and alternative medicine (CAM) 273–8, 320 Comprehensive Assessment of Spoken Language (CASL) 300 comprehensive diagnostic assessment (children) adaptive behavior 190 cognitive/intellectual functioning 189–90 interactive observation 187–8 multidisciplinary team members 186–7, 188 parent interview 187 purpose of 186 speech and language 190, 224–5

comprehensive needs assessment, adults 309–18 computer assisted technology 321 Connexions service 212 conotruncal anomaly facial syndrome 136 continuous spikewave during slow-wave sleep (CSWS) 21 coordination defects 9 copy number variations (CNVs) 148–9, 151 corpus callosum (CC), decreased size 97 cortical abnormalities 91–3 cortical macrocolumns 86 cortical minicolumns 86, 92 cortical thickness (CT) 96 cortical tubers 120–1, 123 Cowden syndrome 153 craving/sensory seeking 71, 221 cytogenetics, methodology 148–9 cytokines 162–3 death 295 acceptance of 295 of loved ones 295, 297 dementia infantilis 6, 17, See also childhood disintegrative disorder (CDD) depression 311–12 descriptive studies, epidemiology research use 34–5 desipramine 259 development 20 adulthood developmental stages 290–7 neurodevelopment See neurodevelopment neuropsychology See developmental neuropsychology normal 62–5 patterns in early autism 19–20, 61–2 Developmental Behavior Checklist (DBC) 196 developmental neuropsychology 60 clinical characteristics 60–2 context of normal development 62–5

329

early developmental deficits 65–71 eye gaze and eye contact deficits 65–6 historical context 59–60 later developmental deficits 71–4 methodologies of developmental precursor studies 62 summary and conclusion 74–5 developmental plateau/ stagnation 62 Developmental, Dimensional and Diagnostic Interview (3di) 289 Developmental–Individual Differences–Relationship Model (DIR) 209 diagnosis adults See diagnosis and assessment (adults) age at 39, 42 children See diagnostic evaluation (children) diagnostic systems See diagnostic and classification systems diagnosis and assessment (adults) 289 developmental considerations 290–7 diagnostic considerations 286–9 functional assessment 298–302 summary 302 diagnostic and classification systems 1 adults with ASDs 286–7 Asperger’s syndrome (AS) 9–15 autistic disorder (AD) 3–9 childhood disintegrative disorder (CDD) 17–19 dimensional approaches 23–6 future trends (DSM-5) 26–7 importance of 1 pervasive developmental disorder not otherwise specified (PDD-NOS) 15–16

330

Index

diagnostic and classification systems (cont.) restricted and repetitive behaviors (RRBs) 258–9 Rett syndrome (RS) 17, 125, 129 rise in prevalence and 38–9, 204–5 terminology 2–3 treatment research implications 240, 251 Diagnostic and Statistical Manual of the American Psychiatric Association (DSM-IV) See diagnostic and classification systems diagnostic evaluation (children) 186 comorbid disorder consideration 192–6 comprehensive diagnostic assessment (children) See comprehensive diagnostic assessment (children) etiologic 191–2 summary 196–7 Diagnostic Interview for Social and Communication Disorders (DISCO) 289 dietary supplements 277–8, See also nutritional treatments diffusion tensor imaging (DTI) studies 97 DiGeorge syndrome 136 digestive enzymes, treatment use 274 dimercaptosuccinic acid (DMSA) 277 dimethylglycine (DMG) 277 diphenhydramine 273 diphtheria–tetanus–pertussis (dTAP) vaccine 45–6 disability evaluations, history 314 disconnection syndrome 98 discrete trial training (DTT) 229, 240–3 divalproex sodium 262, 267–8 DNA microarrays 148–9 etiologic evaluation 192 drug treatment See medication treatments (adults); medication treatments (children); specific drugs/ drug types dyadic communication 63

E3-ubiquitin protein ligase (Ube3A) 131 Early Childhood Inventory-4 196 early intervention services 201–2 autism-specific 202–3 Early Start Denver Model (ESDM) 203 eccentricity 9 echoic language 248 echolalia 4, 73, 220–1 ecologic studies, epidemiology research use 35 ecological assessment 225–6 Education for All Handicapped Children Act (EAHCA) 201, 203, 204, 211 education, family See family support/education educational assessment, adults 301–2 educational treatments (children) 203 comprehensive interventions 206–8, 210, 213, 226–8 early intervention services 201–3 focused interventions 208–10, 213, 228–33 general education inclusion 210–11 scientifically-supported basis of 205 special education services 203–5 summary and conclusion 213 transition services 211–13 EEG 192 epileptiform abnormalities See epilepsy/epileptiform abnormalities etiologic evaluation 191–2 electrical status-epilepticus during slow-wave sleep (ESES) 21, 170–1 empathy 246 employment 212 assessment 293–4, 315–16 future directions 323 importance of 308–9 mentorship 296 strengths-based approach 306 transition services 211, 213 EN2 gene 150

endocrine system, essential autism etiology 167–9 endophenotypes, genetic research 151–2 environmental assessment, adults 316–17 Environmental Rating Scale (ERS) 302 environment–gene interactions 89–90, 94, 155–9 enzymes, digestive 274 epidemiology 35 definitions and methods 34–5 future research directions 52–3 incidence See incidence measles–mumps–rubella (MMR) vaccine 41–4 prevalence See prevalence risk factors See risk factors thimerosal-containing vaccines (TCVs) 44–7 epigenesis 22 essential autism 158 regression 22 syndromic autism 125, 138 epilepsy/epileptiform abnormalities See also seizures continuous spikewave during slow-wave sleep (CSWS) 21 electrical status-epilepticus during slow-wave sleep (ESES) 21, 170–1 essential autism etiology 170–2 etiologic evaluation 191–2 limbic dysfunction 83 regression 20–2, 170–1 tuberous sclerosis complex (TSC) 121, 123 Erikson, E. 291 escitalopram 260, 263 essential autism 3 etiology See etiology (essential autism) terminology 3 establishing operations 248 ethnicity, as risk factor 48 ethylene diamine tetraacetic acid (EDTA) 277 etiologic evaluation 191–2 etiology (essential autism) 144

Index

Asperger’s syndrome (AS) and high-functioning autism (HFA) comparison 13 childhood disintegrative disorder (CDD) 18–19 endocrine system 167–9 environment and gene– environment interactions 155–9 epilepsy 170–2 genetic architecture models 146 genetic research approaches 147–52 immune system 159–63 mitochondrial dysfunction 163–7 research findings on specific genes 152–5 summary and conclusion 172 twin studies 145–6 views on parenting 4, 83 etiology (syndromic autism) 112 22q11 deletion syndrome (22q11DS) 136–7 Angelman syndrome 129–33 examples of associated syndromes 113 fragile X syndrome (FXS) 113–20 Prader–Willi syndrome (PWS) 129, 131, 133–5 Rett syndrome (RS) 124–9 summary and conclusion 137–9 tuberous sclerosis complex (TSC) 120–4 evidence-based practice 205, 226, 253 executive functioning 70 exhaust fumes, maternal exposure 50 extreme autistic aloneness 3 extreme male brain theory 167, 168–9 eye gaze/eye contact 64 early deficits in AD 65–6 fragile X syndrome (FXS) 117 joint attention 218–19 normal development 63–4 face recognition 63–4 factor analysis, ASD subtyping 25

false belief tasks 219 family support/education 113 adult child 293, 296 complementary and alternative medicine (CAM) 278 habilitative treatments 223–4 syndromic autism 113 febrile illness, and regression 166–7 fetal origins hypothesis 89 financial planning/ management 292–3, 316 FISH subtelomere study 191 Floortime program 209, 213, 228 fluoxetine 260, 261, 263 fluvoxamine 260–1, 263 FMR1 gene 94, 114–16 FMR1 protein (FMRP) 114–16, 119–20 folinic acid 276 fragile X syndrome (FXS) 114 and autism 117–20 historical perspective 113–14 phenotype 116–17 tuberous sclerosis complex (TSC) similarities 124 fragile X-associated tremor/ ataxia syndrome (FTXAS) 114 functional analysis 249–50 functional assessment, adults 298–302 functional neuroimaging 98–102 functional outcome See outcome (functional) functional treatment 250 GABA receptor agonists 120 GABA/GABA receptors 116, 132–3, 136 GABRB3 gene 128–9 gastrointestinal (GI) problems 42 adult health assessment 311 diagnostic evaluation 193–4 measles–mumps–rubella (MMR) vaccine 42 treatments targeting GI function 274–5 genetic counseling 293 genetic factors See also specific genes

331

15q11–13 genetic disorders See 15q11–13 genetic disorders 22q11 deletion syndrome (22q11DS) 136–7 Angelman syndrome 129, 132–1, 133 epigenesis See epigenesis essential autism genetic architecture models 146 etiologic evaluation 191, 192 findings on specific genes in essential autism 152–5 fragile X syndrome (FXS) 114–16 gene sequencing 151 gene–environment interactions 89–90, 94, 155–9 genetic association studies 149 genome-wide association studies (GWAS) 150–1 genome-wide nonparametric analysis 147–8 genomic imprinting 129, 131, 136 mitochondrial dysfunction 165–6 neurodevelopmental influence 89–90, 93–4 Prader–Willi syndrome (PWS) 129, 131, 135 regressive autism 22 research approaches 147–52 Rett syndrome (RS) 125–6, 128–9 tuberous sclerosis complex (TSC) 122, 123–4 twin studies in essential autism 145–6 gestures 9 augmentative and alternative communication 231 impairments in autism 9, 68, 218 normal development 64, 65 Gilliam Autism Rating Scale - second edition (GARS-2) 182, 186 glutamate receptor antagonists 120 glutamate/glutamate receptors 115–16, 154–4 glutathione 158, 276

332

Index

gluten and casein-free (GFCF) diet 275 graduation 212 guanfacine 263, 271–2 guardianship 293 habilitative treatments (children) 221 assistive technology (AT) 233–5 comprehensive treatment models (CTMs) 226–8, 235 core communication deficits’ characteristics 217–21 evaluation process 224–6 evidence base for interventions 226, 227 focused interventions 227, 228–33, 236 sensory processing and motor function deficits’ characteristics 221 service delivery models 222–4 service provision guidelines 221–2 summary 235–6 haloperidol 262, 264, 267 hamartin 122 Handicapped Infants and Toddlers Program 201 health assessment, adults 309–14 health care needs assessment, adults 302 heavy metal toxicity 50, 276–7 Heller’s syndrome See childhood disintegrative disorder (CDD) heterogeneity, of autism 1, 112, 305 high-functioning autism (HFA) 14 affective/anxiety disorders in adults 311–12 Asperger’s syndrome (AS) differentiation 12–14 HLA-DRB1*04 160 homocysteine 157 hospitalization 297 housing See living accommodation hyperactivity 117 attention deficit hyperactivity disorder

(ADHD) See attention deficit hyperactivity disorder (ADHD) medication treatments 268–72, 312–13 hyperbilirubinemia 49 hyperphagia 133 hyper-responsiveness 71, 221 hypo-responsiveness 71, 221 hypothesis-driven interventions 250 immune system, autism etiology 51, 159–63 immune therapy 276 immunoglobulin 50, 276 impulsivity, medication treatments 268–72, 312–13 inattention 272 attention deficit hyperactivity disorder (ADHD) See attention deficit hyperactivity disorder (ADHD) medication treatments 268–72, 312–13 incidence 35 definition 34–5 studies of 40, 41, 122 Individual Education Plan (IEP) 203, 211, 212–13 Individualized Family Service Plan (IFSP) 202 Individuals with Disabilities Education Act (IDEA) 203 assistive technology 233–4 general education inclusion 211 special education services 203, 204, 205 transition services 212, 305 infantile autism 6, 59, 83 infant-mother communication, normal development 63 Infant–Toddler Checklist (ITC) 182, 183–4 infection, maternal 47, 51, 159 inferior olivary nucleus, abnormalities 90, 91 inflammation, neuroinflammation 162–3 information processing 299 insomnia 195 diagnostic evaluation 194–5 medication treatments 272–3

intellectual assessment/ intelligence testing See cognitive assessment intellectual disability (ID) 4 Asperger’s syndrome (AS) diagnostic criteria 11 diagnostic substitution and autism prevalence 39 fragile X syndrome (FXS) 116–17, 119 heterogeneity of 112 historical perspective 4, 5 Prader–Willi syndrome (PWS) 133 prevalence in autistic disorder (AD) 4, 310 Rett syndrome (RS) 126–7 tuberous sclerosis complex (TSC) 121, 122–3 interests, restricted See restricted interests International Classification of Diseases (ICD-10) See diagnostic and classification systems interpersonal relatedness 63 intravenous immunoglobulin (IVIG) 276 IQ, ASD subtyping by 24–5 irritability, medication treatments 263–8 jaundice, neonatal 49 joint attention 63 deficits in 66–7, 218–19 normal development 63 pivotal response trainings 245–6 Kanner, Leo 3–5, 59–60, 83 Koegel, R. 244 lactate levels 165 lamotrigine 262, 268 Landau–Kleffner syndrome (LKS) 21, 170, 171 leaky gut hypothesis 193, 274 learning theory 239 Lennox–Gastaut syndrome (LGS) 170 levetiracetam 268 life review 297 life-span approach, personcentred planning 308 limbic dysfunction 83, 90, 95, 101–2

Index

linkage studies, methodology 147–8 living accommodation 292 assistive technology 321–2 developmental tasks 292, 295 environmental assessment 316–17 future aims 322–3 logarithm of odds (LOD) scores 147 loss, managing 295, 297 Lovaas, I. 240, 251 macrocephaly 95 brain volume and 94–5, 102– PTEN gene mutations 153 macrocolumns, cortical 86 magnesium, dietary supplement 277 mand training 247, 248 massage techniques 231 M-CHAT (modified-CHAT) 180, 182–3 measles–mumps–rubella (MMR) vaccine 20, 41–4 MECP2 gene 125–6, 128–9 medication treatments (adults) See also specific drugs/drug types assessment 314, 319 assistive technology 321 prevalence 313 specific types 312–13, 319–20 medication treatments (children) See also specific drugs/drug types inattention, hyperactivity and impulsivity 268–72 irritability 263–8 repetitive and restricted behaviors 258–63 sleep problems 272–3 summary and conclusion 278–9 melatonin 273 memory 101–2 mental retardation See intellectual disability (ID) mentorship, of others 296 mercury toxicity 44–7, 50, 276 messenger RNA (mRNA), fragile X syndrome 115–16 MET gene 150, 152 metabolic syndrome 300 methionine cycle 157–8 methyl B12 276

methyl-CpG-binding protein 2 (MeCP2) 125–6, 128, 131 methylphenidate 269–70, 278 microglia 160, 163 migration, neurodevelopmental phase 85, 92 minicolumns, cortical 86, 92 minocycline 120 mitochondrial dysfunction 163–7 modified-CHAT (M-CHAT) 180, 182–3 motivation 245 motivating operations in Verbal Behavior Training 248 Pivotal Response Trainings 245 motor functioning, deficits 68, 221 MRI 102 etiologic evaluation 191 functional (fMRI) 98–102 mTOR inhibitor 124 mTOR pathway 122, 124, 153 multidisciplinary team 187 adult treatment/services 320–1 comprehensive diagnostic assessment (children) 186–7, 188 educational treatments (children) 202, 211 multiple rare variant model 146 mumps, MMR vaccine 20, 41–4 myelination 88, 96–7 National Standards Project 226, 227 needs assessment, adults 309–18 neurexin genes 154–4 neurobiology 13 Angelman syndrome 130–1 Asperger’s syndrome (AS) and high-functioning autism (HFA) comparison 13 fragile X syndrome (FXS) 114–16 Rett syndrome (RS) 125–6 tuberous sclerosis complex (TSC) 122 neurodevelopment 89

333

gene–environment interactions 89–90, 94, 155–9 maternal autoimmunity effects 161–2 neuroinflammation effects 162–3 postnatal dysregulation of 94–8 process of 84–9 neurogenesis 85 neuroimaging 102 etiologic evaluation 191 functional 98–102 neuroinflammation, essential autism etiology 162–3 neuroleptics See antipsychotic medications neuroligin genes 94, 153–4 neuronal interconnectivity See also synaptic dysfunction alterations in epilepsy 171 autism neuropathology 85–7, 96–8, 102 neuropathology 84 functional neuroimaging in AD 98–102 historical perspective 83–4 neural microstructure in AD 90–4 neurodevelopment considerations 84–90 neuroinflammation 163 postnatal dysregulation of neurodevelopment 94–8 summary 102 neuroplasticity 88, 89 neuropsychological functioning 14 Asperger’s syndrome (AS) and high-functioning autism (HFA) comparison 13–14 assessment 190, 299–300 development See developmental neuropsychology executive function deficits 70 motor function deficits 68, 221 NLGN3/NLGN4 genes 94, 153–4 No Child Left Behind Act 205, 211

334

Index

non-verbal communication See gestures nutritional treatments 273–8 NXRN1 gene 154 observation, interactive 187–8, 224 obsessive–compulsive symptoms 133 medication treatments 259, 263 Prader-Willi syndrome (PWS) 133, 134 occupational therapists 222 American Occupational Therapy Association (AOTA) guidelines 222 assessment process 224, 225 service delivery models 222–4 odds ratio 35 olanzapine 267 omega-3 fatty acids 277–8 onset patterns See regression operant conditioning 239 oromotor development deficits 68 outcome (functional) 13 Asperger’s syndrome (AS) and high-functioning autism (HFA) comparison 13 regressive autism 22 OXTR gene 150, 152–3 oxytocin (OT) 153, 167–8 parametric linkage analysis 147 parent/caregiver interview 187, 224 parental age, as risk factor 48, 49 parental concerns, adult child 286 parental support/advice See family support/education parenting, autism etiology 4, 83 Peabody Picture Vocabulary Test, 3rd Edition (PPVT-III) 299 Pediatric Sleep Questionnaire (PSQ) 194–5 peptides, dietary supplementation 277 perinatal risk factors 47–8, 52, 155 personal and social-emotional adjustment, adults 316

personal involvement, in event recall 102 personal safety 293, 317 personal support technologies (PST) 321 person-centred care 307–9, 317–19 pervasive developmental disorder (PDD) 2 atypical 6 childhood onset (COPDD) 6 diagnosis/classification history 6–7 not otherwise specified See pervasive developmental disorder not otherwise specified (PDD-NOS) prevalence 36, 37 terminology 2 unspecified 16 pervasive developmental disorder not otherwise specified (PDD-NOS) 3 autistic disorder (AD) differentiation 16 diagnosis/classification 6, 15–16, 26, 204–5 prevalence 37 terminology 2 Pervasive Developmental Disorder Screening Test-II (PDDST-II) 182, 183 pesticides, prenatal exposure 51 pharmacotherapy See medication treatments (adults); medication treatments (children); specific drugs/drug types picture boards 231 Picture Exchange Communication System (PECS) 208 educational treatment 208, 213 habilitative treatment 231, 232, 233 picture schedules 230 Pitocin 168 Pivotal Response Trainings 207 behavioral treatment 244–7, 251–2 educational treatment 206–7 habilitative treatment 228 play, in adulthood 294, 296 PON1 gene 159

possessions 297 Prader-Willi syndrome (PWS) 129, 131, 133–5 precedence rule, the 10 pre-eclampsia, as risk factor 49–50 premature birth 50 prenatal risk factors 47–8, 52, 155 prevalence 35 artifactual or real rise in 38–41, 204–5 ASDs in fragile X syndrome (FXS) 117–18 ASDs in tuberous sclerosis complex (TSC) 122 Asperger’s syndrome (AS) 37 autistic disorder (AD) 36–7, 285 autistic spectrum disorders (ASDs) 36, 37–8, 285 definition 34–5 pervasive developmental disorder (PDD) 36, 37 study examples 35–8 primary autism See essential autism privacy 293, 297 probiotics 274 prognosis, autistic disorder (AD) 74 progressive disintegrative psychosis See childhood disintegrative disorder (CDD) proliferation, neurodevelopmental phase 85–6, 92 prompt-dependency 219–20 prompting 228–9, 245 protein synthesis 115, 124 psychiatric disorders 4 22q11 deletion syndrome (22q11DS) comorbidity 136 adult health assessment 311–14 affective disorders 311–12 anxiety disorders 117, 311–12 autistic psychopathy 59 diagnostic evaluation in children 195–6 psychotic disorder classification of autism 4 schizophrenia 311

Index

psychopathy, autistic 59 PTEN gene 153 public health perspective, aging 285–6 Purkinje cells, decreased number/size 90–1, 93 pyruvate levels 164–5 quality of life, assessment 317–18 quetiapine 267 rapamycin 124 redox/methylation hypothesis 157–8 reelin 159 Reflecting on Social Roles Inventory 316 regression 19–23, 61 childhood disintegration disorder (CDD) 17–19 epilepsy 170–1 etiologic evaluation 191 measles-mumps-rubella (MMR) vaccine 41–4 mitochondrial disease and 166–7 Rett syndrome (RS) stages 124–5 Regular Education Initiative 211 rehabilitation services 223 RELN gene 94, 150, 152, 159 repetition (echolalia) 4, 73, 220–1 residual autism 6 respiratory chain inhibition 164–6 restricted and repetitive behaviors (RRBs) 8 ASD subtyping by 25 Asperger’s syndrome (AS) and high-functioning autism (HFA) comparison 14 Asperger’s syndrome (AS) diagnosis 9 autism spectrum disorder (ASD) diagnosis (DSM-5) 26–7 autistic disorder (AD) diagnosis 7, 8 childhood disintegrative disorder (CDD) 18 early deficits in AD 69–71

fragile X syndrome (FXS) 117 functional neuroimaging studies 100 medication treatments 258–63 Prader-Willi syndrome (PWS) 134 restricted interests 8 ASD subtyping by 25 Asperger’s syndrome (AS) 9 autistic disorder (AD) diagnosis 8 retirement 297 Rett syndrome (RS) 17, 124–9 risk factors See also specific risk factors approach to research 47–8 focussed investigations 49–52 future research directions 52–3 gene–environment interactions 89–90, 94, 155–9 large population surveys 48–9 research methods 34–5 risk ratio 35 risperidone 262 irritability treatment 264–5, 268, 278 restricted and repetitive behavior (RRB) treatment 262, 278 rubella 20 congenital infection as risk factor 51 measles–mumps–rubella (MMR) vaccine 20 Rutter, Michael 5–6 S-adenosyl homocysteine (SAH) 157–8 S-adenosyl methionine (SAM) 157–8 safety, personal 293, 317 Sally-Anne task 219 sameness, desire for 4, 5 Scales of Independent Behaviors-Revised (SIB-R) 302 SCERTS (Social, Communication, Emotional Regulation and

335

Transactional Support) 227, 228 schizophrenia 311 School Function Assessment (SFA) 225 screening 72 AAP recommendations 181 in adulthood 287–8 level 1 screening tools 181–4 level 2 screening tools 184–6 motivations for 179–80 parent communication/ support 186 phenomenological basis of tools 180–1 settings for 179 summary 72, 196–7 Screening Test for Autism in Two-year-olds (STAT) 182, 184 secondary autism See syndromic autism secretin 275 seizures See epilepsy/ epileptiform abnormalities adult health assessment 310–11 epilepsy classification 170 limbic dysfunction 83 medication treatments 268 regression 20–2 tuberous sclerosis complex (TSC) 121, 123 selective serotonin reuptake inhibitors (SSRIs) 50, 260–1, 263, 313 self, sense of 292 self-initiation 245 self-injurious behaviors 231 adult treatment/service planning 316, 319 behavioral treatment 249–50 medication treatments 268, 312, 313 sensory integration (SI) 231 self-management/selfregulation 246 sensory function abnormalities 62, 221 restricted and repetitive behaviors (RRBs) association 71 sensory integration (SI) treatment 230–1 sensory integration (SI) 230–1

336

Index

serotonin reuptake inhibitors 50 restricted and repetitive behaviors (RRBs) 259–61, 263, 313 selective (SSRIs) 50, 260–1, 263, 313 serotonin system 153 sertraline 260 SETT model 234 sex 48 as risk factor 48 extreme male brain theory 167, 168–9 sexuality 293, 297 SHANK genes 94, 150, 155 shaping, speech elicitation 229 sign language 231 single nucleotide polymorphisms (SNPs) 149–51 skin, tuberous sclerosis complex (TSC) symptoms 121 Skinner, B.F. 239, 247 SLC25A12 gene 150 SLC6A4 gene 150, 153 sleep disturbance 195 diagnostic evaluation 194–5 medication treatments 272–3 Smarties task 219 smoking, maternal 48 social class, as risk factor 48 social cognition measures 13 Social Communication Questionnaire (SCQ) 182, 184–5 social constructivist approach 253–4 social interaction deficits 3 adult social skills assessment 300–1 adult support 294 ASD subtyping by 23–4, 25 Asperger’s syndrome (AS) and high-functioning autism (HFA) comparison 14 Asperger’s syndrome (AS) diagnosis/classification 9, 10 autism spectrum disorder (ASD) diagnosis (DSM-5) 26–7 autistic disorder (AD) diagnosis/classification 5, 7–8

childhood disintegrative disorder (CDD) diagnosis/ classification 18 context of normal development 62–4 extreme autistic aloneness 3 focused educational treatment interventions 209–10 focused habilitative treatment interventions 231 fragile X syndrome (FXS) 117, 118–19 functional neuroimaging studies 99, 100–2 later developmental deficits in AD 71, 72, 74 oxytocin and argininevasopressin effects 167–8 pervasive developmental disorder not otherwise specified (PDD-NOS) diagnosis/classification 15 Prader-Willi syndrome (PWS) 134–5 social reinforcement receptivity 241 Social Responsiveness Scale (SRS) 24–5, 182, 185 Social Security Disability Income (SSDI) 293 Social Stories 209–10, 231, 234 sociodemographic risk factors 48 special education services (age 3–21) 204 autism and 204–5 history and systems 203–4 speech and language 4 adult assessment 300 Angelman syndrome 130 ASD characteristics affecting communication skills 217–21 ASD subtyping 25 Asperger’s syndrome (AS) diagnosis/classification 9, 10, 11, 12 autism spectrum disorder (ASD) diagnosis (DSM-5) 26–7 autistic disorder (AD) diagnosis/classification 3–4, 5, 7, 8

behavioral treatment 241–2, 247–9 Broca’s area damage and neuroplasticity 88 childhood disintegrative disorder (CDD) diagnosis/ classification 18 comprehensive diagnostic assessment 190 echoic language 248 echolalia 4, 73, 220–1 focused educational treatment interventions 208–9 fragile X syndrome (FXS) 118–19 habilitative treatments See habilitative treatments (children) lack of communication intent 219–20 later developmental deficits in AD 71, 72–3 neuropathology 96, 99–101 normal development 64–5 prelinguistic communication impairment in AD 67–8 regressive autism 20 Rett syndrome (RS) 126 speech generating devices (SGDs) 231, 232–3 speech-language pathologists See speech-language pathologists (SLPs) speech-language pathologists (SLPs) 222 American Speech-LanguageHearing Association (ASHA) guidelines 221–2 communication assessment 224–5 service delivery models 222–4 staff support 223–4 stereotyped behavior See restricted and repetitive behaviors (RRBs) stimulant medications 269–70 strengths-based assessment 306–7 strengths-based treatment/ service plan 317–19 stress, maternal 51 subependymal giant cell astrocytomas (SEGAs) 121 subependymal nodules (SENs) 121

Index

subtractive phase, of neurodevelopment 87 Sukhareva, G.E. 59 superior temporal sulcus (STS) 99 Supplemental Social Security Income (SSI) 292 support assessment, adults 315–16 surveillance 181, 196 synaptic dysfunction 116 Angelman syndrome 131 epilepsy 171 essential autism etiology 154–5 fragile X syndrome (FXS) 115–16 neurogenetic syndrome summary 138–9 Rett syndrome (RS) 126 tuberous sclerosis complex (TSC) 124 synaptogenesis 86–7 syndromic autism 3 etiology See etiology (syndromic autism) family education 113 terminology 3 tact 248 task analyses 230, 234 teachers, skills and training 213, 223–4 technology, assistive (AT) 233–5 temper tantrums, medication treatments 263–8, 312 terminology 1, 2–3 Test of Non-Verbal Intelligence, 4th Edition (TONI-4) 299 testosterone levels 167, 168–9 thalidomide, prenatal exposure 50 theory of mind (TOM) 63, 116, 219 thiamine tetrahydrofurfuryl disulfide (TTFD) 277 thimerosal 47 prenatal exposure 50–1

thimerosal-containing vaccines 44–7 Thorndike, E. 239 topiramate 262–3, 268 transition services 211–13 transplacental transfer, maternal antibodies 161–2 transportation 323 treatment behavioral See behavioral treatments (adults); behavioral treatments (children) complementary and alternative medicine (CAM) 273–8, 320 educational See educational treatments (children) habilitative See habilitative treatments (children) medication See medication treatments (adults); medication treatments (children) nutritional 273–8 Treatment and Education of Autistic and Related Communication Handicapped Children (TEACCH) program 207–8, 213, 228 treatment/service planning (adults) 307 behavior management 319 comprehensive needs assessment 309–18 future directions 322–4 person-centred care models 307–9 psychopharmacological assessment and treatment 319–20 strengths-based assessment 306–7 strengths-based, personcentred plan 317–19 team members 320–1 technology utilization 321–2 treatment 278

337

tricyclic antidepressants 259–60 TSC1/TSC2 genes 122, 123–4, 153 tuberin 122 tuberous sclerosis complex (TSC) 120–4 UBE3A gene 128–9, 131, 133 ubiquitin 131 UCLA model See discrete trial training (DTT) vaccines 20 associated febrile illness and regression 166–7 diphtheria-tetanus-pertussis (dTAP) 45–6 measles-mumps-rubella (MMR) 20, 41–4 thimerosal-containing (TCVs) 44–7 valproic acid (VPA) 50, 267–8 velocardiofacial syndrome (VCFS) 136 Verbal Behavior Training 247–9 video modeling 235 Vineland Adaptive Behavior Scales (VABS) 24, 190, 301 visual schedules 230 vitamin B12 276 vitamin B6 277 vitamin C 276 vocational assessment, adults 301–2 voxel-based morphometry (VBM) 95 Wechsler Adult Intelligence Scale-III (WAIS-III) 299 West syndrome 170 Wing, Lorna 9–10 Wisconsin Assistive Technology Initiative (WATI) 234 writing, assistive technology 234–5 ziprasidone 267